From 0efc3bf780928f15dc654b9b21692b5d544fd8a2 Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Wed, 24 Aug 2022 17:04:30 -0400
Subject: [PATCH 01/58] Add bare bones web UI
---
scripts/dream_web.py | 62 ++++++++++++++++
scripts/static/index.html | 148 ++++++++++++++++++++++++++++++++++++++
2 files changed, 210 insertions(+)
create mode 100644 scripts/dream_web.py
create mode 100644 scripts/static/index.html
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
new file mode 100644
index 0000000000..3b10922c68
--- /dev/null
+++ b/scripts/dream_web.py
@@ -0,0 +1,62 @@
+import json
+import os
+from http.server import BaseHTTPRequestHandler, HTTPServer
+
+print("Loading model...")
+from ldm.simplet2i import T2I
+model = T2I()
+
+class DreamServer(BaseHTTPRequestHandler):
+ def do_GET(self):
+ self.send_response(200)
+ if self.path == "/":
+ self.send_header("Content-type", "text/html")
+ self.end_headers()
+ with open("./scripts/static/index.html", "rb") as content:
+ self.wfile.write(content.read())
+ else:
+ self.send_header("Content-type", "image/png")
+ self.end_headers()
+ with open("." + self.path, "rb") as content:
+ self.wfile.write(content.read())
+
+ def do_POST(self):
+ self.send_response(200)
+ self.send_header("Content-type", "application/json")
+ self.end_headers()
+
+ content_length = int(self.headers['Content-Length'])
+ post_data = json.loads(self.rfile.read(content_length))
+ prompt = post_data['prompt']
+ batch = int(post_data['batch'])
+ steps = int(post_data['steps'])
+ width = int(post_data['width'])
+ height = int(post_data['height'])
+ cfgscale = float(post_data['cfgscale'])
+ seed = None if int(post_data['seed']) == -1 else int(post_data['seed'])
+
+ print(f"Request to generate with data: {post_data}")
+ outputs = model.txt2img(prompt,
+ batch_size = batch,
+ cfg_scale = cfgscale,
+ width = width,
+ height = height,
+ seed = seed,
+ steps = steps);
+ print(f"Prompt generated with output: {outputs}")
+
+ outputs = [x + [prompt] for x in outputs] # Append prompt to each output
+ result = {'outputs': outputs}
+ self.wfile.write(bytes(json.dumps(result), "utf-8"))
+
+if __name__ == "__main__":
+ dream_server = HTTPServer(("0.0.0.0", 9090), DreamServer)
+ print("Started Stable Diffusion dream server!")
+
+ try:
+ dream_server.serve_forever()
+ except KeyboardInterrupt:
+ pass
+
+ dream_server.server_close()
+
diff --git a/scripts/static/index.html b/scripts/static/index.html
new file mode 100644
index 0000000000..326dc50392
--- /dev/null
+++ b/scripts/static/index.html
@@ -0,0 +1,148 @@
+<html>
+ <head>
+ <title>Stable Diffusion WebUI</title>
+ <link rel="icon" href="data:,">
+ <style>
+ * {
+ font-family: 'Arial';
+ }
+ #header {
+ text-decoration: dotted underline;
+ }
+ #search {
+ margin-top: 20vh;
+ text-align: center;
+ }
+ fieldset {
+ border: none;
+ }
+ #prompt {
+ width:500px;
+ border-radius: 20px 0px 0px 20px;
+ padding: 5px 10px 5px 10px;
+ border: 1px solid black;
+ outline: none;
+ }
+ #submit {
+ border-radius: 0px 20px 20px 0px;
+ padding: 5px 10px 5px 10px;
+ border: 1px solid black;
+ }
+ #results {
+ text-align: center;
+ padding-left: 20vw;
+ padding-right: 20vw;
+ padding-top: 10px;
+ }
+ img {
+ height:30vh;
+ border-radius:5px;
+ margin:10px;
+ }
+ input[type="number"] {
+ width:60px;
+ }
+ </style>
+ <script>
+ function append_output(output) {
+ let output_node = document.createElement("img");
+ output_node.src = output[0];
+
+ let alt_text = output[1].toString() + " | " + output[2];
+ output_node.alt = alt_text;
+ output_node.title = alt_text;
+
+ document.querySelector("#results").prepend(output_node);
+ }
+
+ function append_outputs(outputs) {
+ for (const output of outputs) {
+ append_output(output);
+ }
+ }
+
+ function save_fields(form) {
+ for (const [k, v] of new FormData(form)) {
+ localStorage.setItem(k, v);
+ }
+ }
+ function load_fields(form) {
+ for (const [k, v] of new FormData(form)) {
+ const item = localStorage.getItem(k);
+ if (item != null) {
+ form.querySelector(`*[name=${k}]`).value = item;
+ }
+ }
+ }
+
+ window.onload = () => {
+ document.querySelector("#generate_form").addEventListener('submit', (e) => {
+ e.preventDefault();
+ const form = e.target;
+
+ // Post as JSON
+ fetch(form.action, {
+ method: form.method,
+ body: JSON.stringify(Object.fromEntries(new FormData(form))),
+ }).then((result) => {
+ result.json().then((data) => {
+ // Re-enable form, remove no-results-message
+ form.querySelector('fieldset').removeAttribute('disabled');
+ document.querySelector("#prompt").value = '';
+ save_fields(form);
+
+ if (data.outputs.length != 0) {
+ document.querySelector("#no_results_message")?.remove();
+ append_outputs(data.outputs);
+ } else {
+ alert("Error occurred while generating.");
+ }
+ });
+ });
+
+ // Disable form
+ form.querySelector('fieldset').setAttribute('disabled','');
+ document.querySelector("#prompt").value = 'Generating...';
+ });
+ document.querySelector("#generate_form").addEventListener('change', (e) => {
+ save_fields(e.target.form);
+ });
+ load_fields(document.querySelector("#generate_form"));
+ };
+ </script>
+ </head>
+ <body>
+ <div id="search">
+ <h2 id="header">Stable Diffusion</h2>
+
+ <form id="generate_form" method="post" action="#">
+ <fieldset>
+ <input type="text" id="prompt" name="prompt">
+ <input type="submit" id="submit" value="Generate">
+ </fieldset>
+ <fieldset>
+ <label for="batch">Batch Size:</label>
+ <input value="1" type="number" id="batch" name="batch">
+ <label for="steps">Steps:</label>
+ <input value="50" type="number" id="steps" name="steps">
+ <label for="cfgscale">Cfg Scale:</label>
+ <input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any">
+ <span>•</span>
+ <label title="Set to multiple of 64" for="width">Width:</label>
+ <input value="512" type="number" id="width" name="width">
+ <label title="Set to multiple of 64" for="height">Height:</label>
+ <input value="512" type="number" id="height" name="height">
+ <span>•</span>
+ <label title="Set to -1 for random seed" for="seed">Seed:</label>
+ <input value="-1" type="number" id="seed" name="seed">
+ </fieldset>
+ </form>
+ </div>
+ <hr style="width: 200px">
+ <div id="results">
+ <div id="no_results_message">
+ <i><p>No results...</p></i>
+ </div>
+ </div>
+ </body>
+</html>
From b1600d4ca314174d0933c44c2e7a7b1e9813ea23 Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Wed, 24 Aug 2022 17:26:22 -0400
Subject: [PATCH 02/58] Update seed on click
---
scripts/static/index.html | 18 +++++++++++++-----
1 file changed, 13 insertions(+), 5 deletions(-)
diff --git a/scripts/static/index.html b/scripts/static/index.html
index 326dc50392..21d49c9427 100644
--- a/scripts/static/index.html
+++ b/scripts/static/index.html
@@ -17,7 +17,7 @@
border: none;
}
#prompt {
- width:500px;
+ width: 500px;
border-radius: 20px 0px 0px 20px;
padding: 5px 10px 5px 10px;
border: 1px solid black;
@@ -35,12 +35,16 @@
padding-top: 10px;
}
img {
- height:30vh;
- border-radius:5px;
- margin:10px;
+ cursor: pointer;
+ height: 30vh;
+ border-radius: 5px;
+ margin: 10px;
}
input[type="number"] {
- width:60px;
+ width: 60px;
+ }
+ #seed {
+ width: 150px;
}
</style>
<script>
@@ -51,6 +55,10 @@
let alt_text = output[1].toString() + " | " + output[2];
output_node.alt = alt_text;
output_node.title = alt_text;
+ // Update seed on click
+ output_node.addEventListener('click', () => {
+ document.querySelector("#seed").value = output[1];
+ });
document.querySelector("#results").prepend(output_node);
}
From df9f088eb42b9ce0f682ff321a3c39c8ec9ed49c Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Wed, 24 Aug 2022 17:28:59 -0400
Subject: [PATCH 03/58] Preserve prompt across generations
---
scripts/static/index.html | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/scripts/static/index.html b/scripts/static/index.html
index 21d49c9427..d5b27e83ef 100644
--- a/scripts/static/index.html
+++ b/scripts/static/index.html
@@ -87,6 +87,7 @@
document.querySelector("#generate_form").addEventListener('submit', (e) => {
e.preventDefault();
const form = e.target;
+ const prompt = document.querySelector("#prompt").value;
// Post as JSON
fetch(form.action, {
@@ -96,8 +97,7 @@
result.json().then((data) => {
// Re-enable form, remove no-results-message
form.querySelector('fieldset').removeAttribute('disabled');
- document.querySelector("#prompt").value = '';
- save_fields(form);
+ document.querySelector("#prompt").value = prompt;
if (data.outputs.length != 0) {
document.querySelector("#no_results_message")?.remove();
@@ -110,7 +110,7 @@
// Disable form
form.querySelector('fieldset').setAttribute('disabled','');
- document.querySelector("#prompt").value = 'Generating...';
+ document.querySelector("#prompt").value = `Generating: "${prompt}"`;
});
document.querySelector("#generate_form").addEventListener('change', (e) => {
save_fields(e.target.form);
From 91330878503ad1140fb2b2fbd523f01804a3c212 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 24 Aug 2022 17:52:34 -0400
Subject: [PATCH 04/58] first draft at big refactoring; will be broken
---
ldm/dream_util.py | 143 +++++++++++++++++++++++
ldm/simplet2i.py | 288 ++++++++++++++--------------------------------
scripts/dream.py | 130 ++-------------------
3 files changed, 238 insertions(+), 323 deletions(-)
create mode 100644 ldm/dream_util.py
diff --git a/ldm/dream_util.py b/ldm/dream_util.py
new file mode 100644
index 0000000000..1526223cd8
--- /dev/null
+++ b/ldm/dream_util.py
@@ -0,0 +1,143 @@
+'''Utilities for dealing with PNG images and their path names'''
+import os
+import atexit
+from PIL import Image,PngImagePlugin
+
+# ---------------readline utilities---------------------
+try:
+ import readline
+ readline_available = True
+except:
+ readline_available = False
+
+class Completer():
+ def __init__(self,options):
+ self.options = sorted(options)
+ return
+
+ def complete(self,text,state):
+ buffer = readline.get_line_buffer()
+
+ if text.startswith(('-I','--init_img')):
+ return self._path_completions(text,state,('.png'))
+
+ if buffer.strip().endswith('cd') or text.startswith(('.','/')):
+ return self._path_completions(text,state,())
+
+ response = None
+ if state == 0:
+ # This is the first time for this text, so build a match list.
+ if text:
+ self.matches = [s
+ for s in self.options
+ if s and s.startswith(text)]
+ else:
+ self.matches = self.options[:]
+
+ # Return the state'th item from the match list,
+ # if we have that many.
+ try:
+ response = self.matches[state]
+ except IndexError:
+ response = None
+ return response
+
+ def _path_completions(self,text,state,extensions):
+ # get the path so far
+ if text.startswith('-I'):
+ path = text.replace('-I','',1).lstrip()
+ elif text.startswith('--init_img='):
+ path = text.replace('--init_img=','',1).lstrip()
+ else:
+ path = text
+
+ matches = list()
+
+ path = os.path.expanduser(path)
+ if len(path)==0:
+ matches.append(text+'./')
+ else:
+ dir = os.path.dirname(path)
+ dir_list = os.listdir(dir)
+ for n in dir_list:
+ if n.startswith('.') and len(n)>1:
+ continue
+ full_path = os.path.join(dir,n)
+ if full_path.startswith(path):
+ if os.path.isdir(full_path):
+ matches.append(os.path.join(os.path.dirname(text),n)+'/')
+ elif n.endswith(extensions):
+ matches.append(os.path.join(os.path.dirname(text),n))
+
+ try:
+ response = matches[state]
+ except IndexError:
+ response = None
+ return response
+
+if readline_available:
+ readline.set_completer(Completer(['cd','pwd',
+ '--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
+ '--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
+ '--individual','-i','--init_img','-I','--strength','-f','-v','--variants']).complete)
+ readline.set_completer_delims(" ")
+ readline.parse_and_bind('tab: complete')
+
+ histfile = os.path.join(os.path.expanduser('~'),".dream_history")
+ try:
+ readline.read_history_file(histfile)
+ readline.set_history_length(1000)
+ except FileNotFoundError:
+ pass
+ atexit.register(readline.write_history_file,histfile)
+
+# -------------------image generation utils-----
+class PngWriter:
+
+ def __init__(self,opt):
+ self.opt = opt
+ self.filepath = None
+ self.files_written = []
+
+ def write_image(self,image,seed):
+ self.filepath = self.unique_filename(self,opt,seed,self.filepath) # will increment name in some sensible way
+ try:
+ image.save(self.filename)
+ except IOError as e:
+ print(e)
+ self.files_written.append([self.filepath,seed])
+
+ def unique_filename(self,opt,seed,previouspath):
+ revision = 1
+
+ if previouspath is None:
+ # sort reverse alphabetically until we find max+1
+ dirlist = sorted(os.listdir(outdir),reverse=True)
+ # find the first filename that matches our pattern or return 000000.0.png
+ filename = next((f for f in dirlist if re.match('^(\d+)\..*\.png',f)),'0000000.0.png')
+ basecount = int(filename.split('.',1)[0])
+ basecount += 1
+ if opt.batch_size > 1:
+ filename = f'{basecount:06}.{seed}.01.png'
+ else:
+ filename = f'{basecount:06}.{seed}.png'
+ return os.path.join(outdir,filename)
+
+ else:
+ basename = os.path.basename(previouspath)
+ x = re.match('^(\d+)\..*\.png',basename)
+ if not x:
+ return self.unique_filename(opt,seed,previouspath)
+
+ basecount = int(x.groups()[0])
+ series = 0
+ finished = False
+ while not finished:
+ series += 1
+ filename = f'{basecount:06}.{seed}.png'
+ if isbatch or os.path.exists(os.path.join(outdir,filename)):
+ filename = f'{basecount:06}.{seed}.{series:02}.png'
+ finished = not os.path.exists(os.path.join(outdir,filename))
+ return os.path.join(outdir,filename)
+
+
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 4737d90ba7..8e8b077922 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -23,7 +23,6 @@ t2i = T2I(outdir = <path> // outputs/txt2img-samples
width = <integer> // image width, multiple of 64 (512)
height = <integer> // image height, multiple of 64 (512)
cfg_scale = <float> // unconditional guidance scale (7.5)
- fixed_code = <boolean> // False
)
# do the slow model initialization
@@ -79,7 +78,6 @@ class T2I:
"""T2I class
Attributes
----------
- outdir
model
config
iterations
@@ -87,12 +85,9 @@ class T2I:
steps
seed
sampler_name
- grid
- individual
width
height
cfg_scale
- fixed_code
latent_channels
downsampling_factor
precision
@@ -102,11 +97,8 @@ class T2I:
The vast majority of these arguments default to reasonable values.
"""
def __init__(self,
- outdir="outputs/txt2img-samples",
batch_size=1,
iterations = 1,
- width=512,
- height=512,
grid=False,
individual=None, # redundant
steps=50,
@@ -118,7 +110,6 @@ The vast majority of these arguments default to reasonable values.
latent_channels=4,
downsampling_factor=8,
ddim_eta=0.0, # deterministic
- fixed_code=False,
precision='autocast',
full_precision=False,
strength=0.75, # default in scripts/img2img.py
@@ -126,7 +117,6 @@ The vast majority of these arguments default to reasonable values.
latent_diffusion_weights=False, # just to keep track of this parameter when regenerating prompt
device='cuda'
):
- self.outdir = outdir
self.batch_size = batch_size
self.iterations = iterations
self.width = width
@@ -137,7 +127,6 @@ The vast majority of these arguments default to reasonable values.
self.weights = weights
self.config = config
self.sampler_name = sampler_name
- self.fixed_code = fixed_code
self.latent_channels = latent_channels
self.downsampling_factor = downsampling_factor
self.ddim_eta = ddim_eta
@@ -154,16 +143,25 @@ The vast majority of these arguments default to reasonable values.
else:
self.seed = seed
- @torch.no_grad()
- def txt2img(self,prompt,outdir=None,batch_size=None,iterations=None,
- steps=None,seed=None,grid=None,individual=None,width=None,height=None,
- cfg_scale=None,ddim_eta=None,strength=None,embedding_path=None,init_img=None,
- skip_normalize=False,variants=None): # note the "variants" option is an unused hack caused by how options are passed
- """
- Generate an image from the prompt, writing iteration images into the outdir
- The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
- """
- outdir = outdir or self.outdir
+ def generate(self,
+ # these are common
+ prompt,
+ batch_size=None,
+ iterations=None,
+ steps=None,
+ seed=None,
+ cfg_scale=None,
+ ddim_eta=None,
+ skip_normalize=False,
+ image_callback=None,
+ # these are specific to txt2img
+ width=None,
+ height=None,
+ # these are specific to img2img
+ init_img=None,
+ strength=None,
+ variants=None):
+ '''ldm.generate() is the common entry point for txt2img() and img2img()'''
steps = steps or self.steps
seed = seed or self.seed
width = width or self.width
@@ -172,41 +170,57 @@ The vast majority of these arguments default to reasonable values.
ddim_eta = ddim_eta or self.ddim_eta
batch_size = batch_size or self.batch_size
iterations = iterations or self.iterations
- strength = strength or self.strength # not actually used here, but preserved for code refactoring
- embedding_path = embedding_path or self.embedding_path
+ strength = strength or self.strength
model = self.load_model() # will instantiate the model or return it from cache
-
- assert strength<1.0 and strength>=0.0, "strength (-f) must be >=0.0 and <1.0"
assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
+ assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
- # grid and individual are mutually exclusive, with individual taking priority.
- # not necessary, but needed for compatability with dream bot
- if (grid is None):
- grid = self.grid
- if individual:
- grid = False
-
data = [batch_size * [prompt]]
+ scope = autocast if self.precision=="autocast" else nullcontext
+ if grid:
+ callback = self.image2png
+ else:
+ callback = None
- # make directories and establish names for the output files
- os.makedirs(outdir, exist_ok=True)
+ tic = time.time()
+ if init_img:
+ assert os.path.exists(init_img),f'{init_img}: File not found'
+ results = self._img2img(prompt,
+ data=data,precision_scope=scope,
+ batch_size=batch_size,iterations=iterations,
+ steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
+ skip_normalize=skip_normalize,
+ init_img=init_img,strength=strength,variants=variants,
+ callback=image_callback)
+ else:
+ results = self._txt2img(prompt,
+ data=data,precision_scope=scope,
+ batch_size=batch_size,iterations=iterations,
+ steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
+ skip_normalize=skip_normalize,
+ width=width,height=height,
+ callback=image_callback)
+ toc = time.time()
+ print(f'{len(results)} images generated in',"%4.2fs"% (toc-tic))
+ return results
+
+ @torch.no_grad()
+ def _txt2img(self,prompt,
+ data,precision_scope,
+ batch_size,iterations,
+ steps,seed,cfg_scale,ddim_eta,
+ skip_normalize,
+ width,height,
+ callback=callback): # the callback is called each time a new Image is generated
+ """
+ Generate an image from the prompt, writing iteration images into the outdir
+ The output is a list of lists in the format: [[image1,seed1], [image2,seed2],...]
+ """
- start_code = None
- if self.fixed_code:
- start_code = torch.randn([batch_size,
- self.latent_channels,
- height // self.downsampling_factor,
- width // self.downsampling_factor],
- device=self.device)
-
- precision_scope = autocast if self.precision=="autocast" else nullcontext
sampler = self.sampler
images = list()
- seeds = list()
- filename = None
image_count = 0
- tic = time.time()
# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
try:
@@ -239,38 +253,24 @@ The vast majority of these arguments default to reasonable values.
shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
samples_ddim, _ = sampler.sample(S=steps,
- conditioning=c,
- batch_size=batch_size,
- shape=shape,
- verbose=False,
- unconditional_guidance_scale=cfg_scale,
- unconditional_conditioning=uc,
- eta=ddim_eta,
- x_T=start_code)
+ conditioning=c,
+ batch_size=batch_size,
+ shape=shape,
+ verbose=False,
+ unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,
+ eta=ddim_eta)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-
- if not grid:
- for x_sample in x_samples_ddim:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- filename = self._unique_filename(outdir,previousname=filename,
- seed=seed,isbatch=(batch_size>1))
- assert not os.path.exists(filename)
- Image.fromarray(x_sample.astype(np.uint8)).save(filename)
- images.append([filename,seed])
- else:
- all_samples.append(x_samples_ddim)
- seeds.append(seed)
-
- image_count += 1
+ for x_sample in x_samples_ddim:
+ x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+ image = Image.fromarray(x_sample.astype(np.uint8))
+ images.append([image,seed])
+ if callback is not None:
+ callback(image,seed)
+
seed = self._new_seed()
- if grid:
- images = self._make_grid(samples=all_samples,
- seeds=seeds,
- batch_size=batch_size,
- iterations=iterations,
- outdir=outdir)
except KeyboardInterrupt:
print('*interrupted*')
print('Partial results will be returned; if --grid was requested, nothing will be returned.')
@@ -279,48 +279,20 @@ The vast majority of these arguments default to reasonable values.
toc = time.time()
print(f'{image_count} images generated in',"%4.2fs"% (toc-tic))
-
return images
- # There is lots of shared code between this and txt2img and should be refactored.
@torch.no_grad()
- def img2img(self,prompt,outdir=None,init_img=None,batch_size=None,iterations=None,
- steps=None,seed=None,grid=None,individual=None,width=None,height=None,
- cfg_scale=None,ddim_eta=None,strength=None,embedding_path=None,
- skip_normalize=False,variants=None): # note the "variants" option is an unused hack caused by how options are passed
+ def _img2img(self,prompt,
+ data,precision_scope,
+ batch_size,iterations,
+ steps,seed,cfg_scale,ddim_eta,
+ skip_normalize,
+ init_img,strength,variants,
+ callback):
"""
Generate an image from the prompt and the initial image, writing iteration images into the outdir
- The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
+ The output is a list of lists in the format: [[image,seed1], [image,seed2],...]
"""
- outdir = outdir or self.outdir
- steps = steps or self.steps
- seed = seed or self.seed
- cfg_scale = cfg_scale or self.cfg_scale
- ddim_eta = ddim_eta or self.ddim_eta
- batch_size = batch_size or self.batch_size
- iterations = iterations or self.iterations
- strength = strength or self.strength
- embedding_path = embedding_path or self.embedding_path
-
- assert strength<1.0 and strength>=0.0, "strength (-f) must be >=0.0 and <1.0"
- assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
-
- if init_img is None:
- print("no init_img provided!")
- return []
-
- model = self.load_model() # will instantiate the model or return it from cache
-
- precision_scope = autocast if self.precision=="autocast" else nullcontext
-
- # grid and individual are mutually exclusive, with individual taking priority.
- # not necessary, but needed for compatability with dream bot
- if (grid is None):
- grid = self.grid
- if individual:
- grid = False
-
- data = [batch_size * [prompt]]
# PLMS sampler not supported yet, so ignore previous sampler
if self.sampler_name!='ddim':
@@ -329,33 +301,18 @@ The vast majority of these arguments default to reasonable values.
else:
sampler = self.sampler
- # make directories and establish names for the output files
- os.makedirs(outdir, exist_ok=True)
-
- assert os.path.isfile(init_img)
init_image = self._load_img(init_img).to(self.device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
with precision_scope(self.device.type):
init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
-
- try:
- assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
- except AssertionError:
- print(f"strength must be between 0.0 and 1.0, but received value {strength}")
- return []
t_enc = int(strength * steps)
print(f"target t_enc is {t_enc} steps")
images = list()
- seeds = list()
- filename = None
- image_count = 0 # actual number of iterations performed
- tic = time.time()
- # Gawd. Too many levels of indent here. Need to refactor into smaller routines!
try:
with precision_scope(self.device.type), model.ema_scope():
all_samples = list()
@@ -393,25 +350,13 @@ The vast majority of these arguments default to reasonable values.
x_samples = model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
- if not grid:
- for x_sample in x_samples:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- filename = self._unique_filename(outdir,previousname=filename,
- seed=seed,isbatch=(batch_size>1))
- assert not os.path.exists(filename)
- Image.fromarray(x_sample.astype(np.uint8)).save(filename)
- images.append([filename,seed])
- else:
- all_samples.append(x_samples)
- seeds.append(seed)
- image_count +=1
+ for x_sample in x_samples:
+ x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+ image = Image.fromarray(x_sample.astype(np.uint8))
+ images.append([image,seed])
+ if callback is not None:
+ callback(image,seed)
seed = self._new_seed()
- if grid:
- images = self._make_grid(samples=all_samples,
- seeds=seeds,
- batch_size=batch_size,
- iterations=iterations,
- outdir=outdir)
except KeyboardInterrupt:
print('*interrupted*')
@@ -419,26 +364,6 @@ The vast majority of these arguments default to reasonable values.
except RuntimeError as e:
print("Oops! A runtime error has occurred. If this is unexpected, please copy-and-paste this stack trace and post it as an Issue to http://github.com/lstein/stable-diffusion")
traceback.print_exc()
-
- toc = time.time()
- print(f'{image_count} images generated in',"%4.2fs"% (toc-tic))
-
- return images
-
- def _make_grid(self,samples,seeds,batch_size,iterations,outdir):
- images = list()
- n_rows = batch_size if batch_size>1 else int(math.sqrt(batch_size * iterations))
- # save as grid
- grid = torch.stack(samples, 0)
- grid = rearrange(grid, 'n b c h w -> (n b) c h w')
- grid = make_grid(grid, nrow=n_rows)
-
- # to image
- grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
- filename = self._unique_filename(outdir,seed=seeds[0],grid_count=batch_size*iterations)
- Image.fromarray(grid.astype(np.uint8)).save(filename)
- for s in seeds:
- images.append([filename,s])
return images
def _new_seed(self):
@@ -513,43 +438,6 @@ The vast majority of these arguments default to reasonable values.
image = torch.from_numpy(image)
return 2.*image - 1.
- def _unique_filename(self,outdir,previousname=None,seed=0,isbatch=False,grid_count=None):
- revision = 1
-
- if previousname is None:
- # sort reverse alphabetically until we find max+1
- dirlist = sorted(os.listdir(outdir),reverse=True)
- # find the first filename that matches our pattern or return 000000.0.png
- filename = next((f for f in dirlist if re.match('^(\d+)\..*\.png',f)),'0000000.0.png')
- basecount = int(filename.split('.',1)[0])
- basecount += 1
- if grid_count is not None:
- grid_label = f'grid#1-{grid_count}'
- filename = f'{basecount:06}.{seed}.{grid_label}.png'
- elif isbatch:
- filename = f'{basecount:06}.{seed}.01.png'
- else:
- filename = f'{basecount:06}.{seed}.png'
-
- return os.path.join(outdir,filename)
-
- else:
- previousname = os.path.basename(previousname)
- x = re.match('^(\d+)\..*\.png',previousname)
- if not x:
- return self._unique_filename(outdir,previousname,seed)
-
- basecount = int(x.groups()[0])
- series = 0
- finished = False
- while not finished:
- series += 1
- filename = f'{basecount:06}.{seed}.png'
- if isbatch or os.path.exists(os.path.join(outdir,filename)):
- filename = f'{basecount:06}.{seed}.{series:02}.png'
- finished = not os.path.exists(os.path.join(outdir,filename))
- return os.path.join(outdir,filename)
-
def _split_weighted_subprompts(text):
"""
grabs all text up to the first occurrence of ':'
diff --git a/scripts/dream.py b/scripts/dream.py
index dc5fad5bac..e0714dbcbd 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -8,13 +8,7 @@ import os
import sys
import copy
from PIL import Image,PngImagePlugin
-
-# readline unavailable on windows systems
-try:
- import readline
- readline_available = True
-except:
- readline_available = False
+from ldm.dream_util import Completer,PngWriter
debugging = False
@@ -131,13 +125,13 @@ def main_loop(t2i,parser,log,infile):
if elements[0]=='cd' and len(elements)>1:
if os.path.exists(elements[1]):
print(f"setting image output directory to {elements[1]}")
- t2i.outdir=elements[1]
+ opt.outdir=elements[1]
else:
print(f"directory {elements[1]} does not exist")
continue
if elements[0]=='pwd':
- print(f"current output directory is {t2i.outdir}")
+ print(f"current output directory is {opt.outdir}")
continue
if elements[0].startswith('!dream'): # in case a stored prompt still contains the !dream command
@@ -167,47 +161,19 @@ def main_loop(t2i,parser,log,infile):
continue
try:
- if opt.init_img is None:
- results = t2i.txt2img(**vars(opt))
- else:
- assert os.path.exists(opt.init_img),f"No file found at {opt.init_img}. On Linux systems, pressing <tab> after -I will autocomplete a list of possible image files."
- if None not in (opt.width,opt.height):
- print('Warning: width and height options are ignored when modifying an init image')
- results = t2i.img2img(**vars(opt))
+ file_writer = PngWriter(opt)
+ opt.callback = file_writer(write_image)
+ run_generator(**vars(opt))
+ results = file_writer.files_written
except AssertionError as e:
print(e)
continue
-
- allVariantResults = []
- if opt.variants is not None:
- print(f"Generating {opt.variants} variant(s)...")
- newopt = copy.deepcopy(opt)
- newopt.variants = None
- for r in results:
- newopt.init_img = r[0]
- print(f"\t generating variant for {newopt.init_img}")
- for j in range(0, opt.variants):
- try:
- variantResults = t2i.img2img(**vars(newopt))
- allVariantResults.append([newopt,variantResults])
- except AssertionError as e:
- print(e)
- continue
- print(f"{opt.variants} Variants generated!")
-
print("Outputs:")
write_log_message(t2i,opt,results,log)
-
- if allVariantResults:
- print("Variant outputs:")
- for vr in allVariantResults:
- write_log_message(t2i,vr[0],vr[1],log)
-
print("goodbye!")
-
def write_log_message(t2i,opt,results,logfile):
''' logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata '''
switches = _reconstruct_switches(t2i,opt)
@@ -339,89 +305,7 @@ def create_cmd_parser():
parser.add_argument('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
return parser
-if readline_available:
- def setup_readline():
- readline.set_completer(Completer(['cd','pwd',
- '--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
- '--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
- '--individual','-i','--init_img','-I','--strength','-f','-v','--variants']).complete)
- readline.set_completer_delims(" ")
- readline.parse_and_bind('tab: complete')
- load_history()
- def load_history():
- histfile = os.path.join(os.path.expanduser('~'),".dream_history")
- try:
- readline.read_history_file(histfile)
- readline.set_history_length(1000)
- except FileNotFoundError:
- pass
- atexit.register(readline.write_history_file,histfile)
-
- class Completer():
- def __init__(self,options):
- self.options = sorted(options)
- return
-
- def complete(self,text,state):
- buffer = readline.get_line_buffer()
-
- if text.startswith(('-I','--init_img')):
- return self._path_completions(text,state,('.png'))
-
- if buffer.strip().endswith('cd') or text.startswith(('.','/')):
- return self._path_completions(text,state,())
-
- response = None
- if state == 0:
- # This is the first time for this text, so build a match list.
- if text:
- self.matches = [s
- for s in self.options
- if s and s.startswith(text)]
- else:
- self.matches = self.options[:]
-
- # Return the state'th item from the match list,
- # if we have that many.
- try:
- response = self.matches[state]
- except IndexError:
- response = None
- return response
-
- def _path_completions(self,text,state,extensions):
- # get the path so far
- if text.startswith('-I'):
- path = text.replace('-I','',1).lstrip()
- elif text.startswith('--init_img='):
- path = text.replace('--init_img=','',1).lstrip()
- else:
- path = text
-
- matches = list()
-
- path = os.path.expanduser(path)
- if len(path)==0:
- matches.append(text+'./')
- else:
- dir = os.path.dirname(path)
- dir_list = os.listdir(dir)
- for n in dir_list:
- if n.startswith('.') and len(n)>1:
- continue
- full_path = os.path.join(dir,n)
- if full_path.startswith(path):
- if os.path.isdir(full_path):
- matches.append(os.path.join(os.path.dirname(text),n)+'/')
- elif n.endswith(extensions):
- matches.append(os.path.join(os.path.dirname(text),n))
-
- try:
- response = matches[state]
- except IndexError:
- response = None
- return response
if __name__ == "__main__":
main()
From b12955c9631ceec70154690eef072b99b9c71c32 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 24 Aug 2022 17:57:44 -0400
Subject: [PATCH 05/58] remove unneeded imports from dream.py
---
scripts/dream.py | 2 --
1 file changed, 2 deletions(-)
diff --git a/scripts/dream.py b/scripts/dream.py
index e0714dbcbd..6ff7802fa2 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -3,11 +3,9 @@
import argparse
import shlex
-import atexit
import os
import sys
import copy
-from PIL import Image,PngImagePlugin
from ldm.dream_util import Completer,PngWriter
debugging = False
From 0a7fe6f2d9ba03b7da1eef2317476ce4f9de42ca Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Wed, 24 Aug 2022 18:19:50 -0400
Subject: [PATCH 06/58] Switch to ThreadingHTTPServer
---
scripts/dream_web.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index 3b10922c68..7c9155d926 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -1,6 +1,6 @@
import json
import os
-from http.server import BaseHTTPRequestHandler, HTTPServer
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
print("Loading model...")
from ldm.simplet2i import T2I
@@ -50,7 +50,7 @@ class DreamServer(BaseHTTPRequestHandler):
self.wfile.write(bytes(json.dumps(result), "utf-8"))
if __name__ == "__main__":
- dream_server = HTTPServer(("0.0.0.0", 9090), DreamServer)
+ dream_server = ThreadingHTTPServer(("0.0.0.0", 9090), DreamServer)
print("Started Stable Diffusion dream server!")
try:
From b978536385613ab2c4bc4660633337e730020d75 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 24 Aug 2022 19:47:59 -0400
Subject: [PATCH 07/58] code is reorganized and mostly functional. Grid needs
to be brought back online, as well as naming of img2img variants (currently
the variants get written but not logged)
---
ldm/dream_util.py | 64 ++++++++++++++++++++++++++--------
ldm/simplet2i.py | 76 +++++++++++++++++++---------------------
scripts/dream.py | 89 +++++++++--------------------------------------
3 files changed, 101 insertions(+), 128 deletions(-)
diff --git a/ldm/dream_util.py b/ldm/dream_util.py
index 1526223cd8..ceab2940b1 100644
--- a/ldm/dream_util.py
+++ b/ldm/dream_util.py
@@ -1,6 +1,7 @@
'''Utilities for dealing with PNG images and their path names'''
import os
import atexit
+import re
from PIL import Image,PngImagePlugin
# ---------------readline utilities---------------------
@@ -94,40 +95,43 @@ if readline_available:
# -------------------image generation utils-----
class PngWriter:
- def __init__(self,opt):
- self.opt = opt
- self.filepath = None
- self.files_written = []
+ def __init__(self,outdir,opt,prompt):
+ self.outdir = outdir
+ self.opt = opt
+ self.prompt = prompt
+ self.filepath = None
+ self.files_written = []
def write_image(self,image,seed):
- self.filepath = self.unique_filename(self,opt,seed,self.filepath) # will increment name in some sensible way
+ self.filepath = self.unique_filename(seed,self.filepath) # will increment name in some sensible way
try:
- image.save(self.filename)
+ prompt = f'{self.prompt} -S{seed}'
+ self.save_image_and_prompt_to_png(image,prompt,self.filepath)
except IOError as e:
print(e)
self.files_written.append([self.filepath,seed])
- def unique_filename(self,opt,seed,previouspath):
+ def unique_filename(self,seed,previouspath):
revision = 1
if previouspath is None:
# sort reverse alphabetically until we find max+1
- dirlist = sorted(os.listdir(outdir),reverse=True)
+ dirlist = sorted(os.listdir(self.outdir),reverse=True)
# find the first filename that matches our pattern or return 000000.0.png
filename = next((f for f in dirlist if re.match('^(\d+)\..*\.png',f)),'0000000.0.png')
basecount = int(filename.split('.',1)[0])
basecount += 1
- if opt.batch_size > 1:
+ if self.opt.batch_size > 1:
filename = f'{basecount:06}.{seed}.01.png'
else:
filename = f'{basecount:06}.{seed}.png'
- return os.path.join(outdir,filename)
+ return os.path.join(self.outdir,filename)
else:
basename = os.path.basename(previouspath)
x = re.match('^(\d+)\..*\.png',basename)
if not x:
- return self.unique_filename(opt,seed,previouspath)
+ return self.unique_filename(seed,previouspath)
basecount = int(x.groups()[0])
series = 0
@@ -135,9 +139,41 @@ class PngWriter:
while not finished:
series += 1
filename = f'{basecount:06}.{seed}.png'
- if isbatch or os.path.exists(os.path.join(outdir,filename)):
+ if self.opt.batch_size>1 or os.path.exists(os.path.join(self.outdir,filename)):
filename = f'{basecount:06}.{seed}.{series:02}.png'
- finished = not os.path.exists(os.path.join(outdir,filename))
- return os.path.join(outdir,filename)
+ finished = not os.path.exists(os.path.join(self.outdir,filename))
+ return os.path.join(self.outdir,filename)
+ def save_image_and_prompt_to_png(self,image,prompt,path):
+ info = PngImagePlugin.PngInfo()
+ info.add_text("Dream",prompt)
+ image.save(path,"PNG",pnginfo=info)
+
+class PromptFormatter():
+ def __init__(self,t2i,opt):
+ self.t2i = t2i
+ self.opt = opt
+
+ def normalize_prompt(self):
+ '''Normalize the prompt and switches'''
+ t2i = self.t2i
+ opt = self.opt
+
+ switches = list()
+ switches.append(f'"{opt.prompt}"')
+ switches.append(f'-s{opt.steps or t2i.steps}')
+ switches.append(f'-b{opt.batch_size or t2i.batch_size}')
+ switches.append(f'-W{opt.width or t2i.width}')
+ switches.append(f'-H{opt.height or t2i.height}')
+ switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
+ switches.append(f'-m{t2i.sampler_name}')
+ if opt.variants:
+ switches.append(f'-v{opt.variants}')
+ if opt.init_img:
+ switches.append(f'-I{opt.init_img}')
+ if opt.strength and opt.init_img is not None:
+ switches.append(f'-f{opt.strength or t2i.strength}')
+ if t2i.full_precision:
+ switches.append('-F')
+ return ' '.join(switches)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 8e8b077922..3b5aaeb696 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -99,13 +99,13 @@ The vast majority of these arguments default to reasonable values.
def __init__(self,
batch_size=1,
iterations = 1,
- grid=False,
- individual=None, # redundant
steps=50,
seed=None,
cfg_scale=7.5,
weights="models/ldm/stable-diffusion-v1/model.ckpt",
config = "configs/stable-diffusion/v1-inference.yaml",
+ width=512,
+ height=512,
sampler_name="klms",
latent_channels=4,
downsampling_factor=8,
@@ -121,7 +121,6 @@ The vast majority of these arguments default to reasonable values.
self.iterations = iterations
self.width = width
self.height = height
- self.grid = grid
self.steps = steps
self.cfg_scale = cfg_scale
self.weights = weights
@@ -143,25 +142,26 @@ The vast majority of these arguments default to reasonable values.
else:
self.seed = seed
- def generate(self,
- # these are common
- prompt,
- batch_size=None,
- iterations=None,
- steps=None,
- seed=None,
- cfg_scale=None,
- ddim_eta=None,
- skip_normalize=False,
- image_callback=None,
- # these are specific to txt2img
- width=None,
- height=None,
- # these are specific to img2img
- init_img=None,
- strength=None,
- variants=None):
- '''ldm.generate() is the common entry point for txt2img() and img2img()'''
+ def prompt2image(self,
+ # these are common
+ prompt,
+ batch_size=None,
+ iterations=None,
+ steps=None,
+ seed=None,
+ cfg_scale=None,
+ ddim_eta=None,
+ skip_normalize=False,
+ image_callback=None,
+ # these are specific to txt2img
+ width=None,
+ height=None,
+ # these are specific to img2img
+ init_img=None,
+ strength=None,
+ variants=None,
+ **args): # eat up additional cruft
+ '''ldm.prompt2image() is the common entry point for txt2img() and img2img()'''
steps = steps or self.steps
seed = seed or self.seed
width = width or self.width
@@ -178,10 +178,6 @@ The vast majority of these arguments default to reasonable values.
data = [batch_size * [prompt]]
scope = autocast if self.precision=="autocast" else nullcontext
- if grid:
- callback = self.image2png
- else:
- callback = None
tic = time.time()
if init_img:
@@ -212,7 +208,7 @@ The vast majority of these arguments default to reasonable values.
steps,seed,cfg_scale,ddim_eta,
skip_normalize,
width,height,
- callback=callback): # the callback is called each time a new Image is generated
+ callback): # the callback is called each time a new Image is generated
"""
Generate an image from the prompt, writing iteration images into the outdir
The output is a list of lists in the format: [[image1,seed1], [image2,seed2],...]
@@ -224,14 +220,14 @@ The vast majority of these arguments default to reasonable values.
# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
try:
- with precision_scope(self.device.type), model.ema_scope():
+ with precision_scope(self.device.type), self.model.ema_scope():
all_samples = list()
for n in trange(iterations, desc="Sampling"):
seed_everything(seed)
for prompts in tqdm(data, desc="data", dynamic_ncols=True):
uc = None
if cfg_scale != 1.0:
- uc = model.get_learned_conditioning(batch_size * [""])
+ uc = self.model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
@@ -247,9 +243,9 @@ The vast majority of these arguments default to reasonable values.
weight = weights[i]
if not skip_normalize:
weight = weight / totalWeight
- c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
+ c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
else: # just standard 1 prompt
- c = model.get_learned_conditioning(prompts)
+ c = self.model.get_learned_conditioning(prompts)
shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
samples_ddim, _ = sampler.sample(S=steps,
@@ -261,7 +257,7 @@ The vast majority of these arguments default to reasonable values.
unconditional_conditioning=uc,
eta=ddim_eta)
- x_samples_ddim = model.decode_first_stage(samples_ddim)
+ x_samples_ddim = self.model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
@@ -277,8 +273,6 @@ The vast majority of these arguments default to reasonable values.
except RuntimeError as e:
print(str(e))
- toc = time.time()
- print(f'{image_count} images generated in',"%4.2fs"% (toc-tic))
return images
@torch.no_grad()
@@ -297,14 +291,14 @@ The vast majority of these arguments default to reasonable values.
# PLMS sampler not supported yet, so ignore previous sampler
if self.sampler_name!='ddim':
print(f"sampler '{self.sampler_name}' is not yet supported. Using DDM sampler")
- sampler = DDIMSampler(model, device=self.device)
+ sampler = DDIMSampler(self.model, device=self.device)
else:
sampler = self.sampler
init_image = self._load_img(init_img).to(self.device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
with precision_scope(self.device.type):
- init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
+ init_latent = self.model.get_first_stage_encoding(self.model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
@@ -314,14 +308,14 @@ The vast majority of these arguments default to reasonable values.
images = list()
try:
- with precision_scope(self.device.type), model.ema_scope():
+ with precision_scope(self.device.type), self.model.ema_scope():
all_samples = list()
for n in trange(iterations, desc="Sampling"):
seed_everything(seed)
for prompts in tqdm(data, desc="data", dynamic_ncols=True):
uc = None
if cfg_scale != 1.0:
- uc = model.get_learned_conditioning(batch_size * [""])
+ uc = self.model.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
@@ -337,9 +331,9 @@ The vast majority of these arguments default to reasonable values.
weight = weights[i]
if not skip_normalize:
weight = weight / totalWeight
- c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
+ c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
else: # just standard 1 prompt
- c = model.get_learned_conditioning(prompts)
+ c = self.model.get_learned_conditioning(prompts)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
@@ -347,7 +341,7 @@ The vast majority of these arguments default to reasonable values.
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,)
- x_samples = model.decode_first_stage(samples)
+ x_samples = self.model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
for x_sample in x_samples:
diff --git a/scripts/dream.py b/scripts/dream.py
index 6ff7802fa2..ab01e8db01 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -6,7 +6,7 @@ import shlex
import os
import sys
import copy
-from ldm.dream_util import Completer,PngWriter
+from ldm.dream_util import Completer,PngWriter,PromptFormatter
debugging = False
@@ -27,10 +27,6 @@ def main():
config = "configs/stable-diffusion/v1-inference.yaml"
weights = "models/ldm/stable-diffusion-v1/model.ckpt"
- # command line history will be stored in a file called "~/.dream_history"
- if readline_available:
- setup_readline()
-
print("* Initializing, be patient...\n")
sys.path.append('.')
from pytorch_lightning import logging
@@ -46,8 +42,6 @@ def main():
# the user input loop
t2i = T2I(width=width,
height=height,
- batch_size=opt.batch_size,
- outdir=opt.outdir,
sampler_name=opt.sampler_name,
weights=weights,
full_precision=opt.full_precision,
@@ -79,13 +73,13 @@ def main():
log_path = os.path.join(opt.outdir,'dream_log.txt')
with open(log_path,'a') as log:
cmd_parser = create_cmd_parser()
- main_loop(t2i,cmd_parser,log,infile)
+ main_loop(t2i,opt.outdir,cmd_parser,log,infile)
log.close()
if infile:
infile.close()
-def main_loop(t2i,parser,log,infile):
+def main_loop(t2i,outdir,parser,log,infile):
''' prompt/read/execute loop '''
done = False
@@ -123,13 +117,13 @@ def main_loop(t2i,parser,log,infile):
if elements[0]=='cd' and len(elements)>1:
if os.path.exists(elements[1]):
print(f"setting image output directory to {elements[1]}")
- opt.outdir=elements[1]
+ outdir=elements[1]
else:
print(f"directory {elements[1]} does not exist")
continue
if elements[0]=='pwd':
- print(f"current output directory is {opt.outdir}")
+ print(f"current output directory is {outdir}")
continue
if elements[0].startswith('!dream'): # in case a stored prompt still contains the !dream command
@@ -158,88 +152,41 @@ def main_loop(t2i,parser,log,infile):
print("Try again with a prompt!")
continue
+ normalized_prompt = PromptFormatter(t2i,opt).normalize_prompt()
try:
- file_writer = PngWriter(opt)
- opt.callback = file_writer(write_image)
- run_generator(**vars(opt))
+ file_writer = PngWriter(outdir,opt,normalized_prompt)
+ callback = file_writer.write_image
+
+ t2i.prompt2image(image_callback=callback,
+ **vars(opt))
results = file_writer.files_written
+
except AssertionError as e:
print(e)
continue
print("Outputs:")
- write_log_message(t2i,opt,results,log)
+ write_log_message(t2i,normalized_prompt,results,log)
print("goodbye!")
-def write_log_message(t2i,opt,results,logfile):
+def write_log_message(t2i,prompt,results,logfile):
''' logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata '''
- switches = _reconstruct_switches(t2i,opt)
- prompt_str = ' '.join(switches)
-
- # when multiple images are produced in batch, then we keep track of where each starts
last_seed = None
img_num = 1
- batch_size = opt.batch_size or t2i.batch_size
seenit = {}
seeds = [a[1] for a in results]
- if batch_size > 1:
- seeds = f"(seeds for each batch row: {seeds})"
- else:
- seeds = f"(seeds for individual images: {seeds})"
+ seeds = f"(seeds for individual images: {seeds})"
for r in results:
seed = r[1]
- log_message = (f'{r[0]}: {prompt_str} -S{seed}')
+ log_message = (f'{r[0]}: {prompt} -S{seed}')
- if batch_size > 1:
- if seed != last_seed:
- img_num = 1
- log_message += f' # (batch image {img_num} of {batch_size})'
- else:
- img_num += 1
- log_message += f' # (batch image {img_num} of {batch_size})'
- last_seed = seed
print(log_message)
logfile.write(log_message+"\n")
logfile.flush()
- if r[0] not in seenit:
- seenit[r[0]] = True
- try:
- if opt.grid:
- _write_prompt_to_png(r[0],f'{prompt_str} -g -S{seed} {seeds}')
- else:
- _write_prompt_to_png(r[0],f'{prompt_str} -S{seed}')
- except FileNotFoundError:
- print(f"Could not open file '{r[0]}' for reading")
-def _reconstruct_switches(t2i,opt):
- '''Normalize the prompt and switches'''
- switches = list()
- switches.append(f'"{opt.prompt}"')
- switches.append(f'-s{opt.steps or t2i.steps}')
- switches.append(f'-b{opt.batch_size or t2i.batch_size}')
- switches.append(f'-W{opt.width or t2i.width}')
- switches.append(f'-H{opt.height or t2i.height}')
- switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
- switches.append(f'-m{t2i.sampler_name}')
- if opt.variants:
- switches.append(f'-v{opt.variants}')
- if opt.init_img:
- switches.append(f'-I{opt.init_img}')
- if opt.strength and opt.init_img is not None:
- switches.append(f'-f{opt.strength or t2i.strength}')
- if t2i.full_precision:
- switches.append('-F')
- return switches
-
-def _write_prompt_to_png(path,prompt):
- info = PngImagePlugin.PngInfo()
- info.add_text("Dream",prompt)
- im = Image.open(path)
- im.save(path,"PNG",pnginfo=info)
-
def create_argv_parser():
parser = argparse.ArgumentParser(description="Parse script's command line args")
parser.add_argument("--laion400m",
@@ -260,10 +207,6 @@ def create_argv_parser():
dest='full_precision',
action='store_true',
help="use slower full precision math for calculations")
- parser.add_argument('-b','--batch_size',
- type=int,
- default=1,
- help="number of images to produce per iteration (faster, but doesn't generate individual seeds")
parser.add_argument('--sampler','-m',
dest="sampler_name",
choices=['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'],
From 269fcf92d9f2dfe699eb1950176eeebbc1df6620 Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Wed, 24 Aug 2022 21:38:47 -0400
Subject: [PATCH 08/58] Reapply prompt config on image click
---
scripts/dream_web.py | 2 +-
scripts/static/index.html | 17 +++++++++++++++--
2 files changed, 16 insertions(+), 3 deletions(-)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index 7c9155d926..a363dd32ad 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -45,7 +45,7 @@ class DreamServer(BaseHTTPRequestHandler):
steps = steps);
print(f"Prompt generated with output: {outputs}")
- outputs = [x + [prompt] for x in outputs] # Append prompt to each output
+ outputs = [x + [post_data] for x in outputs] # Append config to each output
result = {'outputs': outputs}
self.wfile.write(bytes(json.dumps(result), "utf-8"))
diff --git a/scripts/static/index.html b/scripts/static/index.html
index d5b27e83ef..6d8697846d 100644
--- a/scripts/static/index.html
+++ b/scripts/static/index.html
@@ -52,12 +52,20 @@
let output_node = document.createElement("img");
output_node.src = output[0];
- let alt_text = output[1].toString() + " | " + output[2];
+ let output_config = output[2];
+ let alt_text = output[1].toString() + " | " + output_config.prompt;
output_node.alt = alt_text;
output_node.title = alt_text;
- // Update seed on click
+
+ // Reload image config
output_node.addEventListener('click', () => {
+ let form = document.querySelector("#generate_form");
+ for (const [k, v] of new FormData(form)) {
+ form.querySelector(`*[name=${k}]`).value = output_config[k];
+ }
document.querySelector("#seed").value = output[1];
+
+ save_fields(document.querySelector("#generate_form"));
});
document.querySelector("#results").prepend(output_node);
@@ -115,6 +123,10 @@
document.querySelector("#generate_form").addEventListener('change', (e) => {
save_fields(e.target.form);
});
+ document.querySelector("#reset").addEventListener('click', (e) => {
+ document.querySelector("#seed").value = -1;
+ save_fields(e.target.form);
+ });
load_fields(document.querySelector("#generate_form"));
};
</script>
@@ -143,6 +155,7 @@
<span>•</span>
<label title="Set to -1 for random seed" for="seed">Seed:</label>
<input value="-1" type="number" id="seed" name="seed">
+ <button type="button" id="reset">↺</button>
</fieldset>
</form>
</div>
From ab131cb55e0b806cbbb451c63d4b0ab562c4296b Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Wed, 24 Aug 2022 23:03:02 -0400
Subject: [PATCH 09/58] Add img2img support, fix naming conventions
---
scripts/dream_web.py | 39 +++++++++---
scripts/static/index.html | 123 +++++++++++++++++++++++---------------
2 files changed, 105 insertions(+), 57 deletions(-)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index a363dd32ad..f56f67f7d5 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -1,4 +1,5 @@
import json
+import base64
import os
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
@@ -28,6 +29,7 @@ class DreamServer(BaseHTTPRequestHandler):
content_length = int(self.headers['Content-Length'])
post_data = json.loads(self.rfile.read(content_length))
prompt = post_data['prompt']
+ initimg = post_data['initimg']
batch = int(post_data['batch'])
steps = int(post_data['steps'])
width = int(post_data['width'])
@@ -35,16 +37,37 @@ class DreamServer(BaseHTTPRequestHandler):
cfgscale = float(post_data['cfgscale'])
seed = None if int(post_data['seed']) == -1 else int(post_data['seed'])
- print(f"Request to generate with data: {post_data}")
- outputs = model.txt2img(prompt,
- batch_size = batch,
- cfg_scale = cfgscale,
- width = width,
- height = height,
- seed = seed,
- steps = steps);
+ print(f"Request to generate with prompt: {prompt}")
+
+ outputs = []
+ if initimg is None:
+ # Run txt2img
+ outputs = model.txt2img(prompt,
+ batch_size = batch,
+ cfg_scale = cfgscale,
+ width = width,
+ height = height,
+ seed = seed,
+ steps = steps)
+ else:
+ # Decode initimg as base64 to temp file
+ with open("./img2img-tmp.png", "wb") as f:
+ initimg = initimg.split(",")[1] # Ignore mime type
+ f.write(base64.b64decode(initimg))
+
+ # Run img2img
+ outputs = model.img2img(prompt,
+ init_img = "./img2img-tmp.png",
+ batch_size = batch,
+ cfg_scale = cfgscale,
+ seed = seed,
+ steps = steps)
+ # Remove the temp file
+ os.remove("./img2img-tmp.png")
+
print(f"Prompt generated with output: {outputs}")
+ post_data['initimg'] = '' # Don't send init image back
outputs = [x + [post_data] for x in outputs] # Append config to each output
result = {'outputs': outputs}
self.wfile.write(bytes(json.dumps(result), "utf-8"))
diff --git a/scripts/static/index.html b/scripts/static/index.html
index 6d8697846d..663a32ed12 100644
--- a/scripts/static/index.html
+++ b/scripts/static/index.html
@@ -40,6 +40,9 @@
border-radius: 5px;
margin: 10px;
}
+ #generate-config {
+ line-height:2em;
+ }
input[type="number"] {
width: 60px;
}
@@ -48,41 +51,52 @@
}
</style>
<script>
- function append_output(output) {
- let output_node = document.createElement("img");
- output_node.src = output[0];
+ function toBase64(file) {
+ return new Promise((resolve, reject) => {
+ const r = new FileReader();
+ r.readAsDataURL(file);
+ r.onload = () => resolve(r.result);
+ r.onerror = (error) => reject(error);
+ });
+ }
- let output_config = output[2];
- let alt_text = output[1].toString() + " | " + output_config.prompt;
- output_node.alt = alt_text;
- output_node.title = alt_text;
+ function appendOutput(output) {
+ let outputNode = document.createElement("img");
+ outputNode.src = output[0];
+
+ let outputConfig = output[2];
+ let altText = output[1].toString() + " | " + outputConfig.prompt;
+ outputNode.alt = altText;
+ outputNode.title = altText;
// Reload image config
- output_node.addEventListener('click', () => {
- let form = document.querySelector("#generate_form");
+ outputNode.addEventListener('click', () => {
+ let form = document.querySelector("#generate-form");
for (const [k, v] of new FormData(form)) {
- form.querySelector(`*[name=${k}]`).value = output_config[k];
+ form.querySelector(`*[name=${k}]`).value = outputConfig[k];
}
document.querySelector("#seed").value = output[1];
- save_fields(document.querySelector("#generate_form"));
+ saveFields(document.querySelector("#generate-form"));
});
- document.querySelector("#results").prepend(output_node);
+ document.querySelector("#results").prepend(outputNode);
}
- function append_outputs(outputs) {
+ function appendOutputs(outputs) {
for (const output of outputs) {
- append_output(output);
+ appendOutput(output);
}
}
- function save_fields(form) {
+ function saveFields(form) {
for (const [k, v] of new FormData(form)) {
- localStorage.setItem(k, v);
+ if (typeof v !== 'object') { // Don't save 'file' type
+ localStorage.setItem(k, v);
+ }
}
}
- function load_fields(form) {
+ function loadFields(form) {
for (const [k, v] of new FormData(form)) {
const item = localStorage.getItem(k);
if (item != null) {
@@ -90,44 +104,53 @@
}
}
}
+
+ async function generateSubmit(form) {
+ const prompt = document.querySelector("#prompt").value;
+
+ // Convert file data to base64
+ let formData = Object.fromEntries(new FormData(form));
+ formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
+
+ // Post as JSON
+ fetch(form.action, {
+ method: form.method,
+ body: JSON.stringify(formData),
+ }).then(async (result) => {
+ let data = await result.json();
+
+ // Re-enable form, remove no-results-message
+ form.querySelector('fieldset').removeAttribute('disabled');
+ document.querySelector("#prompt").value = prompt;
+
+ if (data.outputs.length != 0) {
+ document.querySelector("#no-results-message")?.remove();
+ appendOutputs(data.outputs);
+ } else {
+ alert("Error occurred while generating.");
+ }
+ });
+
+ // Disable form while generating
+ form.querySelector('fieldset').setAttribute('disabled','');
+ document.querySelector("#prompt").value = `Generating: "${prompt}"`;
+ }
window.onload = () => {
- document.querySelector("#generate_form").addEventListener('submit', (e) => {
+ document.querySelector("#generate-form").addEventListener('submit', (e) => {
e.preventDefault();
const form = e.target;
- const prompt = document.querySelector("#prompt").value;
- // Post as JSON
- fetch(form.action, {
- method: form.method,
- body: JSON.stringify(Object.fromEntries(new FormData(form))),
- }).then((result) => {
- result.json().then((data) => {
- // Re-enable form, remove no-results-message
- form.querySelector('fieldset').removeAttribute('disabled');
- document.querySelector("#prompt").value = prompt;
-
- if (data.outputs.length != 0) {
- document.querySelector("#no_results_message")?.remove();
- append_outputs(data.outputs);
- } else {
- alert("Error occurred while generating.");
- }
- });
- });
-
- // Disable form
- form.querySelector('fieldset').setAttribute('disabled','');
- document.querySelector("#prompt").value = `Generating: "${prompt}"`;
+ generateSubmit(form);
});
- document.querySelector("#generate_form").addEventListener('change', (e) => {
- save_fields(e.target.form);
+ document.querySelector("#generate-form").addEventListener('change', (e) => {
+ saveFields(e.target.form);
});
document.querySelector("#reset").addEventListener('click', (e) => {
document.querySelector("#seed").value = -1;
- save_fields(e.target.form);
+ saveFields(e.target.form);
});
- load_fields(document.querySelector("#generate_form"));
+ loadFields(document.querySelector("#generate-form"));
};
</script>
</head>
@@ -135,12 +158,12 @@
<div id="search">
<h2 id="header">Stable Diffusion</h2>
- <form id="generate_form" method="post" action="#">
+ <form id="generate-form" method="post" action="#">
<fieldset>
<input type="text" id="prompt" name="prompt">
<input type="submit" id="submit" value="Generate">
</fieldset>
- <fieldset>
+ <fieldset id="generate-config">
<label for="batch">Batch Size:</label>
<input value="1" type="number" id="batch" name="batch">
<label for="steps">Steps:</label>
@@ -152,7 +175,9 @@
<input value="512" type="number" id="width" name="width">
<label title="Set to multiple of 64" for="height">Height:</label>
<input value="512" type="number" id="height" name="height">
- <span>•</span>
+ <br>
+ <label title="Upload an image to use img2img" for="initimg">Img2Img Init:</label>
+ <input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
<label title="Set to -1 for random seed" for="seed">Seed:</label>
<input value="-1" type="number" id="seed" name="seed">
<button type="button" id="reset">↺</button>
@@ -161,7 +186,7 @@
</div>
<hr style="width: 200px">
<div id="results">
- <div id="no_results_message">
+ <div id="no-results-message">
<i><p>No results...</p></i>
</div>
</div>
From 51b9a1d8d3622a9e6a0866241a863bb16afecf09 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Wed, 24 Aug 2022 23:55:31 -0400
Subject: [PATCH 10/58] Update readme.md
---
README.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index a9b03362e2..74deb52d83 100644
--- a/README.md
+++ b/README.md
@@ -172,8 +172,8 @@ repository and associated paper for details and limitations.
* v1.09 (24 August 2022)
* A new -v option allows you to generate multiple variants of an initial image
- in img2img mode. (kudos to Oceanswave)
- * Added ability to personalize text to image generation (kudos to nicolai256)
+ in img2img mode. (kudos to @Oceanswave. [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
+ * Added ability to personalize text to image generation (kudos to @Oceanswave and @nicolai256)
* Enabled all of the samplers from k_diffusion
* v1.08 (24 August 2022)
From 29727f3e12fe918cab2adfa947bf990c399136f5 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Wed, 24 Aug 2022 23:59:37 -0400
Subject: [PATCH 11/58] Another update
---
README.md | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/README.md b/README.md
index 74deb52d83..a14d3dc18c 100644
--- a/README.md
+++ b/README.md
@@ -172,8 +172,8 @@ repository and associated paper for details and limitations.
* v1.09 (24 August 2022)
* A new -v option allows you to generate multiple variants of an initial image
- in img2img mode. (kudos to @Oceanswave. [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
- * Added ability to personalize text to image generation (kudos to @Oceanswave and @nicolai256)
+ in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
+ * Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
* Enabled all of the samplers from k_diffusion
* v1.08 (24 August 2022)
@@ -457,8 +457,8 @@ to send me an email if you use and like the script.
*Contributions by:*
[Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison),
-[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan] (https://github.com/Oceanswave],
-[nicolai256](https://github.com/nicolai256], [Benjamin Warner](https://github.com/warner-benjamin),
+[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan] (https://github.com/Oceanswave),
+[nicolai256](https://github.com/nicolai256), [Benjamin Warner](https://github.com/warner-benjamin),
and [tildebyte](https://github.com/tildebyte)
Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
From c521ac08eea928c99d2122904a8d0ea1822922af Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Thu, 25 Aug 2022 00:00:39 -0400
Subject: [PATCH 12/58] Another update
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index a14d3dc18c..a3347bdb4d 100644
--- a/README.md
+++ b/README.md
@@ -457,7 +457,7 @@ to send me an email if you use and like the script.
*Contributions by:*
[Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison),
-[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan] (https://github.com/Oceanswave),
+[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan](https://github.com/Oceanswave),
[nicolai256](https://github.com/nicolai256), [Benjamin Warner](https://github.com/warner-benjamin),
and [tildebyte](https://github.com/tildebyte)
From 0b4459b7074060314aaafe2c142370d77ac56a46 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 00:42:37 -0400
Subject: [PATCH 13/58] mostly back to full functionality; just missing grid
generation code
---
ldm/dream_util.py | 171 ++++++++++++++++++++++----------------------
ldm/simplet2i.py | 175 ++++++++++++++++++++++++++++++++--------------
scripts/dream.py | 37 +++++++++-
3 files changed, 245 insertions(+), 138 deletions(-)
diff --git a/ldm/dream_util.py b/ldm/dream_util.py
index ceab2940b1..a1d0d3204b 100644
--- a/ldm/dream_util.py
+++ b/ldm/dream_util.py
@@ -4,6 +4,92 @@ import atexit
import re
from PIL import Image,PngImagePlugin
+# -------------------image generation utils-----
+class PngWriter:
+
+ def __init__(self,outdir,prompt=None,batch_size=1):
+ self.outdir = outdir
+ self.batch_size = batch_size
+ self.prompt = prompt
+ self.filepath = None
+ self.files_written = []
+ os.makedirs(outdir, exist_ok=True)
+
+ def write_image(self,image,seed):
+ self.filepath = self.unique_filename(seed,self.filepath) # will increment name in some sensible way
+ try:
+ prompt = f'{self.prompt} -S{seed}'
+ self.save_image_and_prompt_to_png(image,prompt,self.filepath)
+ except IOError as e:
+ print(e)
+ self.files_written.append([self.filepath,seed])
+
+ def unique_filename(self,seed,previouspath):
+ revision = 1
+
+ if previouspath is None:
+ # sort reverse alphabetically until we find max+1
+ dirlist = sorted(os.listdir(self.outdir),reverse=True)
+ # find the first filename that matches our pattern or return 000000.0.png
+ filename = next((f for f in dirlist if re.match('^(\d+)\..*\.png',f)),'0000000.0.png')
+ basecount = int(filename.split('.',1)[0])
+ basecount += 1
+ if self.batch_size > 1:
+ filename = f'{basecount:06}.{seed}.01.png'
+ else:
+ filename = f'{basecount:06}.{seed}.png'
+ return os.path.join(self.outdir,filename)
+
+ else:
+ basename = os.path.basename(previouspath)
+ x = re.match('^(\d+)\..*\.png',basename)
+ if not x:
+ return self.unique_filename(seed,previouspath)
+
+ basecount = int(x.groups()[0])
+ series = 0
+ finished = False
+ while not finished:
+ series += 1
+ filename = f'{basecount:06}.{seed}.png'
+ if self.batch_size>1 or os.path.exists(os.path.join(self.outdir,filename)):
+ filename = f'{basecount:06}.{seed}.{series:02}.png'
+ finished = not os.path.exists(os.path.join(self.outdir,filename))
+ return os.path.join(self.outdir,filename)
+
+ def save_image_and_prompt_to_png(self,image,prompt,path):
+ info = PngImagePlugin.PngInfo()
+ info.add_text("Dream",prompt)
+ image.save(path,"PNG",pnginfo=info)
+
+class PromptFormatter():
+ def __init__(self,t2i,opt):
+ self.t2i = t2i
+ self.opt = opt
+
+ def normalize_prompt(self):
+ '''Normalize the prompt and switches'''
+ t2i = self.t2i
+ opt = self.opt
+
+ switches = list()
+ switches.append(f'"{opt.prompt}"')
+ switches.append(f'-s{opt.steps or t2i.steps}')
+ switches.append(f'-b{opt.batch_size or t2i.batch_size}')
+ switches.append(f'-W{opt.width or t2i.width}')
+ switches.append(f'-H{opt.height or t2i.height}')
+ switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
+ switches.append(f'-m{t2i.sampler_name}')
+ if opt.variants:
+ switches.append(f'-v{opt.variants}')
+ if opt.init_img:
+ switches.append(f'-I{opt.init_img}')
+ if opt.strength and opt.init_img is not None:
+ switches.append(f'-f{opt.strength or t2i.strength}')
+ if t2i.full_precision:
+ switches.append('-F')
+ return ' '.join(switches)
+
# ---------------readline utilities---------------------
try:
import readline
@@ -92,88 +178,3 @@ if readline_available:
pass
atexit.register(readline.write_history_file,histfile)
-# -------------------image generation utils-----
-class PngWriter:
-
- def __init__(self,outdir,opt,prompt):
- self.outdir = outdir
- self.opt = opt
- self.prompt = prompt
- self.filepath = None
- self.files_written = []
-
- def write_image(self,image,seed):
- self.filepath = self.unique_filename(seed,self.filepath) # will increment name in some sensible way
- try:
- prompt = f'{self.prompt} -S{seed}'
- self.save_image_and_prompt_to_png(image,prompt,self.filepath)
- except IOError as e:
- print(e)
- self.files_written.append([self.filepath,seed])
-
- def unique_filename(self,seed,previouspath):
- revision = 1
-
- if previouspath is None:
- # sort reverse alphabetically until we find max+1
- dirlist = sorted(os.listdir(self.outdir),reverse=True)
- # find the first filename that matches our pattern or return 000000.0.png
- filename = next((f for f in dirlist if re.match('^(\d+)\..*\.png',f)),'0000000.0.png')
- basecount = int(filename.split('.',1)[0])
- basecount += 1
- if self.opt.batch_size > 1:
- filename = f'{basecount:06}.{seed}.01.png'
- else:
- filename = f'{basecount:06}.{seed}.png'
- return os.path.join(self.outdir,filename)
-
- else:
- basename = os.path.basename(previouspath)
- x = re.match('^(\d+)\..*\.png',basename)
- if not x:
- return self.unique_filename(seed,previouspath)
-
- basecount = int(x.groups()[0])
- series = 0
- finished = False
- while not finished:
- series += 1
- filename = f'{basecount:06}.{seed}.png'
- if self.opt.batch_size>1 or os.path.exists(os.path.join(self.outdir,filename)):
- filename = f'{basecount:06}.{seed}.{series:02}.png'
- finished = not os.path.exists(os.path.join(self.outdir,filename))
- return os.path.join(self.outdir,filename)
-
- def save_image_and_prompt_to_png(self,image,prompt,path):
- info = PngImagePlugin.PngInfo()
- info.add_text("Dream",prompt)
- image.save(path,"PNG",pnginfo=info)
-
-class PromptFormatter():
- def __init__(self,t2i,opt):
- self.t2i = t2i
- self.opt = opt
-
- def normalize_prompt(self):
- '''Normalize the prompt and switches'''
- t2i = self.t2i
- opt = self.opt
-
- switches = list()
- switches.append(f'"{opt.prompt}"')
- switches.append(f'-s{opt.steps or t2i.steps}')
- switches.append(f'-b{opt.batch_size or t2i.batch_size}')
- switches.append(f'-W{opt.width or t2i.width}')
- switches.append(f'-H{opt.height or t2i.height}')
- switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
- switches.append(f'-m{t2i.sampler_name}')
- if opt.variants:
- switches.append(f'-v{opt.variants}')
- if opt.init_img:
- switches.append(f'-I{opt.init_img}')
- if opt.strength and opt.init_img is not None:
- switches.append(f'-f{opt.strength or t2i.strength}')
- if t2i.full_precision:
- switches.append('-F')
- return ' '.join(switches)
-
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 3b5aaeb696..91b73def43 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -4,52 +4,6 @@
# Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
# Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
-
-"""Simplified text to image API for stable diffusion/latent diffusion
-
-Example Usage:
-
-from ldm.simplet2i import T2I
-# Create an object with default values
-t2i = T2I(outdir = <path> // outputs/txt2img-samples
- model = <path> // models/ldm/stable-diffusion-v1/model.ckpt
- config = <path> // default="configs/stable-diffusion/v1-inference.yaml
- iterations = <integer> // how many times to run the sampling (1)
- batch_size = <integer> // how many images to generate per sampling (1)
- steps = <integer> // 50
- seed = <integer> // current system time
- sampler_name= ['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'] // k_lms
- grid = <boolean> // false
- width = <integer> // image width, multiple of 64 (512)
- height = <integer> // image height, multiple of 64 (512)
- cfg_scale = <float> // unconditional guidance scale (7.5)
- )
-
-# do the slow model initialization
-t2i.load_model()
-
-# Do the fast inference & image generation. Any options passed here
-# override the default values assigned during class initialization
-# Will call load_model() if the model was not previously loaded.
-# The method returns a list of images. Each row of the list is a sub-list of [filename,seed]
-results = t2i.txt2img(prompt = "an astronaut riding a horse"
- outdir = "./outputs/txt2img-samples)
- )
-
-for row in results:
- print(f'filename={row[0]}')
- print(f'seed ={row[1]}')
-
-# Same thing, but using an initial image.
-results = t2i.img2img(prompt = "an astronaut riding a horse"
- outdir = "./outputs/img2img-samples"
- init_img = "./sketches/horse+rider.png")
-
-for row in results:
- print(f'filename={row[0]}')
- print(f'seed ={row[1]}')
-"""
-
import torch
import numpy as np
import random
@@ -64,6 +18,7 @@ from torchvision.utils import make_grid
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext
+import transformers
import time
import math
import re
@@ -73,6 +28,69 @@ from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.ksampler import KSampler
+from ldm.dream_util import PngWriter
+
+"""Simplified text to image API for stable diffusion/latent diffusion
+
+Example Usage:
+
+from ldm.simplet2i import T2I
+
+# Create an object with default values
+t2i = T2I(model = <path> // models/ldm/stable-diffusion-v1/model.ckpt
+ config = <path> // configs/stable-diffusion/v1-inference.yaml
+ iterations = <integer> // how many times to run the sampling (1)
+ batch_size = <integer> // how many images to generate per sampling (1)
+ steps = <integer> // 50
+ seed = <integer> // current system time
+ sampler_name= ['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'] // k_lms
+ grid = <boolean> // false
+ width = <integer> // image width, multiple of 64 (512)
+ height = <integer> // image height, multiple of 64 (512)
+ cfg_scale = <float> // unconditional guidance scale (7.5)
+ )
+
+# do the slow model initialization
+t2i.load_model()
+
+# Do the fast inference & image generation. Any options passed here
+# override the default values assigned during class initialization
+# Will call load_model() if the model was not previously loaded and so
+# may be slow at first.
+# The method returns a list of images. Each row of the list is a sub-list of [filename,seed]
+results = t2i.prompt2png(prompt = "an astronaut riding a horse",
+ outdir = "./outputs/samples",
+ iterations = 3)
+
+for row in results:
+ print(f'filename={row[0]}')
+ print(f'seed ={row[1]}')
+
+# Same thing, but using an initial image.
+results = t2i.prompt2png(prompt = "an astronaut riding a horse",
+ outdir = "./outputs/,
+ iterations = 3,
+ init_img = "./sketches/horse+rider.png")
+
+for row in results:
+ print(f'filename={row[0]}')
+ print(f'seed ={row[1]}')
+
+# Same thing, but we return a series of Image objects, which lets you manipulate them,
+# combine them, and save them under arbitrary names
+
+results = t2i.prompt2image(prompt = "an astronaut riding a horse"
+ outdir = "./outputs/")
+for row in results:
+ im = row[0]
+ seed = row[1]
+ im.save(f'./outputs/samples/an_astronaut_riding_a_horse-{seed}.png')
+ im.thumbnail(100,100).save('./outputs/samples/astronaut_thumb.jpg')
+
+Note that the old txt2img() and img2img() calls are deprecated but will
+still work.
+"""
+
class T2I:
"""T2I class
@@ -141,7 +159,30 @@ The vast majority of these arguments default to reasonable values.
self.seed = self._new_seed()
else:
self.seed = seed
+ transformers.logging.set_verbosity_error()
+ def prompt2png(self,prompt,outdir,**kwargs):
+ '''
+ Takes a prompt and an output directory, writes out the requested number
+ of PNG files, and returns an array of [[filename,seed],[filename,seed]...]
+ Optional named arguments are the same as those passed to T2I and prompt2image()
+ '''
+ results = self.prompt2image(prompt,**kwargs)
+ pngwriter = PngWriter(outdir,prompt,kwargs.get('batch_size',self.batch_size))
+ for r in results:
+ metadata_str = f'prompt2png("{prompt}" {kwargs} seed={r[1]}' # gets written into the PNG
+ pngwriter.write_image(r[0],r[1])
+ return pngwriter.files_written
+
+ def txt2img(self,prompt,**kwargs):
+ outdir = kwargs.get('outdir','outputs/img-samples')
+ return self.prompt2png(prompt,outdir,**kwargs)
+
+ def img2img(self,prompt,**kwargs):
+ outdir = kwargs.get('outdir','outputs/img-samples')
+ assert 'init_img' in kwargs,'call to img2img() must include the init_img argument'
+ return self.prompt2png(prompt,outdir,**kwargs)
+
def prompt2image(self,
# these are common
prompt,
@@ -161,7 +202,34 @@ The vast majority of these arguments default to reasonable values.
strength=None,
variants=None,
**args): # eat up additional cruft
- '''ldm.prompt2image() is the common entry point for txt2img() and img2img()'''
+ '''
+ ldm.prompt2image() is the common entry point for txt2img() and img2img()
+ It takes the following arguments:
+ prompt // prompt string (no default)
+ iterations // iterations (1); image count=iterations x batch_size
+ batch_size // images per iteration (1)
+ steps // refinement steps per iteration
+ seed // seed for random number generator
+ width // width of image, in multiples of 64 (512)
+ height // height of image, in multiples of 64 (512)
+ cfg_scale // how strongly the prompt influences the image (7.5) (must be >1)
+ init_img // path to an initial image - its dimensions override width and height
+ strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
+ ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
+ variants // if >0, the 1st generated image will be passed back to img2img to generate the requested number of variants
+ callback // a function or method that will be called each time an image is generated
+
+ To use the callback, define a function of method that receives two arguments, an Image object
+ and the seed. You can then do whatever you like with the image, including converting it to
+ different formats and manipulating it. For example:
+
+ def process_image(image,seed):
+ image.save(f{'images/seed.png'})
+
+ The callback used by the prompt2png() can be found in ldm/dream_util.py. It contains code
+ to create the requested output directory, select a unique informative name for each image, and
+ write the prompt into the PNG metadata.
+ '''
steps = steps or self.steps
seed = seed or self.seed
width = width or self.width
@@ -175,6 +243,12 @@ The vast majority of these arguments default to reasonable values.
model = self.load_model() # will instantiate the model or return it from cache
assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
+ w = int(width/64) * 64
+ h = int(height/64) * 64
+ if h != height or w != width:
+ print(f'Height and width must be multiples of 64. Resizing to {h}x{w}')
+ height = h
+ width = w
data = [batch_size * [prompt]]
scope = autocast if self.precision=="autocast" else nullcontext
@@ -303,8 +377,7 @@ The vast majority of these arguments default to reasonable values.
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
t_enc = int(strength * steps)
- print(f"target t_enc is {t_enc} steps")
-
+ # print(f"target t_enc is {t_enc} steps")
images = list()
try:
@@ -408,8 +481,8 @@ The vast majority of these arguments default to reasonable values.
def _load_model_from_config(self, config, ckpt):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
- if "global_step" in pl_sd:
- print(f"Global Step: {pl_sd['global_step']}")
+# if "global_step" in pl_sd:
+# print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
diff --git a/scripts/dream.py b/scripts/dream.py
index ab01e8db01..10acccbfc3 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -153,25 +153,58 @@ def main_loop(t2i,outdir,parser,log,infile):
continue
normalized_prompt = PromptFormatter(t2i,opt).normalize_prompt()
+ variants = None
+
try:
- file_writer = PngWriter(outdir,opt,normalized_prompt)
+ file_writer = PngWriter(outdir,normalized_prompt,opt.batch_size)
callback = file_writer.write_image
t2i.prompt2image(image_callback=callback,
**vars(opt))
results = file_writer.files_written
+ if None not in (opt.variants,opt.init_img):
+ variants = generate_variants(t2i,outdir,opt,results)
+
except AssertionError as e:
print(e)
continue
print("Outputs:")
write_log_message(t2i,normalized_prompt,results,log)
+ if variants is not None:
+ print('Variants:')
+ for vr in variants:
+ write_log_message(t2i,vr[0],vr[1],log)
print("goodbye!")
+def generate_variants(t2i,outdir,opt,previous_gens):
+ variants = []
+ print(f"Generating {opt.variants} variant(s)...")
+ newopt = copy.deepcopy(opt)
+ newopt.iterations = 1
+ newopt.variants = None
+ for r in previous_gens:
+ newopt.init_img = r[0]
+ prompt = PromptFormatter(t2i,newopt).normalize_prompt()
+ print(f"] generating variant for {newopt.init_img}")
+ for j in range(0,opt.variants):
+ try:
+ file_writer = PngWriter(outdir,prompt,newopt.batch_size)
+ callback = file_writer.write_image
+ t2i.prompt2image(image_callback=callback,**vars(newopt))
+ results = file_writer.files_written
+ variants.append([prompt,results])
+ except AssertionError as e:
+ print(e)
+ continue
+ print(f'{opt.variants} variants generated')
+ return variants
+
+
def write_log_message(t2i,prompt,results,logfile):
- ''' logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata '''
+ ''' logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata'''
last_seed = None
img_num = 1
seenit = {}
From 72a9d7533091aa2af66f78d81ed5bf52b3e8d731 Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Thu, 25 Aug 2022 01:25:22 -0400
Subject: [PATCH 14/58] 404 on missing file
---
scripts/dream_web.py | 7 +++++--
1 file changed, 5 insertions(+), 2 deletions(-)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index f56f67f7d5..41bc9a9029 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -9,17 +9,20 @@ model = T2I()
class DreamServer(BaseHTTPRequestHandler):
def do_GET(self):
- self.send_response(200)
if self.path == "/":
+ self.send_response(200)
self.send_header("Content-type", "text/html")
self.end_headers()
with open("./scripts/static/index.html", "rb") as content:
self.wfile.write(content.read())
- else:
+ elif os.path.exists("." + self.path):
+ self.send_response(200)
self.send_header("Content-type", "image/png")
self.end_headers()
with open("." + self.path, "rb") as content:
self.wfile.write(content.read())
+ else:
+ self.send_response(404)
def do_POST(self):
self.send_response(200)
From eb58276a2c11f1cbdba1eb7133d4d9404118b7fd Mon Sep 17 00:00:00 2001
From: JigenD <jidaisuke@comcast.net>
Date: Thu, 25 Aug 2022 08:34:51 -0400
Subject: [PATCH 15/58] fix VRAM utilization
---
ldm/simplet2i.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 4737d90ba7..b5dc775c73 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -494,6 +494,7 @@ The vast majority of these arguments default to reasonable values.
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
+ model.cuda()
model.eval()
if self.full_precision:
print('Using slower but more accurate full-precision math (--full_precision)')
From 49247b4aa4f65f98a903b8ad47932d35b940e1fb Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 09:41:12 -0400
Subject: [PATCH 16/58] fix performance regression; closes issue #42
---
ldm/simplet2i.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 91b73def43..a3b4ecfcc7 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -486,6 +486,7 @@ The vast majority of these arguments default to reasonable values.
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
+ model.cuda() # fixes performance issue
model.eval()
if self.full_precision:
print('Using slower but more accurate full-precision math (--full_precision)')
From 26dc05e0e07e1fb93c8988832bf99cdb08f9d195 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 09:47:27 -0400
Subject: [PATCH 17/58] document --from_file flag, closes issue #82
---
README.md | 20 ++++++++++++++++++++
1 file changed, 20 insertions(+)
diff --git a/README.md b/README.md
index 22c6447248..28a610db8c 100644
--- a/README.md
+++ b/README.md
@@ -88,6 +88,26 @@ You may also pass a -v<count> option to generate count variants on the original
passing the first generated image back into img2img the requested number of times. It generates interesting
variants.
+## Reading Prompts from a File
+
+You can automate dream.py by providing a text file with the prompts
+you want to run, one line per prompt. The text file must be composed
+with a text editor (e.g. Notepad) and not a word processor. Each line
+should look like what you would type at the dream> prompt:
+
+~~~~
+a beautiful sunny day in the park, children playing -n4 -C10
+stormy weather on a mountain top, goats grazing -s100
+innovative packaging for a squid's dinner -S137038382
+~~~~
+
+Then pass this file's name to dream.py when you invoke it:
+
+~~~~
+(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file="path/to/prompts.txt"
+~~~~
+
+
## Weighted Prompts
You may weight different sections of the prompt to tell the sampler to attach different levels of
From 1c8ecacddf4d8c8125ce8edbda5f7047c9cc6de8 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 10:43:05 -0400
Subject: [PATCH 18/58] remove src directory, which is gumming up conda
installs; addresses issue #77
---
.gitmodules | 13 -------------
src/clip | 1 -
src/k-diffusion | 1 -
src/taming-transformers | 1 -
4 files changed, 16 deletions(-)
delete mode 160000 src/clip
delete mode 160000 src/k-diffusion
delete mode 160000 src/taming-transformers
diff --git a/.gitmodules b/.gitmodules
index c8f0748436..e69de29bb2 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,13 +0,0 @@
-[submodule "taming-transformers"]
- path = src/taming-transformers
- url = https://github.com/CompVis/taming-transformers.git
- ignore = dirty
-[submodule "clip"]
- path = src/clip
- url = https://github.com/openai/CLIP.git
- ignore = dirty
-[submodule "k-diffusion"]
- path = src/k-diffusion
- url = https://github.com/lstein/k-diffusion.git
- ignore = dirty
-
\ No newline at end of file
diff --git a/src/clip b/src/clip
deleted file mode 160000
index d50d76daa6..0000000000
--- a/src/clip
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
diff --git a/src/k-diffusion b/src/k-diffusion
deleted file mode 160000
index ef1bf07627..0000000000
--- a/src/k-diffusion
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit ef1bf07627c9a10ba9137e68a0206b844544a7d9
diff --git a/src/taming-transformers b/src/taming-transformers
deleted file mode 160000
index 24268930bf..0000000000
--- a/src/taming-transformers
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 24268930bf1dce879235a7fddd0b2355b84d7ea6
From e82c5eba18bc2f2b0e9588b5a9227e19ed1e04a3 Mon Sep 17 00:00:00 2001
From: JigenD <jidaisuke@comcast.net>
Date: Thu, 25 Aug 2022 12:18:35 -0400
Subject: [PATCH 19/58] PR revision: replace cuda call with dynamic type
---
ldm/simplet2i.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index b5dc775c73..c67d02bb6c 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -494,7 +494,7 @@ The vast majority of these arguments default to reasonable values.
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
- model.cuda()
+ model.to(self.device)
model.eval()
if self.full_precision:
print('Using slower but more accurate full-precision math (--full_precision)')
From 39b55ae016c52c82de5158170ecc9205affff7ef Mon Sep 17 00:00:00 2001
From: BlueAmulet <43395286+BlueAmulet@users.noreply.github.com>
Date: Thu, 25 Aug 2022 11:04:57 -0600
Subject: [PATCH 20/58] Remove accelerate library
This library is not required to use k-diffusion
Make k-diffusion wrapper closer to the other samplers
---
environment.yaml | 1 -
ldm/models/diffusion/ksampler.py | 13 ++++---------
ldm/simplet2i.py | 12 ++++++------
requirements.txt | 1 -
scripts/orig_scripts/txt2img.py | 12 +++---------
5 files changed, 13 insertions(+), 26 deletions(-)
diff --git a/environment.yaml b/environment.yaml
index b554cfc035..7d5b4fe9e3 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -10,7 +10,6 @@ dependencies:
- torchvision=0.12.0
- numpy=1.19.2
- pip:
- - accelerate==0.12.0
- albumentations==0.4.3
- opencv-python==4.1.2.30
- pudb==2019.2
diff --git a/ldm/models/diffusion/ksampler.py b/ldm/models/diffusion/ksampler.py
index c48e533410..cea77dac1a 100644
--- a/ldm/models/diffusion/ksampler.py
+++ b/ldm/models/diffusion/ksampler.py
@@ -2,7 +2,6 @@
import k_diffusion as K
import torch
import torch.nn as nn
-import accelerate
class CFGDenoiser(nn.Module):
def __init__(self, model):
@@ -17,12 +16,11 @@ class CFGDenoiser(nn.Module):
return uncond + (cond - uncond) * cond_scale
class KSampler(object):
- def __init__(self,model,schedule="lms", **kwargs):
+ def __init__(self, model, schedule="lms", device="cuda", **kwargs):
super().__init__()
- self.model = K.external.CompVisDenoiser(model)
- self.accelerator = accelerate.Accelerator()
- self.device = self.accelerator.device
+ self.model = K.external.CompVisDenoiser(model)
self.schedule = schedule
+ self.device = device
def forward(self, x, sigma, uncond, cond, cond_scale):
x_in = torch.cat([x] * 2)
@@ -67,8 +65,5 @@ class KSampler(object):
x = torch.randn([batch_size, *shape], device=self.device) * sigmas[0] # for GPU draw
model_wrap_cfg = CFGDenoiser(self.model)
extra_args = {'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}
- return (K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not self.accelerator.is_main_process),
+ return (K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas, extra_args=extra_args),
None)
-
- def gather(samples_ddim):
- return self.accelerator.gather(samples_ddim)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 4737d90ba7..7e5d5d6e7c 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -467,17 +467,17 @@ The vast majority of these arguments default to reasonable values.
elif self.sampler_name == 'ddim':
self.sampler = DDIMSampler(self.model, device=self.device)
elif self.sampler_name == 'k_dpm_2_a':
- self.sampler = KSampler(self.model,'dpm_2_ancestral')
+ self.sampler = KSampler(self.model, 'dpm_2_ancestral', device=self.device)
elif self.sampler_name == 'k_dpm_2':
- self.sampler = KSampler(self.model,'dpm_2')
+ self.sampler = KSampler(self.model, 'dpm_2', device=self.device)
elif self.sampler_name == 'k_euler_a':
- self.sampler = KSampler(self.model,'euler_ancestral')
+ self.sampler = KSampler(self.model, 'euler_ancestral', device=self.device)
elif self.sampler_name == 'k_euler':
- self.sampler = KSampler(self.model,'euler')
+ self.sampler = KSampler(self.model, 'euler', device=self.device)
elif self.sampler_name == 'k_heun':
- self.sampler = KSampler(self.model,'heun')
+ self.sampler = KSampler(self.model, 'heun', device=self.device)
elif self.sampler_name == 'k_lms':
- self.sampler = KSampler(self.model,'lms')
+ self.sampler = KSampler(self.model, 'lms', device=self.device)
else:
msg = f'unsupported sampler {self.sampler_name}, defaulting to plms'
self.sampler = PLMSSampler(self.model, device=self.device)
diff --git a/requirements.txt b/requirements.txt
index 30b2251a1c..a94a4a5382 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,3 @@
-accelerate==0.12.0
albumentations==0.4.3
einops==0.3.0
huggingface-hub==0.8.1
diff --git a/scripts/orig_scripts/txt2img.py b/scripts/orig_scripts/txt2img.py
index 42d5e83496..1edc531309 100644
--- a/scripts/orig_scripts/txt2img.py
+++ b/scripts/orig_scripts/txt2img.py
@@ -12,7 +12,6 @@ from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext
-import accelerate
import k_diffusion as K
import torch.nn as nn
@@ -201,8 +200,6 @@ def main():
#for klms
model_wrap = K.external.CompVisDenoiser(model)
- accelerator = accelerate.Accelerator()
- device = accelerator.device
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
@@ -251,8 +248,8 @@ def main():
with model.ema_scope():
tic = time.time()
all_samples = list()
- for n in trange(opt.n_iter, desc="Sampling", disable =not accelerator.is_main_process):
- for prompts in tqdm(data, desc="data", disable =not accelerator.is_main_process):
+ for n in trange(opt.n_iter, desc="Sampling"):
+ for prompts in tqdm(data, desc="data"):
uc = None
if opt.scale != 1.0:
uc = model.get_learned_conditioning(batch_size * [""])
@@ -279,13 +276,10 @@ def main():
x = torch.randn([opt.n_samples, *shape], device=device) * sigmas[0] # for GPU draw
model_wrap_cfg = CFGDenoiser(model_wrap)
extra_args = {'cond': c, 'uncond': uc, 'cond_scale': opt.scale}
- samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not accelerator.is_main_process)
+ samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args)
x_samples_ddim = model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-
- if opt.klms:
- x_sample = accelerator.gather(x_samples_ddim)
if not opt.skip_save:
for x_sample in x_samples_ddim:
From b7735b3788239f0b0157133efb82884319ef96a9 Mon Sep 17 00:00:00 2001
From: BlueAmulet <43395286+BlueAmulet@users.noreply.github.com>
Date: Thu, 25 Aug 2022 11:13:12 -0600
Subject: [PATCH 21/58] Fix attribution
---
ldm/models/diffusion/ksampler.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/ldm/models/diffusion/ksampler.py b/ldm/models/diffusion/ksampler.py
index cea77dac1a..62912d1a07 100644
--- a/ldm/models/diffusion/ksampler.py
+++ b/ldm/models/diffusion/ksampler.py
@@ -1,4 +1,4 @@
-'''wrapper around part of Karen Crownson's k-duffsion library, making it call compatible with other Samplers'''
+'''wrapper around part of Katherine Crowson's k-diffusion library, making it call compatible with other Samplers'''
import k_diffusion as K
import torch
import torch.nn as nn
From b95908daec46203db8a7a4b594f849d5f05e7052 Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Thu, 25 Aug 2022 14:15:08 -0400
Subject: [PATCH 22/58] Move style and script to individual files
---
scripts/dream_web.py | 15 ++--
scripts/static/index.css | 49 ++++++++++++
scripts/static/index.html | 153 +-------------------------------------
scripts/static/index.js | 101 +++++++++++++++++++++++++
4 files changed, 162 insertions(+), 156 deletions(-)
create mode 100644 scripts/static/index.css
create mode 100644 scripts/static/index.js
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index 41bc9a9029..b429d749d9 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -1,5 +1,6 @@
import json
import base64
+import mimetypes
import os
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
@@ -16,11 +17,15 @@ class DreamServer(BaseHTTPRequestHandler):
with open("./scripts/static/index.html", "rb") as content:
self.wfile.write(content.read())
elif os.path.exists("." + self.path):
- self.send_response(200)
- self.send_header("Content-type", "image/png")
- self.end_headers()
- with open("." + self.path, "rb") as content:
- self.wfile.write(content.read())
+ mime_type = mimetypes.guess_type(self.path)[0]
+ if mime_type is not None:
+ self.send_response(200)
+ self.send_header("Content-type", mime_type)
+ self.end_headers()
+ with open("." + self.path, "rb") as content:
+ self.wfile.write(content.read())
+ else:
+ self.send_response(404)
else:
self.send_response(404)
diff --git a/scripts/static/index.css b/scripts/static/index.css
new file mode 100644
index 0000000000..d3674525f6
--- /dev/null
+++ b/scripts/static/index.css
@@ -0,0 +1,49 @@
+* {
+ font-family: 'Arial';
+}
+#header {
+ text-decoration: dotted underline;
+}
+#search {
+ margin-top: 20vh;
+ text-align: center;
+}
+fieldset {
+ border: none;
+}
+#prompt {
+ width: 500px;
+ border-radius: 20px 0px 0px 20px;
+ padding: 5px 10px 5px 10px;
+ border: 1px solid black;
+ outline: none;
+}
+#submit {
+ border-radius: 0px 20px 20px 0px;
+ padding: 5px 10px 5px 10px;
+ border: 1px solid black;
+}
+#results {
+ text-align: center;
+ padding-left: 20vw;
+ padding-right: 20vw;
+ padding-top: 10px;
+}
+img {
+ cursor: pointer;
+ height: 30vh;
+ border-radius: 5px;
+ margin: 10px;
+}
+#generate-config {
+ line-height:2em;
+}
+input[type="number"] {
+ width: 60px;
+}
+#seed {
+ width: 150px;
+}
+hr {
+ width: 200px;
+}
diff --git a/scripts/static/index.html b/scripts/static/index.html
index 663a32ed12..2e035fc9df 100644
--- a/scripts/static/index.html
+++ b/scripts/static/index.html
@@ -2,157 +2,9 @@
<head>
<title>Stable Diffusion WebUI</title>
<link rel="icon" href="data:,">
- <style>
- * {
- font-family: 'Arial';
- }
- #header {
- text-decoration: dotted underline;
- }
- #search {
- margin-top: 20vh;
- text-align: center;
- }
- fieldset {
- border: none;
- }
- #prompt {
- width: 500px;
- border-radius: 20px 0px 0px 20px;
- padding: 5px 10px 5px 10px;
- border: 1px solid black;
- outline: none;
- }
- #submit {
- border-radius: 0px 20px 20px 0px;
- padding: 5px 10px 5px 10px;
- border: 1px solid black;
- }
- #results {
- text-align: center;
- padding-left: 20vw;
- padding-right: 20vw;
- padding-top: 10px;
- }
- img {
- cursor: pointer;
- height: 30vh;
- border-radius: 5px;
- margin: 10px;
- }
- #generate-config {
- line-height:2em;
- }
- input[type="number"] {
- width: 60px;
- }
- #seed {
- width: 150px;
- }
- </style>
- <script>
- function toBase64(file) {
- return new Promise((resolve, reject) => {
- const r = new FileReader();
- r.readAsDataURL(file);
- r.onload = () => resolve(r.result);
- r.onerror = (error) => reject(error);
- });
- }
- function appendOutput(output) {
- let outputNode = document.createElement("img");
- outputNode.src = output[0];
-
- let outputConfig = output[2];
- let altText = output[1].toString() + " | " + outputConfig.prompt;
- outputNode.alt = altText;
- outputNode.title = altText;
-
- // Reload image config
- outputNode.addEventListener('click', () => {
- let form = document.querySelector("#generate-form");
- for (const [k, v] of new FormData(form)) {
- form.querySelector(`*[name=${k}]`).value = outputConfig[k];
- }
- document.querySelector("#seed").value = output[1];
-
- saveFields(document.querySelector("#generate-form"));
- });
-
- document.querySelector("#results").prepend(outputNode);
- }
-
- function appendOutputs(outputs) {
- for (const output of outputs) {
- appendOutput(output);
- }
- }
-
- function saveFields(form) {
- for (const [k, v] of new FormData(form)) {
- if (typeof v !== 'object') { // Don't save 'file' type
- localStorage.setItem(k, v);
- }
- }
- }
- function loadFields(form) {
- for (const [k, v] of new FormData(form)) {
- const item = localStorage.getItem(k);
- if (item != null) {
- form.querySelector(`*[name=${k}]`).value = item;
- }
- }
- }
-
- async function generateSubmit(form) {
- const prompt = document.querySelector("#prompt").value;
-
- // Convert file data to base64
- let formData = Object.fromEntries(new FormData(form));
- formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
-
- // Post as JSON
- fetch(form.action, {
- method: form.method,
- body: JSON.stringify(formData),
- }).then(async (result) => {
- let data = await result.json();
-
- // Re-enable form, remove no-results-message
- form.querySelector('fieldset').removeAttribute('disabled');
- document.querySelector("#prompt").value = prompt;
-
- if (data.outputs.length != 0) {
- document.querySelector("#no-results-message")?.remove();
- appendOutputs(data.outputs);
- } else {
- alert("Error occurred while generating.");
- }
- });
-
- // Disable form while generating
- form.querySelector('fieldset').setAttribute('disabled','');
- document.querySelector("#prompt").value = `Generating: "${prompt}"`;
- }
-
- window.onload = () => {
- document.querySelector("#generate-form").addEventListener('submit', (e) => {
- e.preventDefault();
- const form = e.target;
-
- generateSubmit(form);
- });
- document.querySelector("#generate-form").addEventListener('change', (e) => {
- saveFields(e.target.form);
- });
- document.querySelector("#reset").addEventListener('click', (e) => {
- document.querySelector("#seed").value = -1;
- saveFields(e.target.form);
- });
- loadFields(document.querySelector("#generate-form"));
- };
- </script>
+ <link rel="stylesheet" href="scripts/static/index.css">
+ <script src="scripts/static/index.js"></script>
</head>
<body>
<div id="search">
@@ -184,7 +36,6 @@
</fieldset>
</form>
</div>
- <hr style="width: 200px">
<div id="results">
<div id="no-results-message">
<i><p>No results...</p></i>
diff --git a/scripts/static/index.js b/scripts/static/index.js
new file mode 100644
index 0000000000..3b99deecf4
--- /dev/null
+++ b/scripts/static/index.js
@@ -0,0 +1,101 @@
+function toBase64(file) {
+ return new Promise((resolve, reject) => {
+ const r = new FileReader();
+ r.readAsDataURL(file);
+ r.onload = () => resolve(r.result);
+ r.onerror = (error) => reject(error);
+ });
+}
+
+function appendOutput(output) {
+ let outputNode = document.createElement("img");
+ outputNode.src = output[0];
+
+ let outputConfig = output[2];
+ let altText = output[1].toString() + " | " + outputConfig.prompt;
+ outputNode.alt = altText;
+ outputNode.title = altText;
+
+ // Reload image config
+ outputNode.addEventListener('click', () => {
+ let form = document.querySelector("#generate-form");
+ for (const [k, v] of new FormData(form)) {
+ form.querySelector(`*[name=${k}]`).value = outputConfig[k];
+ }
+ document.querySelector("#seed").value = output[1];
+
+ saveFields(document.querySelector("#generate-form"));
+ });
+
+ document.querySelector("#results").prepend(outputNode);
+}
+
+function appendOutputs(outputs) {
+ for (const output of outputs) {
+ appendOutput(output);
+ }
+}
+
+function saveFields(form) {
+ for (const [k, v] of new FormData(form)) {
+ if (typeof v !== 'object') { // Don't save 'file' type
+ localStorage.setItem(k, v);
+ }
+ }
+}
+function loadFields(form) {
+ for (const [k, v] of new FormData(form)) {
+ const item = localStorage.getItem(k);
+ if (item != null) {
+ form.querySelector(`*[name=${k}]`).value = item;
+ }
+ }
+}
+
+async function generateSubmit(form) {
+ const prompt = document.querySelector("#prompt").value;
+
+ // Convert file data to base64
+ let formData = Object.fromEntries(new FormData(form));
+ formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
+
+ // Post as JSON
+ fetch(form.action, {
+ method: form.method,
+ body: JSON.stringify(formData),
+ }).then(async (result) => {
+ let data = await result.json();
+
+ // Re-enable form, remove no-results-message
+ form.querySelector('fieldset').removeAttribute('disabled');
+ document.querySelector("#prompt").value = prompt;
+
+ if (data.outputs.length != 0) {
+ document.querySelector("#no-results-message")?.remove();
+ appendOutputs(data.outputs);
+ } else {
+ alert("Error occurred while generating.");
+ }
+ });
+
+ // Disable form while generating
+ form.querySelector('fieldset').setAttribute('disabled','');
+ document.querySelector("#prompt").value = `Generating: "${prompt}"`;
+}
+
+window.onload = () => {
+ document.querySelector("#generate-form").addEventListener('submit', (e) => {
+ e.preventDefault();
+ const form = e.target;
+
+ generateSubmit(form);
+ });
+ document.querySelector("#generate-form").addEventListener('change', (e) => {
+ saveFields(e.target.form);
+ });
+ document.querySelector("#reset").addEventListener('click', (e) => {
+ document.querySelector("#seed").value = -1;
+ saveFields(e.target.form);
+ });
+ loadFields(document.querySelector("#generate-form"));
+};
From d3a802db69b19f3a8362dd296feafed7a664604d Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Thu, 25 Aug 2022 14:18:29 -0400
Subject: [PATCH 23/58] Fix horizontal divider
---
scripts/static/index.html | 1 +
1 file changed, 1 insertion(+)
diff --git a/scripts/static/index.html b/scripts/static/index.html
index 2e035fc9df..d670dc64f5 100644
--- a/scripts/static/index.html
+++ b/scripts/static/index.html
@@ -36,6 +36,7 @@
</fieldset>
</form>
</div>
+ <hr>
<div id="results">
<div id="no-results-message">
<i><p>No results...</p></i>
From 2ad73246f94000624c06a9f8950c4c22a1ee6617 Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Thu, 25 Aug 2022 14:27:33 -0400
Subject: [PATCH 24/58] Normalize working directory
---
scripts/dream_web.py | 6 ++++++
1 file changed, 6 insertions(+)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index b429d749d9..4622f457c5 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -81,6 +81,12 @@ class DreamServer(BaseHTTPRequestHandler):
self.wfile.write(bytes(json.dumps(result), "utf-8"))
if __name__ == "__main__":
+ # Change working directory to the stable-diffusion directory
+ os.chdir(
+ os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..'))
+ )
+
+ # Start server
dream_server = ThreadingHTTPServer(("0.0.0.0", 9090), DreamServer)
print("Started Stable Diffusion dream server!")
From 91966e9ffa01a256ede7b0b49902587f9d41fcf7 Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Thu, 25 Aug 2022 15:01:08 -0400
Subject: [PATCH 25/58] Fix appearance on mobile
---
scripts/static/index.css | 20 ++++++++++++++++----
scripts/static/index.html | 5 +++--
2 files changed, 19 insertions(+), 6 deletions(-)
diff --git a/scripts/static/index.css b/scripts/static/index.css
index d3674525f6..ed840a056a 100644
--- a/scripts/static/index.css
+++ b/scripts/static/index.css
@@ -6,16 +6,25 @@
}
#search {
margin-top: 20vh;
+ margin-left: auto;
+ margin-right: auto;
+ max-width: 800px;
+
text-align: center;
}
fieldset {
border: none;
}
+#fieldset-search {
+ display: flex;
+}
#prompt {
- width: 500px;
+ flex-grow: 1;
+
border-radius: 20px 0px 0px 20px;
padding: 5px 10px 5px 10px;
border: 1px solid black;
+ border-right: none;
outline: none;
}
#submit {
@@ -25,8 +34,8 @@ fieldset {
}
#results {
text-align: center;
- padding-left: 20vw;
- padding-right: 20vw;
+ max-width: 1000px;
+ margin: auto;
padding-top: 10px;
}
img {
@@ -35,7 +44,7 @@ img {
border-radius: 5px;
margin: 10px;
}
-#generate-config {
+#fieldset-config {
line-height:2em;
}
input[type="number"] {
@@ -47,3 +56,6 @@ input[type="number"] {
hr {
width: 200px;
}
+label {
+ white-space: nowrap;
+}
diff --git a/scripts/static/index.html b/scripts/static/index.html
index d670dc64f5..55f338688a 100644
--- a/scripts/static/index.html
+++ b/scripts/static/index.html
@@ -2,6 +2,7 @@
<head>
<title>Stable Diffusion WebUI</title>
<link rel="icon" href="data:,">
+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="scripts/static/index.css">
<script src="scripts/static/index.js"></script>
@@ -11,11 +12,11 @@
<h2 id="header">Stable Diffusion</h2>
<form id="generate-form" method="post" action="#">
- <fieldset>
+ <fieldset id="fieldset-search">
<input type="text" id="prompt" name="prompt">
<input type="submit" id="submit" value="Generate">
</fieldset>
- <fieldset id="generate-config">
+ <fieldset id="fieldset-config">
<label for="batch">Batch Size:</label>
<input value="1" type="number" id="batch" name="batch">
<label for="steps">Steps:</label>
From 2ada3288e71dbd6b99574413ffd47b8e96e02084 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 15:03:40 -0400
Subject: [PATCH 26/58] Small cleanups. - Quenched tokenizer warnings during
model initialization. - Changed "batch" to "iterations" for generating
multiple images in order to conserve vram. - Updated README. - Moved static
folder from under scripts to top level. Can store other static content
there in future. - Added screenshot of web server in action (to static
folder).
---
README.md | 22 +++++++++++++++++++++-
scripts/dream_web.py | 21 +++++++++++++++------
src/k-diffusion | 2 +-
{scripts/static => static}/index.html | 9 +++++----
4 files changed, 42 insertions(+), 12 deletions(-)
rename {scripts/static => static}/index.html (91%)
diff --git a/README.md b/README.md
index a3347bdb4d..15d4b48983 100644
--- a/README.md
+++ b/README.md
@@ -88,6 +88,23 @@ You may also pass a -v<count> option to generate count variants on the original
passing the first generated image back into img2img the requested number of times. It generates interesting
variants.
+## Barebones Web Server
+
+As of version 1.10, this distribution comes with a bare bones web server (see screenshot). To use it,
+run the command:
+
+~~~~
+(ldm) ~/stable-diffusion$ python3 scripts/dream_web.py
+~~~~
+
+You can then connect to the server by pointing your web browser at
+http://localhost:9090, or to the network name or IP address of the server.
+
+Kudos to [Tesseract Cat](https://github.com/TesseractCat) for
+contributing this code.
+
+
+
## Weighted Prompts
You may weight different sections of the prompt to tell the sampler to attach different levels of
@@ -171,6 +188,7 @@ repository and associated paper for details and limitations.
## Changes
* v1.09 (24 August 2022)
+ * A barebone web server for interactive online generation of txt2img and img2img.
* A new -v option allows you to generate multiple variants of an initial image
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
* Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
@@ -459,7 +477,9 @@ to send me an email if you use and like the script.
[Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison),
[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan](https://github.com/Oceanswave),
[nicolai256](https://github.com/nicolai256), [Benjamin Warner](https://github.com/warner-benjamin),
-and [tildebyte](https://github.com/tildebyte)
+[tildebyte](https://github.com/tildebyte),
+and [Tesseract Cat](https://github.com/TesseractCat)
+
Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index 41bc9a9029..378845e89b 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -1,11 +1,20 @@
import json
import base64
import os
+from pytorch_lightning import logging
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
print("Loading model...")
from ldm.simplet2i import T2I
-model = T2I()
+model = T2I(sampler_name='k_lms')
+
+# to get rid of annoying warning messages from pytorch
+import transformers
+transformers.logging.set_verbosity_error()
+logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
+
+print("Initializing model, be patient...")
+model.load_model()
class DreamServer(BaseHTTPRequestHandler):
def do_GET(self):
@@ -13,7 +22,7 @@ class DreamServer(BaseHTTPRequestHandler):
self.send_response(200)
self.send_header("Content-type", "text/html")
self.end_headers()
- with open("./scripts/static/index.html", "rb") as content:
+ with open("./static/index.html", "rb") as content:
self.wfile.write(content.read())
elif os.path.exists("." + self.path):
self.send_response(200)
@@ -33,7 +42,7 @@ class DreamServer(BaseHTTPRequestHandler):
post_data = json.loads(self.rfile.read(content_length))
prompt = post_data['prompt']
initimg = post_data['initimg']
- batch = int(post_data['batch'])
+ iterations = int(post_data['iterations'])
steps = int(post_data['steps'])
width = int(post_data['width'])
height = int(post_data['height'])
@@ -46,7 +55,7 @@ class DreamServer(BaseHTTPRequestHandler):
if initimg is None:
# Run txt2img
outputs = model.txt2img(prompt,
- batch_size = batch,
+ iterations=iterations,
cfg_scale = cfgscale,
width = width,
height = height,
@@ -61,7 +70,7 @@ class DreamServer(BaseHTTPRequestHandler):
# Run img2img
outputs = model.img2img(prompt,
init_img = "./img2img-tmp.png",
- batch_size = batch,
+ iterations = iterations,
cfg_scale = cfgscale,
seed = seed,
steps = steps)
@@ -77,7 +86,7 @@ class DreamServer(BaseHTTPRequestHandler):
if __name__ == "__main__":
dream_server = ThreadingHTTPServer(("0.0.0.0", 9090), DreamServer)
- print("Started Stable Diffusion dream server!")
+ print("\n\n* Started Stable Diffusion dream server! Point your browser at http://localhost:9090 or use the host's DNS name or IP address. *")
try:
dream_server.serve_forever()
diff --git a/src/k-diffusion b/src/k-diffusion
index ef1bf07627..db57990687 160000
--- a/src/k-diffusion
+++ b/src/k-diffusion
@@ -1 +1 @@
-Subproject commit ef1bf07627c9a10ba9137e68a0206b844544a7d9
+Subproject commit db5799068749bf3a6d5845120ed32df16b7d883b
diff --git a/scripts/static/index.html b/static/index.html
similarity index 91%
rename from scripts/static/index.html
rename to static/index.html
index 663a32ed12..a96405aafb 100644
--- a/scripts/static/index.html
+++ b/static/index.html
@@ -1,6 +1,6 @@
<html>
<head>
- <title>Stable Diffusion WebUI</title>
+ <title>Stable Diffusion Dream Server</title>
<link rel="icon" href="data:,">
<style>
* {
@@ -156,7 +156,7 @@
</head>
<body>
<div id="search">
- <h2 id="header">Stable Diffusion</h2>
+ <h2 id="header">Stable Diffusion Dream Server</h2>
<form id="generate-form" method="post" action="#">
<fieldset>
@@ -164,8 +164,8 @@
<input type="submit" id="submit" value="Generate">
</fieldset>
<fieldset id="generate-config">
- <label for="batch">Batch Size:</label>
- <input value="1" type="number" id="batch" name="batch">
+ <label for="iterations">Images to generate:</label>
+ <input value="1" type="number" id="iterations" name="iterations">
<label for="steps">Steps:</label>
<input value="50" type="number" id="steps" name="steps">
<label for="cfgscale">Cfg Scale:</label>
@@ -183,6 +183,7 @@
<button type="button" id="reset">↺</button>
</fieldset>
</form>
+ <div id="about">For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a></div>
</div>
<hr style="width: 200px">
<div id="results">
From b64c902354319360b0deffae2f5d3918874dfc2f Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 15:06:10 -0400
Subject: [PATCH 27/58] added missing image
---
static/dream_web_server.png | Bin 0 -> 548302 bytes
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 static/dream_web_server.png
diff --git a/static/dream_web_server.png b/static/dream_web_server.png
new file mode 100644
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literal 0
HcmV?d00001
From d598b6c79d68d532dee1616ec2419f93ff8afd6b Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 15:11:06 -0400
Subject: [PATCH 28/58] Update README.md
---
README.md | 8 +-------
1 file changed, 1 insertion(+), 7 deletions(-)
diff --git a/README.md b/README.md
index 15d4b48983..189541f95b 100644
--- a/README.md
+++ b/README.md
@@ -31,13 +31,7 @@ runs from the command-line (CMD or Terminal window), and does not have a GUI.
(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
* Initializing, be patient...
Loading model from models/ldm/text2img-large/model.ckpt
-LatentDiffusion: Running in eps-prediction mode
-DiffusionWrapper has 872.30 M params.
-making attention of type 'vanilla' with 512 in_channels
-Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
-making attention of type 'vanilla' with 512 in_channels
-Loading Bert tokenizer from "models/bert"
-setting sampler to plms
+(...more initialization messages...)
* Initialization done! Awaiting your command...
dream> ashley judd riding a camel -n2 -s150
From 2114c386ad7540f17ba7793eca98f9077af4fe15 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 15:27:43 -0400
Subject: [PATCH 29/58] moved index.js .html and .css files into
static/dream_web/; changed batch to iterations again
---
scripts/dream_web.py | 2 +-
{scripts/static => static/dream_web}/index.css | 0
static/{ => dream_web}/index.html | 6 +++---
{scripts/static => static/dream_web}/index.js | 0
4 files changed, 4 insertions(+), 4 deletions(-)
rename {scripts/static => static/dream_web}/index.css (100%)
rename static/{ => dream_web}/index.html (88%)
rename {scripts/static => static/dream_web}/index.js (100%)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index ec909ec5bd..d48d5ac9ec 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -23,7 +23,7 @@ class DreamServer(BaseHTTPRequestHandler):
self.send_response(200)
self.send_header("Content-type", "text/html")
self.end_headers()
- with open("./static/index.html", "rb") as content:
+ with open("./static/dream_web/index.html", "rb") as content:
self.wfile.write(content.read())
elif os.path.exists("." + self.path):
mime_type = mimetypes.guess_type(self.path)[0]
diff --git a/scripts/static/index.css b/static/dream_web/index.css
similarity index 100%
rename from scripts/static/index.css
rename to static/dream_web/index.css
diff --git a/static/index.html b/static/dream_web/index.html
similarity index 88%
rename from static/index.html
rename to static/dream_web/index.html
index 4e7af0f771..5dca10997c 100644
--- a/static/index.html
+++ b/static/dream_web/index.html
@@ -4,8 +4,8 @@
<link rel="icon" href="data:,">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
- <link rel="stylesheet" href="scripts/static/index.css">
- <script src="scripts/static/index.js"></script>
+ <link rel="stylesheet" href="static/dream_web/index.css">
+ <script src="static/dream_web/index.js"></script>
</head>
<body>
<div id="search">
@@ -18,7 +18,7 @@
</fieldset>
<fieldset id="fieldset-config">
<label for="iterations">Images to generate:</label>
- <input value="1" type="number" id="batch" name="batch">
+ <input value="1" type="number" id="iterations" name="iterations">
<label for="steps">Steps:</label>
<input value="50" type="number" id="steps" name="steps">
<label for="cfgscale">Cfg Scale:</label>
diff --git a/scripts/static/index.js b/static/dream_web/index.js
similarity index 100%
rename from scripts/static/index.js
rename to static/dream_web/index.js
From 652c67c90e97729d76404c04f2789c57af567a2e Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 15:29:41 -0400
Subject: [PATCH 30/58] Update README.md
---
README.md | 4 +---
1 file changed, 1 insertion(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 189541f95b..5c049c4d6b 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,7 @@
# Stable Diffusion Dream Script
This is a fork of CompVis/stable-diffusion, the wonderful open source
-text-to-image generator.
-
-The original has been modified in several ways:
+text-to-image generator. The original has been improved in several ways:
## Interactive command-line interface similar to the Discord bot
From 91d3e4605e01430b0472e699a0554d13c345cb83 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 15:32:48 -0400
Subject: [PATCH 31/58] Update README.md
---
README.md | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 5c049c4d6b..315c4127d9 100644
--- a/README.md
+++ b/README.md
@@ -179,8 +179,10 @@ repository and associated paper for details and limitations.
## Changes
-* v1.09 (24 August 2022)
- * A barebone web server for interactive online generation of txt2img and img2img.
+*v1.10 (25 August 2022)
+ * A barebones but fully functional interactive web server for online generation of txt2img and img2img.
+
+*v1.09 (24 August 2022)
* A new -v option allows you to generate multiple variants of an initial image
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
* Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
From f4c95bfec018bb7b822d0c3726bd61a0576c7342 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 15:33:49 -0400
Subject: [PATCH 32/58] Update README.md
---
README.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 315c4127d9..271518a67e 100644
--- a/README.md
+++ b/README.md
@@ -179,10 +179,10 @@ repository and associated paper for details and limitations.
## Changes
-*v1.10 (25 August 2022)
+* v1.10 (25 August 2022)
* A barebones but fully functional interactive web server for online generation of txt2img and img2img.
-*v1.09 (24 August 2022)
+* v1.09 (24 August 2022)
* A new -v option allows you to generate multiple variants of an initial image
in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
* Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
From 888ca39ce23071a54fc32eb8689570757e5a01f4 Mon Sep 17 00:00:00 2001
From: veprogames <veprogames.gamedev@gmail.com>
Date: Thu, 25 Aug 2022 22:29:12 +0200
Subject: [PATCH 33/58] remove k-diffusion from repository (git rm --cached)
should fix conda environment hanging
---
src/k-diffusion | 1 -
1 file changed, 1 deletion(-)
delete mode 160000 src/k-diffusion
diff --git a/src/k-diffusion b/src/k-diffusion
deleted file mode 160000
index ef1bf07627..0000000000
--- a/src/k-diffusion
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit ef1bf07627c9a10ba9137e68a0206b844544a7d9
From dde2994d10f751c57170d750a9557236e701904f Mon Sep 17 00:00:00 2001
From: veprogames <veprogames.gamedev@gmail.com>
Date: Thu, 25 Aug 2022 22:31:24 +0200
Subject: [PATCH 34/58] add inputs/ to .gitignore (a place for initial images)
---
.gitignore | 3 +++
1 file changed, 3 insertions(+)
diff --git a/.gitignore b/.gitignore
index 992cb2be90..ffdc22fed4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,9 @@
outputs/
models/ldm/stable-diffusion-v1/model.ckpt
+# ignore a directory which serves as a place for initial images
+inputs/
+
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
From b3e3b0e8613e7f11e0ae3e5f722233290b0732bb Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 17:26:48 -0400
Subject: [PATCH 35/58] feature complete; looks like ready for merge
---
TODO.txt | 1 +
ldm/dream_util.py | 21 +++++++++++--
ldm/simplet2i.py | 1 -
scripts/dream.py | 78 +++++++++++++++++++++++++----------------------
src/k-diffusion | 2 +-
5 files changed, 61 insertions(+), 42 deletions(-)
diff --git a/TODO.txt b/TODO.txt
index df9aea75ba..32475b43ba 100644
--- a/TODO.txt
+++ b/TODO.txt
@@ -2,6 +2,7 @@ Feature requests:
1. "gobig" mode - split image into strips, scale up, add detail using
img2img and reassemble with feathering. Issue #66.
+ See https://github.com/jquesnelle/txt2imghd
2. Port basujindal low VRAM optimizations. Issue #62
diff --git a/ldm/dream_util.py b/ldm/dream_util.py
index a1d0d3204b..b69a0c1367 100644
--- a/ldm/dream_util.py
+++ b/ldm/dream_util.py
@@ -2,6 +2,7 @@
import os
import atexit
import re
+from math import sqrt,floor,ceil
from PIL import Image,PngImagePlugin
# -------------------image generation utils-----
@@ -24,7 +25,7 @@ class PngWriter:
print(e)
self.files_written.append([self.filepath,seed])
- def unique_filename(self,seed,previouspath):
+ def unique_filename(self,seed,previouspath=None):
revision = 1
if previouspath is None:
@@ -61,6 +62,22 @@ class PngWriter:
info = PngImagePlugin.PngInfo()
info.add_text("Dream",prompt)
image.save(path,"PNG",pnginfo=info)
+
+ def make_grid(self,image_list,rows=None,cols=None):
+ image_cnt = len(image_list)
+ if None in (rows,cols):
+ rows = floor(sqrt(image_cnt)) # try to make it square
+ cols = ceil(image_cnt/rows)
+ width = image_list[0].width
+ height = image_list[0].height
+
+ grid_img = Image.new('RGB',(width*cols,height*rows))
+ for r in range(0,rows):
+ for c in range (0,cols):
+ i = r*rows + c
+ grid_img.paste(image_list[i],(c*width,r*height))
+
+ return grid_img
class PromptFormatter():
def __init__(self,t2i,opt):
@@ -80,8 +97,6 @@ class PromptFormatter():
switches.append(f'-H{opt.height or t2i.height}')
switches.append(f'-C{opt.cfg_scale or t2i.cfg_scale}')
switches.append(f'-m{t2i.sampler_name}')
- if opt.variants:
- switches.append(f'-v{opt.variants}')
if opt.init_img:
switches.append(f'-I{opt.init_img}')
if opt.strength and opt.init_img is not None:
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index a3b4ecfcc7..fe0d3819a1 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -20,7 +20,6 @@ from torch import autocast
from contextlib import contextmanager, nullcontext
import transformers
import time
-import math
import re
import traceback
diff --git a/scripts/dream.py b/scripts/dream.py
index 10acccbfc3..24dac5b927 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -153,54 +153,60 @@ def main_loop(t2i,outdir,parser,log,infile):
continue
normalized_prompt = PromptFormatter(t2i,opt).normalize_prompt()
- variants = None
+ individual_images = not opt.grid
try:
file_writer = PngWriter(outdir,normalized_prompt,opt.batch_size)
- callback = file_writer.write_image
+ callback = file_writer.write_image if individual_images else None
- t2i.prompt2image(image_callback=callback,
- **vars(opt))
- results = file_writer.files_written
+ image_list = t2i.prompt2image(image_callback=callback,**vars(opt))
+ results = file_writer.files_written if individual_images else image_list
- if None not in (opt.variants,opt.init_img):
- variants = generate_variants(t2i,outdir,opt,results)
+ if opt.grid and len(results) > 0:
+ grid_img = file_writer.make_grid([r[0] for r in results])
+ filename = file_writer.unique_filename(results[0][1])
+ seeds = [a[1] for a in results]
+ results = [[filename,seeds]]
+ metadata_prompt = f'{normalized_prompt} -S{results[0][1]}'
+ file_writer.save_image_and_prompt_to_png(grid_img,metadata_prompt,filename)
except AssertionError as e:
print(e)
continue
+ except OSError as e:
+ print(e)
+ continue
+
print("Outputs:")
write_log_message(t2i,normalized_prompt,results,log)
- if variants is not None:
- print('Variants:')
- for vr in variants:
- write_log_message(t2i,vr[0],vr[1],log)
print("goodbye!")
-def generate_variants(t2i,outdir,opt,previous_gens):
- variants = []
- print(f"Generating {opt.variants} variant(s)...")
- newopt = copy.deepcopy(opt)
- newopt.iterations = 1
- newopt.variants = None
- for r in previous_gens:
- newopt.init_img = r[0]
- prompt = PromptFormatter(t2i,newopt).normalize_prompt()
- print(f"] generating variant for {newopt.init_img}")
- for j in range(0,opt.variants):
- try:
- file_writer = PngWriter(outdir,prompt,newopt.batch_size)
- callback = file_writer.write_image
- t2i.prompt2image(image_callback=callback,**vars(newopt))
- results = file_writer.files_written
- variants.append([prompt,results])
- except AssertionError as e:
- print(e)
- continue
- print(f'{opt.variants} variants generated')
- return variants
+# variant generation is going to be superseded by a generalized
+# "prompt-morph" functionality
+# def generate_variants(t2i,outdir,opt,previous_gens):
+# variants = []
+# print(f"Generating {opt.variants} variant(s)...")
+# newopt = copy.deepcopy(opt)
+# newopt.iterations = 1
+# newopt.variants = None
+# for r in previous_gens:
+# newopt.init_img = r[0]
+# prompt = PromptFormatter(t2i,newopt).normalize_prompt()
+# print(f"] generating variant for {newopt.init_img}")
+# for j in range(0,opt.variants):
+# try:
+# file_writer = PngWriter(outdir,prompt,newopt.batch_size)
+# callback = file_writer.write_image
+# t2i.prompt2image(image_callback=callback,**vars(newopt))
+# results = file_writer.files_written
+# variants.append([prompt,results])
+# except AssertionError as e:
+# print(e)
+# continue
+# print(f'{opt.variants} variants generated')
+# return variants
def write_log_message(t2i,prompt,results,logfile):
@@ -209,9 +215,6 @@ def write_log_message(t2i,prompt,results,logfile):
img_num = 1
seenit = {}
- seeds = [a[1] for a in results]
- seeds = f"(seeds for individual images: {seeds})"
-
for r in results:
seed = r[1]
log_message = (f'{r[0]}: {prompt} -S{seed}')
@@ -275,7 +278,8 @@ def create_cmd_parser():
parser.add_argument('-i','--individual',action='store_true',help="generate individual files (default)")
parser.add_argument('-I','--init_img',type=str,help="path to input image for img2img mode (supersedes width and height)")
parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely")
- parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
+# variants is going to be superseded by a generalized "prompt-morph" function
+# parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
parser.add_argument('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
return parser
diff --git a/src/k-diffusion b/src/k-diffusion
index db57990687..ef1bf07627 160000
--- a/src/k-diffusion
+++ b/src/k-diffusion
@@ -1 +1 @@
-Subproject commit db5799068749bf3a6d5845120ed32df16b7d883b
+Subproject commit ef1bf07627c9a10ba9137e68a0206b844544a7d9
From a10baf58082defe05311c2d90614c387651301bd Mon Sep 17 00:00:00 2001
From: Kevin Gibbons <bakkot@gmail.com>
Date: Thu, 25 Aug 2022 15:13:07 -0700
Subject: [PATCH 36/58] factor out exception handler
---
ldm/simplet2i.py | 248 +++++++++++++++++++++++------------------------
1 file changed, 124 insertions(+), 124 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 0ec3d60d98..157f55fbcb 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -52,7 +52,7 @@ t2i = T2I(model = <path> // models/ldm/stable-diffusion-v1/model.ck
# do the slow model initialization
t2i.load_model()
-# Do the fast inference & image generation. Any options passed here
+# Do the fast inference & image generation. Any options passed here
# override the default values assigned during class initialization
# Will call load_model() if the model was not previously loaded and so
# may be slow at first.
@@ -70,7 +70,7 @@ results = t2i.prompt2png(prompt = "an astronaut riding a horse",
outdir = "./outputs/,
iterations = 3,
init_img = "./sketches/horse+rider.png")
-
+
for row in results:
print(f'filename={row[0]}')
print(f'seed ={row[1]}')
@@ -181,7 +181,7 @@ The vast majority of these arguments default to reasonable values.
outdir = kwargs.get('outdir','outputs/img-samples')
assert 'init_img' in kwargs,'call to img2img() must include the init_img argument'
return self.prompt2png(prompt,outdir,**kwargs)
-
+
def prompt2image(self,
# these are common
prompt,
@@ -219,7 +219,7 @@ The vast majority of these arguments default to reasonable values.
callback // a function or method that will be called each time an image is generated
To use the callback, define a function of method that receives two arguments, an Image object
- and the seed. You can then do whatever you like with the image, including converting it to
+ and the seed. You can then do whatever you like with the image, including converting it to
different formats and manipulating it. For example:
def process_image(image,seed):
@@ -252,28 +252,41 @@ The vast majority of these arguments default to reasonable values.
data = [batch_size * [prompt]]
scope = autocast if self.precision=="autocast" else nullcontext
- tic = time.time()
- if init_img:
- assert os.path.exists(init_img),f'{init_img}: File not found'
- results = self._img2img(prompt,
- data=data,precision_scope=scope,
- batch_size=batch_size,iterations=iterations,
- steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
- skip_normalize=skip_normalize,
- init_img=init_img,strength=strength,variants=variants,
- callback=image_callback)
- else:
- results = self._txt2img(prompt,
- data=data,precision_scope=scope,
- batch_size=batch_size,iterations=iterations,
- steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
- skip_normalize=skip_normalize,
- width=width,height=height,
- callback=image_callback)
+ tic = time.time()
+ results = list()
+ def prompt_callback(image, seed):
+ results.append([image, seed])
+ if image_callback is not None:
+ image_callback(image, seed)
+
+ try:
+ if init_img:
+ assert os.path.exists(init_img),f'{init_img}: File not found'
+ self._img2img(prompt,
+ data=data,precision_scope=scope,
+ batch_size=batch_size,iterations=iterations,
+ steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
+ skip_normalize=skip_normalize,
+ init_img=init_img,strength=strength,variants=variants,
+ callback=prompt_callback)
+ else:
+ self._txt2img(prompt,
+ data=data,precision_scope=scope,
+ batch_size=batch_size,iterations=iterations,
+ steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
+ skip_normalize=skip_normalize,
+ width=width,height=height,
+ callback=prompt_callback)
+ except KeyboardInterrupt:
+ print('*interrupted*')
+ print('Partial results will be returned; if --grid was requested, nothing will be returned.')
+ except RuntimeError as e:
+ print(str(e))
+
toc = time.time()
print(f'{len(results)} images generated in',"%4.2fs"% (toc-tic))
return results
-
+
@torch.no_grad()
def _txt2img(self,prompt,
data,precision_scope,
@@ -292,62 +305,56 @@ The vast majority of these arguments default to reasonable values.
image_count = 0
# Gawd. Too many levels of indent here. Need to refactor into smaller routines!
- try:
- with precision_scope(self.device.type), self.model.ema_scope():
- all_samples = list()
- for n in trange(iterations, desc="Sampling"):
- seed_everything(seed)
- for prompts in tqdm(data, desc="data", dynamic_ncols=True):
- uc = None
- if cfg_scale != 1.0:
- uc = self.model.get_learned_conditioning(batch_size * [""])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
+ with precision_scope(self.device.type), self.model.ema_scope():
+ all_samples = list()
+ for n in trange(iterations, desc="Sampling"):
+ seed_everything(seed)
+ for prompts in tqdm(data, desc="data", dynamic_ncols=True):
+ uc = None
+ if cfg_scale != 1.0:
+ uc = self.model.get_learned_conditioning(batch_size * [""])
+ if isinstance(prompts, tuple):
+ prompts = list(prompts)
- # weighted sub-prompts
- subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
- if len(subprompts) > 1:
- # i dont know if this is correct.. but it works
- c = torch.zeros_like(uc)
- # get total weight for normalizing
- totalWeight = sum(weights)
- # normalize each "sub prompt" and add it
- for i in range(0,len(subprompts)):
- weight = weights[i]
- if not skip_normalize:
- weight = weight / totalWeight
- c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
- else: # just standard 1 prompt
- c = self.model.get_learned_conditioning(prompts)
+ # weighted sub-prompts
+ subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
+ if len(subprompts) > 1:
+ # i dont know if this is correct.. but it works
+ c = torch.zeros_like(uc)
+ # get total weight for normalizing
+ totalWeight = sum(weights)
+ # normalize each "sub prompt" and add it
+ for i in range(0,len(subprompts)):
+ weight = weights[i]
+ if not skip_normalize:
+ weight = weight / totalWeight
+ c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
+ else: # just standard 1 prompt
+ c = self.model.get_learned_conditioning(prompts)
- shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
- samples_ddim, _ = sampler.sample(S=steps,
- conditioning=c,
- batch_size=batch_size,
- shape=shape,
- verbose=False,
- unconditional_guidance_scale=cfg_scale,
- unconditional_conditioning=uc,
- eta=ddim_eta)
+ shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
+ samples_ddim, _ = sampler.sample(S=steps,
+ conditioning=c,
+ batch_size=batch_size,
+ shape=shape,
+ verbose=False,
+ unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,
+ eta=ddim_eta)
- x_samples_ddim = self.model.decode_first_stage(samples_ddim)
- x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
- for x_sample in x_samples_ddim:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- image = Image.fromarray(x_sample.astype(np.uint8))
- images.append([image,seed])
- if callback is not None:
- callback(image,seed)
-
- seed = self._new_seed()
- except KeyboardInterrupt:
- print('*interrupted*')
- print('Partial results will be returned; if --grid was requested, nothing will be returned.')
- except RuntimeError as e:
- print(str(e))
+ x_samples_ddim = self.model.decode_first_stage(samples_ddim)
+ x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+ for x_sample in x_samples_ddim:
+ x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+ image = Image.fromarray(x_sample.astype(np.uint8))
+ images.append([image,seed])
+ if callback is not None:
+ callback(image,seed)
+
+ seed = self._new_seed()
return images
-
+
@torch.no_grad()
def _img2img(self,prompt,
data,precision_scope,
@@ -374,62 +381,55 @@ The vast majority of these arguments default to reasonable values.
init_latent = self.model.get_first_stage_encoding(self.model.encode_first_stage(init_image)) # move to latent space
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
-
+
t_enc = int(strength * steps)
# print(f"target t_enc is {t_enc} steps")
images = list()
- try:
- with precision_scope(self.device.type), self.model.ema_scope():
- all_samples = list()
- for n in trange(iterations, desc="Sampling"):
- seed_everything(seed)
- for prompts in tqdm(data, desc="data", dynamic_ncols=True):
- uc = None
- if cfg_scale != 1.0:
- uc = self.model.get_learned_conditioning(batch_size * [""])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
+ with precision_scope(self.device.type), self.model.ema_scope():
+ all_samples = list()
+ for n in trange(iterations, desc="Sampling"):
+ seed_everything(seed)
+ for prompts in tqdm(data, desc="data", dynamic_ncols=True):
+ uc = None
+ if cfg_scale != 1.0:
+ uc = self.model.get_learned_conditioning(batch_size * [""])
+ if isinstance(prompts, tuple):
+ prompts = list(prompts)
- # weighted sub-prompts
- subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
- if len(subprompts) > 1:
- # i dont know if this is correct.. but it works
- c = torch.zeros_like(uc)
- # get total weight for normalizing
- totalWeight = sum(weights)
- # normalize each "sub prompt" and add it
- for i in range(0,len(subprompts)):
- weight = weights[i]
- if not skip_normalize:
- weight = weight / totalWeight
- c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
- else: # just standard 1 prompt
- c = self.model.get_learned_conditioning(prompts)
+ # weighted sub-prompts
+ subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
+ if len(subprompts) > 1:
+ # i dont know if this is correct.. but it works
+ c = torch.zeros_like(uc)
+ # get total weight for normalizing
+ totalWeight = sum(weights)
+ # normalize each "sub prompt" and add it
+ for i in range(0,len(subprompts)):
+ weight = weights[i]
+ if not skip_normalize:
+ weight = weight / totalWeight
+ c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
+ else: # just standard 1 prompt
+ c = self.model.get_learned_conditioning(prompts)
- # encode (scaled latent)
- z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
- # decode it
- samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
- unconditional_conditioning=uc,)
+ # encode (scaled latent)
+ z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
+ # decode it
+ samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,)
- x_samples = self.model.decode_first_stage(samples)
- x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+ x_samples = self.model.decode_first_stage(samples)
+ x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
- for x_sample in x_samples:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- image = Image.fromarray(x_sample.astype(np.uint8))
- images.append([image,seed])
- if callback is not None:
- callback(image,seed)
- seed = self._new_seed()
+ for x_sample in x_samples:
+ x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+ image = Image.fromarray(x_sample.astype(np.uint8))
+ images.append([image,seed])
+ if callback is not None:
+ callback(image,seed)
+ seed = self._new_seed()
- except KeyboardInterrupt:
- print('*interrupted*')
- print('Partial results will be returned; if --grid was requested, nothing will be returned.')
- except RuntimeError as e:
- print("Oops! A runtime error has occurred. If this is unexpected, please copy-and-paste this stack trace and post it as an Issue to http://github.com/lstein/stable-diffusion")
- traceback.print_exc()
return images
def _new_seed(self):
@@ -476,7 +476,7 @@ The vast majority of these arguments default to reasonable values.
print(msg)
return self.model
-
+
def _load_model_from_config(self, config, ckpt):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
@@ -507,7 +507,7 @@ The vast majority of these arguments default to reasonable values.
def _split_weighted_subprompts(text):
"""
- grabs all text up to the first occurrence of ':'
+ grabs all text up to the first occurrence of ':'
uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
if ':' has no value defined, defaults to 1.0
repeats until no text remaining
@@ -523,7 +523,7 @@ The vast majority of these arguments default to reasonable values.
remaining -= idx
# remove from main text
text = text[idx+1:]
- # find value for weight
+ # find value for weight
if " " in text:
idx = text.index(" ") # first occurence
else: # no space, read to end
From 078859207df4a6149cb8cdf7d4d9b4bb1fef1ae6 Mon Sep 17 00:00:00 2001
From: Kevin Gibbons <bakkot@gmail.com>
Date: Thu, 25 Aug 2022 15:19:44 -0700
Subject: [PATCH 37/58] factor out loop
---
ldm/simplet2i.py | 226 ++++++++++++++++++++---------------------------
1 file changed, 94 insertions(+), 132 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 157f55fbcb..31721906d7 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -216,7 +216,7 @@ The vast majority of these arguments default to reasonable values.
strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
variants // if >0, the 1st generated image will be passed back to img2img to generate the requested number of variants
- callback // a function or method that will be called each time an image is generated
+ image_callback // a function or method that will be called each time an image is generated
To use the callback, define a function of method that receives two arguments, an Image object
and the seed. You can then do whatever you like with the image, including converting it to
@@ -249,34 +249,40 @@ The vast majority of these arguments default to reasonable values.
height = h
width = w
- data = [batch_size * [prompt]]
scope = autocast if self.precision=="autocast" else nullcontext
tic = time.time()
results = list()
- def prompt_callback(image, seed):
- results.append([image, seed])
- if image_callback is not None:
- image_callback(image, seed)
try:
if init_img:
assert os.path.exists(init_img),f'{init_img}: File not found'
- self._img2img(prompt,
- data=data,precision_scope=scope,
- batch_size=batch_size,iterations=iterations,
- steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
+ get_images = self._img2img(
+ precision_scope=scope,
+ batch_size=batch_size,
+ steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
- init_img=init_img,strength=strength,variants=variants,
- callback=prompt_callback)
+ init_img=init_img,strength=strength)
else:
- self._txt2img(prompt,
- data=data,precision_scope=scope,
- batch_size=batch_size,iterations=iterations,
- steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
+ get_images = self._txt2img(
+ precision_scope=scope,
+ batch_size=batch_size,
+ steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
- width=width,height=height,
- callback=prompt_callback)
+ width=width,height=height)
+
+ data = [batch_size * [prompt]]
+ with scope(self.device.type), self.model.ema_scope():
+ for n in trange(iterations, desc="Sampling"):
+ seed_everything(seed)
+ for prompts in tqdm(data, desc="data", dynamic_ncols=True):
+ iter_images = get_images(prompts)
+ for image in iter_images:
+ results.append([image, seed])
+ if image_callback is not None:
+ image_callback(image,seed)
+ seed = self._new_seed()
+
except KeyboardInterrupt:
print('*interrupted*')
print('Partial results will be returned; if --grid was requested, nothing will be returned.')
@@ -288,84 +294,41 @@ The vast majority of these arguments default to reasonable values.
return results
@torch.no_grad()
- def _txt2img(self,prompt,
- data,precision_scope,
- batch_size,iterations,
- steps,seed,cfg_scale,ddim_eta,
+ def _txt2img(self,
+ precision_scope,
+ batch_size,
+ steps,cfg_scale,ddim_eta,
skip_normalize,
- width,height,
- callback): # the callback is called each time a new Image is generated
+ width,height):
"""
- Generate an image from the prompt, writing iteration images into the outdir
- The output is a list of lists in the format: [[image1,seed1], [image2,seed2],...]
+ Generate an image from the prompt
"""
- sampler = self.sampler
- images = list()
- image_count = 0
+ sampler = self.sampler
- # Gawd. Too many levels of indent here. Need to refactor into smaller routines!
- with precision_scope(self.device.type), self.model.ema_scope():
- all_samples = list()
- for n in trange(iterations, desc="Sampling"):
- seed_everything(seed)
- for prompts in tqdm(data, desc="data", dynamic_ncols=True):
- uc = None
- if cfg_scale != 1.0:
- uc = self.model.get_learned_conditioning(batch_size * [""])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
-
- # weighted sub-prompts
- subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
- if len(subprompts) > 1:
- # i dont know if this is correct.. but it works
- c = torch.zeros_like(uc)
- # get total weight for normalizing
- totalWeight = sum(weights)
- # normalize each "sub prompt" and add it
- for i in range(0,len(subprompts)):
- weight = weights[i]
- if not skip_normalize:
- weight = weight / totalWeight
- c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
- else: # just standard 1 prompt
- c = self.model.get_learned_conditioning(prompts)
-
- shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
- samples_ddim, _ = sampler.sample(S=steps,
- conditioning=c,
- batch_size=batch_size,
- shape=shape,
- verbose=False,
- unconditional_guidance_scale=cfg_scale,
- unconditional_conditioning=uc,
- eta=ddim_eta)
-
- x_samples_ddim = self.model.decode_first_stage(samples_ddim)
- x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
- for x_sample in x_samples_ddim:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- image = Image.fromarray(x_sample.astype(np.uint8))
- images.append([image,seed])
- if callback is not None:
- callback(image,seed)
-
- seed = self._new_seed()
-
- return images
+ def get_images(prompts):
+ uc, c = self._get_uc_and_c(prompts, batch_size, skip_normalize)
+ shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
+ samples, _ = sampler.sample(S=steps,
+ conditioning=c,
+ batch_size=batch_size,
+ shape=shape,
+ verbose=False,
+ unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,
+ eta=ddim_eta)
+ return self._samples_to_images(samples)
+ return get_images
@torch.no_grad()
- def _img2img(self,prompt,
- data,precision_scope,
- batch_size,iterations,
- steps,seed,cfg_scale,ddim_eta,
+ def _img2img(self,
+ precision_scope,
+ batch_size,
+ steps,cfg_scale,ddim_eta,
skip_normalize,
- init_img,strength,variants,
- callback):
+ init_img,strength):
"""
- Generate an image from the prompt and the initial image, writing iteration images into the outdir
- The output is a list of lists in the format: [[image,seed1], [image,seed2],...]
+ Generate an image from the prompt and the initial image
"""
# PLMS sampler not supported yet, so ignore previous sampler
@@ -384,54 +347,53 @@ The vast majority of these arguments default to reasonable values.
t_enc = int(strength * steps)
# print(f"target t_enc is {t_enc} steps")
+
+ def get_images(prompts):
+ uc, c = self._get_uc_and_c(prompts, batch_size, skip_normalize)
+
+ # encode (scaled latent)
+ z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
+ # decode it
+ samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,)
+ return self._samples_to_images(samples)
+ return get_images
+
+ # TODO: does this actually need to run every loop? does anything in it vary by random seed?
+ def _get_uc_and_c(self, prompts, batch_size, skip_normalize):
+ if isinstance(prompts, tuple):
+ prompts = list(prompts)
+
+ uc = self.model.get_learned_conditioning(batch_size * [""])
+
+ # weighted sub-prompts
+ subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
+ if len(subprompts) > 1:
+ # i dont know if this is correct.. but it works
+ c = torch.zeros_like(uc)
+ # get total weight for normalizing
+ totalWeight = sum(weights)
+ # normalize each "sub prompt" and add it
+ for i in range(0,len(subprompts)):
+ weight = weights[i]
+ if not skip_normalize:
+ weight = weight / totalWeight
+ c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
+ else: # just standard 1 prompt
+ c = self.model.get_learned_conditioning(prompts)
+ return (uc, c)
+
+ def _samples_to_images(self, samples):
+ x_samples = self.model.decode_first_stage(samples)
+ x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
images = list()
-
- with precision_scope(self.device.type), self.model.ema_scope():
- all_samples = list()
- for n in trange(iterations, desc="Sampling"):
- seed_everything(seed)
- for prompts in tqdm(data, desc="data", dynamic_ncols=True):
- uc = None
- if cfg_scale != 1.0:
- uc = self.model.get_learned_conditioning(batch_size * [""])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
-
- # weighted sub-prompts
- subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
- if len(subprompts) > 1:
- # i dont know if this is correct.. but it works
- c = torch.zeros_like(uc)
- # get total weight for normalizing
- totalWeight = sum(weights)
- # normalize each "sub prompt" and add it
- for i in range(0,len(subprompts)):
- weight = weights[i]
- if not skip_normalize:
- weight = weight / totalWeight
- c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
- else: # just standard 1 prompt
- c = self.model.get_learned_conditioning(prompts)
-
- # encode (scaled latent)
- z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
- # decode it
- samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
- unconditional_conditioning=uc,)
-
- x_samples = self.model.decode_first_stage(samples)
- x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
- for x_sample in x_samples:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- image = Image.fromarray(x_sample.astype(np.uint8))
- images.append([image,seed])
- if callback is not None:
- callback(image,seed)
- seed = self._new_seed()
-
+ for x_sample in x_samples:
+ x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+ image = Image.fromarray(x_sample.astype(np.uint8))
+ images.append(image)
return images
+
def _new_seed(self):
self.seed = random.randrange(0,np.iinfo(np.uint32).max)
return self.seed
From 31b22e057d12daddd9a9b79c8d156288dfad3b95 Mon Sep 17 00:00:00 2001
From: Kevin Gibbons <bakkot@gmail.com>
Date: Thu, 25 Aug 2022 17:01:17 -0700
Subject: [PATCH 38/58] switch to generators
---
ldm/simplet2i.py | 30 ++++++++++++++----------------
1 file changed, 14 insertions(+), 16 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 31721906d7..d63502831d 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -257,26 +257,25 @@ The vast majority of these arguments default to reasonable values.
try:
if init_img:
assert os.path.exists(init_img),f'{init_img}: File not found'
- get_images = self._img2img(
+ images_iterator = self._img2img(prompt,
precision_scope=scope,
batch_size=batch_size,
steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
init_img=init_img,strength=strength)
else:
- get_images = self._txt2img(
+ images_iterator = self._txt2img(prompt,
precision_scope=scope,
batch_size=batch_size,
steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
width=width,height=height)
- data = [batch_size * [prompt]]
with scope(self.device.type), self.model.ema_scope():
for n in trange(iterations, desc="Sampling"):
seed_everything(seed)
- for prompts in tqdm(data, desc="data", dynamic_ncols=True):
- iter_images = get_images(prompts)
+ for batch_item in tqdm(range(batch_size), desc="data", dynamic_ncols=True):
+ iter_images = next(images_iterator)
for image in iter_images:
results.append([image, seed])
if image_callback is not None:
@@ -295,19 +294,20 @@ The vast majority of these arguments default to reasonable values.
@torch.no_grad()
def _txt2img(self,
+ prompt,
precision_scope,
batch_size,
steps,cfg_scale,ddim_eta,
skip_normalize,
width,height):
"""
- Generate an image from the prompt
+ An infinite iterator of images from the prompt.
"""
sampler = self.sampler
- def get_images(prompts):
- uc, c = self._get_uc_and_c(prompts, batch_size, skip_normalize)
+ while True:
+ uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
samples, _ = sampler.sample(S=steps,
conditioning=c,
@@ -317,18 +317,18 @@ The vast majority of these arguments default to reasonable values.
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,
eta=ddim_eta)
- return self._samples_to_images(samples)
- return get_images
+ yield self._samples_to_images(samples)
@torch.no_grad()
def _img2img(self,
+ prompt,
precision_scope,
batch_size,
steps,cfg_scale,ddim_eta,
skip_normalize,
init_img,strength):
"""
- Generate an image from the prompt and the initial image
+ An infinite iterator of images from the prompt and the initial image
"""
# PLMS sampler not supported yet, so ignore previous sampler
@@ -348,16 +348,15 @@ The vast majority of these arguments default to reasonable values.
t_enc = int(strength * steps)
# print(f"target t_enc is {t_enc} steps")
- def get_images(prompts):
- uc, c = self._get_uc_and_c(prompts, batch_size, skip_normalize)
+ while True:
+ uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
# encode (scaled latent)
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,)
- return self._samples_to_images(samples)
- return get_images
+ yield self._samples_to_images(samples)
# TODO: does this actually need to run every loop? does anything in it vary by random seed?
def _get_uc_and_c(self, prompts, batch_size, skip_normalize):
@@ -393,7 +392,6 @@ The vast majority of these arguments default to reasonable values.
images.append(image)
return images
-
def _new_seed(self):
self.seed = random.randrange(0,np.iinfo(np.uint32).max)
return self.seed
From 797de3257cf3507dba8672f4387995821a42adcc Mon Sep 17 00:00:00 2001
From: Kevin Gibbons <bakkot@gmail.com>
Date: Thu, 25 Aug 2022 17:16:07 -0700
Subject: [PATCH 39/58] fix batch_size
---
ldm/simplet2i.py | 21 +++++++++------------
1 file changed, 9 insertions(+), 12 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index d63502831d..3187bee090 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -274,12 +274,11 @@ The vast majority of these arguments default to reasonable values.
with scope(self.device.type), self.model.ema_scope():
for n in trange(iterations, desc="Sampling"):
seed_everything(seed)
- for batch_item in tqdm(range(batch_size), desc="data", dynamic_ncols=True):
- iter_images = next(images_iterator)
- for image in iter_images:
- results.append([image, seed])
- if image_callback is not None:
- image_callback(image,seed)
+ iter_images = next(images_iterator)
+ for image in iter_images:
+ results.append([image, seed])
+ if image_callback is not None:
+ image_callback(image,seed)
seed = self._new_seed()
except KeyboardInterrupt:
@@ -359,14 +358,12 @@ The vast majority of these arguments default to reasonable values.
yield self._samples_to_images(samples)
# TODO: does this actually need to run every loop? does anything in it vary by random seed?
- def _get_uc_and_c(self, prompts, batch_size, skip_normalize):
- if isinstance(prompts, tuple):
- prompts = list(prompts)
+ def _get_uc_and_c(self, prompt, batch_size, skip_normalize):
uc = self.model.get_learned_conditioning(batch_size * [""])
# weighted sub-prompts
- subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
+ subprompts,weights = T2I._split_weighted_subprompts(prompt)
if len(subprompts) > 1:
# i dont know if this is correct.. but it works
c = torch.zeros_like(uc)
@@ -377,9 +374,9 @@ The vast majority of these arguments default to reasonable values.
weight = weights[i]
if not skip_normalize:
weight = weight / totalWeight
- c = torch.add(c,self.model.get_learned_conditioning(subprompts[i]), alpha=weight)
+ c = torch.add(c, self.model.get_learned_conditioning(batch_size * [subprompts[i]]), alpha=weight)
else: # just standard 1 prompt
- c = self.model.get_learned_conditioning(prompts)
+ c = self.model.get_learned_conditioning(batch_size * [prompt])
return (uc, c)
def _samples_to_images(self, samples):
From b49475a54ff3f49ff68d129cea6146ca4b55996c Mon Sep 17 00:00:00 2001
From: tesseractcat <tesseractcats@gmail.com>
Date: Thu, 25 Aug 2022 21:06:17 -0400
Subject: [PATCH 40/58] Keep a log of requests for dream_web
---
scripts/dream_web.py | 6 ++++++
1 file changed, 6 insertions(+)
diff --git a/scripts/dream_web.py b/scripts/dream_web.py
index d48d5ac9ec..227e8acdc7 100644
--- a/scripts/dream_web.py
+++ b/scripts/dream_web.py
@@ -85,6 +85,12 @@ class DreamServer(BaseHTTPRequestHandler):
print(f"Prompt generated with output: {outputs}")
post_data['initimg'] = '' # Don't send init image back
+
+ # Append post_data to log
+ with open("./outputs/img-samples/dream_web_log.txt", "a") as log:
+ for output in outputs:
+ log.write(f"{output[0]}: {json.dumps(post_data)}\n")
+
outputs = [x + [post_data] for x in outputs] # Append config to each output
result = {'outputs': outputs}
self.wfile.write(bytes(json.dumps(result), "utf-8"))
From c38b6964b4e6c350004bcd4180caeae3b77f8e4b Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 22:19:12 -0400
Subject: [PATCH 41/58] improved inline error messages slightly
---
ldm/simplet2i.py | 1 +
scripts/dream.py | 2 +-
2 files changed, 2 insertions(+), 1 deletion(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 3187bee090..21f973988b 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -286,6 +286,7 @@ The vast majority of these arguments default to reasonable values.
print('Partial results will be returned; if --grid was requested, nothing will be returned.')
except RuntimeError as e:
print(str(e))
+ print('Are you sure your system has an adequate NVIDIA GPU?')
toc = time.time()
print(f'{len(results)} images generated in',"%4.2fs"% (toc-tic))
diff --git a/scripts/dream.py b/scripts/dream.py
index 24dac5b927..b0a31b63e0 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -252,7 +252,7 @@ def create_argv_parser():
'-o',
type=str,
default="outputs/img-samples",
- help="directory in which to place generated images and a log of prompts and seeds")
+ help="directory in which to place generated images and a log of prompts and seeds (outputs/img-samples")
parser.add_argument('--embedding_path',
type=str,
help="Path to a pre-trained embedding manager checkpoint - can only be set on command line")
From f1bed52530fdbabeab9f9ef029ac6a9b479ff8a9 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 22:49:15 -0400
Subject: [PATCH 42/58] moved dream utilities into their own subfolder
---
ldm/{dream_util.py => dream/pngwriter.py} | 100 +++-------------------
ldm/dream/readline.py | 94 ++++++++++++++++++++
ldm/simplet2i.py | 2 +-
scripts/dream.py | 4 +-
4 files changed, 107 insertions(+), 93 deletions(-)
rename ldm/{dream_util.py => dream/pngwriter.py} (56%)
create mode 100644 ldm/dream/readline.py
diff --git a/ldm/dream_util.py b/ldm/dream/pngwriter.py
similarity index 56%
rename from ldm/dream_util.py
rename to ldm/dream/pngwriter.py
index b69a0c1367..aca7b47c21 100644
--- a/ldm/dream_util.py
+++ b/ldm/dream/pngwriter.py
@@ -1,6 +1,14 @@
-'''Utilities for dealing with PNG images and their path names'''
+'''
+Two helper classes for dealing with PNG images and their path names.
+PngWriter -- Converts Images generated by T2I into PNGs, finds
+ appropriate names for them, and writes prompt metadata
+ into the PNG. Intended to be subclassable in order to
+ create more complex naming schemes, including using the
+ prompt for file/directory names.
+PromptFormatter -- Utility for converting a Namespace of prompt parameters
+ back into a formatted prompt string with command-line switches.
+'''
import os
-import atexit
import re
from math import sqrt,floor,ceil
from PIL import Image,PngImagePlugin
@@ -105,91 +113,3 @@ class PromptFormatter():
switches.append('-F')
return ' '.join(switches)
-# ---------------readline utilities---------------------
-try:
- import readline
- readline_available = True
-except:
- readline_available = False
-
-class Completer():
- def __init__(self,options):
- self.options = sorted(options)
- return
-
- def complete(self,text,state):
- buffer = readline.get_line_buffer()
-
- if text.startswith(('-I','--init_img')):
- return self._path_completions(text,state,('.png'))
-
- if buffer.strip().endswith('cd') or text.startswith(('.','/')):
- return self._path_completions(text,state,())
-
- response = None
- if state == 0:
- # This is the first time for this text, so build a match list.
- if text:
- self.matches = [s
- for s in self.options
- if s and s.startswith(text)]
- else:
- self.matches = self.options[:]
-
- # Return the state'th item from the match list,
- # if we have that many.
- try:
- response = self.matches[state]
- except IndexError:
- response = None
- return response
-
- def _path_completions(self,text,state,extensions):
- # get the path so far
- if text.startswith('-I'):
- path = text.replace('-I','',1).lstrip()
- elif text.startswith('--init_img='):
- path = text.replace('--init_img=','',1).lstrip()
- else:
- path = text
-
- matches = list()
-
- path = os.path.expanduser(path)
- if len(path)==0:
- matches.append(text+'./')
- else:
- dir = os.path.dirname(path)
- dir_list = os.listdir(dir)
- for n in dir_list:
- if n.startswith('.') and len(n)>1:
- continue
- full_path = os.path.join(dir,n)
- if full_path.startswith(path):
- if os.path.isdir(full_path):
- matches.append(os.path.join(os.path.dirname(text),n)+'/')
- elif n.endswith(extensions):
- matches.append(os.path.join(os.path.dirname(text),n))
-
- try:
- response = matches[state]
- except IndexError:
- response = None
- return response
-
-if readline_available:
- readline.set_completer(Completer(['cd','pwd',
- '--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
- '--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
- '--individual','-i','--init_img','-I','--strength','-f','-v','--variants']).complete)
- readline.set_completer_delims(" ")
- readline.parse_and_bind('tab: complete')
-
- histfile = os.path.join(os.path.expanduser('~'),".dream_history")
- try:
- readline.read_history_file(histfile)
- readline.set_history_length(1000)
- except FileNotFoundError:
- pass
- atexit.register(readline.write_history_file,histfile)
-
diff --git a/ldm/dream/readline.py b/ldm/dream/readline.py
new file mode 100644
index 0000000000..f46ac6e23a
--- /dev/null
+++ b/ldm/dream/readline.py
@@ -0,0 +1,94 @@
+'''
+Readline helper functions for dream.py (linux and mac only).
+'''
+import os
+import re
+import atexit
+# ---------------readline utilities---------------------
+try:
+ import readline
+ readline_available = True
+except:
+ readline_available = False
+
+class Completer():
+ def __init__(self,options):
+ self.options = sorted(options)
+ return
+
+ def complete(self,text,state):
+ buffer = readline.get_line_buffer()
+
+ if text.startswith(('-I','--init_img')):
+ return self._path_completions(text,state,('.png'))
+
+ if buffer.strip().endswith('cd') or text.startswith(('.','/')):
+ return self._path_completions(text,state,())
+
+ response = None
+ if state == 0:
+ # This is the first time for this text, so build a match list.
+ if text:
+ self.matches = [s
+ for s in self.options
+ if s and s.startswith(text)]
+ else:
+ self.matches = self.options[:]
+
+ # Return the state'th item from the match list,
+ # if we have that many.
+ try:
+ response = self.matches[state]
+ except IndexError:
+ response = None
+ return response
+
+ def _path_completions(self,text,state,extensions):
+ # get the path so far
+ if text.startswith('-I'):
+ path = text.replace('-I','',1).lstrip()
+ elif text.startswith('--init_img='):
+ path = text.replace('--init_img=','',1).lstrip()
+ else:
+ path = text
+
+ matches = list()
+
+ path = os.path.expanduser(path)
+ if len(path)==0:
+ matches.append(text+'./')
+ else:
+ dir = os.path.dirname(path)
+ dir_list = os.listdir(dir)
+ for n in dir_list:
+ if n.startswith('.') and len(n)>1:
+ continue
+ full_path = os.path.join(dir,n)
+ if full_path.startswith(path):
+ if os.path.isdir(full_path):
+ matches.append(os.path.join(os.path.dirname(text),n)+'/')
+ elif n.endswith(extensions):
+ matches.append(os.path.join(os.path.dirname(text),n))
+
+ try:
+ response = matches[state]
+ except IndexError:
+ response = None
+ return response
+
+if readline_available:
+ readline.set_completer(Completer(['cd','pwd',
+ '--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
+ '--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
+ '--individual','-i','--init_img','-I','--strength','-f','-v','--variants']).complete)
+ readline.set_completer_delims(" ")
+ readline.parse_and_bind('tab: complete')
+
+ histfile = os.path.join(os.path.expanduser('~'),".dream_history")
+ try:
+ readline.read_history_file(histfile)
+ readline.set_history_length(1000)
+ except FileNotFoundError:
+ pass
+ atexit.register(readline.write_history_file,histfile)
+
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 21f973988b..80a0194957 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -27,7 +27,7 @@ from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.ksampler import KSampler
-from ldm.dream_util import PngWriter
+from ldm.dream.pngwriter import PngWriter
"""Simplified text to image API for stable diffusion/latent diffusion
diff --git a/scripts/dream.py b/scripts/dream.py
index b0a31b63e0..b9090e79f4 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -6,8 +6,8 @@ import shlex
import os
import sys
import copy
-from ldm.dream_util import Completer,PngWriter,PromptFormatter
-
+import ldm.dream.readline
+from ldm.dream.pngwriter import PngWriter,PromptFormatter
debugging = False
def main():
From 5711b6d611ed7fb892ade5e45ebf17850d3abb0d Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Thu, 25 Aug 2022 22:57:30 -0400
Subject: [PATCH 43/58] Add optional GFPGAN support
---
README.md | 30 +++++++++++++++++++
ldm/simplet2i.py | 31 ++++++++++++++++++--
scripts/dream.py | 75 ++++++++++++++++++++++++++++++++++++++++++++++++
3 files changed, 134 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index 2af4a2a53a..6260032d3f 100644
--- a/README.md
+++ b/README.md
@@ -80,6 +80,36 @@ You may also pass a -v<count> option to generate count variants on the original
passing the first generated image back into img2img the requested number of times. It generates interesting
variants.
+## GFPGAN Support
+
+This script also provides the ability to invoke GFPGAN after image generation. Doing so will enhance faces
+and optionally upscale the image to a higher resolution.
+
+To use the ability, clone the [GFPGAN repository](https://github.com/TencentARC/GFPGAN) and follow their
+installation instructions. By default, we expect GFPGAN to be installed in a 'gfpgan' sibling directory.
+
+You may also want to install Real-ESRGAN, if you want to enhance non-face regions in the image by installing
+the pip Real-ESRGAN package.
+```
+pip install realesrgan
+
+```
+
+Now, you can run this script by adding the --gfpgan option. Any issues with GFPGAN will be reported on initialization.
+
+When generating prompts, add a -G or --gfpgan_strenth option to control the strength of the GFPGAN enhancement.
+0.0 is no enhancement, 1.0 is maximum enhancement.
+
+So for instance, to apply the maximum strength:
+~~~~
+dream> a man wearing a pineapple hat -G 1
+~~~~
+
+That's it!
+
+There's also a bunch of options to control GFPGAN settings when starting the script for different configs that you can
+read about in the help text. This will let you control where GFPGAN is installed, if upsampling is enapled, the upsampler to use and the model path.
+
## Barebones Web Server
As of version 1.10, this distribution comes with a bare bones web server (see screenshot). To use it,
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 0ec3d60d98..ab40330e43 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -132,7 +132,8 @@ The vast majority of these arguments default to reasonable values.
strength=0.75, # default in scripts/img2img.py
embedding_path=None,
latent_diffusion_weights=False, # just to keep track of this parameter when regenerating prompt
- device='cuda'
+ device='cuda',
+ gfpgan=None,
):
self.batch_size = batch_size
self.iterations = iterations
@@ -154,6 +155,7 @@ The vast majority of these arguments default to reasonable values.
self.sampler = None
self.latent_diffusion_weights=latent_diffusion_weights
self.device = device
+ self.gfpgan = gfpgan
if seed is None:
self.seed = self._new_seed()
else:
@@ -199,6 +201,7 @@ The vast majority of these arguments default to reasonable values.
# these are specific to img2img
init_img=None,
strength=None,
+ gfpgan_strength=None,
variants=None,
**args): # eat up additional cruft
'''
@@ -214,6 +217,7 @@ The vast majority of these arguments default to reasonable values.
cfg_scale // how strongly the prompt influences the image (7.5) (must be >1)
init_img // path to an initial image - its dimensions override width and height
strength // strength for noising/unnoising init_img. 0.0 preserves image exactly, 1.0 replaces it completely
+ gfpgan_strength // strength for GFPGAN. 0.0 preserves image exactly, 1.0 replaces it completely
ddim_eta // image randomness (eta=0.0 means the same seed always produces the same image)
variants // if >0, the 1st generated image will be passed back to img2img to generate the requested number of variants
callback // a function or method that will be called each time an image is generated
@@ -260,7 +264,8 @@ The vast majority of these arguments default to reasonable values.
batch_size=batch_size,iterations=iterations,
steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
- init_img=init_img,strength=strength,variants=variants,
+ init_img=init_img,strength=strength,
+ gfpgan_strength=gfpgan_strength,variants=variants,
callback=image_callback)
else:
results = self._txt2img(prompt,
@@ -268,6 +273,7 @@ The vast majority of these arguments default to reasonable values.
batch_size=batch_size,iterations=iterations,
steps=steps,seed=seed,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
+ gfpgan_strength=gfpgan_strength,
width=width,height=height,
callback=image_callback)
toc = time.time()
@@ -280,6 +286,7 @@ The vast majority of these arguments default to reasonable values.
batch_size,iterations,
steps,seed,cfg_scale,ddim_eta,
skip_normalize,
+ gfpgan_strength,
width,height,
callback): # the callback is called each time a new Image is generated
"""
@@ -335,6 +342,8 @@ The vast majority of these arguments default to reasonable values.
for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
image = Image.fromarray(x_sample.astype(np.uint8))
+ if gfpgan_strength > 0:
+ image = self._run_gfpgan(image, gfpgan_strength)
images.append([image,seed])
if callback is not None:
callback(image,seed)
@@ -354,6 +363,7 @@ The vast majority of these arguments default to reasonable values.
batch_size,iterations,
steps,seed,cfg_scale,ddim_eta,
skip_normalize,
+ gfpgan_strength,
init_img,strength,variants,
callback):
"""
@@ -419,6 +429,8 @@ The vast majority of these arguments default to reasonable values.
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
image = Image.fromarray(x_sample.astype(np.uint8))
+ if gfpgan_strength > 0:
+ image = self._run_gfpgan(image, gfpgan_strength)
images.append([image,seed])
if callback is not None:
callback(image,seed)
@@ -549,3 +561,18 @@ The vast majority of these arguments default to reasonable values.
weights.append(1.0)
remaining = 0
return prompts, weights
+
+ def _run_gfpgan(self, image, strength):
+ if (self.gfpgan is None):
+ print(f"GFPGAN not initialized, it must be loaded via the --gfpgan argument")
+ return image
+
+ image = image.convert("RGB")
+
+ cropped_faces, restored_faces, restored_img = self.gfpgan.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
+ res = Image.fromarray(restored_img)
+
+ if strength < 1.0:
+ res = Image.blend(image, res, strength)
+
+ return res
diff --git a/scripts/dream.py b/scripts/dream.py
index 24dac5b927..c49340d655 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -6,6 +6,7 @@ import shlex
import os
import sys
import copy
+
from ldm.dream_util import Completer,PngWriter,PromptFormatter
debugging = False
@@ -68,6 +69,28 @@ def main():
# preload the model
t2i.load_model()
+
+ # load GFPGAN if requested
+ if opt.use_gfpgan:
+ print("\n* --gfpgan was specified, loading gfpgan...")
+ try:
+ model_path = os.path.join(opt.gfpgan_dir, opt.gfpgan_model_path)
+ if not os.path.isfile(model_path):
+ raise Exception("GFPGAN model not found at path "+model_path)
+
+ sys.path.append(os.path.abspath(opt.gfpgan_dir))
+ from gfpgan import GFPGANer
+
+ bg_upsampler = None
+ if opt.gfpgan_bg_upsampler is not None:
+ bg_upsampler = load_gfpgan_bg_upsampler(opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile)
+
+ t2i.gfpgan = GFPGANer(model_path=model_path, upscale=opt.gfpgan_upscale, arch='clean', channel_multiplier=2, bg_upsampler=bg_upsampler)
+ except Exception:
+ import traceback
+ print("Error loading GFPGAN:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+
print("\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)...")
log_path = os.path.join(opt.outdir,'dream_log.txt')
@@ -183,6 +206,32 @@ def main_loop(t2i,outdir,parser,log,infile):
print("goodbye!")
+def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
+ import torch
+
+ if bg_upsampler == 'realesrgan':
+ if not torch.cuda.is_available(): # CPU
+ import warnings
+ warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
+ 'If you really want to use it, please modify the corresponding codes.')
+ bg_upsampler = None
+ else:
+ from basicsr.archs.rrdbnet_arch import RRDBNet
+ from realesrgan import RealESRGANer
+ model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+ bg_upsampler = RealESRGANer(
+ scale=2,
+ model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
+ model=model,
+ tile=bg_tile,
+ tile_pad=10,
+ pre_pad=0,
+ half=True) # need to set False in CPU mode
+ else:
+ bg_upsampler = None
+
+ return bg_upsampler
+
# variant generation is going to be superseded by a generalized
# "prompt-morph" functionality
# def generate_variants(t2i,outdir,opt,previous_gens):
@@ -261,6 +310,31 @@ def create_argv_parser():
type=str,
default="cuda",
help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if avalible")
+ # GFPGAN related args
+ parser.add_argument('--gfpgan',
+ dest='use_gfpgan',
+ action='store_true',
+ help="load gfpgan for use in the dreambot. Note: Enabling GFPGAN will require more GPU memory")
+ parser.add_argument("--gfpgan_upscale",
+ type=int,
+ default=2,
+ help="The final upsampling scale of the image. Default: 2. Only used if --gfpgan is specified")
+ parser.add_argument("--gfpgan_bg_upsampler",
+ type=str,
+ default='realesrgan',
+ help="Background upsampler. Default: None. Options: realesrgan, none. Only used if --gfpgan is specified")
+ parser.add_argument("--gfpgan_bg_tile",
+ type=int,
+ default=400,
+ help="Tile size for background sampler, 0 for no tile during testing. Default: 400. Only used if --gfpgan is specified")
+ parser.add_argument("--gfpgan_model_path",
+ type=str,
+ default='experiments/pretrained_models/GFPGANv1.3.pth',
+ help="indicates the path to the GFPGAN model, relative to --gfpgan_dir. Only used if --gfpgan is specified")
+ parser.add_argument("--gfpgan_dir",
+ type=str,
+ default='../gfpgan',
+ help="indicates the directory containing the GFPGAN code. Only used if --gfpgan is specified")
return parser
@@ -278,6 +352,7 @@ def create_cmd_parser():
parser.add_argument('-i','--individual',action='store_true',help="generate individual files (default)")
parser.add_argument('-I','--init_img',type=str,help="path to input image for img2img mode (supersedes width and height)")
parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely")
+ parser.add_argument('-G','--gfpgan_strength', default=0.5, type=float, help="The strength at which to apply the GFPGAN model to the result, in order to improve faces.")
# variants is going to be superseded by a generalized "prompt-morph" function
# parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
parser.add_argument('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
From 60ed00432837bc393f205321750788f91f3aa4b0 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Thu, 25 Aug 2022 23:31:08 -0400
Subject: [PATCH 44/58] Update readme, fix defaults for case-sensitive fs's
---
README.md | 14 ++++++++++++--
scripts/dream.py | 2 +-
2 files changed, 13 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 28063203c8..f3cf91877f 100644
--- a/README.md
+++ b/README.md
@@ -86,7 +86,7 @@ This script also provides the ability to invoke GFPGAN after image generation. D
and optionally upscale the image to a higher resolution.
To use the ability, clone the [GFPGAN repository](https://github.com/TencentARC/GFPGAN) and follow their
-installation instructions. By default, we expect GFPGAN to be installed in a 'gfpgan' sibling directory.
+installation instructions. By default, we expect GFPGAN to be installed in a 'GFPGAN' sibling directory.
You may also want to install Real-ESRGAN, if you want to enhance non-face regions in the image by installing
the pip Real-ESRGAN package.
@@ -97,6 +97,15 @@ pip install realesrgan
Now, you can run this script by adding the --gfpgan option. Any issues with GFPGAN will be reported on initialization.
+~~~~
+(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
+* Initializing, be patient...
+(...more initialization messages...)
+* --gfpgan was specified, loading gfpgan...
+(...even more initialization messages...)
+* Initialization done! Awaiting your command...
+~~~~
+
When generating prompts, add a -G or --gfpgan_strenth option to control the strength of the GFPGAN enhancement.
0.0 is no enhancement, 1.0 is maximum enhancement.
@@ -115,7 +124,8 @@ That's it!
There's also a bunch of options to control GFPGAN settings when starting the script for different configs that you can
read about in the help text. This will let you control where GFPGAN is installed, if upsampling is enapled, the upsampler to use and the model path.
-Note that loading GFPGAN consumes additional GPU memory, additionaly, a couple of seconds will be tacked on when generating your images.
+Note that loading GFPGAN consumes additional GPU memory, but hey, 3090s with 24Gi of VRAM are cheap now *cough*.
+Additionally, a couple of seconds will be tacked on when generating your images, but hey, it's worth it.
## Barebones Web Server
diff --git a/scripts/dream.py b/scripts/dream.py
index 66980bf1f1..0e888bf99c 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -333,7 +333,7 @@ def create_argv_parser():
help="indicates the path to the GFPGAN model, relative to --gfpgan_dir. Only used if --gfpgan is specified")
parser.add_argument("--gfpgan_dir",
type=str,
- default='../gfpgan',
+ default='../GFPGAN',
help="indicates the directory containing the GFPGAN code. Only used if --gfpgan is specified")
return parser
From 3a30a8f2d246c0f7934064233de271b00adb3477 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Thu, 25 Aug 2022 23:39:03 -0400
Subject: [PATCH 45/58] Fix not being able to disable bgupscaler; update readme
---
README.md | 2 +-
scripts/dream.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index f3cf91877f..179be569b2 100644
--- a/README.md
+++ b/README.md
@@ -88,7 +88,7 @@ and optionally upscale the image to a higher resolution.
To use the ability, clone the [GFPGAN repository](https://github.com/TencentARC/GFPGAN) and follow their
installation instructions. By default, we expect GFPGAN to be installed in a 'GFPGAN' sibling directory.
-You may also want to install Real-ESRGAN, if you want to enhance non-face regions in the image by installing
+You may also want to install Real-ESRGAN, if you want to enhance non-face regions in the image, by installing
the pip Real-ESRGAN package.
```
pip install realesrgan
diff --git a/scripts/dream.py b/scripts/dream.py
index 0e888bf99c..e2825f8142 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -82,7 +82,7 @@ def main():
from gfpgan import GFPGANer
bg_upsampler = None
- if opt.gfpgan_bg_upsampler is not None:
+ if opt.gfpgan_bg_upsampler == 'realesrgan':
bg_upsampler = load_gfpgan_bg_upsampler(opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile)
t2i.gfpgan = GFPGANer(model_path=model_path, upscale=opt.gfpgan_upscale, arch='clean', channel_multiplier=2, bg_upsampler=bg_upsampler)
From cb86b9ae6e849cbe4609a62eaf686388eda9b2df Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Thu, 25 Aug 2022 23:48:35 -0400
Subject: [PATCH 46/58] Remove the redundancy, better logging
---
ldm/simplet2i.py | 4 ++--
scripts/dream.py | 4 +---
2 files changed, 3 insertions(+), 5 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index f0713081c8..1cc199c119 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -399,8 +399,8 @@ The vast majority of these arguments default to reasonable values.
try:
if gfpgan_strength > 0:
image = self._run_gfpgan(image, gfpgan_strength)
- except Exception:
- print(f"Error running GFPGAN - Your image was not enhanced.")
+ except Exception as e:
+ print(f"Error running GFPGAN - Your image was not enhanced.\n{e}")
images.append(image)
return images
diff --git a/scripts/dream.py b/scripts/dream.py
index e2825f8142..4def627519 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -81,9 +81,7 @@ def main():
sys.path.append(os.path.abspath(opt.gfpgan_dir))
from gfpgan import GFPGANer
- bg_upsampler = None
- if opt.gfpgan_bg_upsampler == 'realesrgan':
- bg_upsampler = load_gfpgan_bg_upsampler(opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile)
+ bg_upsampler = load_gfpgan_bg_upsampler(opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile)
t2i.gfpgan = GFPGANer(model_path=model_path, upscale=opt.gfpgan_upscale, arch='clean', channel_multiplier=2, bg_upsampler=bg_upsampler)
except Exception:
From 5f844807cb11792f68ed360338c4f28fb71919ef Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 23:50:56 -0400
Subject: [PATCH 47/58] Update README.md
Removed a bit of an uncaught merge conflict warning.
---
README.md | 2 --
1 file changed, 2 deletions(-)
diff --git a/README.md b/README.md
index 2af4a2a53a..cd834f9c2f 100644
--- a/README.md
+++ b/README.md
@@ -116,8 +116,6 @@ Then pass this file's name to dream.py when you invoke it:
(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file="path/to/prompts.txt"
~~~~
->>>>>>> big-refactoring
-
## Weighted Prompts
You may weight different sections of the prompt to tell the sampler to attach different levels of
From 539c15966d78a7d37aea68a14c7caf91ad70dcd5 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 25 Aug 2022 23:54:44 -0400
Subject: [PATCH 48/58] Update README.md
Put in a plug for Yansuki's morphing code.
---
README.md | 6 ++++++
1 file changed, 6 insertions(+)
diff --git a/README.md b/README.md
index cd834f9c2f..17215f9ea0 100644
--- a/README.md
+++ b/README.md
@@ -198,6 +198,12 @@ repository and associated paper for details and limitations.
## Changes
+ * v1.11 (pending)
+ * Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
+ iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
+ for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
+ significantly.
+
* v1.10 (25 August 2022)
* A barebones but fully functional interactive web server for online generation of txt2img and img2img.
From fcdd95b652b8f503528d3e3526590e3c7ab2f3dc Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 00:39:57 -0400
Subject: [PATCH 49/58] Refactor so that behavior is consolidated at top level
---
ldm/simplet2i.py | 18 ++++++++----------
1 file changed, 8 insertions(+), 10 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 0de3a33237..6ae45b36bf 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -282,6 +282,11 @@ The vast majority of these arguments default to reasonable values.
seed_everything(seed)
iter_images = next(images_iterator)
for image in iter_images:
+ try:
+ if gfpgan_strength > 0:
+ image = self._run_gfpgan(image, gfpgan_strength)
+ except Exception as e:
+ print(f"Error running GFPGAN - Your image was not enhanced.\n{e}")
results.append([image, seed])
if image_callback is not None:
image_callback(image,seed)
@@ -305,7 +310,6 @@ The vast majority of these arguments default to reasonable values.
batch_size,
steps,cfg_scale,ddim_eta,
skip_normalize,
- gfpgan_strength,
width,height):
"""
An infinite iterator of images from the prompt.
@@ -325,7 +329,7 @@ The vast majority of these arguments default to reasonable values.
unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,
eta=ddim_eta)
- yield self._samples_to_images(samples, gfpgan_strength=gfpgan_strength)
+ yield self._samples_to_images(samples)
@torch.no_grad()
def _img2img(self,
@@ -334,7 +338,6 @@ The vast majority of these arguments default to reasonable values.
batch_size,
steps,cfg_scale,ddim_eta,
skip_normalize,
- gfpgan_strength,
init_img,strength):
"""
An infinite iterator of images from the prompt and the initial image
@@ -365,7 +368,7 @@ The vast majority of these arguments default to reasonable values.
# decode it
samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
unconditional_conditioning=uc,)
- yield self._samples_to_images(samples, gfpgan_strength)
+ yield self._samples_to_images(samples)
# TODO: does this actually need to run every loop? does anything in it vary by random seed?
def _get_uc_and_c(self, prompt, batch_size, skip_normalize):
@@ -389,18 +392,13 @@ The vast majority of these arguments default to reasonable values.
c = self.model.get_learned_conditioning(batch_size * [prompt])
return (uc, c)
- def _samples_to_images(self, samples, gfpgan_strength=0):
+ def _samples_to_images(self, samples):
x_samples = self.model.decode_first_stage(samples)
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
images = list()
for x_sample in x_samples:
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
image = Image.fromarray(x_sample.astype(np.uint8))
- try:
- if gfpgan_strength > 0:
- image = self._run_gfpgan(image, gfpgan_strength)
- except Exception as e:
- print(f"Error running GFPGAN - Your image was not enhanced.\n{e}")
images.append(image)
return images
From 4f1664ec4f7b95addaf8f531ebe0c2bd68f0f516 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 00:41:41 -0400
Subject: [PATCH 50/58] remove params
---
ldm/simplet2i.py | 2 --
1 file changed, 2 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 6ae45b36bf..66fdbb48b4 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -266,7 +266,6 @@ The vast majority of these arguments default to reasonable values.
batch_size=batch_size,
steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
- gfpgan_strength=gfpgan_strength,
init_img=init_img,strength=strength)
else:
images_iterator = self._txt2img(prompt,
@@ -274,7 +273,6 @@ The vast majority of these arguments default to reasonable values.
batch_size=batch_size,
steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
skip_normalize=skip_normalize,
- gfpgan_strength=gfpgan_strength,
width=width,height=height)
with scope(self.device.type), self.model.ema_scope():
From f1ffb5b51b2d2adb48799d367e285d573b108792 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 00:45:19 -0400
Subject: [PATCH 51/58] Fix blend if the target image has been upscaled
---
ldm/simplet2i.py | 3 +++
1 file changed, 3 insertions(+)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 66fdbb48b4..1b7e34a7dc 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -529,6 +529,9 @@ The vast majority of these arguments default to reasonable values.
res = Image.fromarray(restored_img)
if strength < 1.0:
+ # Resize the image to the new image if the sizes have changed
+ if restored_img.size != image.size:
+ res = res.resize(image.size)
res = Image.blend(image, res, strength)
return res
From 407d70a98789d0f14ccfaf4aea8a0ed156af96d8 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 00:49:12 -0400
Subject: [PATCH 52/58] Fix backwards logic
---
ldm/simplet2i.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 1b7e34a7dc..f1f88bba5e 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -531,7 +531,7 @@ The vast majority of these arguments default to reasonable values.
if strength < 1.0:
# Resize the image to the new image if the sizes have changed
if restored_img.size != image.size:
- res = res.resize(image.size)
+ image = image.resize(res.size)
res = Image.blend(image, res, strength)
return res
From 4f02b72c9ca464f53c0f7f23ee483f7a24b631ad Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Fri, 26 Aug 2022 03:15:42 -0400
Subject: [PATCH 53/58] prettified all the code using "blue" at the urging of
@tildebyte
---
ldm/data/base.py | 18 +-
ldm/data/imagenet.py | 322 ++--
ldm/data/lsun.py | 104 +-
ldm/data/personalized.py | 116 +-
ldm/data/personalized_style.py | 108 +-
ldm/dream/pngwriter.py | 98 +-
ldm/dream/readline.py | 95 +-
ldm/lr_scheduler.py | 89 +-
ldm/models/autoencoder.py | 425 +++--
ldm/models/diffusion/classifier.py | 226 ++-
ldm/models/diffusion/ddim.py | 389 ++--
ldm/models/diffusion/ddpm.py | 1581 ++++++++++++-----
ldm/models/diffusion/ksampler.py | 79 +-
ldm/models/diffusion/plms.py | 373 ++--
ldm/modules/attention.py | 167 +-
ldm/modules/diffusionmodules/model.py | 808 +++++----
ldm/modules/diffusionmodules/openaimodel.py | 203 ++-
ldm/modules/diffusionmodules/util.py | 105 +-
ldm/modules/distributions/distributions.py | 38 +-
ldm/modules/ema.py | 34 +-
ldm/modules/embedding_manager.py | 191 +-
ldm/modules/encoders/modules.py | 386 ++--
ldm/modules/image_degradation/__init__.py | 8 +-
ldm/modules/image_degradation/bsrgan.py | 306 +++-
ldm/modules/image_degradation/bsrgan_light.py | 257 ++-
ldm/modules/image_degradation/utils_image.py | 346 ++--
ldm/modules/losses/__init__.py | 2 +-
ldm/modules/losses/contperceptual.py | 146 +-
ldm/modules/losses/vqperceptual.py | 197 +-
ldm/modules/x_transformer.py | 382 ++--
ldm/simplet2i.py | 501 +++---
ldm/util.py | 56 +-
main.py | 685 ++++---
scripts/dream.py | 482 +++--
scripts/preload_models.py | 48 +-
35 files changed, 6252 insertions(+), 3119 deletions(-)
diff --git a/ldm/data/base.py b/ldm/data/base.py
index b196c2f7aa..de9493fc1e 100644
--- a/ldm/data/base.py
+++ b/ldm/data/base.py
@@ -1,11 +1,17 @@
from abc import abstractmethod
-from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
+from torch.utils.data import (
+ Dataset,
+ ConcatDataset,
+ ChainDataset,
+ IterableDataset,
+)
class Txt2ImgIterableBaseDataset(IterableDataset):
- '''
+ """
Define an interface to make the IterableDatasets for text2img data chainable
- '''
+ """
+
def __init__(self, num_records=0, valid_ids=None, size=256):
super().__init__()
self.num_records = num_records
@@ -13,11 +19,13 @@ class Txt2ImgIterableBaseDataset(IterableDataset):
self.sample_ids = valid_ids
self.size = size
- print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
+ print(
+ f'{self.__class__.__name__} dataset contains {self.__len__()} examples.'
+ )
def __len__(self):
return self.num_records
@abstractmethod
def __iter__(self):
- pass
\ No newline at end of file
+ pass
diff --git a/ldm/data/imagenet.py b/ldm/data/imagenet.py
index 1c473f9c69..d155f6d6ae 100644
--- a/ldm/data/imagenet.py
+++ b/ldm/data/imagenet.py
@@ -11,24 +11,34 @@ from tqdm import tqdm
from torch.utils.data import Dataset, Subset
import taming.data.utils as tdu
-from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
+from taming.data.imagenet import (
+ str_to_indices,
+ give_synsets_from_indices,
+ download,
+ retrieve,
+)
from taming.data.imagenet import ImagePaths
-from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
+from ldm.modules.image_degradation import (
+ degradation_fn_bsr,
+ degradation_fn_bsr_light,
+)
-def synset2idx(path_to_yaml="data/index_synset.yaml"):
+def synset2idx(path_to_yaml='data/index_synset.yaml'):
with open(path_to_yaml) as f:
di2s = yaml.load(f)
- return dict((v,k) for k,v in di2s.items())
+ return dict((v, k) for k, v in di2s.items())
class ImageNetBase(Dataset):
def __init__(self, config=None):
self.config = config or OmegaConf.create()
- if not type(self.config)==dict:
+ if not type(self.config) == dict:
self.config = OmegaConf.to_container(self.config)
- self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
+ self.keep_orig_class_label = self.config.get(
+ 'keep_orig_class_label', False
+ )
self.process_images = True # if False we skip loading & processing images and self.data contains filepaths
self._prepare()
self._prepare_synset_to_human()
@@ -46,17 +56,23 @@ class ImageNetBase(Dataset):
raise NotImplementedError()
def _filter_relpaths(self, relpaths):
- ignore = set([
- "n06596364_9591.JPEG",
- ])
- relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
- if "sub_indices" in self.config:
- indices = str_to_indices(self.config["sub_indices"])
- synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings
+ ignore = set(
+ [
+ 'n06596364_9591.JPEG',
+ ]
+ )
+ relpaths = [
+ rpath for rpath in relpaths if not rpath.split('/')[-1] in ignore
+ ]
+ if 'sub_indices' in self.config:
+ indices = str_to_indices(self.config['sub_indices'])
+ synsets = give_synsets_from_indices(
+ indices, path_to_yaml=self.idx2syn
+ ) # returns a list of strings
self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
files = []
for rpath in relpaths:
- syn = rpath.split("/")[0]
+ syn = rpath.split('/')[0]
if syn in synsets:
files.append(rpath)
return files
@@ -65,78 +81,89 @@ class ImageNetBase(Dataset):
def _prepare_synset_to_human(self):
SIZE = 2655750
- URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
- self.human_dict = os.path.join(self.root, "synset_human.txt")
- if (not os.path.exists(self.human_dict) or
- not os.path.getsize(self.human_dict)==SIZE):
+ URL = 'https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1'
+ self.human_dict = os.path.join(self.root, 'synset_human.txt')
+ if (
+ not os.path.exists(self.human_dict)
+ or not os.path.getsize(self.human_dict) == SIZE
+ ):
download(URL, self.human_dict)
def _prepare_idx_to_synset(self):
- URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
- self.idx2syn = os.path.join(self.root, "index_synset.yaml")
- if (not os.path.exists(self.idx2syn)):
+ URL = 'https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1'
+ self.idx2syn = os.path.join(self.root, 'index_synset.yaml')
+ if not os.path.exists(self.idx2syn):
download(URL, self.idx2syn)
def _prepare_human_to_integer_label(self):
- URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
- self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
- if (not os.path.exists(self.human2integer)):
+ URL = 'https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1'
+ self.human2integer = os.path.join(
+ self.root, 'imagenet1000_clsidx_to_labels.txt'
+ )
+ if not os.path.exists(self.human2integer):
download(URL, self.human2integer)
- with open(self.human2integer, "r") as f:
+ with open(self.human2integer, 'r') as f:
lines = f.read().splitlines()
assert len(lines) == 1000
self.human2integer_dict = dict()
for line in lines:
- value, key = line.split(":")
+ value, key = line.split(':')
self.human2integer_dict[key] = int(value)
def _load(self):
- with open(self.txt_filelist, "r") as f:
+ with open(self.txt_filelist, 'r') as f:
self.relpaths = f.read().splitlines()
l1 = len(self.relpaths)
self.relpaths = self._filter_relpaths(self.relpaths)
- print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
+ print(
+ 'Removed {} files from filelist during filtering.'.format(
+ l1 - len(self.relpaths)
+ )
+ )
- self.synsets = [p.split("/")[0] for p in self.relpaths]
+ self.synsets = [p.split('/')[0] for p in self.relpaths]
self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
unique_synsets = np.unique(self.synsets)
- class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
+ class_dict = dict(
+ (synset, i) for i, synset in enumerate(unique_synsets)
+ )
if not self.keep_orig_class_label:
self.class_labels = [class_dict[s] for s in self.synsets]
else:
self.class_labels = [self.synset2idx[s] for s in self.synsets]
- with open(self.human_dict, "r") as f:
+ with open(self.human_dict, 'r') as f:
human_dict = f.read().splitlines()
human_dict = dict(line.split(maxsplit=1) for line in human_dict)
self.human_labels = [human_dict[s] for s in self.synsets]
labels = {
- "relpath": np.array(self.relpaths),
- "synsets": np.array(self.synsets),
- "class_label": np.array(self.class_labels),
- "human_label": np.array(self.human_labels),
+ 'relpath': np.array(self.relpaths),
+ 'synsets': np.array(self.synsets),
+ 'class_label': np.array(self.class_labels),
+ 'human_label': np.array(self.human_labels),
}
if self.process_images:
- self.size = retrieve(self.config, "size", default=256)
- self.data = ImagePaths(self.abspaths,
- labels=labels,
- size=self.size,
- random_crop=self.random_crop,
- )
+ self.size = retrieve(self.config, 'size', default=256)
+ self.data = ImagePaths(
+ self.abspaths,
+ labels=labels,
+ size=self.size,
+ random_crop=self.random_crop,
+ )
else:
self.data = self.abspaths
class ImageNetTrain(ImageNetBase):
- NAME = "ILSVRC2012_train"
- URL = "http://www.image-net.org/challenges/LSVRC/2012/"
- AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
+ NAME = 'ILSVRC2012_train'
+ URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
+ AT_HASH = 'a306397ccf9c2ead27155983c254227c0fd938e2'
FILES = [
- "ILSVRC2012_img_train.tar",
+ 'ILSVRC2012_img_train.tar',
]
SIZES = [
147897477120,
@@ -151,57 +178,64 @@ class ImageNetTrain(ImageNetBase):
if self.data_root:
self.root = os.path.join(self.data_root, self.NAME)
else:
- cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
- self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
+ cachedir = os.environ.get(
+ 'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
+ )
+ self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
- self.datadir = os.path.join(self.root, "data")
- self.txt_filelist = os.path.join(self.root, "filelist.txt")
+ self.datadir = os.path.join(self.root, 'data')
+ self.txt_filelist = os.path.join(self.root, 'filelist.txt')
self.expected_length = 1281167
- self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
- default=True)
+ self.random_crop = retrieve(
+ self.config, 'ImageNetTrain/random_crop', default=True
+ )
if not tdu.is_prepared(self.root):
# prep
- print("Preparing dataset {} in {}".format(self.NAME, self.root))
+ print('Preparing dataset {} in {}'.format(self.NAME, self.root))
datadir = self.datadir
if not os.path.exists(datadir):
path = os.path.join(self.root, self.FILES[0])
- if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
+ if (
+ not os.path.exists(path)
+ or not os.path.getsize(path) == self.SIZES[0]
+ ):
import academictorrents as at
+
atpath = at.get(self.AT_HASH, datastore=self.root)
assert atpath == path
- print("Extracting {} to {}".format(path, datadir))
+ print('Extracting {} to {}'.format(path, datadir))
os.makedirs(datadir, exist_ok=True)
- with tarfile.open(path, "r:") as tar:
+ with tarfile.open(path, 'r:') as tar:
tar.extractall(path=datadir)
- print("Extracting sub-tars.")
- subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
+ print('Extracting sub-tars.')
+ subpaths = sorted(glob.glob(os.path.join(datadir, '*.tar')))
for subpath in tqdm(subpaths):
- subdir = subpath[:-len(".tar")]
+ subdir = subpath[: -len('.tar')]
os.makedirs(subdir, exist_ok=True)
- with tarfile.open(subpath, "r:") as tar:
+ with tarfile.open(subpath, 'r:') as tar:
tar.extractall(path=subdir)
- filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
+ filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
filelist = [os.path.relpath(p, start=datadir) for p in filelist]
filelist = sorted(filelist)
- filelist = "\n".join(filelist)+"\n"
- with open(self.txt_filelist, "w") as f:
+ filelist = '\n'.join(filelist) + '\n'
+ with open(self.txt_filelist, 'w') as f:
f.write(filelist)
tdu.mark_prepared(self.root)
class ImageNetValidation(ImageNetBase):
- NAME = "ILSVRC2012_validation"
- URL = "http://www.image-net.org/challenges/LSVRC/2012/"
- AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
- VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
+ NAME = 'ILSVRC2012_validation'
+ URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
+ AT_HASH = '5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5'
+ VS_URL = 'https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1'
FILES = [
- "ILSVRC2012_img_val.tar",
- "validation_synset.txt",
+ 'ILSVRC2012_img_val.tar',
+ 'validation_synset.txt',
]
SIZES = [
6744924160,
@@ -217,39 +251,49 @@ class ImageNetValidation(ImageNetBase):
if self.data_root:
self.root = os.path.join(self.data_root, self.NAME)
else:
- cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
- self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
- self.datadir = os.path.join(self.root, "data")
- self.txt_filelist = os.path.join(self.root, "filelist.txt")
+ cachedir = os.environ.get(
+ 'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
+ )
+ self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
+ self.datadir = os.path.join(self.root, 'data')
+ self.txt_filelist = os.path.join(self.root, 'filelist.txt')
self.expected_length = 50000
- self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
- default=False)
+ self.random_crop = retrieve(
+ self.config, 'ImageNetValidation/random_crop', default=False
+ )
if not tdu.is_prepared(self.root):
# prep
- print("Preparing dataset {} in {}".format(self.NAME, self.root))
+ print('Preparing dataset {} in {}'.format(self.NAME, self.root))
datadir = self.datadir
if not os.path.exists(datadir):
path = os.path.join(self.root, self.FILES[0])
- if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
+ if (
+ not os.path.exists(path)
+ or not os.path.getsize(path) == self.SIZES[0]
+ ):
import academictorrents as at
+
atpath = at.get(self.AT_HASH, datastore=self.root)
assert atpath == path
- print("Extracting {} to {}".format(path, datadir))
+ print('Extracting {} to {}'.format(path, datadir))
os.makedirs(datadir, exist_ok=True)
- with tarfile.open(path, "r:") as tar:
+ with tarfile.open(path, 'r:') as tar:
tar.extractall(path=datadir)
vspath = os.path.join(self.root, self.FILES[1])
- if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
+ if (
+ not os.path.exists(vspath)
+ or not os.path.getsize(vspath) == self.SIZES[1]
+ ):
download(self.VS_URL, vspath)
- with open(vspath, "r") as f:
+ with open(vspath, 'r') as f:
synset_dict = f.read().splitlines()
synset_dict = dict(line.split() for line in synset_dict)
- print("Reorganizing into synset folders")
+ print('Reorganizing into synset folders')
synsets = np.unique(list(synset_dict.values()))
for s in synsets:
os.makedirs(os.path.join(datadir, s), exist_ok=True)
@@ -258,21 +302,26 @@ class ImageNetValidation(ImageNetBase):
dst = os.path.join(datadir, v)
shutil.move(src, dst)
- filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
+ filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
filelist = [os.path.relpath(p, start=datadir) for p in filelist]
filelist = sorted(filelist)
- filelist = "\n".join(filelist)+"\n"
- with open(self.txt_filelist, "w") as f:
+ filelist = '\n'.join(filelist) + '\n'
+ with open(self.txt_filelist, 'w') as f:
f.write(filelist)
tdu.mark_prepared(self.root)
-
class ImageNetSR(Dataset):
- def __init__(self, size=None,
- degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
- random_crop=True):
+ def __init__(
+ self,
+ size=None,
+ degradation=None,
+ downscale_f=4,
+ min_crop_f=0.5,
+ max_crop_f=1.0,
+ random_crop=True,
+ ):
"""
Imagenet Superresolution Dataloader
Performs following ops in order:
@@ -296,67 +345,86 @@ class ImageNetSR(Dataset):
self.LR_size = int(size / downscale_f)
self.min_crop_f = min_crop_f
self.max_crop_f = max_crop_f
- assert(max_crop_f <= 1.)
+ assert max_crop_f <= 1.0
self.center_crop = not random_crop
- self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
+ self.image_rescaler = albumentations.SmallestMaxSize(
+ max_size=size, interpolation=cv2.INTER_AREA
+ )
- self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
+ self.pil_interpolation = (
+ False # gets reset later if incase interp_op is from pillow
+ )
- if degradation == "bsrgan":
- self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
+ if degradation == 'bsrgan':
+ self.degradation_process = partial(
+ degradation_fn_bsr, sf=downscale_f
+ )
- elif degradation == "bsrgan_light":
- self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
+ elif degradation == 'bsrgan_light':
+ self.degradation_process = partial(
+ degradation_fn_bsr_light, sf=downscale_f
+ )
else:
interpolation_fn = {
- "cv_nearest": cv2.INTER_NEAREST,
- "cv_bilinear": cv2.INTER_LINEAR,
- "cv_bicubic": cv2.INTER_CUBIC,
- "cv_area": cv2.INTER_AREA,
- "cv_lanczos": cv2.INTER_LANCZOS4,
- "pil_nearest": PIL.Image.NEAREST,
- "pil_bilinear": PIL.Image.BILINEAR,
- "pil_bicubic": PIL.Image.BICUBIC,
- "pil_box": PIL.Image.BOX,
- "pil_hamming": PIL.Image.HAMMING,
- "pil_lanczos": PIL.Image.LANCZOS,
+ 'cv_nearest': cv2.INTER_NEAREST,
+ 'cv_bilinear': cv2.INTER_LINEAR,
+ 'cv_bicubic': cv2.INTER_CUBIC,
+ 'cv_area': cv2.INTER_AREA,
+ 'cv_lanczos': cv2.INTER_LANCZOS4,
+ 'pil_nearest': PIL.Image.NEAREST,
+ 'pil_bilinear': PIL.Image.BILINEAR,
+ 'pil_bicubic': PIL.Image.BICUBIC,
+ 'pil_box': PIL.Image.BOX,
+ 'pil_hamming': PIL.Image.HAMMING,
+ 'pil_lanczos': PIL.Image.LANCZOS,
}[degradation]
- self.pil_interpolation = degradation.startswith("pil_")
+ self.pil_interpolation = degradation.startswith('pil_')
if self.pil_interpolation:
- self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
+ self.degradation_process = partial(
+ TF.resize,
+ size=self.LR_size,
+ interpolation=interpolation_fn,
+ )
else:
- self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
- interpolation=interpolation_fn)
+ self.degradation_process = albumentations.SmallestMaxSize(
+ max_size=self.LR_size, interpolation=interpolation_fn
+ )
def __len__(self):
return len(self.base)
def __getitem__(self, i):
example = self.base[i]
- image = Image.open(example["file_path_"])
+ image = Image.open(example['file_path_'])
- if not image.mode == "RGB":
- image = image.convert("RGB")
+ if not image.mode == 'RGB':
+ image = image.convert('RGB')
image = np.array(image).astype(np.uint8)
min_side_len = min(image.shape[:2])
- crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
+ crop_side_len = min_side_len * np.random.uniform(
+ self.min_crop_f, self.max_crop_f, size=None
+ )
crop_side_len = int(crop_side_len)
if self.center_crop:
- self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
+ self.cropper = albumentations.CenterCrop(
+ height=crop_side_len, width=crop_side_len
+ )
else:
- self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
+ self.cropper = albumentations.RandomCrop(
+ height=crop_side_len, width=crop_side_len
+ )
- image = self.cropper(image=image)["image"]
- image = self.image_rescaler(image=image)["image"]
+ image = self.cropper(image=image)['image']
+ image = self.image_rescaler(image=image)['image']
if self.pil_interpolation:
image_pil = PIL.Image.fromarray(image)
@@ -364,10 +432,10 @@ class ImageNetSR(Dataset):
LR_image = np.array(LR_image).astype(np.uint8)
else:
- LR_image = self.degradation_process(image=image)["image"]
+ LR_image = self.degradation_process(image=image)['image']
- example["image"] = (image/127.5 - 1.0).astype(np.float32)
- example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
+ example['image'] = (image / 127.5 - 1.0).astype(np.float32)
+ example['LR_image'] = (LR_image / 127.5 - 1.0).astype(np.float32)
return example
@@ -377,9 +445,11 @@ class ImageNetSRTrain(ImageNetSR):
super().__init__(**kwargs)
def get_base(self):
- with open("data/imagenet_train_hr_indices.p", "rb") as f:
+ with open('data/imagenet_train_hr_indices.p', 'rb') as f:
indices = pickle.load(f)
- dset = ImageNetTrain(process_images=False,)
+ dset = ImageNetTrain(
+ process_images=False,
+ )
return Subset(dset, indices)
@@ -388,7 +458,9 @@ class ImageNetSRValidation(ImageNetSR):
super().__init__(**kwargs)
def get_base(self):
- with open("data/imagenet_val_hr_indices.p", "rb") as f:
+ with open('data/imagenet_val_hr_indices.p', 'rb') as f:
indices = pickle.load(f)
- dset = ImageNetValidation(process_images=False,)
+ dset = ImageNetValidation(
+ process_images=False,
+ )
return Subset(dset, indices)
diff --git a/ldm/data/lsun.py b/ldm/data/lsun.py
index 6256e45715..4a7ecb147e 100644
--- a/ldm/data/lsun.py
+++ b/ldm/data/lsun.py
@@ -7,30 +7,33 @@ from torchvision import transforms
class LSUNBase(Dataset):
- def __init__(self,
- txt_file,
- data_root,
- size=None,
- interpolation="bicubic",
- flip_p=0.5
- ):
+ def __init__(
+ self,
+ txt_file,
+ data_root,
+ size=None,
+ interpolation='bicubic',
+ flip_p=0.5,
+ ):
self.data_paths = txt_file
self.data_root = data_root
- with open(self.data_paths, "r") as f:
+ with open(self.data_paths, 'r') as f:
self.image_paths = f.read().splitlines()
self._length = len(self.image_paths)
self.labels = {
- "relative_file_path_": [l for l in self.image_paths],
- "file_path_": [os.path.join(self.data_root, l)
- for l in self.image_paths],
+ 'relative_file_path_': [l for l in self.image_paths],
+ 'file_path_': [
+ os.path.join(self.data_root, l) for l in self.image_paths
+ ],
}
self.size = size
- self.interpolation = {"linear": PIL.Image.LINEAR,
- "bilinear": PIL.Image.BILINEAR,
- "bicubic": PIL.Image.BICUBIC,
- "lanczos": PIL.Image.LANCZOS,
- }[interpolation]
+ self.interpolation = {
+ 'linear': PIL.Image.LINEAR,
+ 'bilinear': PIL.Image.BILINEAR,
+ 'bicubic': PIL.Image.BICUBIC,
+ 'lanczos': PIL.Image.LANCZOS,
+ }[interpolation]
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
def __len__(self):
@@ -38,55 +41,86 @@ class LSUNBase(Dataset):
def __getitem__(self, i):
example = dict((k, self.labels[k][i]) for k in self.labels)
- image = Image.open(example["file_path_"])
- if not image.mode == "RGB":
- image = image.convert("RGB")
+ image = Image.open(example['file_path_'])
+ if not image.mode == 'RGB':
+ image = image.convert('RGB')
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
crop = min(img.shape[0], img.shape[1])
- h, w, = img.shape[0], img.shape[1]
- img = img[(h - crop) // 2:(h + crop) // 2,
- (w - crop) // 2:(w + crop) // 2]
+ h, w, = (
+ img.shape[0],
+ img.shape[1],
+ )
+ img = img[
+ (h - crop) // 2 : (h + crop) // 2,
+ (w - crop) // 2 : (w + crop) // 2,
+ ]
image = Image.fromarray(img)
if self.size is not None:
- image = image.resize((self.size, self.size), resample=self.interpolation)
+ image = image.resize(
+ (self.size, self.size), resample=self.interpolation
+ )
image = self.flip(image)
image = np.array(image).astype(np.uint8)
- example["image"] = (image / 127.5 - 1.0).astype(np.float32)
+ example['image'] = (image / 127.5 - 1.0).astype(np.float32)
return example
class LSUNChurchesTrain(LSUNBase):
def __init__(self, **kwargs):
- super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
+ super().__init__(
+ txt_file='data/lsun/church_outdoor_train.txt',
+ data_root='data/lsun/churches',
+ **kwargs
+ )
class LSUNChurchesValidation(LSUNBase):
- def __init__(self, flip_p=0., **kwargs):
- super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
- flip_p=flip_p, **kwargs)
+ def __init__(self, flip_p=0.0, **kwargs):
+ super().__init__(
+ txt_file='data/lsun/church_outdoor_val.txt',
+ data_root='data/lsun/churches',
+ flip_p=flip_p,
+ **kwargs
+ )
class LSUNBedroomsTrain(LSUNBase):
def __init__(self, **kwargs):
- super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
+ super().__init__(
+ txt_file='data/lsun/bedrooms_train.txt',
+ data_root='data/lsun/bedrooms',
+ **kwargs
+ )
class LSUNBedroomsValidation(LSUNBase):
def __init__(self, flip_p=0.0, **kwargs):
- super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
- flip_p=flip_p, **kwargs)
+ super().__init__(
+ txt_file='data/lsun/bedrooms_val.txt',
+ data_root='data/lsun/bedrooms',
+ flip_p=flip_p,
+ **kwargs
+ )
class LSUNCatsTrain(LSUNBase):
def __init__(self, **kwargs):
- super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
+ super().__init__(
+ txt_file='data/lsun/cat_train.txt',
+ data_root='data/lsun/cats',
+ **kwargs
+ )
class LSUNCatsValidation(LSUNBase):
- def __init__(self, flip_p=0., **kwargs):
- super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
- flip_p=flip_p, **kwargs)
+ def __init__(self, flip_p=0.0, **kwargs):
+ super().__init__(
+ txt_file='data/lsun/cat_val.txt',
+ data_root='data/lsun/cats',
+ flip_p=flip_p,
+ **kwargs
+ )
diff --git a/ldm/data/personalized.py b/ldm/data/personalized.py
index c8a57d09fa..15fc8a8d2d 100644
--- a/ldm/data/personalized.py
+++ b/ldm/data/personalized.py
@@ -72,31 +72,57 @@ imagenet_dual_templates_small = [
]
per_img_token_list = [
- 'א', 'ב', 'ג', 'ד', 'ה', 'ו', 'ז', 'ח', 'ט', 'י', 'כ', 'ל', 'מ', 'נ', 'ס', 'ע', 'פ', 'צ', 'ק', 'ר', 'ש', 'ת',
+ 'א',
+ 'ב',
+ 'ג',
+ 'ד',
+ 'ה',
+ 'ו',
+ 'ז',
+ 'ח',
+ 'ט',
+ 'י',
+ 'כ',
+ 'ל',
+ 'מ',
+ 'נ',
+ 'ס',
+ 'ע',
+ 'פ',
+ 'צ',
+ 'ק',
+ 'ר',
+ 'ש',
+ 'ת',
]
+
class PersonalizedBase(Dataset):
- def __init__(self,
- data_root,
- size=None,
- repeats=100,
- interpolation="bicubic",
- flip_p=0.5,
- set="train",
- placeholder_token="*",
- per_image_tokens=False,
- center_crop=False,
- mixing_prob=0.25,
- coarse_class_text=None,
- ):
+ def __init__(
+ self,
+ data_root,
+ size=None,
+ repeats=100,
+ interpolation='bicubic',
+ flip_p=0.5,
+ set='train',
+ placeholder_token='*',
+ per_image_tokens=False,
+ center_crop=False,
+ mixing_prob=0.25,
+ coarse_class_text=None,
+ ):
self.data_root = data_root
- self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
+ self.image_paths = [
+ os.path.join(self.data_root, file_path)
+ for file_path in os.listdir(self.data_root)
+ ]
# self._length = len(self.image_paths)
self.num_images = len(self.image_paths)
- self._length = self.num_images
+ self._length = self.num_images
self.placeholder_token = placeholder_token
@@ -107,17 +133,20 @@ class PersonalizedBase(Dataset):
self.coarse_class_text = coarse_class_text
if per_image_tokens:
- assert self.num_images < len(per_img_token_list), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
+ assert self.num_images < len(
+ per_img_token_list
+ ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
- if set == "train":
+ if set == 'train':
self._length = self.num_images * repeats
self.size = size
- self.interpolation = {"linear": PIL.Image.LINEAR,
- "bilinear": PIL.Image.BILINEAR,
- "bicubic": PIL.Image.BICUBIC,
- "lanczos": PIL.Image.LANCZOS,
- }[interpolation]
+ self.interpolation = {
+ 'linear': PIL.Image.LINEAR,
+ 'bilinear': PIL.Image.BILINEAR,
+ 'bicubic': PIL.Image.BICUBIC,
+ 'lanczos': PIL.Image.LANCZOS,
+ }[interpolation]
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
def __len__(self):
@@ -127,34 +156,47 @@ class PersonalizedBase(Dataset):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
- if not image.mode == "RGB":
- image = image.convert("RGB")
+ if not image.mode == 'RGB':
+ image = image.convert('RGB')
placeholder_string = self.placeholder_token
if self.coarse_class_text:
- placeholder_string = f"{self.coarse_class_text} {placeholder_string}"
+ placeholder_string = (
+ f'{self.coarse_class_text} {placeholder_string}'
+ )
if self.per_image_tokens and np.random.uniform() < self.mixing_prob:
- text = random.choice(imagenet_dual_templates_small).format(placeholder_string, per_img_token_list[i % self.num_images])
+ text = random.choice(imagenet_dual_templates_small).format(
+ placeholder_string, per_img_token_list[i % self.num_images]
+ )
else:
- text = random.choice(imagenet_templates_small).format(placeholder_string)
-
- example["caption"] = text
+ text = random.choice(imagenet_templates_small).format(
+ placeholder_string
+ )
+
+ example['caption'] = text
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
-
+
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
- h, w, = img.shape[0], img.shape[1]
- img = img[(h - crop) // 2:(h + crop) // 2,
- (w - crop) // 2:(w + crop) // 2]
+ h, w, = (
+ img.shape[0],
+ img.shape[1],
+ )
+ img = img[
+ (h - crop) // 2 : (h + crop) // 2,
+ (w - crop) // 2 : (w + crop) // 2,
+ ]
image = Image.fromarray(img)
if self.size is not None:
- image = image.resize((self.size, self.size), resample=self.interpolation)
+ image = image.resize(
+ (self.size, self.size), resample=self.interpolation
+ )
image = self.flip(image)
image = np.array(image).astype(np.uint8)
- example["image"] = (image / 127.5 - 1.0).astype(np.float32)
- return example
\ No newline at end of file
+ example['image'] = (image / 127.5 - 1.0).astype(np.float32)
+ return example
diff --git a/ldm/data/personalized_style.py b/ldm/data/personalized_style.py
index b6be7b15c4..56d77d7e81 100644
--- a/ldm/data/personalized_style.py
+++ b/ldm/data/personalized_style.py
@@ -50,29 +50,55 @@ imagenet_dual_templates_small = [
]
per_img_token_list = [
- 'א', 'ב', 'ג', 'ד', 'ה', 'ו', 'ז', 'ח', 'ט', 'י', 'כ', 'ל', 'מ', 'נ', 'ס', 'ע', 'פ', 'צ', 'ק', 'ר', 'ש', 'ת',
+ 'א',
+ 'ב',
+ 'ג',
+ 'ד',
+ 'ה',
+ 'ו',
+ 'ז',
+ 'ח',
+ 'ט',
+ 'י',
+ 'כ',
+ 'ל',
+ 'מ',
+ 'נ',
+ 'ס',
+ 'ע',
+ 'פ',
+ 'צ',
+ 'ק',
+ 'ר',
+ 'ש',
+ 'ת',
]
+
class PersonalizedBase(Dataset):
- def __init__(self,
- data_root,
- size=None,
- repeats=100,
- interpolation="bicubic",
- flip_p=0.5,
- set="train",
- placeholder_token="*",
- per_image_tokens=False,
- center_crop=False,
- ):
+ def __init__(
+ self,
+ data_root,
+ size=None,
+ repeats=100,
+ interpolation='bicubic',
+ flip_p=0.5,
+ set='train',
+ placeholder_token='*',
+ per_image_tokens=False,
+ center_crop=False,
+ ):
self.data_root = data_root
- self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
+ self.image_paths = [
+ os.path.join(self.data_root, file_path)
+ for file_path in os.listdir(self.data_root)
+ ]
# self._length = len(self.image_paths)
self.num_images = len(self.image_paths)
- self._length = self.num_images
+ self._length = self.num_images
self.placeholder_token = placeholder_token
@@ -80,17 +106,20 @@ class PersonalizedBase(Dataset):
self.center_crop = center_crop
if per_image_tokens:
- assert self.num_images < len(per_img_token_list), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
+ assert self.num_images < len(
+ per_img_token_list
+ ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
- if set == "train":
+ if set == 'train':
self._length = self.num_images * repeats
self.size = size
- self.interpolation = {"linear": PIL.Image.LINEAR,
- "bilinear": PIL.Image.BILINEAR,
- "bicubic": PIL.Image.BICUBIC,
- "lanczos": PIL.Image.LANCZOS,
- }[interpolation]
+ self.interpolation = {
+ 'linear': PIL.Image.LINEAR,
+ 'bilinear': PIL.Image.BILINEAR,
+ 'bicubic': PIL.Image.BICUBIC,
+ 'lanczos': PIL.Image.LANCZOS,
+ }[interpolation]
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
def __len__(self):
@@ -100,30 +129,41 @@ class PersonalizedBase(Dataset):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
- if not image.mode == "RGB":
- image = image.convert("RGB")
+ if not image.mode == 'RGB':
+ image = image.convert('RGB')
if self.per_image_tokens and np.random.uniform() < 0.25:
- text = random.choice(imagenet_dual_templates_small).format(self.placeholder_token, per_img_token_list[i % self.num_images])
+ text = random.choice(imagenet_dual_templates_small).format(
+ self.placeholder_token, per_img_token_list[i % self.num_images]
+ )
else:
- text = random.choice(imagenet_templates_small).format(self.placeholder_token)
-
- example["caption"] = text
+ text = random.choice(imagenet_templates_small).format(
+ self.placeholder_token
+ )
+
+ example['caption'] = text
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
-
+
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
- h, w, = img.shape[0], img.shape[1]
- img = img[(h - crop) // 2:(h + crop) // 2,
- (w - crop) // 2:(w + crop) // 2]
+ h, w, = (
+ img.shape[0],
+ img.shape[1],
+ )
+ img = img[
+ (h - crop) // 2 : (h + crop) // 2,
+ (w - crop) // 2 : (w + crop) // 2,
+ ]
image = Image.fromarray(img)
if self.size is not None:
- image = image.resize((self.size, self.size), resample=self.interpolation)
+ image = image.resize(
+ (self.size, self.size), resample=self.interpolation
+ )
image = self.flip(image)
image = np.array(image).astype(np.uint8)
- example["image"] = (image / 127.5 - 1.0).astype(np.float32)
- return example
\ No newline at end of file
+ example['image'] = (image / 127.5 - 1.0).astype(np.float32)
+ return example
diff --git a/ldm/dream/pngwriter.py b/ldm/dream/pngwriter.py
index ecbbbd4ff7..3a3f205512 100644
--- a/ldm/dream/pngwriter.py
+++ b/ldm/dream/pngwriter.py
@@ -1,4 +1,4 @@
-'''
+"""
Two helper classes for dealing with PNG images and their path names.
PngWriter -- Converts Images generated by T2I into PNGs, finds
appropriate names for them, and writes prompt metadata
@@ -7,95 +7,104 @@ PngWriter -- Converts Images generated by T2I into PNGs, finds
prompt for file/directory names.
PromptFormatter -- Utility for converting a Namespace of prompt parameters
back into a formatted prompt string with command-line switches.
-'''
+"""
import os
import re
-from math import sqrt,floor,ceil
-from PIL import Image,PngImagePlugin
+from math import sqrt, floor, ceil
+from PIL import Image, PngImagePlugin
# -------------------image generation utils-----
class PngWriter:
-
- def __init__(self,outdir,prompt=None,batch_size=1):
- self.outdir = outdir
- self.batch_size = batch_size
- self.prompt = prompt
- self.filepath = None
- self.files_written = []
+ def __init__(self, outdir, prompt=None, batch_size=1):
+ self.outdir = outdir
+ self.batch_size = batch_size
+ self.prompt = prompt
+ self.filepath = None
+ self.files_written = []
os.makedirs(outdir, exist_ok=True)
- def write_image(self,image,seed):
- self.filepath = self.unique_filename(seed,self.filepath) # will increment name in some sensible way
+ def write_image(self, image, seed):
+ self.filepath = self.unique_filename(
+ seed, self.filepath
+ ) # will increment name in some sensible way
try:
prompt = f'{self.prompt} -S{seed}'
- self.save_image_and_prompt_to_png(image,prompt,self.filepath)
+ self.save_image_and_prompt_to_png(image, prompt, self.filepath)
except IOError as e:
print(e)
- self.files_written.append([self.filepath,seed])
+ self.files_written.append([self.filepath, seed])
- def unique_filename(self,seed,previouspath=None):
+ def unique_filename(self, seed, previouspath=None):
revision = 1
if previouspath is None:
# sort reverse alphabetically until we find max+1
- dirlist = sorted(os.listdir(self.outdir),reverse=True)
+ dirlist = sorted(os.listdir(self.outdir), reverse=True)
# find the first filename that matches our pattern or return 000000.0.png
- filename = next((f for f in dirlist if re.match('^(\d+)\..*\.png',f)),'0000000.0.png')
- basecount = int(filename.split('.',1)[0])
+ filename = next(
+ (f for f in dirlist if re.match('^(\d+)\..*\.png', f)),
+ '0000000.0.png',
+ )
+ basecount = int(filename.split('.', 1)[0])
basecount += 1
if self.batch_size > 1:
filename = f'{basecount:06}.{seed}.01.png'
else:
filename = f'{basecount:06}.{seed}.png'
- return os.path.join(self.outdir,filename)
+ return os.path.join(self.outdir, filename)
else:
basename = os.path.basename(previouspath)
- x = re.match('^(\d+)\..*\.png',basename)
+ x = re.match('^(\d+)\..*\.png', basename)
if not x:
- return self.unique_filename(seed,previouspath)
+ return self.unique_filename(seed, previouspath)
basecount = int(x.groups()[0])
- series = 0
+ series = 0
finished = False
while not finished:
series += 1
filename = f'{basecount:06}.{seed}.png'
- if self.batch_size>1 or os.path.exists(os.path.join(self.outdir,filename)):
+ if self.batch_size > 1 or os.path.exists(
+ os.path.join(self.outdir, filename)
+ ):
filename = f'{basecount:06}.{seed}.{series:02}.png'
- finished = not os.path.exists(os.path.join(self.outdir,filename))
- return os.path.join(self.outdir,filename)
+ finished = not os.path.exists(
+ os.path.join(self.outdir, filename)
+ )
+ return os.path.join(self.outdir, filename)
- def save_image_and_prompt_to_png(self,image,prompt,path):
+ def save_image_and_prompt_to_png(self, image, prompt, path):
info = PngImagePlugin.PngInfo()
- info.add_text("Dream",prompt)
- image.save(path,"PNG",pnginfo=info)
+ info.add_text('Dream', prompt)
+ image.save(path, 'PNG', pnginfo=info)
- def make_grid(self,image_list,rows=None,cols=None):
+ def make_grid(self, image_list, rows=None, cols=None):
image_cnt = len(image_list)
- if None in (rows,cols):
+ if None in (rows, cols):
rows = floor(sqrt(image_cnt)) # try to make it square
- cols = ceil(image_cnt/rows)
- width = image_list[0].width
+ cols = ceil(image_cnt / rows)
+ width = image_list[0].width
height = image_list[0].height
- grid_img = Image.new('RGB',(width*cols,height*rows))
- for r in range(0,rows):
- for c in range (0,cols):
- i = r*rows + c
- grid_img.paste(image_list[i],(c*width,r*height))
+ grid_img = Image.new('RGB', (width * cols, height * rows))
+ for r in range(0, rows):
+ for c in range(0, cols):
+ i = r * rows + c
+ grid_img.paste(image_list[i], (c * width, r * height))
return grid_img
-
-class PromptFormatter():
- def __init__(self,t2i,opt):
+
+
+class PromptFormatter:
+ def __init__(self, t2i, opt):
self.t2i = t2i
self.opt = opt
def normalize_prompt(self):
- '''Normalize the prompt and switches'''
- t2i = self.t2i
- opt = self.opt
+ """Normalize the prompt and switches"""
+ t2i = self.t2i
+ opt = self.opt
switches = list()
switches.append(f'"{opt.prompt}"')
@@ -114,4 +123,3 @@ class PromptFormatter():
if t2i.full_precision:
switches.append('-F')
return ' '.join(switches)
-
diff --git a/ldm/dream/readline.py b/ldm/dream/readline.py
index f46ac6e23a..f40fe83316 100644
--- a/ldm/dream/readline.py
+++ b/ldm/dream/readline.py
@@ -1,37 +1,40 @@
-'''
+"""
Readline helper functions for dream.py (linux and mac only).
-'''
+"""
import os
import re
import atexit
+
# ---------------readline utilities---------------------
try:
import readline
+
readline_available = True
except:
readline_available = False
-class Completer():
- def __init__(self,options):
+
+class Completer:
+ def __init__(self, options):
self.options = sorted(options)
return
- def complete(self,text,state):
+ def complete(self, text, state):
buffer = readline.get_line_buffer()
- if text.startswith(('-I','--init_img')):
- return self._path_completions(text,state,('.png'))
+ if text.startswith(('-I', '--init_img')):
+ return self._path_completions(text, state, ('.png'))
- if buffer.strip().endswith('cd') or text.startswith(('.','/')):
- return self._path_completions(text,state,())
+ if buffer.strip().endswith('cd') or text.startswith(('.', '/')):
+ return self._path_completions(text, state, ())
response = None
if state == 0:
# This is the first time for this text, so build a match list.
if text:
- self.matches = [s
- for s in self.options
- if s and s.startswith(text)]
+ self.matches = [
+ s for s in self.options if s and s.startswith(text)
+ ]
else:
self.matches = self.options[:]
@@ -43,32 +46,34 @@ class Completer():
response = None
return response
- def _path_completions(self,text,state,extensions):
+ def _path_completions(self, text, state, extensions):
# get the path so far
if text.startswith('-I'):
- path = text.replace('-I','',1).lstrip()
+ path = text.replace('-I', '', 1).lstrip()
elif text.startswith('--init_img='):
- path = text.replace('--init_img=','',1).lstrip()
+ path = text.replace('--init_img=', '', 1).lstrip()
else:
path = text
- matches = list()
+ matches = list()
path = os.path.expanduser(path)
- if len(path)==0:
- matches.append(text+'./')
+ if len(path) == 0:
+ matches.append(text + './')
else:
- dir = os.path.dirname(path)
+ dir = os.path.dirname(path)
dir_list = os.listdir(dir)
for n in dir_list:
- if n.startswith('.') and len(n)>1:
+ if n.startswith('.') and len(n) > 1:
continue
- full_path = os.path.join(dir,n)
+ full_path = os.path.join(dir, n)
if full_path.startswith(path):
if os.path.isdir(full_path):
- matches.append(os.path.join(os.path.dirname(text),n)+'/')
+ matches.append(
+ os.path.join(os.path.dirname(text), n) + '/'
+ )
elif n.endswith(extensions):
- matches.append(os.path.join(os.path.dirname(text),n))
+ matches.append(os.path.join(os.path.dirname(text), n))
try:
response = matches[state]
@@ -76,19 +81,47 @@ class Completer():
response = None
return response
+
if readline_available:
- readline.set_completer(Completer(['cd','pwd',
- '--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
- '--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
- '--individual','-i','--init_img','-I','--strength','-f','-v','--variants']).complete)
- readline.set_completer_delims(" ")
+ readline.set_completer(
+ Completer(
+ [
+ 'cd',
+ 'pwd',
+ '--steps',
+ '-s',
+ '--seed',
+ '-S',
+ '--iterations',
+ '-n',
+ '--batch_size',
+ '-b',
+ '--width',
+ '-W',
+ '--height',
+ '-H',
+ '--cfg_scale',
+ '-C',
+ '--grid',
+ '-g',
+ '--individual',
+ '-i',
+ '--init_img',
+ '-I',
+ '--strength',
+ '-f',
+ '-v',
+ '--variants',
+ ]
+ ).complete
+ )
+ readline.set_completer_delims(' ')
readline.parse_and_bind('tab: complete')
- histfile = os.path.join(os.path.expanduser('~'),".dream_history")
+ histfile = os.path.join(os.path.expanduser('~'), '.dream_history')
try:
readline.read_history_file(histfile)
readline.set_history_length(1000)
except FileNotFoundError:
pass
- atexit.register(readline.write_history_file,histfile)
-
+ atexit.register(readline.write_history_file, histfile)
diff --git a/ldm/lr_scheduler.py b/ldm/lr_scheduler.py
index be39da9ca6..79c1d1978e 100644
--- a/ldm/lr_scheduler.py
+++ b/ldm/lr_scheduler.py
@@ -5,32 +5,49 @@ class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
- def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
+
+ def __init__(
+ self,
+ warm_up_steps,
+ lr_min,
+ lr_max,
+ lr_start,
+ max_decay_steps,
+ verbosity_interval=0,
+ ):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
self.lr_max = lr_max
self.lr_max_decay_steps = max_decay_steps
- self.last_lr = 0.
+ self.last_lr = 0.0
self.verbosity_interval = verbosity_interval
def schedule(self, n, **kwargs):
if self.verbosity_interval > 0:
- if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
+ if n % self.verbosity_interval == 0:
+ print(
+ f'current step: {n}, recent lr-multiplier: {self.last_lr}'
+ )
if n < self.lr_warm_up_steps:
- lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
+ lr = (
+ self.lr_max - self.lr_start
+ ) / self.lr_warm_up_steps * n + self.lr_start
self.last_lr = lr
return lr
else:
- t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
+ t = (n - self.lr_warm_up_steps) / (
+ self.lr_max_decay_steps - self.lr_warm_up_steps
+ )
t = min(t, 1.0)
lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
- 1 + np.cos(t * np.pi))
+ 1 + np.cos(t * np.pi)
+ )
self.last_lr = lr
return lr
def __call__(self, n, **kwargs):
- return self.schedule(n,**kwargs)
+ return self.schedule(n, **kwargs)
class LambdaWarmUpCosineScheduler2:
@@ -38,15 +55,30 @@ class LambdaWarmUpCosineScheduler2:
supports repeated iterations, configurable via lists
note: use with a base_lr of 1.0.
"""
- def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
- assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
+
+ def __init__(
+ self,
+ warm_up_steps,
+ f_min,
+ f_max,
+ f_start,
+ cycle_lengths,
+ verbosity_interval=0,
+ ):
+ assert (
+ len(warm_up_steps)
+ == len(f_min)
+ == len(f_max)
+ == len(f_start)
+ == len(cycle_lengths)
+ )
self.lr_warm_up_steps = warm_up_steps
self.f_start = f_start
self.f_min = f_min
self.f_max = f_max
self.cycle_lengths = cycle_lengths
self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
- self.last_f = 0.
+ self.last_f = 0.0
self.verbosity_interval = verbosity_interval
def find_in_interval(self, n):
@@ -60,17 +92,25 @@ class LambdaWarmUpCosineScheduler2:
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
- if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
- f"current cycle {cycle}")
+ if n % self.verbosity_interval == 0:
+ print(
+ f'current step: {n}, recent lr-multiplier: {self.last_f}, '
+ f'current cycle {cycle}'
+ )
if n < self.lr_warm_up_steps[cycle]:
- f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+ f = (
+ self.f_max[cycle] - self.f_start[cycle]
+ ) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
- t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
+ t = (n - self.lr_warm_up_steps[cycle]) / (
+ self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
+ )
t = min(t, 1.0)
- f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
- 1 + np.cos(t * np.pi))
+ f = self.f_min[cycle] + 0.5 * (
+ self.f_max[cycle] - self.f_min[cycle]
+ ) * (1 + np.cos(t * np.pi))
self.last_f = f
return f
@@ -79,20 +119,25 @@ class LambdaWarmUpCosineScheduler2:
class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
-
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
- if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
- f"current cycle {cycle}")
+ if n % self.verbosity_interval == 0:
+ print(
+ f'current step: {n}, recent lr-multiplier: {self.last_f}, '
+ f'current cycle {cycle}'
+ )
if n < self.lr_warm_up_steps[cycle]:
- f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+ f = (
+ self.f_max[cycle] - self.f_start[cycle]
+ ) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
- f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
+ f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
+ self.cycle_lengths[cycle] - n
+ ) / (self.cycle_lengths[cycle])
self.last_f = f
return f
-
diff --git a/ldm/models/autoencoder.py b/ldm/models/autoencoder.py
index 6a9c4f4549..359f5688d1 100644
--- a/ldm/models/autoencoder.py
+++ b/ldm/models/autoencoder.py
@@ -6,29 +6,32 @@ from contextlib import contextmanager
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+from ldm.modules.distributions.distributions import (
+ DiagonalGaussianDistribution,
+)
from ldm.util import instantiate_from_config
class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
+ def __init__(
+ self,
+ ddconfig,
+ lossconfig,
+ n_embed,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key='image',
+ colorize_nlabels=None,
+ monitor=None,
+ batch_resize_range=None,
+ scheduler_config=None,
+ lr_g_factor=1.0,
+ remap=None,
+ sane_index_shape=False, # tell vector quantizer to return indices as bhw
+ use_ema=False,
+ ):
super().__init__()
self.embed_dim = embed_dim
self.n_embed = n_embed
@@ -36,24 +39,34 @@ class VQModel(pl.LightningModule):
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ self.quantize = VectorQuantizer(
+ n_embed,
+ embed_dim,
+ beta=0.25,
+ remap=remap,
+ sane_index_shape=sane_index_shape,
+ )
+ self.quant_conv = torch.nn.Conv2d(ddconfig['z_channels'], embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(
+ embed_dim, ddconfig['z_channels'], 1
+ )
if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ assert type(colorize_nlabels) == int
+ self.register_buffer(
+ 'colorize', torch.randn(3, colorize_nlabels, 1, 1)
+ )
if monitor is not None:
self.monitor = monitor
self.batch_resize_range = batch_resize_range
if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+ print(
+ f'{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.'
+ )
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+ print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
@@ -66,28 +79,30 @@ class VQModel(pl.LightningModule):
self.model_ema.store(self.parameters())
self.model_ema.copy_to(self)
if context is not None:
- print(f"{context}: Switched to EMA weights")
+ print(f'{context}: Switched to EMA weights')
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.parameters())
if context is not None:
- print(f"{context}: Restored training weights")
+ print(f'{context}: Restored training weights')
def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
+ sd = torch.load(path, map_location='cpu')['state_dict']
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
+ print('Deleting key {} from state_dict.'.format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ print(
+ f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+ )
if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
+ print(f'Missing Keys: {missing}')
+ print(f'Unexpected Keys: {unexpected}')
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
@@ -115,7 +130,7 @@ class VQModel(pl.LightningModule):
return dec
def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
+ quant, diff, (_, _, ind) = self.encode(input)
dec = self.decode(quant)
if return_pred_indices:
return dec, diff, ind
@@ -125,7 +140,11 @@ class VQModel(pl.LightningModule):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ x = (
+ x.permute(0, 3, 1, 2)
+ .to(memory_format=torch.contiguous_format)
+ .float()
+ )
if self.batch_resize_range is not None:
lower_size = self.batch_resize_range[0]
upper_size = self.batch_resize_range[1]
@@ -133,9 +152,11 @@ class VQModel(pl.LightningModule):
# do the first few batches with max size to avoid later oom
new_resize = upper_size
else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+ new_resize = np.random.choice(
+ np.arange(lower_size, upper_size + 16, 16)
+ )
if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
+ x = F.interpolate(x, size=new_resize, mode='bicubic')
x = x.detach()
return x
@@ -147,81 +168,139 @@ class VQModel(pl.LightningModule):
if optimizer_idx == 0:
# autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
+ aeloss, log_dict_ae = self.loss(
+ qloss,
+ x,
+ xrec,
+ optimizer_idx,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='train',
+ predicted_indices=ind,
+ )
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(
+ log_dict_ae,
+ prog_bar=False,
+ logger=True,
+ on_step=True,
+ on_epoch=True,
+ )
return aeloss
if optimizer_idx == 1:
# discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+ discloss, log_dict_disc = self.loss(
+ qloss,
+ x,
+ xrec,
+ optimizer_idx,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='train',
+ )
+ self.log_dict(
+ log_dict_disc,
+ prog_bar=False,
+ logger=True,
+ on_step=True,
+ on_epoch=True,
+ )
return discloss
def validation_step(self, batch, batch_idx):
log_dict = self._validation_step(batch, batch_idx)
with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+ log_dict_ema = self._validation_step(
+ batch, batch_idx, suffix='_ema'
+ )
return log_dict
- def _validation_step(self, batch, batch_idx, suffix=""):
+ def _validation_step(self, batch, batch_idx, suffix=''):
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
+ aeloss, log_dict_ae = self.loss(
+ qloss,
+ x,
+ xrec,
+ 0,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='val' + suffix,
+ predicted_indices=ind,
+ )
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+ discloss, log_dict_disc = self.loss(
+ qloss,
+ x,
+ xrec,
+ 1,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='val' + suffix,
+ predicted_indices=ind,
+ )
+ rec_loss = log_dict_ae[f'val{suffix}/rec_loss']
+ self.log(
+ f'val{suffix}/rec_loss',
+ rec_loss,
+ prog_bar=True,
+ logger=True,
+ on_step=False,
+ on_epoch=True,
+ sync_dist=True,
+ )
+ self.log(
+ f'val{suffix}/aeloss',
+ aeloss,
+ prog_bar=True,
+ logger=True,
+ on_step=False,
+ on_epoch=True,
+ sync_dist=True,
+ )
if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
+ del log_dict_ae[f'val{suffix}/rec_loss']
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
+ lr_g = self.lr_g_factor * self.learning_rate
+ print('lr_d', lr_d)
+ print('lr_g', lr_g)
+ opt_ae = torch.optim.Adam(
+ list(self.encoder.parameters())
+ + list(self.decoder.parameters())
+ + list(self.quantize.parameters())
+ + list(self.quant_conv.parameters())
+ + list(self.post_quant_conv.parameters()),
+ lr=lr_g,
+ betas=(0.5, 0.9),
+ )
+ opt_disc = torch.optim.Adam(
+ self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)
+ )
if self.scheduler_config is not None:
scheduler = instantiate_from_config(self.scheduler_config)
- print("Setting up LambdaLR scheduler...")
+ print('Setting up LambdaLR scheduler...')
scheduler = [
{
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+ 'scheduler': LambdaLR(
+ opt_ae, lr_lambda=scheduler.schedule
+ ),
'interval': 'step',
- 'frequency': 1
+ 'frequency': 1,
},
{
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+ 'scheduler': LambdaLR(
+ opt_disc, lr_lambda=scheduler.schedule
+ ),
'interval': 'step',
- 'frequency': 1
+ 'frequency': 1,
},
]
return [opt_ae, opt_disc], scheduler
@@ -235,7 +314,7 @@ class VQModel(pl.LightningModule):
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if only_inputs:
- log["inputs"] = x
+ log['inputs'] = x
return log
xrec, _ = self(x)
if x.shape[1] > 3:
@@ -243,21 +322,24 @@ class VQModel(pl.LightningModule):
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
+ log['inputs'] = x
+ log['reconstructions'] = xrec
if plot_ema:
with self.ema_scope():
xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
+ if x.shape[1] > 3:
+ xrec_ema = self.to_rgb(xrec_ema)
+ log['reconstructions_ema'] = xrec_ema
return log
def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ assert self.image_key == 'segmentation'
+ if not hasattr(self, 'colorize'):
+ self.register_buffer(
+ 'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
+ )
x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
return x
@@ -283,43 +365,50 @@ class VQModelInterface(VQModel):
class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
+ def __init__(
+ self,
+ ddconfig,
+ lossconfig,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key='image',
+ colorize_nlabels=None,
+ monitor=None,
+ ):
super().__init__()
self.image_key = image_key
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ assert ddconfig['double_z']
+ self.quant_conv = torch.nn.Conv2d(
+ 2 * ddconfig['z_channels'], 2 * embed_dim, 1
+ )
+ self.post_quant_conv = torch.nn.Conv2d(
+ embed_dim, ddconfig['z_channels'], 1
+ )
self.embed_dim = embed_dim
if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ assert type(colorize_nlabels) == int
+ self.register_buffer(
+ 'colorize', torch.randn(3, colorize_nlabels, 1, 1)
+ )
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
+ sd = torch.load(path, map_location='cpu')['state_dict']
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
+ print('Deleting key {} from state_dict.'.format(k))
del sd[k]
self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
+ print(f'Restored from {path}')
def encode(self, x):
h = self.encoder(x)
@@ -345,7 +434,11 @@ class AutoencoderKL(pl.LightningModule):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ x = (
+ x.permute(0, 3, 1, 2)
+ .to(memory_format=torch.contiguous_format)
+ .float()
+ )
return x
def training_step(self, batch, batch_idx, optimizer_idx):
@@ -354,44 +447,102 @@ class AutoencoderKL(pl.LightningModule):
if optimizer_idx == 0:
# train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ aeloss, log_dict_ae = self.loss(
+ inputs,
+ reconstructions,
+ posterior,
+ optimizer_idx,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='train',
+ )
+ self.log(
+ 'aeloss',
+ aeloss,
+ prog_bar=True,
+ logger=True,
+ on_step=True,
+ on_epoch=True,
+ )
+ self.log_dict(
+ log_dict_ae,
+ prog_bar=False,
+ logger=True,
+ on_step=True,
+ on_epoch=False,
+ )
return aeloss
if optimizer_idx == 1:
# train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
+ discloss, log_dict_disc = self.loss(
+ inputs,
+ reconstructions,
+ posterior,
+ optimizer_idx,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='train',
+ )
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ self.log(
+ 'discloss',
+ discloss,
+ prog_bar=True,
+ logger=True,
+ on_step=True,
+ on_epoch=True,
+ )
+ self.log_dict(
+ log_dict_disc,
+ prog_bar=False,
+ logger=True,
+ on_step=True,
+ on_epoch=False,
+ )
return discloss
def validation_step(self, batch, batch_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
+ aeloss, log_dict_ae = self.loss(
+ inputs,
+ reconstructions,
+ posterior,
+ 0,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='val',
+ )
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
+ discloss, log_dict_disc = self.loss(
+ inputs,
+ reconstructions,
+ posterior,
+ 1,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split='val',
+ )
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
+ self.log('val/rec_loss', log_dict_ae['val/rec_loss'])
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
+ opt_ae = torch.optim.Adam(
+ list(self.encoder.parameters())
+ + list(self.decoder.parameters())
+ + list(self.quant_conv.parameters())
+ + list(self.post_quant_conv.parameters()),
+ lr=lr,
+ betas=(0.5, 0.9),
+ )
+ opt_disc = torch.optim.Adam(
+ self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)
+ )
return [opt_ae, opt_disc], []
def get_last_layer(self):
@@ -409,17 +560,19 @@ class AutoencoderKL(pl.LightningModule):
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
+ log['samples'] = self.decode(torch.randn_like(posterior.sample()))
+ log['reconstructions'] = xrec
+ log['inputs'] = x
return log
def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ assert self.image_key == 'segmentation'
+ if not hasattr(self, 'colorize'):
+ self.register_buffer(
+ 'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
+ )
x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
return x
diff --git a/ldm/models/diffusion/classifier.py b/ldm/models/diffusion/classifier.py
index 67e98b9d8f..be0d8c1919 100644
--- a/ldm/models/diffusion/classifier.py
+++ b/ldm/models/diffusion/classifier.py
@@ -10,13 +10,13 @@ from einops import rearrange
from glob import glob
from natsort import natsorted
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
+from ldm.modules.diffusionmodules.openaimodel import (
+ EncoderUNetModel,
+ UNetModel,
+)
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-__models__ = {
- 'class_label': EncoderUNetModel,
- 'segmentation': UNetModel
-}
+__models__ = {'class_label': EncoderUNetModel, 'segmentation': UNetModel}
def disabled_train(self, mode=True):
@@ -26,37 +26,49 @@ def disabled_train(self, mode=True):
class NoisyLatentImageClassifier(pl.LightningModule):
-
- def __init__(self,
- diffusion_path,
- num_classes,
- ckpt_path=None,
- pool='attention',
- label_key=None,
- diffusion_ckpt_path=None,
- scheduler_config=None,
- weight_decay=1.e-2,
- log_steps=10,
- monitor='val/loss',
- *args,
- **kwargs):
+ def __init__(
+ self,
+ diffusion_path,
+ num_classes,
+ ckpt_path=None,
+ pool='attention',
+ label_key=None,
+ diffusion_ckpt_path=None,
+ scheduler_config=None,
+ weight_decay=1.0e-2,
+ log_steps=10,
+ monitor='val/loss',
+ *args,
+ **kwargs,
+ ):
super().__init__(*args, **kwargs)
self.num_classes = num_classes
# get latest config of diffusion model
- diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
+ diffusion_config = natsorted(
+ glob(os.path.join(diffusion_path, 'configs', '*-project.yaml'))
+ )[-1]
self.diffusion_config = OmegaConf.load(diffusion_config).model
self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
self.load_diffusion()
self.monitor = monitor
- self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
- self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
+ self.numd = (
+ self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
+ )
+ self.log_time_interval = (
+ self.diffusion_model.num_timesteps // log_steps
+ )
self.log_steps = log_steps
- self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
+ self.label_key = (
+ label_key
+ if not hasattr(self.diffusion_model, 'cond_stage_key')
else self.diffusion_model.cond_stage_key
+ )
- assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
+ assert (
+ self.label_key is not None
+ ), 'label_key neither in diffusion model nor in model.params'
if self.label_key not in __models__:
raise NotImplementedError()
@@ -68,22 +80,27 @@ class NoisyLatentImageClassifier(pl.LightningModule):
self.weight_decay = weight_decay
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
- sd = torch.load(path, map_location="cpu")
- if "state_dict" in list(sd.keys()):
- sd = sd["state_dict"]
+ sd = torch.load(path, map_location='cpu')
+ if 'state_dict' in list(sd.keys()):
+ sd = sd['state_dict']
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
+ print('Deleting key {} from state_dict.'.format(k))
del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
- sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ missing, unexpected = (
+ self.load_state_dict(sd, strict=False)
+ if not only_model
+ else self.model.load_state_dict(sd, strict=False)
+ )
+ print(
+ f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+ )
if len(missing) > 0:
- print(f"Missing Keys: {missing}")
+ print(f'Missing Keys: {missing}')
if len(unexpected) > 0:
- print(f"Unexpected Keys: {unexpected}")
+ print(f'Unexpected Keys: {unexpected}')
def load_diffusion(self):
model = instantiate_from_config(self.diffusion_config)
@@ -93,17 +110,25 @@ class NoisyLatentImageClassifier(pl.LightningModule):
param.requires_grad = False
def load_classifier(self, ckpt_path, pool):
- model_config = deepcopy(self.diffusion_config.params.unet_config.params)
- model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
+ model_config = deepcopy(
+ self.diffusion_config.params.unet_config.params
+ )
+ model_config.in_channels = (
+ self.diffusion_config.params.unet_config.params.out_channels
+ )
model_config.out_channels = self.num_classes
if self.label_key == 'class_label':
model_config.pool = pool
self.model = __models__[self.label_key](**model_config)
if ckpt_path is not None:
- print('#####################################################################')
+ print(
+ '#####################################################################'
+ )
print(f'load from ckpt "{ckpt_path}"')
- print('#####################################################################')
+ print(
+ '#####################################################################'
+ )
self.init_from_ckpt(ckpt_path)
@torch.no_grad()
@@ -111,11 +136,19 @@ class NoisyLatentImageClassifier(pl.LightningModule):
noise = default(noise, lambda: torch.randn_like(x))
continuous_sqrt_alpha_cumprod = None
if self.diffusion_model.use_continuous_noise:
- continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
+ continuous_sqrt_alpha_cumprod = (
+ self.diffusion_model.sample_continuous_noise_level(
+ x.shape[0], t + 1
+ )
+ )
# todo: make sure t+1 is correct here
- return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
- continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
+ return self.diffusion_model.q_sample(
+ x_start=x,
+ t=t,
+ noise=noise,
+ continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod,
+ )
def forward(self, x_noisy, t, *args, **kwargs):
return self.model(x_noisy, t)
@@ -141,17 +174,21 @@ class NoisyLatentImageClassifier(pl.LightningModule):
targets = rearrange(targets, 'b h w c -> b c h w')
for down in range(self.numd):
h, w = targets.shape[-2:]
- targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
+ targets = F.interpolate(
+ targets, size=(h // 2, w // 2), mode='nearest'
+ )
# targets = rearrange(targets,'b c h w -> b h w c')
return targets
- def compute_top_k(self, logits, labels, k, reduction="mean"):
+ def compute_top_k(self, logits, labels, k, reduction='mean'):
_, top_ks = torch.topk(logits, k, dim=1)
- if reduction == "mean":
- return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
- elif reduction == "none":
+ if reduction == 'mean':
+ return (
+ (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
+ )
+ elif reduction == 'none':
return (top_ks == labels[:, None]).float().sum(dim=-1)
def on_train_epoch_start(self):
@@ -162,29 +199,59 @@ class NoisyLatentImageClassifier(pl.LightningModule):
def write_logs(self, loss, logits, targets):
log_prefix = 'train' if self.training else 'val'
log = {}
- log[f"{log_prefix}/loss"] = loss.mean()
- log[f"{log_prefix}/acc@1"] = self.compute_top_k(
- logits, targets, k=1, reduction="mean"
+ log[f'{log_prefix}/loss'] = loss.mean()
+ log[f'{log_prefix}/acc@1'] = self.compute_top_k(
+ logits, targets, k=1, reduction='mean'
)
- log[f"{log_prefix}/acc@5"] = self.compute_top_k(
- logits, targets, k=5, reduction="mean"
+ log[f'{log_prefix}/acc@5'] = self.compute_top_k(
+ logits, targets, k=5, reduction='mean'
)
- self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
- self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
- self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
+ self.log_dict(
+ log,
+ prog_bar=False,
+ logger=True,
+ on_step=self.training,
+ on_epoch=True,
+ )
+ self.log(
+ 'loss', log[f'{log_prefix}/loss'], prog_bar=True, logger=False
+ )
+ self.log(
+ 'global_step',
+ self.global_step,
+ logger=False,
+ on_epoch=False,
+ prog_bar=True,
+ )
lr = self.optimizers().param_groups[0]['lr']
- self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
+ self.log(
+ 'lr_abs',
+ lr,
+ on_step=True,
+ logger=True,
+ on_epoch=False,
+ prog_bar=True,
+ )
def shared_step(self, batch, t=None):
- x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
+ x, *_ = self.diffusion_model.get_input(
+ batch, k=self.diffusion_model.first_stage_key
+ )
targets = self.get_conditioning(batch)
if targets.dim() == 4:
targets = targets.argmax(dim=1)
if t is None:
- t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
+ t = torch.randint(
+ 0,
+ self.diffusion_model.num_timesteps,
+ (x.shape[0],),
+ device=self.device,
+ ).long()
else:
- t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
+ t = torch.full(
+ size=(x.shape[0],), fill_value=t, device=self.device
+ ).long()
x_noisy = self.get_x_noisy(x, t)
logits = self(x_noisy, t)
@@ -200,8 +267,14 @@ class NoisyLatentImageClassifier(pl.LightningModule):
return loss
def reset_noise_accs(self):
- self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
- range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
+ self.noisy_acc = {
+ t: {'acc@1': [], 'acc@5': []}
+ for t in range(
+ 0,
+ self.diffusion_model.num_timesteps,
+ self.diffusion_model.log_every_t,
+ )
+ }
def on_validation_start(self):
self.reset_noise_accs()
@@ -212,24 +285,35 @@ class NoisyLatentImageClassifier(pl.LightningModule):
for t in self.noisy_acc:
_, logits, _, targets = self.shared_step(batch, t)
- self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
- self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
+ self.noisy_acc[t]['acc@1'].append(
+ self.compute_top_k(logits, targets, k=1, reduction='mean')
+ )
+ self.noisy_acc[t]['acc@5'].append(
+ self.compute_top_k(logits, targets, k=5, reduction='mean')
+ )
return loss
def configure_optimizers(self):
- optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
+ optimizer = AdamW(
+ self.model.parameters(),
+ lr=self.learning_rate,
+ weight_decay=self.weight_decay,
+ )
if self.use_scheduler:
scheduler = instantiate_from_config(self.scheduler_config)
- print("Setting up LambdaLR scheduler...")
+ print('Setting up LambdaLR scheduler...')
scheduler = [
{
- 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
+ 'scheduler': LambdaLR(
+ optimizer, lr_lambda=scheduler.schedule
+ ),
'interval': 'step',
- 'frequency': 1
- }]
+ 'frequency': 1,
+ }
+ ]
return [optimizer], scheduler
return optimizer
@@ -243,7 +327,7 @@ class NoisyLatentImageClassifier(pl.LightningModule):
y = self.get_conditioning(batch)
if self.label_key == 'class_label':
- y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+ y = log_txt_as_img((x.shape[2], x.shape[3]), batch['human_label'])
log['labels'] = y
if ismap(y):
@@ -256,10 +340,14 @@ class NoisyLatentImageClassifier(pl.LightningModule):
log[f'inputs@t{current_time}'] = x_noisy
- pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
+ pred = F.one_hot(
+ logits.argmax(dim=1), num_classes=self.num_classes
+ )
pred = rearrange(pred, 'b h w c -> b c h w')
- log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
+ log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(
+ pred
+ )
for key in log:
log[key] = log[key][:N]
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index ddf786b5a8..3d9086eb1d 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -5,12 +5,16 @@ import numpy as np
from tqdm import tqdm
from functools import partial
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
- extract_into_tensor
+from ldm.modules.diffusionmodules.util import (
+ make_ddim_sampling_parameters,
+ make_ddim_timesteps,
+ noise_like,
+ extract_into_tensor,
+)
class DDIMSampler(object):
- def __init__(self, model, schedule="linear", device="cuda", **kwargs):
+ def __init__(self, model, schedule='linear', device='cuda', **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
@@ -23,70 +27,122 @@ class DDIMSampler(object):
attr = attr.to(torch.device(self.device))
setattr(self, name, attr)
- def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
- self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
- num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ def make_schedule(
+ self,
+ ddim_num_steps,
+ ddim_discretize='uniform',
+ ddim_eta=0.0,
+ verbose=True,
+ ):
+ self.ddim_timesteps = make_ddim_timesteps(
+ ddim_discr_method=ddim_discretize,
+ num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,
+ verbose=verbose,
+ )
alphas_cumprod = self.model.alphas_cumprod
- assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
- to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+ assert (
+ alphas_cumprod.shape[0] == self.ddpm_num_timesteps
+ ), 'alphas have to be defined for each timestep'
+ to_torch = (
+ lambda x: x.clone()
+ .detach()
+ .to(torch.float32)
+ .to(self.model.device)
+ )
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
- self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+ self.register_buffer(
+ 'alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)
+ )
# calculations for diffusion q(x_t | x_{t-1}) and others
- self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
- self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
- self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+ self.register_buffer(
+ 'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))
+ )
+ self.register_buffer(
+ 'sqrt_one_minus_alphas_cumprod',
+ to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
+ )
+ self.register_buffer(
+ 'log_one_minus_alphas_cumprod',
+ to_torch(np.log(1.0 - alphas_cumprod.cpu())),
+ )
+ self.register_buffer(
+ 'sqrt_recip_alphas_cumprod',
+ to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())),
+ )
+ self.register_buffer(
+ 'sqrt_recipm1_alphas_cumprod',
+ to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
+ )
# ddim sampling parameters
- ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
- ddim_timesteps=self.ddim_timesteps,
- eta=ddim_eta,verbose=verbose)
+ (
+ ddim_sigmas,
+ ddim_alphas,
+ ddim_alphas_prev,
+ ) = make_ddim_sampling_parameters(
+ alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,
+ verbose=verbose,
+ )
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
- self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ self.register_buffer(
+ 'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
+ )
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
- (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
- 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
- self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+ (1 - self.alphas_cumprod_prev)
+ / (1 - self.alphas_cumprod)
+ * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
+ )
+ self.register_buffer(
+ 'ddim_sigmas_for_original_num_steps',
+ sigmas_for_original_sampling_steps,
+ )
@torch.no_grad()
- def sample(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
+ def sample(
+ self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.0,
+ mask=None,
+ x0=None,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ **kwargs,
+ ):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ print(
+ f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'
+ )
else:
if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+ print(
+ f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'
+ )
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
@@ -94,30 +150,47 @@ class DDIMSampler(object):
size = (batch_size, C, H, W)
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
- samples, intermediates = self.ddim_sampling(conditioning, size,
- callback=callback,
- img_callback=img_callback,
- quantize_denoised=quantize_x0,
- mask=mask, x0=x0,
- ddim_use_original_steps=False,
- noise_dropout=noise_dropout,
- temperature=temperature,
- score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- x_T=x_T,
- log_every_t=log_every_t,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- )
+ samples, intermediates = self.ddim_sampling(
+ conditioning,
+ size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask,
+ x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ )
return samples, intermediates
@torch.no_grad()
- def ddim_sampling(self, cond, shape,
- x_T=None, ddim_use_original_steps=False,
- callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, log_every_t=100,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None,):
+ def ddim_sampling(
+ self,
+ cond,
+ shape,
+ x_T=None,
+ ddim_use_original_steps=False,
+ callback=None,
+ timesteps=None,
+ quantize_denoised=False,
+ mask=None,
+ x0=None,
+ img_callback=None,
+ log_every_t=100,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ ):
device = self.model.betas.device
b = shape[0]
if x_T is None:
@@ -126,17 +199,38 @@ class DDIMSampler(object):
img = x_T
if timesteps is None:
- timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ timesteps = (
+ self.ddpm_num_timesteps
+ if ddim_use_original_steps
+ else self.ddim_timesteps
+ )
elif timesteps is not None and not ddim_use_original_steps:
- subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ subset_end = (
+ int(
+ min(timesteps / self.ddim_timesteps.shape[0], 1)
+ * self.ddim_timesteps.shape[0]
+ )
+ - 1
+ )
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
- time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
- total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
- print(f"Running DDIM Sampling with {total_steps} timesteps")
+ time_range = (
+ reversed(range(0, timesteps))
+ if ddim_use_original_steps
+ else np.flip(timesteps)
+ )
+ total_steps = (
+ timesteps if ddim_use_original_steps else timesteps.shape[0]
+ )
+ print(f'Running DDIM Sampling with {total_steps} timesteps')
- iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps, dynamic_ncols=True)
+ iterator = tqdm(
+ time_range,
+ desc='DDIM Sampler',
+ total=total_steps,
+ dynamic_ncols=True,
+ )
for i, step in enumerate(iterator):
index = total_steps - i - 1
@@ -144,18 +238,30 @@ class DDIMSampler(object):
if mask is not None:
assert x0 is not None
- img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
- img = img_orig * mask + (1. - mask) * img
+ img_orig = self.model.q_sample(
+ x0, ts
+ ) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1.0 - mask) * img
- outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
- quantize_denoised=quantize_denoised, temperature=temperature,
- noise_dropout=noise_dropout, score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning)
+ outs = self.p_sample_ddim(
+ img,
+ cond,
+ ts,
+ index=index,
+ use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised,
+ temperature=temperature,
+ noise_dropout=noise_dropout,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ )
img, pred_x0 = outs
- if callback: callback(i)
- if img_callback: img_callback(pred_x0, i)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
@@ -164,42 +270,82 @@ class DDIMSampler(object):
return img, intermediates
@torch.no_grad()
- def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None):
+ def p_sample_ddim(
+ self,
+ x,
+ c,
+ t,
+ index,
+ repeat_noise=False,
+ use_original_steps=False,
+ quantize_denoised=False,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ ):
b, *_, device = *x.shape, x.device
- if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ if (
+ unconditional_conditioning is None
+ or unconditional_guidance_scale == 1.0
+ ):
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
- e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+ e_t = e_t_uncond + unconditional_guidance_scale * (
+ e_t - e_t_uncond
+ )
if score_corrector is not None:
- assert self.model.parameterization == "eps"
- e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+ assert self.model.parameterization == 'eps'
+ e_t = score_corrector.modify_score(
+ self.model, e_t, x, t, c, **corrector_kwargs
+ )
- alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
- alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
- sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
- sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ alphas = (
+ self.model.alphas_cumprod
+ if use_original_steps
+ else self.ddim_alphas
+ )
+ alphas_prev = (
+ self.model.alphas_cumprod_prev
+ if use_original_steps
+ else self.ddim_alphas_prev
+ )
+ sqrt_one_minus_alphas = (
+ self.model.sqrt_one_minus_alphas_cumprod
+ if use_original_steps
+ else self.ddim_sqrt_one_minus_alphas
+ )
+ sigmas = (
+ self.model.ddim_sigmas_for_original_num_steps
+ if use_original_steps
+ else self.ddim_sigmas
+ )
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
- sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+ sqrt_one_minus_at = torch.full(
+ (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
+ )
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
- dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
- noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
+ dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
+ noise = (
+ sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ )
+ if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
@@ -217,26 +363,51 @@ class DDIMSampler(object):
if noise is None:
noise = torch.randn_like(x0)
- return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
- extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+ return (
+ extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
+ + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)
+ * noise
+ )
@torch.no_grad()
- def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
- use_original_steps=False):
+ def decode(
+ self,
+ x_latent,
+ cond,
+ t_start,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ use_original_steps=False,
+ ):
- timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+ timesteps = (
+ np.arange(self.ddpm_num_timesteps)
+ if use_original_steps
+ else self.ddim_timesteps
+ )
timesteps = timesteps[:t_start]
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
- print(f"Running DDIM Sampling with {total_steps} timesteps")
+ print(f'Running DDIM Sampling with {total_steps} timesteps')
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
x_dec = x_latent
for i, step in enumerate(iterator):
index = total_steps - i - 1
- ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
- x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning)
+ ts = torch.full(
+ (x_latent.shape[0],),
+ step,
+ device=x_latent.device,
+ dtype=torch.long,
+ )
+ x_dec, _ = self.p_sample_ddim(
+ x_dec,
+ cond,
+ ts,
+ index=index,
+ use_original_steps=use_original_steps,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ )
return x_dec
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
index d5f74a0fbe..ccfffa9b9b 100644
--- a/ldm/models/diffusion/ddpm.py
+++ b/ldm/models/diffusion/ddpm.py
@@ -21,17 +21,39 @@ from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
import urllib
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.util import (
+ log_txt_as_img,
+ exists,
+ default,
+ ismap,
+ isimage,
+ mean_flat,
+ count_params,
+ instantiate_from_config,
+)
from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.modules.distributions.distributions import (
+ normal_kl,
+ DiagonalGaussianDistribution,
+)
+from ldm.models.autoencoder import (
+ VQModelInterface,
+ IdentityFirstStage,
+ AutoencoderKL,
+)
+from ldm.modules.diffusionmodules.util import (
+ make_beta_schedule,
+ extract_into_tensor,
+ noise_like,
+)
from ldm.models.diffusion.ddim import DDIMSampler
-__conditioning_keys__ = {'concat': 'c_concat',
- 'crossattn': 'c_crossattn',
- 'adm': 'y'}
+__conditioning_keys__ = {
+ 'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y',
+}
def disabled_train(self, mode=True):
@@ -46,40 +68,46 @@ def uniform_on_device(r1, r2, shape, device):
class DDPM(pl.LightningModule):
# classic DDPM with Gaussian diffusion, in image space
- def __init__(self,
- unet_config,
- timesteps=1000,
- beta_schedule="linear",
- loss_type="l2",
- ckpt_path=None,
- ignore_keys=[],
- load_only_unet=False,
- monitor="val/loss",
- use_ema=True,
- first_stage_key="image",
- image_size=256,
- channels=3,
- log_every_t=100,
- clip_denoised=True,
- linear_start=1e-4,
- linear_end=2e-2,
- cosine_s=8e-3,
- given_betas=None,
- original_elbo_weight=0.,
- embedding_reg_weight=0.,
- v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
- l_simple_weight=1.,
- conditioning_key=None,
- parameterization="eps", # all assuming fixed variance schedules
- scheduler_config=None,
- use_positional_encodings=False,
- learn_logvar=False,
- logvar_init=0.,
- ):
+ def __init__(
+ self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule='linear',
+ loss_type='l2',
+ ckpt_path=None,
+ ignore_keys=[],
+ load_only_unet=False,
+ monitor='val/loss',
+ use_ema=True,
+ first_stage_key='image',
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.0,
+ embedding_reg_weight=0.0,
+ v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.0,
+ conditioning_key=None,
+ parameterization='eps', # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.0,
+ ):
super().__init__()
- assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+ assert parameterization in [
+ 'eps',
+ 'x0',
+ ], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
- print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ print(
+ f'{self.__class__.__name__}: Running in {self.parameterization}-prediction mode'
+ )
self.cond_stage_model = None
self.clip_denoised = clip_denoised
self.log_every_t = log_every_t
@@ -92,7 +120,7 @@ class DDPM(pl.LightningModule):
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self.model)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+ print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
self.use_scheduler = scheduler_config is not None
if self.use_scheduler:
@@ -106,68 +134,131 @@ class DDPM(pl.LightningModule):
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+ self.init_from_ckpt(
+ ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
+ )
- self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
- linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+ self.register_schedule(
+ given_betas=given_betas,
+ beta_schedule=beta_schedule,
+ timesteps=timesteps,
+ linear_start=linear_start,
+ linear_end=linear_end,
+ cosine_s=cosine_s,
+ )
self.loss_type = loss_type
self.learn_logvar = learn_logvar
- self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ self.logvar = torch.full(
+ fill_value=logvar_init, size=(self.num_timesteps,)
+ )
if self.learn_logvar:
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
- def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
- linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ def register_schedule(
+ self,
+ given_betas=None,
+ beta_schedule='linear',
+ timesteps=1000,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ ):
if exists(given_betas):
betas = given_betas
else:
- betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
- cosine_s=cosine_s)
- alphas = 1. - betas
+ betas = make_beta_schedule(
+ beta_schedule,
+ timesteps,
+ linear_start=linear_start,
+ linear_end=linear_end,
+ cosine_s=cosine_s,
+ )
+ alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
- alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+ alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
- timesteps, = betas.shape
+ (timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
- assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+ assert (
+ alphas_cumprod.shape[0] == self.num_timesteps
+ ), 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
- self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+ self.register_buffer(
+ 'alphas_cumprod_prev', to_torch(alphas_cumprod_prev)
+ )
# calculations for diffusion q(x_t | x_{t-1}) and others
- self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
- self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
- self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
- self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
- self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+ self.register_buffer(
+ 'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))
+ )
+ self.register_buffer(
+ 'sqrt_one_minus_alphas_cumprod',
+ to_torch(np.sqrt(1.0 - alphas_cumprod)),
+ )
+ self.register_buffer(
+ 'log_one_minus_alphas_cumprod',
+ to_torch(np.log(1.0 - alphas_cumprod)),
+ )
+ self.register_buffer(
+ 'sqrt_recip_alphas_cumprod',
+ to_torch(np.sqrt(1.0 / alphas_cumprod)),
+ )
+ self.register_buffer(
+ 'sqrt_recipm1_alphas_cumprod',
+ to_torch(np.sqrt(1.0 / alphas_cumprod - 1)),
+ )
# calculations for posterior q(x_{t-1} | x_t, x_0)
- posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
- 1. - alphas_cumprod) + self.v_posterior * betas
+ posterior_variance = (1 - self.v_posterior) * betas * (
+ 1.0 - alphas_cumprod_prev
+ ) / (1.0 - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
- self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ self.register_buffer(
+ 'posterior_variance', to_torch(posterior_variance)
+ )
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
- self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
- self.register_buffer('posterior_mean_coef1', to_torch(
- betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
- self.register_buffer('posterior_mean_coef2', to_torch(
- (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+ self.register_buffer(
+ 'posterior_log_variance_clipped',
+ to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
+ )
+ self.register_buffer(
+ 'posterior_mean_coef1',
+ to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
+ ),
+ )
+ self.register_buffer(
+ 'posterior_mean_coef2',
+ to_torch(
+ (1.0 - alphas_cumprod_prev)
+ * np.sqrt(alphas)
+ / (1.0 - alphas_cumprod)
+ ),
+ )
- if self.parameterization == "eps":
- lvlb_weights = self.betas ** 2 / (
- 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
- elif self.parameterization == "x0":
- lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ if self.parameterization == 'eps':
+ lvlb_weights = self.betas**2 / (
+ 2
+ * self.posterior_variance
+ * to_torch(alphas)
+ * (1 - self.alphas_cumprod)
+ )
+ elif self.parameterization == 'x0':
+ lvlb_weights = (
+ 0.5
+ * np.sqrt(torch.Tensor(alphas_cumprod))
+ / (2.0 * 1 - torch.Tensor(alphas_cumprod))
+ )
else:
- raise NotImplementedError("mu not supported")
+ raise NotImplementedError('mu not supported')
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
@@ -179,32 +270,37 @@ class DDPM(pl.LightningModule):
self.model_ema.store(self.model.parameters())
self.model_ema.copy_to(self.model)
if context is not None:
- print(f"{context}: Switched to EMA weights")
+ print(f'{context}: Switched to EMA weights')
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.model.parameters())
if context is not None:
- print(f"{context}: Restored training weights")
+ print(f'{context}: Restored training weights')
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
- sd = torch.load(path, map_location="cpu")
- if "state_dict" in list(sd.keys()):
- sd = sd["state_dict"]
+ sd = torch.load(path, map_location='cpu')
+ if 'state_dict' in list(sd.keys()):
+ sd = sd['state_dict']
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
+ print('Deleting key {} from state_dict.'.format(k))
del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
- sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ missing, unexpected = (
+ self.load_state_dict(sd, strict=False)
+ if not only_model
+ else self.model.load_state_dict(sd, strict=False)
+ )
+ print(
+ f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+ )
if len(missing) > 0:
- print(f"Missing Keys: {missing}")
+ print(f'Missing Keys: {missing}')
if len(unexpected) > 0:
- print(f"Unexpected Keys: {unexpected}")
+ print(f'Unexpected Keys: {unexpected}')
def q_mean_variance(self, x_start, t):
"""
@@ -213,46 +309,78 @@ class DDPM(pl.LightningModule):
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
- mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
- variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
- log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ mean = (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape)
+ * x_start
+ )
+ variance = extract_into_tensor(
+ 1.0 - self.alphas_cumprod, t, x_start.shape
+ )
+ log_variance = extract_into_tensor(
+ self.log_one_minus_alphas_cumprod, t, x_start.shape
+ )
return mean, variance, log_variance
def predict_start_from_noise(self, x_t, t, noise):
return (
- extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape)
+ * x_t
+ - extract_into_tensor(
+ self.sqrt_recipm1_alphas_cumprod, t, x_t.shape
+ )
+ * noise
)
def q_posterior(self, x_start, x_t, t):
posterior_mean = (
- extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
- extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape)
+ * x_start
+ + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape)
+ * x_t
+ )
+ posterior_variance = extract_into_tensor(
+ self.posterior_variance, t, x_t.shape
+ )
+ posterior_log_variance_clipped = extract_into_tensor(
+ self.posterior_log_variance_clipped, t, x_t.shape
+ )
+ return (
+ posterior_mean,
+ posterior_variance,
+ posterior_log_variance_clipped,
)
- posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
- posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
- return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, x, t, clip_denoised: bool):
model_out = self.model(x, t)
- if self.parameterization == "eps":
+ if self.parameterization == 'eps':
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
- elif self.parameterization == "x0":
+ elif self.parameterization == 'x0':
x_recon = model_out
if clip_denoised:
- x_recon.clamp_(-1., 1.)
+ x_recon.clamp_(-1.0, 1.0)
- model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ (
+ model_mean,
+ posterior_variance,
+ posterior_log_variance,
+ ) = self.q_posterior(x_start=x_recon, x_t=x, t=t)
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
b, *_, device = *x.shape, x.device
- model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ model_mean, _, model_log_variance = self.p_mean_variance(
+ x=x, t=t, clip_denoised=clip_denoised
+ )
noise = noise_like(x.shape, device, repeat_noise)
# no noise when t == 0
- nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+ nonzero_mask = (1 - (t == 0).float()).reshape(
+ b, *((1,) * (len(x.shape) - 1))
+ )
+ return (
+ model_mean
+ + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+ )
@torch.no_grad()
def p_sample_loop(self, shape, return_intermediates=False):
@@ -260,9 +388,17 @@ class DDPM(pl.LightningModule):
b = shape[0]
img = torch.randn(shape, device=device)
intermediates = [img]
- for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps, dynamic_ncols=True):
- img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
- clip_denoised=self.clip_denoised)
+ for i in tqdm(
+ reversed(range(0, self.num_timesteps)),
+ desc='Sampling t',
+ total=self.num_timesteps,
+ dynamic_ncols=True,
+ ):
+ img = self.p_sample(
+ img,
+ torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised,
+ )
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
intermediates.append(img)
if return_intermediates:
@@ -273,13 +409,21 @@ class DDPM(pl.LightningModule):
def sample(self, batch_size=16, return_intermediates=False):
image_size = self.image_size
channels = self.channels
- return self.p_sample_loop((batch_size, channels, image_size, image_size),
- return_intermediates=return_intermediates)
+ return self.p_sample_loop(
+ (batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates,
+ )
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
- return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
- extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape)
+ * x_start
+ + extract_into_tensor(
+ self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
+ )
+ * noise
+ )
def get_loss(self, pred, target, mean=True):
if self.loss_type == 'l1':
@@ -290,7 +434,9 @@ class DDPM(pl.LightningModule):
if mean:
loss = torch.nn.functional.mse_loss(target, pred)
else:
- loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+ loss = torch.nn.functional.mse_loss(
+ target, pred, reduction='none'
+ )
else:
raise NotImplementedError("unknown loss type '{loss_type}'")
@@ -302,12 +448,14 @@ class DDPM(pl.LightningModule):
model_out = self.model(x_noisy, t)
loss_dict = {}
- if self.parameterization == "eps":
+ if self.parameterization == 'eps':
target = noise
- elif self.parameterization == "x0":
+ elif self.parameterization == 'x0':
target = x_start
else:
- raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+ raise NotImplementedError(
+ f'Paramterization {self.parameterization} not yet supported'
+ )
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
@@ -328,7 +476,9 @@ class DDPM(pl.LightningModule):
def forward(self, x, *args, **kwargs):
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
- t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ t = torch.randint(
+ 0, self.num_timesteps, (x.shape[0],), device=self.device
+ ).long()
return self.p_losses(x, t, *args, **kwargs)
def get_input(self, batch, k):
@@ -347,15 +497,29 @@ class DDPM(pl.LightningModule):
def training_step(self, batch, batch_idx):
loss, loss_dict = self.shared_step(batch)
- self.log_dict(loss_dict, prog_bar=True,
- logger=True, on_step=True, on_epoch=True)
+ self.log_dict(
+ loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
+ )
- self.log("global_step", self.global_step,
- prog_bar=True, logger=True, on_step=True, on_epoch=False)
+ self.log(
+ 'global_step',
+ self.global_step,
+ prog_bar=True,
+ logger=True,
+ on_step=True,
+ on_epoch=False,
+ )
if self.use_scheduler:
lr = self.optimizers().param_groups[0]['lr']
- self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+ self.log(
+ 'lr_abs',
+ lr,
+ prog_bar=True,
+ logger=True,
+ on_step=True,
+ on_epoch=False,
+ )
return loss
@@ -364,9 +528,23 @@ class DDPM(pl.LightningModule):
_, loss_dict_no_ema = self.shared_step(batch)
with self.ema_scope():
_, loss_dict_ema = self.shared_step(batch)
- loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
- self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
- self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+ loss_dict_ema = {
+ key + '_ema': loss_dict_ema[key] for key in loss_dict_ema
+ }
+ self.log_dict(
+ loss_dict_no_ema,
+ prog_bar=False,
+ logger=True,
+ on_step=False,
+ on_epoch=True,
+ )
+ self.log_dict(
+ loss_dict_ema,
+ prog_bar=False,
+ logger=True,
+ on_step=False,
+ on_epoch=True,
+ )
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
@@ -380,13 +558,15 @@ class DDPM(pl.LightningModule):
return denoise_grid
@torch.no_grad()
- def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ def log_images(
+ self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs
+ ):
log = dict()
x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
x = x.to(self.device)[:N]
- log["inputs"] = x
+ log['inputs'] = x
# get diffusion row
diffusion_row = list()
@@ -400,15 +580,17 @@ class DDPM(pl.LightningModule):
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
diffusion_row.append(x_noisy)
- log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+ log['diffusion_row'] = self._get_rows_from_list(diffusion_row)
if sample:
# get denoise row
- with self.ema_scope("Plotting"):
- samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+ with self.ema_scope('Plotting'):
+ samples, denoise_row = self.sample(
+ batch_size=N, return_intermediates=True
+ )
- log["samples"] = samples
- log["denoise_row"] = self._get_rows_from_list(denoise_row)
+ log['samples'] = samples
+ log['denoise_row'] = self._get_rows_from_list(denoise_row)
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
@@ -428,19 +610,23 @@ class DDPM(pl.LightningModule):
class LatentDiffusion(DDPM):
"""main class"""
- def __init__(self,
- first_stage_config,
- cond_stage_config,
- personalization_config,
- num_timesteps_cond=None,
- cond_stage_key="image",
- cond_stage_trainable=False,
- concat_mode=True,
- cond_stage_forward=None,
- conditioning_key=None,
- scale_factor=1.0,
- scale_by_std=False,
- *args, **kwargs):
+
+ def __init__(
+ self,
+ first_stage_config,
+ cond_stage_config,
+ personalization_config,
+ num_timesteps_cond=None,
+ cond_stage_key='image',
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ *args,
+ **kwargs,
+ ):
self.num_timesteps_cond = default(num_timesteps_cond, 1)
self.scale_by_std = scale_by_std
@@ -450,15 +636,17 @@ class LatentDiffusion(DDPM):
conditioning_key = 'concat' if concat_mode else 'crossattn'
if cond_stage_config == '__is_unconditional__':
conditioning_key = None
- ckpt_path = kwargs.pop("ckpt_path", None)
- ignore_keys = kwargs.pop("ignore_keys", [])
+ ckpt_path = kwargs.pop('ckpt_path', None)
+ ignore_keys = kwargs.pop('ignore_keys', [])
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
self.concat_mode = concat_mode
self.cond_stage_trainable = cond_stage_trainable
self.cond_stage_key = cond_stage_key
try:
- self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ self.num_downs = (
+ len(first_stage_config.params.ddconfig.ch_mult) - 1
+ )
except:
self.num_downs = 0
if not scale_by_std:
@@ -470,7 +658,7 @@ class LatentDiffusion(DDPM):
self.cond_stage_forward = cond_stage_forward
self.clip_denoised = False
- self.bbox_tokenizer = None
+ self.bbox_tokenizer = None
self.restarted_from_ckpt = False
if ckpt_path is not None:
@@ -485,8 +673,10 @@ class LatentDiffusion(DDPM):
self.model.train = disabled_train
for param in self.model.parameters():
param.requires_grad = False
-
- self.embedding_manager = self.instantiate_embedding_manager(personalization_config, self.cond_stage_model)
+
+ self.embedding_manager = self.instantiate_embedding_manager(
+ personalization_config, self.cond_stage_model
+ )
self.emb_ckpt_counter = 0
@@ -496,32 +686,61 @@ class LatentDiffusion(DDPM):
for param in self.embedding_manager.embedding_parameters():
param.requires_grad = True
- def make_cond_schedule(self, ):
- self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
- ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
- self.cond_ids[:self.num_timesteps_cond] = ids
+ def make_cond_schedule(
+ self,
+ ):
+ self.cond_ids = torch.full(
+ size=(self.num_timesteps,),
+ fill_value=self.num_timesteps - 1,
+ dtype=torch.long,
+ )
+ ids = torch.round(
+ torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
+ ).long()
+ self.cond_ids[: self.num_timesteps_cond] = ids
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
# only for very first batch
- if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
- assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ if (
+ self.scale_by_std
+ and self.current_epoch == 0
+ and self.global_step == 0
+ and batch_idx == 0
+ and not self.restarted_from_ckpt
+ ):
+ assert (
+ self.scale_factor == 1.0
+ ), 'rather not use custom rescaling and std-rescaling simultaneously'
# set rescale weight to 1./std of encodings
- print("### USING STD-RESCALING ###")
+ print('### USING STD-RESCALING ###')
x = super().get_input(batch, self.first_stage_key)
x = x.to(self.device)
encoder_posterior = self.encode_first_stage(x)
z = self.get_first_stage_encoding(encoder_posterior).detach()
del self.scale_factor
- self.register_buffer('scale_factor', 1. / z.flatten().std())
- print(f"setting self.scale_factor to {self.scale_factor}")
- print("### USING STD-RESCALING ###")
+ self.register_buffer('scale_factor', 1.0 / z.flatten().std())
+ print(f'setting self.scale_factor to {self.scale_factor}')
+ print('### USING STD-RESCALING ###')
- def register_schedule(self,
- given_betas=None, beta_schedule="linear", timesteps=1000,
- linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+ def register_schedule(
+ self,
+ given_betas=None,
+ beta_schedule='linear',
+ timesteps=1000,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ ):
+ super().register_schedule(
+ given_betas,
+ beta_schedule,
+ timesteps,
+ linear_start,
+ linear_end,
+ cosine_s,
+ )
self.shorten_cond_schedule = self.num_timesteps_cond > 1
if self.shorten_cond_schedule:
@@ -536,11 +755,13 @@ class LatentDiffusion(DDPM):
def instantiate_cond_stage(self, config):
if not self.cond_stage_trainable:
- if config == "__is_first_stage__":
- print("Using first stage also as cond stage.")
+ if config == '__is_first_stage__':
+ print('Using first stage also as cond stage.')
self.cond_stage_model = self.first_stage_model
- elif config == "__is_unconditional__":
- print(f"Training {self.__class__.__name__} as an unconditional model.")
+ elif config == '__is_unconditional__':
+ print(
+ f'Training {self.__class__.__name__} as an unconditional model.'
+ )
self.cond_stage_model = None
# self.be_unconditional = True
else:
@@ -555,23 +776,32 @@ class LatentDiffusion(DDPM):
try:
model = instantiate_from_config(config)
except urllib.error.URLError:
- raise SystemExit("* Couldn't load a dependency. Try running scripts/preload_models.py from an internet-conected machine.")
+ raise SystemExit(
+ "* Couldn't load a dependency. Try running scripts/preload_models.py from an internet-conected machine."
+ )
self.cond_stage_model = model
-
-
+
def instantiate_embedding_manager(self, config, embedder):
model = instantiate_from_config(config, embedder=embedder)
- if config.params.get("embedding_manager_ckpt", None): # do not load if missing OR empty string
+ if config.params.get(
+ 'embedding_manager_ckpt', None
+ ): # do not load if missing OR empty string
model.load(config.params.embedding_manager_ckpt)
-
+
return model
- def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ def _get_denoise_row_from_list(
+ self, samples, desc='', force_no_decoder_quantization=False
+ ):
denoise_row = []
for zd in tqdm(samples, desc=desc):
- denoise_row.append(self.decode_first_stage(zd.to(self.device),
- force_not_quantize=force_no_decoder_quantization))
+ denoise_row.append(
+ self.decode_first_stage(
+ zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization,
+ )
+ )
n_imgs_per_row = len(denoise_row)
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
@@ -585,13 +815,19 @@ class LatentDiffusion(DDPM):
elif isinstance(encoder_posterior, torch.Tensor):
z = encoder_posterior
else:
- raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ raise NotImplementedError(
+ f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
+ )
return self.scale_factor * z
def get_learned_conditioning(self, c):
if self.cond_stage_forward is None:
- if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
- c = self.cond_stage_model.encode(c, embedding_manager=self.embedding_manager)
+ if hasattr(self.cond_stage_model, 'encode') and callable(
+ self.cond_stage_model.encode
+ ):
+ c = self.cond_stage_model.encode(
+ c, embedding_manager=self.embedding_manager
+ )
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
else:
@@ -619,26 +855,37 @@ class LatentDiffusion(DDPM):
arr = self.meshgrid(h, w) / lower_right_corner
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
- edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ edge_dist = torch.min(
+ torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1
+ )[0]
return edge_dist
def get_weighting(self, h, w, Ly, Lx, device):
weighting = self.delta_border(h, w)
- weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
- self.split_input_params["clip_max_weight"], )
- weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+ weighting = torch.clip(
+ weighting,
+ self.split_input_params['clip_min_weight'],
+ self.split_input_params['clip_max_weight'],
+ )
+ weighting = (
+ weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+ )
- if self.split_input_params["tie_braker"]:
+ if self.split_input_params['tie_braker']:
L_weighting = self.delta_border(Ly, Lx)
- L_weighting = torch.clip(L_weighting,
- self.split_input_params["clip_min_tie_weight"],
- self.split_input_params["clip_max_tie_weight"])
+ L_weighting = torch.clip(
+ L_weighting,
+ self.split_input_params['clip_min_tie_weight'],
+ self.split_input_params['clip_max_tie_weight'],
+ )
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
weighting = weighting * L_weighting
return weighting
- def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ def get_fold_unfold(
+ self, x, kernel_size, stride, uf=1, df=1
+ ): # todo load once not every time, shorten code
"""
:param x: img of size (bs, c, h, w)
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
@@ -650,40 +897,75 @@ class LatentDiffusion(DDPM):
Lx = (w - kernel_size[1]) // stride[1] + 1
if uf == 1 and df == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ fold_params = dict(
+ kernel_size=kernel_size, dilation=1, padding=0, stride=stride
+ )
unfold = torch.nn.Unfold(**fold_params)
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
- weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+ weighting = self.get_weighting(
+ kernel_size[0], kernel_size[1], Ly, Lx, x.device
+ ).to(x.dtype)
+ normalization = fold(weighting).view(
+ 1, 1, h, w
+ ) # normalizes the overlap
+ weighting = weighting.view(
+ (1, 1, kernel_size[0], kernel_size[1], Ly * Lx)
+ )
elif uf > 1 and df == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ fold_params = dict(
+ kernel_size=kernel_size, dilation=1, padding=0, stride=stride
+ )
unfold = torch.nn.Unfold(**fold_params)
- fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
- dilation=1, padding=0,
- stride=(stride[0] * uf, stride[1] * uf))
- fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+ fold_params2 = dict(
+ kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1,
+ padding=0,
+ stride=(stride[0] * uf, stride[1] * uf),
+ )
+ fold = torch.nn.Fold(
+ output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
+ )
- weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+ weighting = self.get_weighting(
+ kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device
+ ).to(x.dtype)
+ normalization = fold(weighting).view(
+ 1, 1, h * uf, w * uf
+ ) # normalizes the overlap
+ weighting = weighting.view(
+ (1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)
+ )
elif df > 1 and uf == 1:
- fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ fold_params = dict(
+ kernel_size=kernel_size, dilation=1, padding=0, stride=stride
+ )
unfold = torch.nn.Unfold(**fold_params)
- fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
- dilation=1, padding=0,
- stride=(stride[0] // df, stride[1] // df))
- fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+ fold_params2 = dict(
+ kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1,
+ padding=0,
+ stride=(stride[0] // df, stride[1] // df),
+ )
+ fold = torch.nn.Fold(
+ output_size=(x.shape[2] // df, x.shape[3] // df),
+ **fold_params2,
+ )
- weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
- normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
- weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+ weighting = self.get_weighting(
+ kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
+ ).to(x.dtype)
+ normalization = fold(weighting).view(
+ 1, 1, h // df, w // df
+ ) # normalizes the overlap
+ weighting = weighting.view(
+ (1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
+ )
else:
raise NotImplementedError
@@ -691,8 +973,16 @@ class LatentDiffusion(DDPM):
return fold, unfold, normalization, weighting
@torch.no_grad()
- def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
- cond_key=None, return_original_cond=False, bs=None):
+ def get_input(
+ self,
+ batch,
+ k,
+ return_first_stage_outputs=False,
+ force_c_encode=False,
+ cond_key=None,
+ return_original_cond=False,
+ bs=None,
+ ):
x = super().get_input(batch, k)
if bs is not None:
x = x[:bs]
@@ -743,155 +1033,211 @@ class LatentDiffusion(DDPM):
return out
@torch.no_grad()
- def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ def decode_first_stage(
+ self, z, predict_cids=False, force_not_quantize=False
+ ):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
- z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = self.first_stage_model.quantize.get_codebook_entry(
+ z, shape=None
+ )
z = rearrange(z, 'b h w c -> b c h w').contiguous()
- z = 1. / self.scale_factor * z
+ z = 1.0 / self.scale_factor * z
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- uf = self.split_input_params["vqf"]
+ if hasattr(self, 'split_input_params'):
+ if self.split_input_params['patch_distributed_vq']:
+ ks = self.split_input_params['ks'] # eg. (128, 128)
+ stride = self.split_input_params['stride'] # eg. (64, 64)
+ uf = self.split_input_params['vqf']
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
+ print('reducing Kernel')
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
+ print('reducing stride')
- fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+ fold, unfold, normalization, weighting = self.get_fold_unfold(
+ z, ks, stride, uf=uf
+ )
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+ z = z.view(
+ (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
+ ) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
if isinstance(self.first_stage_model, VQModelInterface):
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
- force_not_quantize=predict_cids or force_not_quantize)
- for i in range(z.shape[-1])]
+ output_list = [
+ self.first_stage_model.decode(
+ z[:, :, :, :, i],
+ force_not_quantize=predict_cids
+ or force_not_quantize,
+ )
+ for i in range(z.shape[-1])
+ ]
else:
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
+ output_list = [
+ self.first_stage_model.decode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])
+ ]
- o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
+ o = torch.stack(
+ output_list, axis=-1
+ ) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ o = o.view(
+ (o.shape[0], -1, o.shape[-1])
+ ) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ return self.first_stage_model.decode(
+ z,
+ force_not_quantize=predict_cids or force_not_quantize,
+ )
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ return self.first_stage_model.decode(
+ z, force_not_quantize=predict_cids or force_not_quantize
+ )
else:
return self.first_stage_model.decode(z)
# same as above but without decorator
- def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ def differentiable_decode_first_stage(
+ self, z, predict_cids=False, force_not_quantize=False
+ ):
if predict_cids:
if z.dim() == 4:
z = torch.argmax(z.exp(), dim=1).long()
- z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = self.first_stage_model.quantize.get_codebook_entry(
+ z, shape=None
+ )
z = rearrange(z, 'b h w c -> b c h w').contiguous()
- z = 1. / self.scale_factor * z
+ z = 1.0 / self.scale_factor * z
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- uf = self.split_input_params["vqf"]
+ if hasattr(self, 'split_input_params'):
+ if self.split_input_params['patch_distributed_vq']:
+ ks = self.split_input_params['ks'] # eg. (128, 128)
+ stride = self.split_input_params['stride'] # eg. (64, 64)
+ uf = self.split_input_params['vqf']
bs, nc, h, w = z.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
+ print('reducing Kernel')
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
+ print('reducing stride')
- fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+ fold, unfold, normalization, weighting = self.get_fold_unfold(
+ z, ks, stride, uf=uf
+ )
z = unfold(z) # (bn, nc * prod(**ks), L)
# 1. Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+ z = z.view(
+ (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
+ ) # (bn, nc, ks[0], ks[1], L )
# 2. apply model loop over last dim
- if isinstance(self.first_stage_model, VQModelInterface):
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
- force_not_quantize=predict_cids or force_not_quantize)
- for i in range(z.shape[-1])]
+ if isinstance(self.first_stage_model, VQModelInterface):
+ output_list = [
+ self.first_stage_model.decode(
+ z[:, :, :, :, i],
+ force_not_quantize=predict_cids
+ or force_not_quantize,
+ )
+ for i in range(z.shape[-1])
+ ]
else:
- output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
+ output_list = [
+ self.first_stage_model.decode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])
+ ]
- o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
+ o = torch.stack(
+ output_list, axis=-1
+ ) # # (bn, nc, ks[0], ks[1], L)
o = o * weighting
# Reverse 1. reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ o = o.view(
+ (o.shape[0], -1, o.shape[-1])
+ ) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization # norm is shape (1, 1, h, w)
return decoded
else:
if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ return self.first_stage_model.decode(
+ z,
+ force_not_quantize=predict_cids or force_not_quantize,
+ )
else:
return self.first_stage_model.decode(z)
else:
if isinstance(self.first_stage_model, VQModelInterface):
- return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ return self.first_stage_model.decode(
+ z, force_not_quantize=predict_cids or force_not_quantize
+ )
else:
return self.first_stage_model.decode(z)
@torch.no_grad()
def encode_first_stage(self, x):
- if hasattr(self, "split_input_params"):
- if self.split_input_params["patch_distributed_vq"]:
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
- df = self.split_input_params["vqf"]
+ if hasattr(self, 'split_input_params'):
+ if self.split_input_params['patch_distributed_vq']:
+ ks = self.split_input_params['ks'] # eg. (128, 128)
+ stride = self.split_input_params['stride'] # eg. (64, 64)
+ df = self.split_input_params['vqf']
self.split_input_params['original_image_size'] = x.shape[-2:]
bs, nc, h, w = x.shape
if ks[0] > h or ks[1] > w:
ks = (min(ks[0], h), min(ks[1], w))
- print("reducing Kernel")
+ print('reducing Kernel')
if stride[0] > h or stride[1] > w:
stride = (min(stride[0], h), min(stride[1], w))
- print("reducing stride")
+ print('reducing stride')
- fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+ fold, unfold, normalization, weighting = self.get_fold_unfold(
+ x, ks, stride, df=df
+ )
z = unfold(x) # (bn, nc * prod(**ks), L)
# Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+ z = z.view(
+ (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
+ ) # (bn, nc, ks[0], ks[1], L )
- output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
- for i in range(z.shape[-1])]
+ output_list = [
+ self.first_stage_model.encode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])
+ ]
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ o = o.view(
+ (o.shape[0], -1, o.shape[-1])
+ ) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
decoded = fold(o)
decoded = decoded / normalization
@@ -908,18 +1254,24 @@ class LatentDiffusion(DDPM):
return loss
def forward(self, x, c, *args, **kwargs):
- t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ t = torch.randint(
+ 0, self.num_timesteps, (x.shape[0],), device=self.device
+ ).long()
if self.model.conditioning_key is not None:
assert c is not None
if self.cond_stage_trainable:
c = self.get_learned_conditioning(c)
if self.shorten_cond_schedule: # TODO: drop this option
tc = self.cond_ids[t].to(self.device)
- c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ c = self.q_sample(
+ x_start=c, t=tc, noise=torch.randn_like(c.float())
+ )
return self.p_losses(x, c, t, *args, **kwargs)
- def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
+ def _rescale_annotations(
+ self, bboxes, crop_coordinates
+ ): # TODO: move to dataset
def rescale_bbox(bbox):
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
@@ -937,42 +1289,65 @@ class LatentDiffusion(DDPM):
else:
if not isinstance(cond, list):
cond = [cond]
- key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ key = (
+ 'c_concat'
+ if self.model.conditioning_key == 'concat'
+ else 'c_crossattn'
+ )
cond = {key: cond}
- if hasattr(self, "split_input_params"):
- assert len(cond) == 1 # todo can only deal with one conditioning atm
- assert not return_ids
- ks = self.split_input_params["ks"] # eg. (128, 128)
- stride = self.split_input_params["stride"] # eg. (64, 64)
+ if hasattr(self, 'split_input_params'):
+ assert (
+ len(cond) == 1
+ ) # todo can only deal with one conditioning atm
+ assert not return_ids
+ ks = self.split_input_params['ks'] # eg. (128, 128)
+ stride = self.split_input_params['stride'] # eg. (64, 64)
h, w = x_noisy.shape[-2:]
- fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+ fold, unfold, normalization, weighting = self.get_fold_unfold(
+ x_noisy, ks, stride
+ )
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
# Reshape to img shape
- z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+ z = z.view(
+ (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
+ ) # (bn, nc, ks[0], ks[1], L )
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
- if self.cond_stage_key in ["image", "LR_image", "segmentation",
- 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
+ if (
+ self.cond_stage_key
+ in ['image', 'LR_image', 'segmentation', 'bbox_img']
+ and self.model.conditioning_key
+ ): # todo check for completeness
c_key = next(iter(cond.keys())) # get key
c = next(iter(cond.values())) # get value
- assert (len(c) == 1) # todo extend to list with more than one elem
+ assert (
+ len(c) == 1
+ ) # todo extend to list with more than one elem
c = c[0] # get element
c = unfold(c)
- c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+ c = c.view(
+ (c.shape[0], -1, ks[0], ks[1], c.shape[-1])
+ ) # (bn, nc, ks[0], ks[1], L )
- cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+ cond_list = [
+ {c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])
+ ]
elif self.cond_stage_key == 'coordinates_bbox':
- assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+ assert (
+ 'original_image_size' in self.split_input_params
+ ), 'BoudingBoxRescaling is missing original_image_size'
# assuming padding of unfold is always 0 and its dilation is always 1
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
- full_img_h, full_img_w = self.split_input_params['original_image_size']
+ full_img_h, full_img_w = self.split_input_params[
+ 'original_image_size'
+ ]
# as we are operating on latents, we need the factor from the original image size to the
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
num_downs = self.first_stage_model.encoder.num_resolutions - 1
@@ -980,47 +1355,84 @@ class LatentDiffusion(DDPM):
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
# need to rescale the tl patch coordinates to be in between (0,1)
- tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
- rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
- for patch_nr in range(z.shape[-1])]
+ tl_patch_coordinates = [
+ (
+ rescale_latent
+ * stride[0]
+ * (patch_nr % n_patches_per_row)
+ / full_img_w,
+ rescale_latent
+ * stride[1]
+ * (patch_nr // n_patches_per_row)
+ / full_img_h,
+ )
+ for patch_nr in range(z.shape[-1])
+ ]
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
- patch_limits = [(x_tl, y_tl,
- rescale_latent * ks[0] / full_img_w,
- rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+ patch_limits = [
+ (
+ x_tl,
+ y_tl,
+ rescale_latent * ks[0] / full_img_w,
+ rescale_latent * ks[1] / full_img_h,
+ )
+ for x_tl, y_tl in tl_patch_coordinates
+ ]
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
# tokenize crop coordinates for the bounding boxes of the respective patches
- patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
- for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
+ patch_limits_tknzd = [
+ torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[
+ None
+ ].to(self.device)
+ for bbox in patch_limits
+ ] # list of length l with tensors of shape (1, 2)
print(patch_limits_tknzd[0].shape)
# cut tknzd crop position from conditioning
- assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+ assert isinstance(
+ cond, dict
+ ), 'cond must be dict to be fed into model'
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
print(cut_cond.shape)
- adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+ adapted_cond = torch.stack(
+ [
+ torch.cat([cut_cond, p], dim=1)
+ for p in patch_limits_tknzd
+ ]
+ )
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
print(adapted_cond.shape)
adapted_cond = self.get_learned_conditioning(adapted_cond)
print(adapted_cond.shape)
- adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+ adapted_cond = rearrange(
+ adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]
+ )
print(adapted_cond.shape)
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
else:
- cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
+ cond_list = [
+ cond for i in range(z.shape[-1])
+ ] # Todo make this more efficient
# apply model by loop over crops
- output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
- assert not isinstance(output_list[0],
- tuple) # todo cant deal with multiple model outputs check this never happens
+ output_list = [
+ self.model(z_list[i], t, **cond_list[i])
+ for i in range(z.shape[-1])
+ ]
+ assert not isinstance(
+ output_list[0], tuple
+ ) # todo cant deal with multiple model outputs check this never happens
o = torch.stack(output_list, axis=-1)
o = o * weighting
# Reverse reshape to img shape
- o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ o = o.view(
+ (o.shape[0], -1, o.shape[-1])
+ ) # (bn, nc * ks[0] * ks[1], L)
# stitch crops together
x_recon = fold(o) / normalization
@@ -1033,8 +1445,11 @@ class LatentDiffusion(DDPM):
return x_recon
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
- return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
- extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape)
+ * x_t
+ - pred_xstart
+ ) / extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def _prior_bpd(self, x_start):
"""
@@ -1045,9 +1460,13 @@ class LatentDiffusion(DDPM):
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
- t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ t = torch.tensor(
+ [self.num_timesteps - 1] * batch_size, device=x_start.device
+ )
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
- kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ kl_prior = normal_kl(
+ mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
+ )
return mean_flat(kl_prior) / np.log(2.0)
def p_losses(self, x_start, cond, t, noise=None):
@@ -1058,14 +1477,16 @@ class LatentDiffusion(DDPM):
loss_dict = {}
prefix = 'train' if self.training else 'val'
- if self.parameterization == "x0":
+ if self.parameterization == 'x0':
target = x_start
- elif self.parameterization == "eps":
+ elif self.parameterization == 'eps':
target = noise
else:
raise NotImplementedError()
- loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_simple = self.get_loss(model_output, target, mean=False).mean(
+ [1, 2, 3]
+ )
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
logvar_t = self.logvar[t].to(self.device)
@@ -1077,65 +1498,117 @@ class LatentDiffusion(DDPM):
loss = self.l_simple_weight * loss.mean()
- loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(
+ dim=(1, 2, 3)
+ )
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
- loss += (self.original_elbo_weight * loss_vlb)
+ loss += self.original_elbo_weight * loss_vlb
loss_dict.update({f'{prefix}/loss': loss})
if self.embedding_reg_weight > 0:
- loss_embedding_reg = self.embedding_manager.embedding_to_coarse_loss().mean()
+ loss_embedding_reg = (
+ self.embedding_manager.embedding_to_coarse_loss().mean()
+ )
loss_dict.update({f'{prefix}/loss_emb_reg': loss_embedding_reg})
- loss += (self.embedding_reg_weight * loss_embedding_reg)
+ loss += self.embedding_reg_weight * loss_embedding_reg
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
- def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
- return_x0=False, score_corrector=None, corrector_kwargs=None):
+ def p_mean_variance(
+ self,
+ x,
+ c,
+ t,
+ clip_denoised: bool,
+ return_codebook_ids=False,
+ quantize_denoised=False,
+ return_x0=False,
+ score_corrector=None,
+ corrector_kwargs=None,
+ ):
t_in = t
- model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+ model_out = self.apply_model(
+ x, t_in, c, return_ids=return_codebook_ids
+ )
if score_corrector is not None:
- assert self.parameterization == "eps"
- model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+ assert self.parameterization == 'eps'
+ model_out = score_corrector.modify_score(
+ self, model_out, x, t, c, **corrector_kwargs
+ )
if return_codebook_ids:
model_out, logits = model_out
- if self.parameterization == "eps":
+ if self.parameterization == 'eps':
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
- elif self.parameterization == "x0":
+ elif self.parameterization == 'x0':
x_recon = model_out
else:
raise NotImplementedError()
if clip_denoised:
- x_recon.clamp_(-1., 1.)
+ x_recon.clamp_(-1.0, 1.0)
if quantize_denoised:
- x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
- model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(
+ x_recon
+ )
+ (
+ model_mean,
+ posterior_variance,
+ posterior_log_variance,
+ ) = self.q_posterior(x_start=x_recon, x_t=x, t=t)
if return_codebook_ids:
- return model_mean, posterior_variance, posterior_log_variance, logits
+ return (
+ model_mean,
+ posterior_variance,
+ posterior_log_variance,
+ logits,
+ )
elif return_x0:
- return model_mean, posterior_variance, posterior_log_variance, x_recon
+ return (
+ model_mean,
+ posterior_variance,
+ posterior_log_variance,
+ x_recon,
+ )
else:
return model_mean, posterior_variance, posterior_log_variance
@torch.no_grad()
- def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
- return_codebook_ids=False, quantize_denoised=False, return_x0=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ def p_sample(
+ self,
+ x,
+ c,
+ t,
+ clip_denoised=False,
+ repeat_noise=False,
+ return_codebook_ids=False,
+ quantize_denoised=False,
+ return_x0=False,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ ):
b, *_, device = *x.shape, x.device
- outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
- return_codebook_ids=return_codebook_ids,
- quantize_denoised=quantize_denoised,
- return_x0=return_x0,
- score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ outputs = self.p_mean_variance(
+ x=x,
+ c=c,
+ t=t,
+ clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ )
if return_codebook_ids:
- raise DeprecationWarning("Support dropped.")
+ raise DeprecationWarning('Support dropped.')
model_mean, _, model_log_variance, logits = outputs
elif return_x0:
model_mean, _, model_log_variance, x0 = outputs
@@ -1143,23 +1616,49 @@ class LatentDiffusion(DDPM):
model_mean, _, model_log_variance = outputs
noise = noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
+ if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# no noise when t == 0
- nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ nonzero_mask = (1 - (t == 0).float()).reshape(
+ b, *((1,) * (len(x.shape) - 1))
+ )
if return_codebook_ids:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ return model_mean + nonzero_mask * (
+ 0.5 * model_log_variance
+ ).exp() * noise, logits.argmax(dim=1)
if return_x0:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ return (
+ model_mean
+ + nonzero_mask * (0.5 * model_log_variance).exp() * noise,
+ x0,
+ )
else:
- return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+ return (
+ model_mean
+ + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+ )
@torch.no_grad()
- def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
- img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
- score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
- log_every_t=None):
+ def progressive_denoising(
+ self,
+ cond,
+ shape,
+ verbose=True,
+ callback=None,
+ quantize_denoised=False,
+ img_callback=None,
+ mask=None,
+ x0=None,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ batch_size=None,
+ x_T=None,
+ start_T=None,
+ log_every_t=None,
+ ):
if not log_every_t:
log_every_t = self.log_every_t
timesteps = self.num_timesteps
@@ -1175,16 +1674,30 @@ class LatentDiffusion(DDPM):
intermediates = []
if cond is not None:
if isinstance(cond, dict):
- cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
- list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ cond = {
+ key: cond[key][:batch_size]
+ if not isinstance(cond[key], list)
+ else list(map(lambda x: x[:batch_size], cond[key]))
+ for key in cond
+ }
else:
- cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ cond = (
+ [c[:batch_size] for c in cond]
+ if isinstance(cond, list)
+ else cond[:batch_size]
+ )
if start_T is not None:
timesteps = min(timesteps, start_T)
- iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
- total=timesteps) if verbose else reversed(
- range(0, timesteps))
+ iterator = (
+ tqdm(
+ reversed(range(0, timesteps)),
+ desc='Progressive Generation',
+ total=timesteps,
+ )
+ if verbose
+ else reversed(range(0, timesteps))
+ )
if type(temperature) == float:
temperature = [temperature] * timesteps
@@ -1193,29 +1706,52 @@ class LatentDiffusion(DDPM):
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
- cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+ cond = self.q_sample(
+ x_start=cond, t=tc, noise=torch.randn_like(cond)
+ )
- img, x0_partial = self.p_sample(img, cond, ts,
- clip_denoised=self.clip_denoised,
- quantize_denoised=quantize_denoised, return_x0=True,
- temperature=temperature[i], noise_dropout=noise_dropout,
- score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ img, x0_partial = self.p_sample(
+ img,
+ cond,
+ ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised,
+ return_x0=True,
+ temperature=temperature[i],
+ noise_dropout=noise_dropout,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ )
if mask is not None:
assert x0 is not None
img_orig = self.q_sample(x0, ts)
- img = img_orig * mask + (1. - mask) * img
+ img = img_orig * mask + (1.0 - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(x0_partial)
- if callback: callback(i)
- if img_callback: img_callback(img, i)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(img, i)
return img, intermediates
@torch.no_grad()
- def p_sample_loop(self, cond, shape, return_intermediates=False,
- x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, start_T=None,
- log_every_t=None):
+ def p_sample_loop(
+ self,
+ cond,
+ shape,
+ return_intermediates=False,
+ x_T=None,
+ verbose=True,
+ callback=None,
+ timesteps=None,
+ quantize_denoised=False,
+ mask=None,
+ x0=None,
+ img_callback=None,
+ start_T=None,
+ log_every_t=None,
+ ):
if not log_every_t:
log_every_t = self.log_every_t
@@ -1232,100 +1768,170 @@ class LatentDiffusion(DDPM):
if start_T is not None:
timesteps = min(timesteps, start_T)
- iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
- range(0, timesteps))
+ iterator = (
+ tqdm(
+ reversed(range(0, timesteps)),
+ desc='Sampling t',
+ total=timesteps,
+ )
+ if verbose
+ else reversed(range(0, timesteps))
+ )
if mask is not None:
assert x0 is not None
- assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+ assert (
+ x0.shape[2:3] == mask.shape[2:3]
+ ) # spatial size has to match
for i in iterator:
ts = torch.full((b,), i, device=device, dtype=torch.long)
if self.shorten_cond_schedule:
assert self.model.conditioning_key != 'hybrid'
tc = self.cond_ids[ts].to(cond.device)
- cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+ cond = self.q_sample(
+ x_start=cond, t=tc, noise=torch.randn_like(cond)
+ )
- img = self.p_sample(img, cond, ts,
- clip_denoised=self.clip_denoised,
- quantize_denoised=quantize_denoised)
+ img = self.p_sample(
+ img,
+ cond,
+ ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised,
+ )
if mask is not None:
img_orig = self.q_sample(x0, ts)
- img = img_orig * mask + (1. - mask) * img
+ img = img_orig * mask + (1.0 - mask) * img
if i % log_every_t == 0 or i == timesteps - 1:
intermediates.append(img)
- if callback: callback(i)
- if img_callback: img_callback(img, i)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(img, i)
if return_intermediates:
return img, intermediates
return img
@torch.no_grad()
- def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
- verbose=True, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, shape=None,**kwargs):
+ def sample(
+ self,
+ cond,
+ batch_size=16,
+ return_intermediates=False,
+ x_T=None,
+ verbose=True,
+ timesteps=None,
+ quantize_denoised=False,
+ mask=None,
+ x0=None,
+ shape=None,
+ **kwargs,
+ ):
if shape is None:
- shape = (batch_size, self.channels, self.image_size, self.image_size)
+ shape = (
+ batch_size,
+ self.channels,
+ self.image_size,
+ self.image_size,
+ )
if cond is not None:
if isinstance(cond, dict):
- cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
- list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ cond = {
+ key: cond[key][:batch_size]
+ if not isinstance(cond[key], list)
+ else list(map(lambda x: x[:batch_size], cond[key]))
+ for key in cond
+ }
else:
- cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
- return self.p_sample_loop(cond,
- shape,
- return_intermediates=return_intermediates, x_T=x_T,
- verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
- mask=mask, x0=x0)
+ cond = (
+ [c[:batch_size] for c in cond]
+ if isinstance(cond, list)
+ else cond[:batch_size]
+ )
+ return self.p_sample_loop(
+ cond,
+ shape,
+ return_intermediates=return_intermediates,
+ x_T=x_T,
+ verbose=verbose,
+ timesteps=timesteps,
+ quantize_denoised=quantize_denoised,
+ mask=mask,
+ x0=x0,
+ )
@torch.no_grad()
- def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+ def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
if ddim:
ddim_sampler = DDIMSampler(self)
shape = (self.channels, self.image_size, self.image_size)
- samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
- shape,cond,verbose=False,**kwargs)
+ samples, intermediates = ddim_sampler.sample(
+ ddim_steps, batch_size, shape, cond, verbose=False, **kwargs
+ )
else:
- samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
- return_intermediates=True,**kwargs)
+ samples, intermediates = self.sample(
+ cond=cond,
+ batch_size=batch_size,
+ return_intermediates=True,
+ **kwargs,
+ )
return samples, intermediates
@torch.no_grad()
- def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
- quantize_denoised=True, inpaint=False, plot_denoise_rows=False, plot_progressive_rows=False,
- plot_diffusion_rows=False, **kwargs):
+ def log_images(
+ self,
+ batch,
+ N=8,
+ n_row=4,
+ sample=True,
+ ddim_steps=200,
+ ddim_eta=1.0,
+ return_keys=None,
+ quantize_denoised=True,
+ inpaint=False,
+ plot_denoise_rows=False,
+ plot_progressive_rows=False,
+ plot_diffusion_rows=False,
+ **kwargs,
+ ):
use_ddim = ddim_steps is not None
log = dict()
- z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
- return_first_stage_outputs=True,
- force_c_encode=True,
- return_original_cond=True,
- bs=N)
+ z, c, x, xrec, xc = self.get_input(
+ batch,
+ self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ bs=N,
+ )
N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row)
- log["inputs"] = x
- log["reconstruction"] = xrec
+ log['inputs'] = x
+ log['reconstruction'] = xrec
if self.model.conditioning_key is not None:
- if hasattr(self.cond_stage_model, "decode"):
+ if hasattr(self.cond_stage_model, 'decode'):
xc = self.cond_stage_model.decode(c)
- log["conditioning"] = xc
- elif self.cond_stage_key in ["caption"]:
- xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
- log["conditioning"] = xc
+ log['conditioning'] = xc
+ elif self.cond_stage_key in ['caption']:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch['caption'])
+ log['conditioning'] = xc
elif self.cond_stage_key == 'class_label':
- xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+ xc = log_txt_as_img(
+ (x.shape[2], x.shape[3]), batch['human_label']
+ )
log['conditioning'] = xc
elif isimage(xc):
- log["conditioning"] = xc
+ log['conditioning'] = xc
if ismap(xc):
- log["original_conditioning"] = self.to_rgb(xc)
+ log['original_conditioning'] = self.to_rgb(xc)
if plot_diffusion_rows:
# get diffusion row
@@ -1339,75 +1945,114 @@ class LatentDiffusion(DDPM):
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
- diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_row = torch.stack(
+ diffusion_row
+ ) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
- diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
- diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
- log["diffusion_row"] = diffusion_grid
+ diffusion_grid = rearrange(
+ diffusion_grid, 'b n c h w -> (b n) c h w'
+ )
+ diffusion_grid = make_grid(
+ diffusion_grid, nrow=diffusion_row.shape[0]
+ )
+ log['diffusion_row'] = diffusion_grid
if sample:
# get denoise row
- with self.ema_scope("Plotting"):
- samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
- ddim_steps=ddim_steps,eta=ddim_eta)
+ with self.ema_scope('Plotting'):
+ samples, z_denoise_row = self.sample_log(
+ cond=c,
+ batch_size=N,
+ ddim=use_ddim,
+ ddim_steps=ddim_steps,
+ eta=ddim_eta,
+ )
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
x_samples = self.decode_first_stage(samples)
- log["samples"] = x_samples
+ log['samples'] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
- log["denoise_row"] = denoise_grid
-
- uc = self.get_learned_conditioning(len(c) * [""])
- sample_scaled, _ = self.sample_log(cond=c,
- batch_size=N,
- ddim=use_ddim,
- ddim_steps=ddim_steps,
- eta=ddim_eta,
- unconditional_guidance_scale=5.0,
- unconditional_conditioning=uc)
- log["samples_scaled"] = self.decode_first_stage(sample_scaled)
+ log['denoise_row'] = denoise_grid
- if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
- self.first_stage_model, IdentityFirstStage):
+ uc = self.get_learned_conditioning(len(c) * [''])
+ sample_scaled, _ = self.sample_log(
+ cond=c,
+ batch_size=N,
+ ddim=use_ddim,
+ ddim_steps=ddim_steps,
+ eta=ddim_eta,
+ unconditional_guidance_scale=5.0,
+ unconditional_conditioning=uc,
+ )
+ log['samples_scaled'] = self.decode_first_stage(sample_scaled)
+
+ if (
+ quantize_denoised
+ and not isinstance(self.first_stage_model, AutoencoderKL)
+ and not isinstance(self.first_stage_model, IdentityFirstStage)
+ ):
# also display when quantizing x0 while sampling
- with self.ema_scope("Plotting Quantized Denoised"):
- samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
- ddim_steps=ddim_steps,eta=ddim_eta,
- quantize_denoised=True)
+ with self.ema_scope('Plotting Quantized Denoised'):
+ samples, z_denoise_row = self.sample_log(
+ cond=c,
+ batch_size=N,
+ ddim=use_ddim,
+ ddim_steps=ddim_steps,
+ eta=ddim_eta,
+ quantize_denoised=True,
+ )
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
# quantize_denoised=True)
x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_x0_quantized"] = x_samples
+ log['samples_x0_quantized'] = x_samples
if inpaint:
# make a simple center square
b, h, w = z.shape[0], z.shape[2], z.shape[3]
mask = torch.ones(N, h, w).to(self.device)
# zeros will be filled in
- mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask[:, h // 4 : 3 * h // 4, w // 4 : 3 * w // 4] = 0.0
mask = mask[:, None, ...]
- with self.ema_scope("Plotting Inpaint"):
+ with self.ema_scope('Plotting Inpaint'):
- samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
- ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ samples, _ = self.sample_log(
+ cond=c,
+ batch_size=N,
+ ddim=use_ddim,
+ eta=ddim_eta,
+ ddim_steps=ddim_steps,
+ x0=z[:N],
+ mask=mask,
+ )
x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_inpainting"] = x_samples
- log["mask"] = mask
+ log['samples_inpainting'] = x_samples
+ log['mask'] = mask
# outpaint
- with self.ema_scope("Plotting Outpaint"):
- samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
- ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ with self.ema_scope('Plotting Outpaint'):
+ samples, _ = self.sample_log(
+ cond=c,
+ batch_size=N,
+ ddim=use_ddim,
+ eta=ddim_eta,
+ ddim_steps=ddim_steps,
+ x0=z[:N],
+ mask=mask,
+ )
x_samples = self.decode_first_stage(samples.to(self.device))
- log["samples_outpainting"] = x_samples
+ log['samples_outpainting'] = x_samples
if plot_progressive_rows:
- with self.ema_scope("Plotting Progressives"):
- img, progressives = self.progressive_denoising(c,
- shape=(self.channels, self.image_size, self.image_size),
- batch_size=N)
- prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
- log["progressive_row"] = prog_row
+ with self.ema_scope('Plotting Progressives'):
+ img, progressives = self.progressive_denoising(
+ c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N,
+ )
+ prog_row = self._get_denoise_row_from_list(
+ progressives, desc='Progressive Generation'
+ )
+ log['progressive_row'] = prog_row
if return_keys:
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
@@ -1425,7 +2070,9 @@ class LatentDiffusion(DDPM):
else:
params = list(self.model.parameters())
if self.cond_stage_trainable:
- print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ print(
+ f'{self.__class__.__name__}: Also optimizing conditioner params!'
+ )
params = params + list(self.cond_stage_model.parameters())
if self.learn_logvar:
print('Diffusion model optimizing logvar')
@@ -1435,34 +2082,44 @@ class LatentDiffusion(DDPM):
assert 'target' in self.scheduler_config
scheduler = instantiate_from_config(self.scheduler_config)
- print("Setting up LambdaLR scheduler...")
+ print('Setting up LambdaLR scheduler...')
scheduler = [
{
'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
'interval': 'step',
- 'frequency': 1
- }]
+ 'frequency': 1,
+ }
+ ]
return [opt], scheduler
return opt
@torch.no_grad()
def to_rgb(self, x):
x = x.float()
- if not hasattr(self, "colorize"):
+ if not hasattr(self, 'colorize'):
self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
x = nn.functional.conv2d(x, weight=self.colorize)
- x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
return x
@rank_zero_only
def on_save_checkpoint(self, checkpoint):
checkpoint.clear()
-
+
if os.path.isdir(self.trainer.checkpoint_callback.dirpath):
- self.embedding_manager.save(os.path.join(self.trainer.checkpoint_callback.dirpath, "embeddings.pt"))
+ self.embedding_manager.save(
+ os.path.join(
+ self.trainer.checkpoint_callback.dirpath, 'embeddings.pt'
+ )
+ )
if (self.global_step - self.emb_ckpt_counter) > 500:
- self.embedding_manager.save(os.path.join(self.trainer.checkpoint_callback.dirpath, f"embeddings_gs-{self.global_step}.pt"))
+ self.embedding_manager.save(
+ os.path.join(
+ self.trainer.checkpoint_callback.dirpath,
+ f'embeddings_gs-{self.global_step}.pt',
+ )
+ )
self.emb_ckpt_counter += 500
@@ -1472,7 +2129,13 @@ class DiffusionWrapper(pl.LightningModule):
super().__init__()
self.diffusion_model = instantiate_from_config(diff_model_config)
self.conditioning_key = conditioning_key
- assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+ assert self.conditioning_key in [
+ None,
+ 'concat',
+ 'crossattn',
+ 'hybrid',
+ 'adm',
+ ]
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
if self.conditioning_key is None:
@@ -1499,7 +2162,9 @@ class DiffusionWrapper(pl.LightningModule):
class Layout2ImgDiffusion(LatentDiffusion):
# TODO: move all layout-specific hacks to this class
def __init__(self, cond_stage_key, *args, **kwargs):
- assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+ assert (
+ cond_stage_key == 'coordinates_bbox'
+ ), 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
def log_images(self, batch, N=8, *args, **kwargs):
@@ -1510,9 +2175,13 @@ class Layout2ImgDiffusion(LatentDiffusion):
mapper = dset.conditional_builders[self.cond_stage_key]
bbox_imgs = []
- map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+ map_fn = lambda catno: dset.get_textual_label(
+ dset.get_category_id(catno)
+ )
for tknzd_bbox in batch[self.cond_stage_key][:N]:
- bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+ bboximg = mapper.plot(
+ tknzd_bbox.detach().cpu(), map_fn, (256, 256)
+ )
bbox_imgs.append(bboximg)
cond_img = torch.stack(bbox_imgs, dim=0)
diff --git a/ldm/models/diffusion/ksampler.py b/ldm/models/diffusion/ksampler.py
index 62912d1a07..1da81eee5a 100644
--- a/ldm/models/diffusion/ksampler.py
+++ b/ldm/models/diffusion/ksampler.py
@@ -1,8 +1,9 @@
-'''wrapper around part of Katherine Crowson's k-diffusion library, making it call compatible with other Samplers'''
+"""wrapper around part of Katherine Crowson's k-diffusion library, making it call compatible with other Samplers"""
import k_diffusion as K
import torch
import torch.nn as nn
+
class CFGDenoiser(nn.Module):
def __init__(self, model):
super().__init__()
@@ -15,8 +16,9 @@ class CFGDenoiser(nn.Module):
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
return uncond + (cond - uncond) * cond_scale
+
class KSampler(object):
- def __init__(self, model, schedule="lms", device="cuda", **kwargs):
+ def __init__(self, model, schedule='lms', device='cuda', **kwargs):
super().__init__()
self.model = K.external.CompVisDenoiser(model)
self.schedule = schedule
@@ -26,44 +28,57 @@ class KSampler(object):
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigma] * 2)
cond_in = torch.cat([uncond, cond])
- uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+ uncond, cond = self.inner_model(
+ x_in, sigma_in, cond=cond_in
+ ).chunk(2)
return uncond + (cond - uncond) * cond_scale
-
# most of these arguments are ignored and are only present for compatibility with
# other samples
@torch.no_grad()
- def sample(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
+ def sample(
+ self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.0,
+ mask=None,
+ x0=None,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ **kwargs,
+ ):
sigmas = self.model.get_sigmas(S)
if x_T:
x = x_T
else:
- x = torch.randn([batch_size, *shape], device=self.device) * sigmas[0] # for GPU draw
+ x = (
+ torch.randn([batch_size, *shape], device=self.device)
+ * sigmas[0]
+ ) # for GPU draw
model_wrap_cfg = CFGDenoiser(self.model)
- extra_args = {'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}
- return (K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas, extra_args=extra_args),
- None)
+ extra_args = {
+ 'cond': conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': unconditional_guidance_scale,
+ }
+ return (
+ K.sampling.__dict__[f'sample_{self.schedule}'](
+ model_wrap_cfg, x, sigmas, extra_args=extra_args
+ ),
+ None,
+ )
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
index 5eafe1d7ce..7b9dc4706b 100644
--- a/ldm/models/diffusion/plms.py
+++ b/ldm/models/diffusion/plms.py
@@ -5,11 +5,15 @@ import numpy as np
from tqdm import tqdm
from functools import partial
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.modules.diffusionmodules.util import (
+ make_ddim_sampling_parameters,
+ make_ddim_timesteps,
+ noise_like,
+)
class PLMSSampler(object):
- def __init__(self, model, schedule="linear", device="cuda", **kwargs):
+ def __init__(self, model, schedule='linear', device='cuda', **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
@@ -23,103 +27,172 @@ class PLMSSampler(object):
setattr(self, name, attr)
- def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ def make_schedule(
+ self,
+ ddim_num_steps,
+ ddim_discretize='uniform',
+ ddim_eta=0.0,
+ verbose=True,
+ ):
if ddim_eta != 0:
raise ValueError('ddim_eta must be 0 for PLMS')
- self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
- num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ self.ddim_timesteps = make_ddim_timesteps(
+ ddim_discr_method=ddim_discretize,
+ num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,
+ verbose=verbose,
+ )
alphas_cumprod = self.model.alphas_cumprod
- assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
- to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+ assert (
+ alphas_cumprod.shape[0] == self.ddpm_num_timesteps
+ ), 'alphas have to be defined for each timestep'
+ to_torch = (
+ lambda x: x.clone()
+ .detach()
+ .to(torch.float32)
+ .to(self.model.device)
+ )
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
- self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+ self.register_buffer(
+ 'alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)
+ )
# calculations for diffusion q(x_t | x_{t-1}) and others
- self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
- self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
- self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+ self.register_buffer(
+ 'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))
+ )
+ self.register_buffer(
+ 'sqrt_one_minus_alphas_cumprod',
+ to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
+ )
+ self.register_buffer(
+ 'log_one_minus_alphas_cumprod',
+ to_torch(np.log(1.0 - alphas_cumprod.cpu())),
+ )
+ self.register_buffer(
+ 'sqrt_recip_alphas_cumprod',
+ to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())),
+ )
+ self.register_buffer(
+ 'sqrt_recipm1_alphas_cumprod',
+ to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
+ )
# ddim sampling parameters
- ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
- ddim_timesteps=self.ddim_timesteps,
- eta=ddim_eta,verbose=verbose)
+ (
+ ddim_sigmas,
+ ddim_alphas,
+ ddim_alphas_prev,
+ ) = make_ddim_sampling_parameters(
+ alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,
+ verbose=verbose,
+ )
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
- self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ self.register_buffer(
+ 'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
+ )
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
- (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
- 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
- self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+ (1 - self.alphas_cumprod_prev)
+ / (1 - self.alphas_cumprod)
+ * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
+ )
+ self.register_buffer(
+ 'ddim_sigmas_for_original_num_steps',
+ sigmas_for_original_sampling_steps,
+ )
@torch.no_grad()
- def sample(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
+ def sample(
+ self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.0,
+ mask=None,
+ x0=None,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ **kwargs,
+ ):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ print(
+ f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'
+ )
else:
if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+ print(
+ f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'
+ )
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
-# print(f'Data shape for PLMS sampling is {size}')
+ # print(f'Data shape for PLMS sampling is {size}')
- samples, intermediates = self.plms_sampling(conditioning, size,
- callback=callback,
- img_callback=img_callback,
- quantize_denoised=quantize_x0,
- mask=mask, x0=x0,
- ddim_use_original_steps=False,
- noise_dropout=noise_dropout,
- temperature=temperature,
- score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- x_T=x_T,
- log_every_t=log_every_t,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- )
+ samples, intermediates = self.plms_sampling(
+ conditioning,
+ size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask,
+ x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ )
return samples, intermediates
@torch.no_grad()
- def plms_sampling(self, cond, shape,
- x_T=None, ddim_use_original_steps=False,
- callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, log_every_t=100,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None,):
+ def plms_sampling(
+ self,
+ cond,
+ shape,
+ x_T=None,
+ ddim_use_original_steps=False,
+ callback=None,
+ timesteps=None,
+ quantize_denoised=False,
+ mask=None,
+ x0=None,
+ img_callback=None,
+ log_every_t=100,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ ):
device = self.model.betas.device
b = shape[0]
if x_T is None:
@@ -128,42 +201,81 @@ class PLMSSampler(object):
img = x_T
if timesteps is None:
- timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ timesteps = (
+ self.ddpm_num_timesteps
+ if ddim_use_original_steps
+ else self.ddim_timesteps
+ )
elif timesteps is not None and not ddim_use_original_steps:
- subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ subset_end = (
+ int(
+ min(timesteps / self.ddim_timesteps.shape[0], 1)
+ * self.ddim_timesteps.shape[0]
+ )
+ - 1
+ )
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
- time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
- total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-# print(f"Running PLMS Sampling with {total_steps} timesteps")
+ time_range = (
+ list(reversed(range(0, timesteps)))
+ if ddim_use_original_steps
+ else np.flip(timesteps)
+ )
+ total_steps = (
+ timesteps if ddim_use_original_steps else timesteps.shape[0]
+ )
+ # print(f"Running PLMS Sampling with {total_steps} timesteps")
- iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps, dynamic_ncols=True)
+ iterator = tqdm(
+ time_range,
+ desc='PLMS Sampler',
+ total=total_steps,
+ dynamic_ncols=True,
+ )
old_eps = []
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((b,), step, device=device, dtype=torch.long)
- ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+ ts_next = torch.full(
+ (b,),
+ time_range[min(i + 1, len(time_range) - 1)],
+ device=device,
+ dtype=torch.long,
+ )
if mask is not None:
assert x0 is not None
- img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
- img = img_orig * mask + (1. - mask) * img
+ img_orig = self.model.q_sample(
+ x0, ts
+ ) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1.0 - mask) * img
- outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
- quantize_denoised=quantize_denoised, temperature=temperature,
- noise_dropout=noise_dropout, score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- old_eps=old_eps, t_next=ts_next)
+ outs = self.p_sample_plms(
+ img,
+ cond,
+ ts,
+ index=index,
+ use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised,
+ temperature=temperature,
+ noise_dropout=noise_dropout,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ old_eps=old_eps,
+ t_next=ts_next,
+ )
img, pred_x0, e_t = outs
old_eps.append(e_t)
if len(old_eps) >= 4:
old_eps.pop(0)
- if callback: callback(i)
- if img_callback: img_callback(pred_x0, i)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
@@ -172,47 +284,95 @@ class PLMSSampler(object):
return img, intermediates
@torch.no_grad()
- def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+ def p_sample_plms(
+ self,
+ x,
+ c,
+ t,
+ index,
+ repeat_noise=False,
+ use_original_steps=False,
+ quantize_denoised=False,
+ temperature=1.0,
+ noise_dropout=0.0,
+ score_corrector=None,
+ corrector_kwargs=None,
+ unconditional_guidance_scale=1.0,
+ unconditional_conditioning=None,
+ old_eps=None,
+ t_next=None,
+ ):
b, *_, device = *x.shape, x.device
def get_model_output(x, t):
- if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ if (
+ unconditional_conditioning is None
+ or unconditional_guidance_scale == 1.0
+ ):
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
- e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
- e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+ e_t_uncond, e_t = self.model.apply_model(
+ x_in, t_in, c_in
+ ).chunk(2)
+ e_t = e_t_uncond + unconditional_guidance_scale * (
+ e_t - e_t_uncond
+ )
if score_corrector is not None:
- assert self.model.parameterization == "eps"
- e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+ assert self.model.parameterization == 'eps'
+ e_t = score_corrector.modify_score(
+ self.model, e_t, x, t, c, **corrector_kwargs
+ )
return e_t
- alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
- alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
- sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
- sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ alphas = (
+ self.model.alphas_cumprod
+ if use_original_steps
+ else self.ddim_alphas
+ )
+ alphas_prev = (
+ self.model.alphas_cumprod_prev
+ if use_original_steps
+ else self.ddim_alphas_prev
+ )
+ sqrt_one_minus_alphas = (
+ self.model.sqrt_one_minus_alphas_cumprod
+ if use_original_steps
+ else self.ddim_sqrt_one_minus_alphas
+ )
+ sigmas = (
+ self.model.ddim_sigmas_for_original_num_steps
+ if use_original_steps
+ else self.ddim_sigmas
+ )
def get_x_prev_and_pred_x0(e_t, index):
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
- a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ a_prev = torch.full(
+ (b, 1, 1, 1), alphas_prev[index], device=device
+ )
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
- sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+ sqrt_one_minus_at = torch.full(
+ (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
+ )
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
- dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
- noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
+ dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
+ noise = (
+ sigma_t
+ * noise_like(x.shape, device, repeat_noise)
+ * temperature
+ )
+ if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
@@ -231,7 +391,12 @@ class PLMSSampler(object):
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
elif len(old_eps) >= 3:
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
- e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+ e_t_prime = (
+ 55 * e_t
+ - 59 * old_eps[-1]
+ + 37 * old_eps[-2]
+ - 9 * old_eps[-3]
+ ) / 24
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
index f4eff39ccb..960a112001 100644
--- a/ldm/modules/attention.py
+++ b/ldm/modules/attention.py
@@ -13,7 +13,7 @@ def exists(val):
def uniq(arr):
- return{el: True for el in arr}.keys()
+ return {el: True for el in arr}.keys()
def default(val, d):
@@ -45,19 +45,18 @@ class GEGLU(nn.Module):
class FeedForward(nn.Module):
- def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
- project_in = nn.Sequential(
- nn.Linear(dim, inner_dim),
- nn.GELU()
- ) if not glu else GEGLU(dim, inner_dim)
+ project_in = (
+ nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+ if not glu
+ else GEGLU(dim, inner_dim)
+ )
self.net = nn.Sequential(
- project_in,
- nn.Dropout(dropout),
- nn.Linear(inner_dim, dim_out)
+ project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
@@ -74,7 +73,9 @@ def zero_module(module):
def Normalize(in_channels):
- return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ return torch.nn.GroupNorm(
+ num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+ )
class LinearAttention(nn.Module):
@@ -82,17 +83,28 @@ class LinearAttention(nn.Module):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
- self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+ self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
- q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
- k = k.softmax(dim=-1)
+ q, k, v = rearrange(
+ qkv,
+ 'b (qkv heads c) h w -> qkv b heads c (h w)',
+ heads=self.heads,
+ qkv=3,
+ )
+ k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
- out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+ out = rearrange(
+ out,
+ 'b heads c (h w) -> b (heads c) h w',
+ heads=self.heads,
+ h=h,
+ w=w,
+ )
return self.to_out(out)
@@ -102,26 +114,18 @@ class SpatialSelfAttention(nn.Module):
self.in_channels = in_channels
self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
+ self.q = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
+ self.k = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
+ self.v = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
+ self.proj_out = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
def forward(self, x):
h_ = x
@@ -131,12 +135,12 @@ class SpatialSelfAttention(nn.Module):
v = self.v(h_)
# compute attention
- b,c,h,w = q.shape
+ b, c, h, w = q.shape
q = rearrange(q, 'b c h w -> b (h w) c')
k = rearrange(k, 'b c h w -> b c (h w)')
w_ = torch.einsum('bij,bjk->bik', q, k)
- w_ = w_ * (int(c)**(-0.5))
+ w_ = w_ * (int(c) ** (-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
@@ -146,16 +150,18 @@ class SpatialSelfAttention(nn.Module):
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
h_ = self.proj_out(h_)
- return x+h_
+ return x + h_
class CrossAttention(nn.Module):
- def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+ def __init__(
+ self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0
+ ):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
- self.scale = dim_head ** -0.5
+ self.scale = dim_head**-0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
@@ -163,8 +169,7 @@ class CrossAttention(nn.Module):
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
- nn.Linear(inner_dim, query_dim),
- nn.Dropout(dropout)
+ nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None):
@@ -175,7 +180,9 @@ class CrossAttention(nn.Module):
k = self.to_k(context)
v = self.to_v(context)
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+ q, k, v = map(
+ lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)
+ )
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
@@ -194,19 +201,37 @@ class CrossAttention(nn.Module):
class BasicTransformerBlock(nn.Module):
- def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
+ def __init__(
+ self,
+ dim,
+ n_heads,
+ d_head,
+ dropout=0.0,
+ context_dim=None,
+ gated_ff=True,
+ checkpoint=True,
+ ):
super().__init__()
- self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
+ self.attn1 = CrossAttention(
+ query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
+ ) # is a self-attention
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
- self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
- heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
+ self.attn2 = CrossAttention(
+ query_dim=dim,
+ context_dim=context_dim,
+ heads=n_heads,
+ dim_head=d_head,
+ dropout=dropout,
+ ) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def forward(self, x, context=None):
- return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+ return checkpoint(
+ self._forward, (x, context), self.parameters(), self.checkpoint
+ )
def _forward(self, x, context=None):
x = self.attn1(self.norm1(x)) + x
@@ -223,29 +248,43 @@ class SpatialTransformer(nn.Module):
Then apply standard transformer action.
Finally, reshape to image
"""
- def __init__(self, in_channels, n_heads, d_head,
- depth=1, dropout=0., context_dim=None):
+
+ def __init__(
+ self,
+ in_channels,
+ n_heads,
+ d_head,
+ depth=1,
+ dropout=0.0,
+ context_dim=None,
+ ):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
- self.proj_in = nn.Conv2d(in_channels,
- inner_dim,
- kernel_size=1,
- stride=1,
- padding=0)
-
- self.transformer_blocks = nn.ModuleList(
- [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
- for d in range(depth)]
+ self.proj_in = nn.Conv2d(
+ in_channels, inner_dim, kernel_size=1, stride=1, padding=0
)
- self.proj_out = zero_module(nn.Conv2d(inner_dim,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0))
+ self.transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ inner_dim,
+ n_heads,
+ d_head,
+ dropout=dropout,
+ context_dim=context_dim,
+ )
+ for d in range(depth)
+ ]
+ )
+
+ self.proj_out = zero_module(
+ nn.Conv2d(
+ inner_dim, in_channels, kernel_size=1, stride=1, padding=0
+ )
+ )
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
@@ -258,4 +297,4 @@ class SpatialTransformer(nn.Module):
x = block(x, context=context)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
x = self.proj_out(x)
- return x + x_in
\ No newline at end of file
+ return x + x_in
diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
index 533e589a20..cd79e37565 100644
--- a/ldm/modules/diffusionmodules/model.py
+++ b/ldm/modules/diffusionmodules/model.py
@@ -26,17 +26,19 @@ def get_timestep_embedding(timesteps, embedding_dim):
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
- emb = torch.nn.functional.pad(emb, (0,1,0,0))
+ emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
def nonlinearity(x):
# swish
- return x*torch.sigmoid(x)
+ return x * torch.sigmoid(x)
def Normalize(in_channels, num_groups=32):
- return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+ return torch.nn.GroupNorm(
+ num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
+ )
class Upsample(nn.Module):
@@ -44,14 +46,14 @@ class Upsample(nn.Module):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=3, stride=1, padding=1
+ )
def forward(self, x):
- x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+ x = torch.nn.functional.interpolate(
+ x, scale_factor=2.0, mode='nearest'
+ )
if self.with_conv:
x = self.conv(x)
return x
@@ -63,16 +65,14 @@ class Downsample(nn.Module):
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=2,
- padding=0)
+ self.conv = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=3, stride=2, padding=0
+ )
def forward(self, x):
if self.with_conv:
- pad = (0,1,0,1)
- x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+ pad = (0, 1, 0, 1)
+ x = torch.nn.functional.pad(x, pad, mode='constant', value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
@@ -80,8 +80,15 @@ class Downsample(nn.Module):
class ResnetBlock(nn.Module):
- def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
- dropout, temb_channels=512):
+ def __init__(
+ self,
+ *,
+ in_channels,
+ out_channels=None,
+ conv_shortcut=False,
+ dropout,
+ temb_channels=512,
+ ):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
@@ -89,34 +96,33 @@ class ResnetBlock(nn.Module):
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
- self.conv1 = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv1 = torch.nn.Conv2d(
+ in_channels, out_channels, kernel_size=3, stride=1, padding=1
+ )
if temb_channels > 0:
- self.temb_proj = torch.nn.Linear(temb_channels,
- out_channels)
+ self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
- self.conv2 = torch.nn.Conv2d(out_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv2 = torch.nn.Conv2d(
+ out_channels, out_channels, kernel_size=3, stride=1, padding=1
+ )
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
- self.conv_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_shortcut = torch.nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ )
else:
- self.nin_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=0)
+ self.nin_shortcut = torch.nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ )
def forward(self, x, temb):
h = x
@@ -125,7 +131,7 @@ class ResnetBlock(nn.Module):
h = self.conv1(h)
if temb is not None:
- h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+ h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
h = self.norm2(h)
h = nonlinearity(h)
@@ -138,11 +144,12 @@ class ResnetBlock(nn.Module):
else:
x = self.nin_shortcut(x)
- return x+h
+ return x + h
class LinAttnBlock(LinearAttention):
"""to match AttnBlock usage"""
+
def __init__(self, in_channels):
super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
@@ -153,27 +160,18 @@ class AttnBlock(nn.Module):
self.in_channels = in_channels
self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
+ self.q = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
+ self.k = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
+ self.v = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
+ self.proj_out = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=1, stride=1, padding=0
+ )
def forward(self, x):
h_ = x
@@ -183,44 +181,66 @@ class AttnBlock(nn.Module):
v = self.v(h_)
# compute attention
- b,c,h,w = q.shape
- q = q.reshape(b,c,h*w)
- q = q.permute(0,2,1) # b,hw,c
- k = k.reshape(b,c,h*w) # b,c,hw
- w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
- w_ = w_ * (int(c)**(-0.5))
+ b, c, h, w = q.shape
+ q = q.reshape(b, c, h * w)
+ q = q.permute(0, 2, 1) # b,hw,c
+ k = k.reshape(b, c, h * w) # b,c,hw
+ w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+ w_ = w_ * (int(c) ** (-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
- v = v.reshape(b,c,h*w)
- w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
- h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
- h_ = h_.reshape(b,c,h,w)
+ v = v.reshape(b, c, h * w)
+ w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
+ h_ = torch.bmm(
+ v, w_
+ ) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+ h_ = h_.reshape(b, c, h, w)
h_ = self.proj_out(h_)
- return x+h_
+ return x + h_
-def make_attn(in_channels, attn_type="vanilla"):
- assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
- print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
- if attn_type == "vanilla":
+def make_attn(in_channels, attn_type='vanilla'):
+ assert attn_type in [
+ 'vanilla',
+ 'linear',
+ 'none',
+ ], f'attn_type {attn_type} unknown'
+ print(
+ f"making attention of type '{attn_type}' with {in_channels} in_channels"
+ )
+ if attn_type == 'vanilla':
return AttnBlock(in_channels)
- elif attn_type == "none":
+ elif attn_type == 'none':
return nn.Identity(in_channels)
else:
return LinAttnBlock(in_channels)
class Model(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
+ def __init__(
+ self,
+ *,
+ ch,
+ out_ch,
+ ch_mult=(1, 2, 4, 8),
+ num_res_blocks,
+ attn_resolutions,
+ dropout=0.0,
+ resamp_with_conv=True,
+ in_channels,
+ resolution,
+ use_timestep=True,
+ use_linear_attn=False,
+ attn_type='vanilla',
+ ):
super().__init__()
- if use_linear_attn: attn_type = "linear"
+ if use_linear_attn:
+ attn_type = 'linear'
self.ch = ch
- self.temb_ch = self.ch*4
+ self.temb_ch = self.ch * 4
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.resolution = resolution
@@ -230,70 +250,80 @@ class Model(nn.Module):
if self.use_timestep:
# timestep embedding
self.temb = nn.Module()
- self.temb.dense = nn.ModuleList([
- torch.nn.Linear(self.ch,
- self.temb_ch),
- torch.nn.Linear(self.temb_ch,
- self.temb_ch),
- ])
+ self.temb.dense = nn.ModuleList(
+ [
+ torch.nn.Linear(self.ch, self.temb_ch),
+ torch.nn.Linear(self.temb_ch, self.temb_ch),
+ ]
+ )
# downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_in = torch.nn.Conv2d(
+ in_channels, self.ch, kernel_size=3, stride=1, padding=1
+ )
curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
+ in_ch_mult = (1,) + tuple(ch_mult)
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
+ block_in = ch * in_ch_mult[i_level]
+ block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
+ block.append(
+ ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
+ )
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
down = nn.Module()
down.block = block
down.attn = attn
- if i_level != self.num_resolutions-1:
+ if i_level != self.num_resolutions - 1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
+ self.mid.block_1 = ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
+ self.mid.block_2 = ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- skip_in = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
+ block_out = ch * ch_mult[i_level]
+ skip_in = ch * ch_mult[i_level]
+ for i_block in range(self.num_res_blocks + 1):
if i_block == self.num_res_blocks:
- skip_in = ch*in_ch_mult[i_level]
- block.append(ResnetBlock(in_channels=block_in+skip_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
+ skip_in = ch * in_ch_mult[i_level]
+ block.append(
+ ResnetBlock(
+ in_channels=block_in + skip_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
+ )
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
@@ -303,18 +333,16 @@ class Model(nn.Module):
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
+ self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_out = torch.nn.Conv2d(
+ block_in, out_ch, kernel_size=3, stride=1, padding=1
+ )
def forward(self, x, t=None, context=None):
- #assert x.shape[2] == x.shape[3] == self.resolution
+ # assert x.shape[2] == x.shape[3] == self.resolution
if context is not None:
# assume aligned context, cat along channel axis
x = torch.cat((x, context), dim=1)
@@ -336,7 +364,7 @@ class Model(nn.Module):
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
- if i_level != self.num_resolutions-1:
+ if i_level != self.num_resolutions - 1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
@@ -347,9 +375,10 @@ class Model(nn.Module):
# upsampling
for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
+ for i_block in range(self.num_res_blocks + 1):
h = self.up[i_level].block[i_block](
- torch.cat([h, hs.pop()], dim=1), temb)
+ torch.cat([h, hs.pop()], dim=1), temb
+ )
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
@@ -366,12 +395,27 @@ class Model(nn.Module):
class Encoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
- **ignore_kwargs):
+ def __init__(
+ self,
+ *,
+ ch,
+ out_ch,
+ ch_mult=(1, 2, 4, 8),
+ num_res_blocks,
+ attn_resolutions,
+ dropout=0.0,
+ resamp_with_conv=True,
+ in_channels,
+ resolution,
+ z_channels,
+ double_z=True,
+ use_linear_attn=False,
+ attn_type='vanilla',
+ **ignore_kwargs,
+ ):
super().__init__()
- if use_linear_attn: attn_type = "linear"
+ if use_linear_attn:
+ attn_type = 'linear'
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
@@ -380,56 +424,64 @@ class Encoder(nn.Module):
self.in_channels = in_channels
# downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_in = torch.nn.Conv2d(
+ in_channels, self.ch, kernel_size=3, stride=1, padding=1
+ )
curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
+ in_ch_mult = (1,) + tuple(ch_mult)
self.in_ch_mult = in_ch_mult
self.down = nn.ModuleList()
for i_level in range(self.num_resolutions):
block = nn.ModuleList()
attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
+ block_in = ch * in_ch_mult[i_level]
+ block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
+ block.append(
+ ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
+ )
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
down = nn.Module()
down.block = block
down.attn = attn
- if i_level != self.num_resolutions-1:
+ if i_level != self.num_resolutions - 1:
down.downsample = Downsample(block_in, resamp_with_conv)
curr_res = curr_res // 2
self.down.append(down)
# middle
self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
+ self.mid.block_1 = ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
+ self.mid.block_2 = ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
# end
self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- 2*z_channels if double_z else z_channels,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_out = torch.nn.Conv2d(
+ block_in,
+ 2 * z_channels if double_z else z_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ )
def forward(self, x):
# timestep embedding
@@ -443,7 +495,7 @@ class Encoder(nn.Module):
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
- if i_level != self.num_resolutions-1:
+ if i_level != self.num_resolutions - 1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
@@ -460,12 +512,28 @@ class Encoder(nn.Module):
class Decoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
- attn_type="vanilla", **ignorekwargs):
+ def __init__(
+ self,
+ *,
+ ch,
+ out_ch,
+ ch_mult=(1, 2, 4, 8),
+ num_res_blocks,
+ attn_resolutions,
+ dropout=0.0,
+ resamp_with_conv=True,
+ in_channels,
+ resolution,
+ z_channels,
+ give_pre_end=False,
+ tanh_out=False,
+ use_linear_attn=False,
+ attn_type='vanilla',
+ **ignorekwargs,
+ ):
super().__init__()
- if use_linear_attn: attn_type = "linear"
+ if use_linear_attn:
+ attn_type = 'linear'
self.ch = ch
self.temb_ch = 0
self.num_resolutions = len(ch_mult)
@@ -476,43 +544,52 @@ class Decoder(nn.Module):
self.tanh_out = tanh_out
# compute in_ch_mult, block_in and curr_res at lowest res
- in_ch_mult = (1,)+tuple(ch_mult)
- block_in = ch*ch_mult[self.num_resolutions-1]
- curr_res = resolution // 2**(self.num_resolutions-1)
- self.z_shape = (1,z_channels,curr_res,curr_res)
- print("Working with z of shape {} = {} dimensions.".format(
- self.z_shape, np.prod(self.z_shape)))
+ in_ch_mult = (1,) + tuple(ch_mult)
+ block_in = ch * ch_mult[self.num_resolutions - 1]
+ curr_res = resolution // 2 ** (self.num_resolutions - 1)
+ self.z_shape = (1, z_channels, curr_res, curr_res)
+ print(
+ 'Working with z of shape {} = {} dimensions.'.format(
+ self.z_shape, np.prod(self.z_shape)
+ )
+ )
# z to block_in
- self.conv_in = torch.nn.Conv2d(z_channels,
- block_in,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_in = torch.nn.Conv2d(
+ z_channels, block_in, kernel_size=3, stride=1, padding=1
+ )
# middle
self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
+ self.mid.block_1 = ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
+ self.mid.block_2 = ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = nn.ModuleList()
attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
+ block_out = ch * ch_mult[i_level]
+ for i_block in range(self.num_res_blocks + 1):
+ block.append(
+ ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
+ )
block_in = block_out
if curr_res in attn_resolutions:
attn.append(make_attn(block_in, attn_type=attn_type))
@@ -522,18 +599,16 @@ class Decoder(nn.Module):
if i_level != 0:
up.upsample = Upsample(block_in, resamp_with_conv)
curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
+ self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_out = torch.nn.Conv2d(
+ block_in, out_ch, kernel_size=3, stride=1, padding=1
+ )
def forward(self, z):
- #assert z.shape[1:] == self.z_shape[1:]
+ # assert z.shape[1:] == self.z_shape[1:]
self.last_z_shape = z.shape
# timestep embedding
@@ -549,7 +624,7 @@ class Decoder(nn.Module):
# upsampling
for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
+ for i_block in range(self.num_res_blocks + 1):
h = self.up[i_level].block[i_block](h, temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
@@ -571,29 +646,40 @@ class Decoder(nn.Module):
class SimpleDecoder(nn.Module):
def __init__(self, in_channels, out_channels, *args, **kwargs):
super().__init__()
- self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
- ResnetBlock(in_channels=in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=2 * in_channels,
- out_channels=4 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=4 * in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- nn.Conv2d(2*in_channels, in_channels, 1),
- Upsample(in_channels, with_conv=True)])
+ self.model = nn.ModuleList(
+ [
+ nn.Conv2d(in_channels, in_channels, 1),
+ ResnetBlock(
+ in_channels=in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0,
+ dropout=0.0,
+ ),
+ ResnetBlock(
+ in_channels=2 * in_channels,
+ out_channels=4 * in_channels,
+ temb_channels=0,
+ dropout=0.0,
+ ),
+ ResnetBlock(
+ in_channels=4 * in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0,
+ dropout=0.0,
+ ),
+ nn.Conv2d(2 * in_channels, in_channels, 1),
+ Upsample(in_channels, with_conv=True),
+ ]
+ )
# end
self.norm_out = Normalize(in_channels)
- self.conv_out = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_out = torch.nn.Conv2d(
+ in_channels, out_channels, kernel_size=3, stride=1, padding=1
+ )
def forward(self, x):
for i, layer in enumerate(self.model):
- if i in [1,2,3]:
+ if i in [1, 2, 3]:
x = layer(x, None)
else:
x = layer(x)
@@ -605,8 +691,16 @@ class SimpleDecoder(nn.Module):
class UpsampleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
- ch_mult=(2,2), dropout=0.0):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ ch,
+ num_res_blocks,
+ resolution,
+ ch_mult=(2, 2),
+ dropout=0.0,
+ ):
super().__init__()
# upsampling
self.temb_ch = 0
@@ -620,10 +714,14 @@ class UpsampleDecoder(nn.Module):
res_block = []
block_out = ch * ch_mult[i_level]
for i_block in range(self.num_res_blocks + 1):
- res_block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
+ res_block.append(
+ ResnetBlock(
+ in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout,
+ )
+ )
block_in = block_out
self.res_blocks.append(nn.ModuleList(res_block))
if i_level != self.num_resolutions - 1:
@@ -632,11 +730,9 @@ class UpsampleDecoder(nn.Module):
# end
self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
+ self.conv_out = torch.nn.Conv2d(
+ block_in, out_channels, kernel_size=3, stride=1, padding=1
+ )
def forward(self, x):
# upsampling
@@ -653,35 +749,56 @@ class UpsampleDecoder(nn.Module):
class LatentRescaler(nn.Module):
- def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
+ def __init__(
+ self, factor, in_channels, mid_channels, out_channels, depth=2
+ ):
super().__init__()
# residual block, interpolate, residual block
self.factor = factor
- self.conv_in = nn.Conv2d(in_channels,
- mid_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
+ self.conv_in = nn.Conv2d(
+ in_channels, mid_channels, kernel_size=3, stride=1, padding=1
+ )
+ self.res_block1 = nn.ModuleList(
+ [
+ ResnetBlock(
+ in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0,
+ )
+ for _ in range(depth)
+ ]
+ )
self.attn = AttnBlock(mid_channels)
- self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
+ self.res_block2 = nn.ModuleList(
+ [
+ ResnetBlock(
+ in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0,
+ )
+ for _ in range(depth)
+ ]
+ )
- self.conv_out = nn.Conv2d(mid_channels,
- out_channels,
- kernel_size=1,
- )
+ self.conv_out = nn.Conv2d(
+ mid_channels,
+ out_channels,
+ kernel_size=1,
+ )
def forward(self, x):
x = self.conv_in(x)
for block in self.res_block1:
x = block(x, None)
- x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+ x = torch.nn.functional.interpolate(
+ x,
+ size=(
+ int(round(x.shape[2] * self.factor)),
+ int(round(x.shape[3] * self.factor)),
+ ),
+ )
x = self.attn(x)
for block in self.res_block2:
x = block(x, None)
@@ -690,17 +807,42 @@ class LatentRescaler(nn.Module):
class MergedRescaleEncoder(nn.Module):
- def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True,
- ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
+ def __init__(
+ self,
+ in_channels,
+ ch,
+ resolution,
+ out_ch,
+ num_res_blocks,
+ attn_resolutions,
+ dropout=0.0,
+ resamp_with_conv=True,
+ ch_mult=(1, 2, 4, 8),
+ rescale_factor=1.0,
+ rescale_module_depth=1,
+ ):
super().__init__()
intermediate_chn = ch * ch_mult[-1]
- self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
- z_channels=intermediate_chn, double_z=False, resolution=resolution,
- attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
- out_ch=None)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
- mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
+ self.encoder = Encoder(
+ in_channels=in_channels,
+ num_res_blocks=num_res_blocks,
+ ch=ch,
+ ch_mult=ch_mult,
+ z_channels=intermediate_chn,
+ double_z=False,
+ resolution=resolution,
+ attn_resolutions=attn_resolutions,
+ dropout=dropout,
+ resamp_with_conv=resamp_with_conv,
+ out_ch=None,
+ )
+ self.rescaler = LatentRescaler(
+ factor=rescale_factor,
+ in_channels=intermediate_chn,
+ mid_channels=intermediate_chn,
+ out_channels=out_ch,
+ depth=rescale_module_depth,
+ )
def forward(self, x):
x = self.encoder(x)
@@ -709,15 +851,41 @@ class MergedRescaleEncoder(nn.Module):
class MergedRescaleDecoder(nn.Module):
- def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
- dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
+ def __init__(
+ self,
+ z_channels,
+ out_ch,
+ resolution,
+ num_res_blocks,
+ attn_resolutions,
+ ch,
+ ch_mult=(1, 2, 4, 8),
+ dropout=0.0,
+ resamp_with_conv=True,
+ rescale_factor=1.0,
+ rescale_module_depth=1,
+ ):
super().__init__()
- tmp_chn = z_channels*ch_mult[-1]
- self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
- resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
- ch_mult=ch_mult, resolution=resolution, ch=ch)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
- out_channels=tmp_chn, depth=rescale_module_depth)
+ tmp_chn = z_channels * ch_mult[-1]
+ self.decoder = Decoder(
+ out_ch=out_ch,
+ z_channels=tmp_chn,
+ attn_resolutions=attn_resolutions,
+ dropout=dropout,
+ resamp_with_conv=resamp_with_conv,
+ in_channels=None,
+ num_res_blocks=num_res_blocks,
+ ch_mult=ch_mult,
+ resolution=resolution,
+ ch=ch,
+ )
+ self.rescaler = LatentRescaler(
+ factor=rescale_factor,
+ in_channels=z_channels,
+ mid_channels=tmp_chn,
+ out_channels=tmp_chn,
+ depth=rescale_module_depth,
+ )
def forward(self, x):
x = self.rescaler(x)
@@ -726,17 +894,32 @@ class MergedRescaleDecoder(nn.Module):
class Upsampler(nn.Module):
- def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
+ def __init__(
+ self, in_size, out_size, in_channels, out_channels, ch_mult=2
+ ):
super().__init__()
assert out_size >= in_size
- num_blocks = int(np.log2(out_size//in_size))+1
- factor_up = 1.+ (out_size % in_size)
- print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
- self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
- out_channels=in_channels)
- self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
- attn_resolutions=[], in_channels=None, ch=in_channels,
- ch_mult=[ch_mult for _ in range(num_blocks)])
+ num_blocks = int(np.log2(out_size // in_size)) + 1
+ factor_up = 1.0 + (out_size % in_size)
+ print(
+ f'Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}'
+ )
+ self.rescaler = LatentRescaler(
+ factor=factor_up,
+ in_channels=in_channels,
+ mid_channels=2 * in_channels,
+ out_channels=in_channels,
+ )
+ self.decoder = Decoder(
+ out_ch=out_channels,
+ resolution=out_size,
+ z_channels=in_channels,
+ num_res_blocks=2,
+ attn_resolutions=[],
+ in_channels=None,
+ ch=in_channels,
+ ch_mult=[ch_mult for _ in range(num_blocks)],
+ )
def forward(self, x):
x = self.rescaler(x)
@@ -745,42 +928,55 @@ class Upsampler(nn.Module):
class Resize(nn.Module):
- def __init__(self, in_channels=None, learned=False, mode="bilinear"):
+ def __init__(self, in_channels=None, learned=False, mode='bilinear'):
super().__init__()
self.with_conv = learned
self.mode = mode
if self.with_conv:
- print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+ print(
+ f'Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode'
+ )
raise NotImplementedError()
assert in_channels is not None
# no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=4,
- stride=2,
- padding=1)
+ self.conv = torch.nn.Conv2d(
+ in_channels, in_channels, kernel_size=4, stride=2, padding=1
+ )
def forward(self, x, scale_factor=1.0):
- if scale_factor==1.0:
+ if scale_factor == 1.0:
return x
else:
- x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+ x = torch.nn.functional.interpolate(
+ x,
+ mode=self.mode,
+ align_corners=False,
+ scale_factor=scale_factor,
+ )
return x
-class FirstStagePostProcessor(nn.Module):
- def __init__(self, ch_mult:list, in_channels,
- pretrained_model:nn.Module=None,
- reshape=False,
- n_channels=None,
- dropout=0.,
- pretrained_config=None):
+class FirstStagePostProcessor(nn.Module):
+ def __init__(
+ self,
+ ch_mult: list,
+ in_channels,
+ pretrained_model: nn.Module = None,
+ reshape=False,
+ n_channels=None,
+ dropout=0.0,
+ pretrained_config=None,
+ ):
super().__init__()
if pretrained_config is None:
- assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+ assert (
+ pretrained_model is not None
+ ), 'Either "pretrained_model" or "pretrained_config" must not be None'
self.pretrained_model = pretrained_model
else:
- assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
+ assert (
+ pretrained_config is not None
+ ), 'Either "pretrained_model" or "pretrained_config" must not be None'
self.instantiate_pretrained(pretrained_config)
self.do_reshape = reshape
@@ -788,22 +984,28 @@ class FirstStagePostProcessor(nn.Module):
if n_channels is None:
n_channels = self.pretrained_model.encoder.ch
- self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
- self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
- stride=1,padding=1)
+ self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2)
+ self.proj = nn.Conv2d(
+ in_channels, n_channels, kernel_size=3, stride=1, padding=1
+ )
blocks = []
downs = []
ch_in = n_channels
for m in ch_mult:
- blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
+ blocks.append(
+ ResnetBlock(
+ in_channels=ch_in,
+ out_channels=m * n_channels,
+ dropout=dropout,
+ )
+ )
ch_in = m * n_channels
downs.append(Downsample(ch_in, with_conv=False))
self.model = nn.ModuleList(blocks)
self.downsampler = nn.ModuleList(downs)
-
def instantiate_pretrained(self, config):
model = instantiate_from_config(config)
self.pretrained_model = model.eval()
@@ -811,25 +1013,23 @@ class FirstStagePostProcessor(nn.Module):
for param in self.pretrained_model.parameters():
param.requires_grad = False
-
@torch.no_grad()
- def encode_with_pretrained(self,x):
+ def encode_with_pretrained(self, x):
c = self.pretrained_model.encode(x)
if isinstance(c, DiagonalGaussianDistribution):
c = c.mode()
- return c
+ return c
- def forward(self,x):
+ def forward(self, x):
z_fs = self.encode_with_pretrained(x)
z = self.proj_norm(z_fs)
z = self.proj(z)
z = nonlinearity(z)
- for submodel, downmodel in zip(self.model,self.downsampler):
- z = submodel(z,temb=None)
+ for submodel, downmodel in zip(self.model, self.downsampler):
+ z = submodel(z, temb=None)
z = downmodel(z)
if self.do_reshape:
- z = rearrange(z,'b c h w -> b (h w) c')
+ z = rearrange(z, 'b c h w -> b (h w) c')
return z
-
diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py
index fcf95d1ea8..d6baa76a1c 100644
--- a/ldm/modules/diffusionmodules/openaimodel.py
+++ b/ldm/modules/diffusionmodules/openaimodel.py
@@ -24,6 +24,7 @@ from ldm.modules.attention import SpatialTransformer
def convert_module_to_f16(x):
pass
+
def convert_module_to_f32(x):
pass
@@ -42,7 +43,9 @@ class AttentionPool2d(nn.Module):
output_dim: int = None,
):
super().__init__()
- self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+ self.positional_embedding = nn.Parameter(
+ th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
+ )
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
self.num_heads = embed_dim // num_heads_channels
@@ -97,37 +100,45 @@ class Upsample(nn.Module):
upsampling occurs in the inner-two dimensions.
"""
- def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ def __init__(
+ self, channels, use_conv, dims=2, out_channels=None, padding=1
+ ):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
- self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+ self.conv = conv_nd(
+ dims, self.channels, self.out_channels, 3, padding=padding
+ )
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
- x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+ x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest'
)
else:
- x = F.interpolate(x, scale_factor=2, mode="nearest")
+ x = F.interpolate(x, scale_factor=2, mode='nearest')
if self.use_conv:
x = self.conv(x)
return x
+
class TransposedUpsample(nn.Module):
- 'Learned 2x upsampling without padding'
+ """Learned 2x upsampling without padding"""
+
def __init__(self, channels, out_channels=None, ks=5):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
- self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+ self.up = nn.ConvTranspose2d(
+ self.channels, self.out_channels, kernel_size=ks, stride=2
+ )
- def forward(self,x):
+ def forward(self, x):
return self.up(x)
@@ -140,7 +151,9 @@ class Downsample(nn.Module):
downsampling occurs in the inner-two dimensions.
"""
- def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+ def __init__(
+ self, channels, use_conv, dims=2, out_channels=None, padding=1
+ ):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
@@ -149,7 +162,12 @@ class Downsample(nn.Module):
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
- dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ dims,
+ self.channels,
+ self.out_channels,
+ 3,
+ stride=stride,
+ padding=padding,
)
else:
assert self.channels == self.out_channels
@@ -219,7 +237,9 @@ class ResBlock(TimestepBlock):
nn.SiLU(),
linear(
emb_channels,
- 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+ 2 * self.out_channels
+ if use_scale_shift_norm
+ else self.out_channels,
),
)
self.out_layers = nn.Sequential(
@@ -227,7 +247,9 @@ class ResBlock(TimestepBlock):
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
- conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+ conv_nd(
+ dims, self.out_channels, self.out_channels, 3, padding=1
+ )
),
)
@@ -238,7 +260,9 @@ class ResBlock(TimestepBlock):
dims, channels, self.out_channels, 3, padding=1
)
else:
- self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+ self.skip_connection = conv_nd(
+ dims, channels, self.out_channels, 1
+ )
def forward(self, x, emb):
"""
@@ -251,7 +275,6 @@ class ResBlock(TimestepBlock):
self._forward, (x, emb), self.parameters(), self.use_checkpoint
)
-
def _forward(self, x, emb):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
@@ -297,7 +320,7 @@ class AttentionBlock(nn.Module):
else:
assert (
channels % num_head_channels == 0
- ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+ ), f'q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}'
self.num_heads = channels // num_head_channels
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels)
@@ -312,8 +335,10 @@ class AttentionBlock(nn.Module):
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
def forward(self, x):
- return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
- #return pt_checkpoint(self._forward, x) # pytorch
+ return checkpoint(
+ self._forward, (x,), self.parameters(), True
+ ) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+ # return pt_checkpoint(self._forward, x) # pytorch
def _forward(self, x):
b, c, *spatial = x.shape
@@ -340,7 +365,7 @@ def count_flops_attn(model, _x, y):
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
- matmul_ops = 2 * b * (num_spatial ** 2) * c
+ matmul_ops = 2 * b * (num_spatial**2) * c
model.total_ops += th.DoubleTensor([matmul_ops])
@@ -362,13 +387,15 @@ class QKVAttentionLegacy(nn.Module):
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
- q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+ q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(
+ ch, dim=1
+ )
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
- "bct,bcs->bts", q * scale, k * scale
+ 'bct,bcs->bts', q * scale, k * scale
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
- a = th.einsum("bts,bcs->bct", weight, v)
+ a = th.einsum('bts,bcs->bct', weight, v)
return a.reshape(bs, -1, length)
@staticmethod
@@ -397,12 +424,14 @@ class QKVAttention(nn.Module):
q, k, v = qkv.chunk(3, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
- "bct,bcs->bts",
+ 'bct,bcs->bts',
(q * scale).view(bs * self.n_heads, ch, length),
(k * scale).view(bs * self.n_heads, ch, length),
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
- a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+ a = th.einsum(
+ 'bts,bcs->bct', weight, v.reshape(bs * self.n_heads, ch, length)
+ )
return a.reshape(bs, -1, length)
@staticmethod
@@ -461,19 +490,24 @@ class UNetModel(nn.Module):
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
- use_spatial_transformer=False, # custom transformer support
- transformer_depth=1, # custom transformer support
- context_dim=None, # custom transformer support
- n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
):
super().__init__()
if use_spatial_transformer:
- assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+ assert (
+ context_dim is not None
+ ), 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
- assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ assert (
+ use_spatial_transformer
+ ), 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
+
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
@@ -481,10 +515,14 @@ class UNetModel(nn.Module):
num_heads_upsample = num_heads
if num_heads == -1:
- assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+ assert (
+ num_head_channels != -1
+ ), 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
- assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+ assert (
+ num_heads != -1
+ ), 'Either num_heads or num_head_channels has to be set'
self.image_size = image_size
self.in_channels = in_channels
@@ -545,8 +583,12 @@ class UNetModel(nn.Module):
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ # num_heads = 1
+ dim_head = (
+ ch // num_heads
+ if use_spatial_transformer
+ else num_head_channels
+ )
layers.append(
AttentionBlock(
ch,
@@ -554,8 +596,14 @@ class UNetModel(nn.Module):
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+ )
+ if not use_spatial_transformer
+ else SpatialTransformer(
+ ch,
+ num_heads,
+ dim_head,
+ depth=transformer_depth,
+ context_dim=context_dim,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
@@ -592,8 +640,12 @@ class UNetModel(nn.Module):
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ # num_heads = 1
+ dim_head = (
+ ch // num_heads
+ if use_spatial_transformer
+ else num_head_channels
+ )
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
@@ -609,9 +661,15 @@ class UNetModel(nn.Module):
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
- ),
+ )
+ if not use_spatial_transformer
+ else SpatialTransformer(
+ ch,
+ num_heads,
+ dim_head,
+ depth=transformer_depth,
+ context_dim=context_dim,
+ ),
ResBlock(
ch,
time_embed_dim,
@@ -646,8 +704,12 @@ class UNetModel(nn.Module):
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
- #num_heads = 1
- dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ # num_heads = 1
+ dim_head = (
+ ch // num_heads
+ if use_spatial_transformer
+ else num_head_channels
+ )
layers.append(
AttentionBlock(
ch,
@@ -655,8 +717,14 @@ class UNetModel(nn.Module):
num_heads=num_heads_upsample,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
- ) if not use_spatial_transformer else SpatialTransformer(
- ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+ )
+ if not use_spatial_transformer
+ else SpatialTransformer(
+ ch,
+ num_heads,
+ dim_head,
+ depth=transformer_depth,
+ context_dim=context_dim,
)
)
if level and i == num_res_blocks:
@@ -673,7 +741,9 @@ class UNetModel(nn.Module):
up=True,
)
if resblock_updown
- else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ else Upsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
@@ -682,14 +752,16 @@ class UNetModel(nn.Module):
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
- zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+ zero_module(
+ conv_nd(dims, model_channels, out_channels, 3, padding=1)
+ ),
)
if self.predict_codebook_ids:
self.id_predictor = nn.Sequential(
- normalization(ch),
- conv_nd(dims, model_channels, n_embed, 1),
- #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
- )
+ normalization(ch),
+ conv_nd(dims, model_channels, n_embed, 1),
+ # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
+ )
def convert_to_fp16(self):
"""
@@ -707,7 +779,7 @@ class UNetModel(nn.Module):
self.middle_block.apply(convert_module_to_f32)
self.output_blocks.apply(convert_module_to_f32)
- def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+ def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
"""
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
@@ -718,9 +790,11 @@ class UNetModel(nn.Module):
"""
assert (y is not None) == (
self.num_classes is not None
- ), "must specify y if and only if the model is class-conditional"
+ ), 'must specify y if and only if the model is class-conditional'
hs = []
- t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ t_emb = timestep_embedding(
+ timesteps, self.model_channels, repeat_only=False
+ )
emb = self.time_embed(t_emb)
if self.num_classes is not None:
@@ -768,9 +842,9 @@ class EncoderUNetModel(nn.Module):
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
- pool="adaptive",
+ pool='adaptive',
*args,
- **kwargs
+ **kwargs,
):
super().__init__()
@@ -888,7 +962,7 @@ class EncoderUNetModel(nn.Module):
)
self._feature_size += ch
self.pool = pool
- if pool == "adaptive":
+ if pool == 'adaptive':
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
@@ -896,7 +970,7 @@ class EncoderUNetModel(nn.Module):
zero_module(conv_nd(dims, ch, out_channels, 1)),
nn.Flatten(),
)
- elif pool == "attention":
+ elif pool == 'attention':
assert num_head_channels != -1
self.out = nn.Sequential(
normalization(ch),
@@ -905,13 +979,13 @@ class EncoderUNetModel(nn.Module):
(image_size // ds), ch, num_head_channels, out_channels
),
)
- elif pool == "spatial":
+ elif pool == 'spatial':
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
nn.ReLU(),
nn.Linear(2048, self.out_channels),
)
- elif pool == "spatial_v2":
+ elif pool == 'spatial_v2':
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
normalization(2048),
@@ -919,7 +993,7 @@ class EncoderUNetModel(nn.Module):
nn.Linear(2048, self.out_channels),
)
else:
- raise NotImplementedError(f"Unexpected {pool} pooling")
+ raise NotImplementedError(f'Unexpected {pool} pooling')
def convert_to_fp16(self):
"""
@@ -942,20 +1016,21 @@ class EncoderUNetModel(nn.Module):
:param timesteps: a 1-D batch of timesteps.
:return: an [N x K] Tensor of outputs.
"""
- emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+ emb = self.time_embed(
+ timestep_embedding(timesteps, self.model_channels)
+ )
results = []
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb)
- if self.pool.startswith("spatial"):
+ if self.pool.startswith('spatial'):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = self.middle_block(h, emb)
- if self.pool.startswith("spatial"):
+ if self.pool.startswith('spatial'):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = th.cat(results, axis=-1)
return self.out(h)
else:
h = h.type(x.dtype)
return self.out(h)
-
diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py
index 6b5b9dc9e2..197b42b2bc 100644
--- a/ldm/modules/diffusionmodules/util.py
+++ b/ldm/modules/diffusionmodules/util.py
@@ -18,15 +18,24 @@ from einops import repeat
from ldm.util import instantiate_from_config
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- if schedule == "linear":
+def make_beta_schedule(
+ schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
+):
+ if schedule == 'linear':
betas = (
- torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ torch.linspace(
+ linear_start**0.5,
+ linear_end**0.5,
+ n_timestep,
+ dtype=torch.float64,
+ )
+ ** 2
)
- elif schedule == "cosine":
+ elif schedule == 'cosine':
timesteps = (
- torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep
+ + cosine_s
)
alphas = timesteps / (1 + cosine_s) * np.pi / 2
alphas = torch.cos(alphas).pow(2)
@@ -34,23 +43,41 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
- elif schedule == "sqrt_linear":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
- elif schedule == "sqrt":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ elif schedule == 'sqrt_linear':
+ betas = torch.linspace(
+ linear_start, linear_end, n_timestep, dtype=torch.float64
+ )
+ elif schedule == 'sqrt':
+ betas = (
+ torch.linspace(
+ linear_start, linear_end, n_timestep, dtype=torch.float64
+ )
+ ** 0.5
+ )
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas.numpy()
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+def make_ddim_timesteps(
+ ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
+):
if ddim_discr_method == 'uniform':
c = num_ddpm_timesteps // num_ddim_timesteps
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
elif ddim_discr_method == 'quad':
- ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+ ddim_timesteps = (
+ (
+ np.linspace(
+ 0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps
+ )
+ )
+ ** 2
+ ).astype(int)
else:
- raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+ raise NotImplementedError(
+ f'There is no ddim discretization method called "{ddim_discr_method}"'
+ )
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
@@ -60,17 +87,27 @@ def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timestep
return steps_out
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+def make_ddim_sampling_parameters(
+ alphacums, ddim_timesteps, eta, verbose=True
+):
# select alphas for computing the variance schedule
alphas = alphacums[ddim_timesteps]
- alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+ alphas_prev = np.asarray(
+ [alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()
+ )
# according the the formula provided in https://arxiv.org/abs/2010.02502
- sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+ sigmas = eta * np.sqrt(
+ (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
+ )
if verbose:
- print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
- print(f'For the chosen value of eta, which is {eta}, '
- f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+ print(
+ f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}'
+ )
+ print(
+ f'For the chosen value of eta, which is {eta}, '
+ f'this results in the following sigma_t schedule for ddim sampler {sigmas}'
+ )
return sigmas, alphas, alphas_prev
@@ -109,7 +146,9 @@ def checkpoint(func, inputs, params, flag):
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
- if False: # disabled checkpointing to allow requires_grad = False for main model
+ if (
+ False
+ ): # disabled checkpointing to allow requires_grad = False for main model
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
@@ -129,7 +168,9 @@ class CheckpointFunction(torch.autograd.Function):
@staticmethod
def backward(ctx, *output_grads):
- ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+ ctx.input_tensors = [
+ x.detach().requires_grad_(True) for x in ctx.input_tensors
+ ]
with torch.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
@@ -160,12 +201,16 @@ def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
if not repeat_only:
half = dim // 2
freqs = torch.exp(
- -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+ -math.log(max_period)
+ * torch.arange(start=0, end=half, dtype=torch.float32)
+ / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
- embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ embedding = torch.cat(
+ [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+ )
else:
embedding = repeat(timesteps, 'b -> b d', d=dim)
return embedding
@@ -215,6 +260,7 @@ class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
+
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
@@ -225,7 +271,7 @@ def conv_nd(dims, *args, **kwargs):
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
+ raise ValueError(f'unsupported dimensions: {dims}')
def linear(*args, **kwargs):
@@ -245,15 +291,16 @@ def avg_pool_nd(dims, *args, **kwargs):
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
+ raise ValueError(f'unsupported dimensions: {dims}')
class HybridConditioner(nn.Module):
-
def __init__(self, c_concat_config, c_crossattn_config):
super().__init__()
self.concat_conditioner = instantiate_from_config(c_concat_config)
- self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+ self.crossattn_conditioner = instantiate_from_config(
+ c_crossattn_config
+ )
def forward(self, c_concat, c_crossattn):
c_concat = self.concat_conditioner(c_concat)
@@ -262,6 +309,8 @@ class HybridConditioner(nn.Module):
def noise_like(shape, device, repeat=False):
- repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+ repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
+ shape[0], *((1,) * (len(shape) - 1))
+ )
noise = lambda: torch.randn(shape, device=device)
- return repeat_noise() if repeat else noise()
\ No newline at end of file
+ return repeat_noise() if repeat else noise()
diff --git a/ldm/modules/distributions/distributions.py b/ldm/modules/distributions/distributions.py
index f2b8ef9011..67ed535791 100644
--- a/ldm/modules/distributions/distributions.py
+++ b/ldm/modules/distributions/distributions.py
@@ -30,33 +30,45 @@ class DiagonalGaussianDistribution(object):
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
- self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+ self.var = self.std = torch.zeros_like(self.mean).to(
+ device=self.parameters.device
+ )
def sample(self):
- x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+ x = self.mean + self.std * torch.randn(self.mean.shape).to(
+ device=self.parameters.device
+ )
return x
def kl(self, other=None):
if self.deterministic:
- return torch.Tensor([0.])
+ return torch.Tensor([0.0])
else:
if other is None:
- return 0.5 * torch.sum(torch.pow(self.mean, 2)
- + self.var - 1.0 - self.logvar,
- dim=[1, 2, 3])
+ return 0.5 * torch.sum(
+ torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+ dim=[1, 2, 3],
+ )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean, 2) / other.var
- + self.var / other.var - 1.0 - self.logvar + other.logvar,
- dim=[1, 2, 3])
+ + self.var / other.var
+ - 1.0
+ - self.logvar
+ + other.logvar,
+ dim=[1, 2, 3],
+ )
- def nll(self, sample, dims=[1,2,3]):
+ def nll(self, sample, dims=[1, 2, 3]):
if self.deterministic:
- return torch.Tensor([0.])
+ return torch.Tensor([0.0])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
- logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
- dim=dims)
+ logtwopi
+ + self.logvar
+ + torch.pow(sample - self.mean, 2) / self.var,
+ dim=dims,
+ )
def mode(self):
return self.mean
@@ -74,7 +86,7 @@ def normal_kl(mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
- assert tensor is not None, "at least one argument must be a Tensor"
+ assert tensor is not None, 'at least one argument must be a Tensor'
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().
diff --git a/ldm/modules/ema.py b/ldm/modules/ema.py
index c8c75af435..2ceec5f0e7 100644
--- a/ldm/modules/ema.py
+++ b/ldm/modules/ema.py
@@ -10,24 +10,30 @@ class LitEma(nn.Module):
self.m_name2s_name = {}
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
- self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
- else torch.tensor(-1,dtype=torch.int))
+ self.register_buffer(
+ 'num_updates',
+ torch.tensor(0, dtype=torch.int)
+ if use_num_upates
+ else torch.tensor(-1, dtype=torch.int),
+ )
for name, p in model.named_parameters():
if p.requires_grad:
- #remove as '.'-character is not allowed in buffers
- s_name = name.replace('.','')
- self.m_name2s_name.update({name:s_name})
- self.register_buffer(s_name,p.clone().detach().data)
+ # remove as '.'-character is not allowed in buffers
+ s_name = name.replace('.', '')
+ self.m_name2s_name.update({name: s_name})
+ self.register_buffer(s_name, p.clone().detach().data)
self.collected_params = []
- def forward(self,model):
+ def forward(self, model):
decay = self.decay
if self.num_updates >= 0:
self.num_updates += 1
- decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
+ decay = min(
+ self.decay, (1 + self.num_updates) / (10 + self.num_updates)
+ )
one_minus_decay = 1.0 - decay
@@ -38,8 +44,12 @@ class LitEma(nn.Module):
for key in m_param:
if m_param[key].requires_grad:
sname = self.m_name2s_name[key]
- shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
- shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+ shadow_params[sname] = shadow_params[sname].type_as(
+ m_param[key]
+ )
+ shadow_params[sname].sub_(
+ one_minus_decay * (shadow_params[sname] - m_param[key])
+ )
else:
assert not key in self.m_name2s_name
@@ -48,7 +58,9 @@ class LitEma(nn.Module):
shadow_params = dict(self.named_buffers())
for key in m_param:
if m_param[key].requires_grad:
- m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+ m_param[key].data.copy_(
+ shadow_params[self.m_name2s_name[key]].data
+ )
else:
assert not key in self.m_name2s_name
diff --git a/ldm/modules/embedding_manager.py b/ldm/modules/embedding_manager.py
index 7020a27b9a..677bc4ad3a 100644
--- a/ldm/modules/embedding_manager.py
+++ b/ldm/modules/embedding_manager.py
@@ -8,18 +8,29 @@ from ldm.data.personalized import per_img_token_list
from transformers import CLIPTokenizer
from functools import partial
-DEFAULT_PLACEHOLDER_TOKEN = ["*"]
+DEFAULT_PLACEHOLDER_TOKEN = ['*']
PROGRESSIVE_SCALE = 2000
+
def get_clip_token_for_string(tokenizer, string):
- batch_encoding = tokenizer(string, truncation=True, max_length=77, return_length=True,
- return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
- tokens = batch_encoding["input_ids"]
- assert torch.count_nonzero(tokens - 49407) == 2, f"String '{string}' maps to more than a single token. Please use another string"
+ batch_encoding = tokenizer(
+ string,
+ truncation=True,
+ max_length=77,
+ return_length=True,
+ return_overflowing_tokens=False,
+ padding='max_length',
+ return_tensors='pt',
+ )
+ tokens = batch_encoding['input_ids']
+ assert (
+ torch.count_nonzero(tokens - 49407) == 2
+ ), f"String '{string}' maps to more than a single token. Please use another string"
return tokens[0, 1]
+
def get_bert_token_for_string(tokenizer, string):
token = tokenizer(string)
# assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
@@ -28,42 +39,54 @@ def get_bert_token_for_string(tokenizer, string):
return token
+
def get_embedding_for_clip_token(embedder, token):
return embedder(token.unsqueeze(0))[0, 0]
class EmbeddingManager(nn.Module):
def __init__(
- self,
- embedder,
- placeholder_strings=None,
- initializer_words=None,
- per_image_tokens=False,
- num_vectors_per_token=1,
- progressive_words=False,
- **kwargs
+ self,
+ embedder,
+ placeholder_strings=None,
+ initializer_words=None,
+ per_image_tokens=False,
+ num_vectors_per_token=1,
+ progressive_words=False,
+ **kwargs,
):
super().__init__()
self.string_to_token_dict = {}
-
+
self.string_to_param_dict = nn.ParameterDict()
- self.initial_embeddings = nn.ParameterDict() # These should not be optimized
+ self.initial_embeddings = (
+ nn.ParameterDict()
+ ) # These should not be optimized
self.progressive_words = progressive_words
self.progressive_counter = 0
self.max_vectors_per_token = num_vectors_per_token
- if hasattr(embedder, 'tokenizer'): # using Stable Diffusion's CLIP encoder
+ if hasattr(
+ embedder, 'tokenizer'
+ ): # using Stable Diffusion's CLIP encoder
self.is_clip = True
- get_token_for_string = partial(get_clip_token_for_string, embedder.tokenizer)
- get_embedding_for_tkn = partial(get_embedding_for_clip_token, embedder.transformer.text_model.embeddings)
+ get_token_for_string = partial(
+ get_clip_token_for_string, embedder.tokenizer
+ )
+ get_embedding_for_tkn = partial(
+ get_embedding_for_clip_token,
+ embedder.transformer.text_model.embeddings,
+ )
token_dim = 1280
- else: # using LDM's BERT encoder
+ else: # using LDM's BERT encoder
self.is_clip = False
- get_token_for_string = partial(get_bert_token_for_string, embedder.tknz_fn)
+ get_token_for_string = partial(
+ get_bert_token_for_string, embedder.tknz_fn
+ )
get_embedding_for_tkn = embedder.transformer.token_emb
token_dim = 1280
@@ -71,79 +94,140 @@ class EmbeddingManager(nn.Module):
placeholder_strings.extend(per_img_token_list)
for idx, placeholder_string in enumerate(placeholder_strings):
-
+
token = get_token_for_string(placeholder_string)
if initializer_words and idx < len(initializer_words):
init_word_token = get_token_for_string(initializer_words[idx])
with torch.no_grad():
- init_word_embedding = get_embedding_for_tkn(init_word_token.cpu())
+ init_word_embedding = get_embedding_for_tkn(
+ init_word_token.cpu()
+ )
- token_params = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=True)
- self.initial_embeddings[placeholder_string] = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=False)
+ token_params = torch.nn.Parameter(
+ init_word_embedding.unsqueeze(0).repeat(
+ num_vectors_per_token, 1
+ ),
+ requires_grad=True,
+ )
+ self.initial_embeddings[
+ placeholder_string
+ ] = torch.nn.Parameter(
+ init_word_embedding.unsqueeze(0).repeat(
+ num_vectors_per_token, 1
+ ),
+ requires_grad=False,
+ )
else:
- token_params = torch.nn.Parameter(torch.rand(size=(num_vectors_per_token, token_dim), requires_grad=True))
-
+ token_params = torch.nn.Parameter(
+ torch.rand(
+ size=(num_vectors_per_token, token_dim),
+ requires_grad=True,
+ )
+ )
+
self.string_to_token_dict[placeholder_string] = token
self.string_to_param_dict[placeholder_string] = token_params
def forward(
- self,
- tokenized_text,
- embedded_text,
+ self,
+ tokenized_text,
+ embedded_text,
):
b, n, device = *tokenized_text.shape, tokenized_text.device
- for placeholder_string, placeholder_token in self.string_to_token_dict.items():
+ for (
+ placeholder_string,
+ placeholder_token,
+ ) in self.string_to_token_dict.items():
- placeholder_embedding = self.string_to_param_dict[placeholder_string].to(device)
+ placeholder_embedding = self.string_to_param_dict[
+ placeholder_string
+ ].to(device)
- if self.max_vectors_per_token == 1: # If there's only one vector per token, we can do a simple replacement
- placeholder_idx = torch.where(tokenized_text == placeholder_token.to(device))
+ if (
+ self.max_vectors_per_token == 1
+ ): # If there's only one vector per token, we can do a simple replacement
+ placeholder_idx = torch.where(
+ tokenized_text == placeholder_token.to(device)
+ )
embedded_text[placeholder_idx] = placeholder_embedding
- else: # otherwise, need to insert and keep track of changing indices
+ else: # otherwise, need to insert and keep track of changing indices
if self.progressive_words:
self.progressive_counter += 1
- max_step_tokens = 1 + self.progressive_counter // PROGRESSIVE_SCALE
+ max_step_tokens = (
+ 1 + self.progressive_counter // PROGRESSIVE_SCALE
+ )
else:
max_step_tokens = self.max_vectors_per_token
- num_vectors_for_token = min(placeholder_embedding.shape[0], max_step_tokens)
+ num_vectors_for_token = min(
+ placeholder_embedding.shape[0], max_step_tokens
+ )
- placeholder_rows, placeholder_cols = torch.where(tokenized_text == placeholder_token.to(device))
+ placeholder_rows, placeholder_cols = torch.where(
+ tokenized_text == placeholder_token.to(device)
+ )
if placeholder_rows.nelement() == 0:
continue
- sorted_cols, sort_idx = torch.sort(placeholder_cols, descending=True)
+ sorted_cols, sort_idx = torch.sort(
+ placeholder_cols, descending=True
+ )
sorted_rows = placeholder_rows[sort_idx]
for idx in range(len(sorted_rows)):
row = sorted_rows[idx]
col = sorted_cols[idx]
- new_token_row = torch.cat([tokenized_text[row][:col], placeholder_token.repeat(num_vectors_for_token).to(device), tokenized_text[row][col + 1:]], axis=0)[:n]
- new_embed_row = torch.cat([embedded_text[row][:col], placeholder_embedding[:num_vectors_for_token], embedded_text[row][col + 1:]], axis=0)[:n]
+ new_token_row = torch.cat(
+ [
+ tokenized_text[row][:col],
+ placeholder_token.repeat(num_vectors_for_token).to(
+ device
+ ),
+ tokenized_text[row][col + 1 :],
+ ],
+ axis=0,
+ )[:n]
+ new_embed_row = torch.cat(
+ [
+ embedded_text[row][:col],
+ placeholder_embedding[:num_vectors_for_token],
+ embedded_text[row][col + 1 :],
+ ],
+ axis=0,
+ )[:n]
- embedded_text[row] = new_embed_row
+ embedded_text[row] = new_embed_row
tokenized_text[row] = new_token_row
return embedded_text
def save(self, ckpt_path):
- torch.save({"string_to_token": self.string_to_token_dict,
- "string_to_param": self.string_to_param_dict}, ckpt_path)
+ torch.save(
+ {
+ 'string_to_token': self.string_to_token_dict,
+ 'string_to_param': self.string_to_param_dict,
+ },
+ ckpt_path,
+ )
def load(self, ckpt_path):
ckpt = torch.load(ckpt_path, map_location='cpu')
- self.string_to_token_dict = ckpt["string_to_token"]
- self.string_to_param_dict = ckpt["string_to_param"]
+ self.string_to_token_dict = ckpt['string_to_token']
+ self.string_to_param_dict = ckpt['string_to_param']
def get_embedding_norms_squared(self):
- all_params = torch.cat(list(self.string_to_param_dict.values()), axis=0) # num_placeholders x embedding_dim
- param_norm_squared = (all_params * all_params).sum(axis=-1) # num_placeholders
+ all_params = torch.cat(
+ list(self.string_to_param_dict.values()), axis=0
+ ) # num_placeholders x embedding_dim
+ param_norm_squared = (all_params * all_params).sum(
+ axis=-1
+ ) # num_placeholders
return param_norm_squared
@@ -151,14 +235,19 @@ class EmbeddingManager(nn.Module):
return self.string_to_param_dict.parameters()
def embedding_to_coarse_loss(self):
-
- loss = 0.
+
+ loss = 0.0
num_embeddings = len(self.initial_embeddings)
for key in self.initial_embeddings:
optimized = self.string_to_param_dict[key]
coarse = self.initial_embeddings[key].clone().to(optimized.device)
- loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings
+ loss = (
+ loss
+ + (optimized - coarse)
+ @ (optimized - coarse).T
+ / num_embeddings
+ )
- return loss
\ No newline at end of file
+ return loss
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index def6d2136d..2c25948b5c 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -6,29 +6,39 @@ from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel
import kornia
-from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+from ldm.modules.x_transformer import (
+ Encoder,
+ TransformerWrapper,
+) # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
-def _expand_mask(mask, dtype, tgt_len = None):
+
+def _expand_mask(mask, dtype, tgt_len=None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
- expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+ expanded_mask = (
+ mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+ )
inverted_mask = 1.0 - expanded_mask
- return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+ return inverted_mask.masked_fill(
+ inverted_mask.to(torch.bool), torch.finfo(dtype).min
+ )
+
def _build_causal_attention_mask(bsz, seq_len, dtype):
- # lazily create causal attention mask, with full attention between the vision tokens
- # pytorch uses additive attention mask; fill with -inf
- mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
- mask.fill_(torch.tensor(torch.finfo(dtype).min))
- mask.triu_(1) # zero out the lower diagonal
- mask = mask.unsqueeze(1) # expand mask
- return mask
+ # lazily create causal attention mask, with full attention between the vision tokens
+ # pytorch uses additive attention mask; fill with -inf
+ mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
+ mask.fill_(torch.tensor(torch.finfo(dtype).min))
+ mask.triu_(1) # zero out the lower diagonal
+ mask = mask.unsqueeze(1) # expand mask
+ return mask
+
class AbstractEncoder(nn.Module):
def __init__(self):
@@ -38,7 +48,6 @@ class AbstractEncoder(nn.Module):
raise NotImplementedError
-
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class'):
super().__init__()
@@ -56,11 +65,17 @@ class ClassEmbedder(nn.Module):
class TransformerEmbedder(AbstractEncoder):
"""Some transformer encoder layers"""
- def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
+
+ def __init__(
+ self, n_embed, n_layer, vocab_size, max_seq_len=77, device='cuda'
+ ):
super().__init__()
self.device = device
- self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
- attn_layers=Encoder(dim=n_embed, depth=n_layer))
+ self.transformer = TransformerWrapper(
+ num_tokens=vocab_size,
+ max_seq_len=max_seq_len,
+ attn_layers=Encoder(dim=n_embed, depth=n_layer),
+ )
def forward(self, tokens):
tokens = tokens.to(self.device) # meh
@@ -72,27 +87,42 @@ class TransformerEmbedder(AbstractEncoder):
class BERTTokenizer(AbstractEncoder):
- """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
- def __init__(self, device="cuda", vq_interface=True, max_length=77):
+ """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
+
+ def __init__(self, device='cuda', vq_interface=True, max_length=77):
super().__init__()
- from transformers import BertTokenizerFast # TODO: add to reuquirements
+ from transformers import (
+ BertTokenizerFast,
+ ) # TODO: add to reuquirements
+
# Modified to allow to run on non-internet connected compute nodes.
# Model needs to be loaded into cache from an internet-connected machine
# by running:
# from transformers import BertTokenizerFast
# BertTokenizerFast.from_pretrained("bert-base-uncased")
try:
- self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased",local_files_only=True)
+ self.tokenizer = BertTokenizerFast.from_pretrained(
+ 'bert-base-uncased', local_files_only=True
+ )
except OSError:
- raise SystemExit("* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine.")
+ raise SystemExit(
+ "* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine."
+ )
self.device = device
self.vq_interface = vq_interface
self.max_length = max_length
def forward(self, text):
- batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
- return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
- tokens = batch_encoding["input_ids"].to(self.device)
+ batch_encoding = self.tokenizer(
+ text,
+ truncation=True,
+ max_length=self.max_length,
+ return_length=True,
+ return_overflowing_tokens=False,
+ padding='max_length',
+ return_tensors='pt',
+ )
+ tokens = batch_encoding['input_ids'].to(self.device)
return tokens
@torch.no_grad()
@@ -108,53 +138,84 @@ class BERTTokenizer(AbstractEncoder):
class BERTEmbedder(AbstractEncoder):
"""Uses the BERT tokenizr model and add some transformer encoder layers"""
- def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
- device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+
+ def __init__(
+ self,
+ n_embed,
+ n_layer,
+ vocab_size=30522,
+ max_seq_len=77,
+ device='cuda',
+ use_tokenizer=True,
+ embedding_dropout=0.0,
+ ):
super().__init__()
self.use_tknz_fn = use_tokenizer
if self.use_tknz_fn:
- self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+ self.tknz_fn = BERTTokenizer(
+ vq_interface=False, max_length=max_seq_len
+ )
self.device = device
- self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
- attn_layers=Encoder(dim=n_embed, depth=n_layer),
- emb_dropout=embedding_dropout)
+ self.transformer = TransformerWrapper(
+ num_tokens=vocab_size,
+ max_seq_len=max_seq_len,
+ attn_layers=Encoder(dim=n_embed, depth=n_layer),
+ emb_dropout=embedding_dropout,
+ )
def forward(self, text, embedding_manager=None):
if self.use_tknz_fn:
- tokens = self.tknz_fn(text)#.to(self.device)
+ tokens = self.tknz_fn(text) # .to(self.device)
else:
tokens = text
- z = self.transformer(tokens, return_embeddings=True, embedding_manager=embedding_manager)
+ z = self.transformer(
+ tokens, return_embeddings=True, embedding_manager=embedding_manager
+ )
return z
def encode(self, text, **kwargs):
# output of length 77
return self(text, **kwargs)
+
class SpatialRescaler(nn.Module):
- def __init__(self,
- n_stages=1,
- method='bilinear',
- multiplier=0.5,
- in_channels=3,
- out_channels=None,
- bias=False):
+ def __init__(
+ self,
+ n_stages=1,
+ method='bilinear',
+ multiplier=0.5,
+ in_channels=3,
+ out_channels=None,
+ bias=False,
+ ):
super().__init__()
self.n_stages = n_stages
assert self.n_stages >= 0
- assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
+ assert method in [
+ 'nearest',
+ 'linear',
+ 'bilinear',
+ 'trilinear',
+ 'bicubic',
+ 'area',
+ ]
self.multiplier = multiplier
- self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
+ self.interpolator = partial(
+ torch.nn.functional.interpolate, mode=method
+ )
self.remap_output = out_channels is not None
if self.remap_output:
- print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
- self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
+ print(
+ f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.'
+ )
+ self.channel_mapper = nn.Conv2d(
+ in_channels, out_channels, 1, bias=bias
+ )
- def forward(self,x):
+ def forward(self, x):
for stage in range(self.n_stages):
x = self.interpolator(x, scale_factor=self.multiplier)
-
if self.remap_output:
x = self.channel_mapper(x)
return x
@@ -162,57 +223,83 @@ class SpatialRescaler(nn.Module):
def encode(self, x):
return self(x)
+
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
- def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
+
+ def __init__(
+ self,
+ version='openai/clip-vit-large-patch14',
+ device='cuda',
+ max_length=77,
+ ):
super().__init__()
- self.tokenizer = CLIPTokenizer.from_pretrained(version,local_files_only=True)
- self.transformer = CLIPTextModel.from_pretrained(version,local_files_only=True)
+ self.tokenizer = CLIPTokenizer.from_pretrained(
+ version, local_files_only=True
+ )
+ self.transformer = CLIPTextModel.from_pretrained(
+ version, local_files_only=True
+ )
self.device = device
self.max_length = max_length
self.freeze()
def embedding_forward(
- self,
- input_ids = None,
- position_ids = None,
- inputs_embeds = None,
- embedding_manager = None,
- ) -> torch.Tensor:
+ self,
+ input_ids=None,
+ position_ids=None,
+ inputs_embeds=None,
+ embedding_manager=None,
+ ) -> torch.Tensor:
- seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+ seq_length = (
+ input_ids.shape[-1]
+ if input_ids is not None
+ else inputs_embeds.shape[-2]
+ )
- if position_ids is None:
- position_ids = self.position_ids[:, :seq_length]
+ if position_ids is None:
+ position_ids = self.position_ids[:, :seq_length]
- if inputs_embeds is None:
- inputs_embeds = self.token_embedding(input_ids)
+ if inputs_embeds is None:
+ inputs_embeds = self.token_embedding(input_ids)
- if embedding_manager is not None:
- inputs_embeds = embedding_manager(input_ids, inputs_embeds)
+ if embedding_manager is not None:
+ inputs_embeds = embedding_manager(input_ids, inputs_embeds)
+ position_embeddings = self.position_embedding(position_ids)
+ embeddings = inputs_embeds + position_embeddings
- position_embeddings = self.position_embedding(position_ids)
- embeddings = inputs_embeds + position_embeddings
-
- return embeddings
+ return embeddings
- self.transformer.text_model.embeddings.forward = embedding_forward.__get__(self.transformer.text_model.embeddings)
+ self.transformer.text_model.embeddings.forward = (
+ embedding_forward.__get__(self.transformer.text_model.embeddings)
+ )
def encoder_forward(
self,
inputs_embeds,
- attention_mask = None,
- causal_attention_mask = None,
- output_attentions = None,
- output_hidden_states = None,
- return_dict = None,
+ attention_mask=None,
+ causal_attention_mask=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
):
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ output_attentions = (
+ output_attentions
+ if output_attentions is not None
+ else self.config.output_attentions
+ )
+ output_hidden_states = (
+ output_hidden_states
+ if output_hidden_states is not None
+ else self.config.output_hidden_states
+ )
+ return_dict = (
+ return_dict
+ if return_dict is not None
+ else self.config.use_return_dict
)
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
@@ -239,44 +326,61 @@ class FrozenCLIPEmbedder(AbstractEncoder):
return hidden_states
- self.transformer.text_model.encoder.forward = encoder_forward.__get__(self.transformer.text_model.encoder)
-
+ self.transformer.text_model.encoder.forward = encoder_forward.__get__(
+ self.transformer.text_model.encoder
+ )
def text_encoder_forward(
self,
- input_ids = None,
- attention_mask = None,
- position_ids = None,
- output_attentions = None,
- output_hidden_states = None,
- return_dict = None,
- embedding_manager = None,
+ input_ids=None,
+ attention_mask=None,
+ position_ids=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ embedding_manager=None,
):
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
- output_hidden_states = (
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ output_attentions = (
+ output_attentions
+ if output_attentions is not None
+ else self.config.output_attentions
+ )
+ output_hidden_states = (
+ output_hidden_states
+ if output_hidden_states is not None
+ else self.config.output_hidden_states
+ )
+ return_dict = (
+ return_dict
+ if return_dict is not None
+ else self.config.use_return_dict
)
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is None:
- raise ValueError("You have to specify either input_ids")
+ raise ValueError('You have to specify either input_ids')
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
- hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, embedding_manager=embedding_manager)
+ hidden_states = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ embedding_manager=embedding_manager,
+ )
bsz, seq_len = input_shape
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
- causal_attention_mask = _build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
- hidden_states.device
- )
+ causal_attention_mask = _build_causal_attention_mask(
+ bsz, seq_len, hidden_states.dtype
+ ).to(hidden_states.device)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
- attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
+ attention_mask = _expand_mask(
+ attention_mask, hidden_states.dtype
+ )
last_hidden_state = self.encoder(
inputs_embeds=hidden_states,
@@ -291,17 +395,19 @@ class FrozenCLIPEmbedder(AbstractEncoder):
return last_hidden_state
- self.transformer.text_model.forward = text_encoder_forward.__get__(self.transformer.text_model)
+ self.transformer.text_model.forward = text_encoder_forward.__get__(
+ self.transformer.text_model
+ )
def transformer_forward(
self,
- input_ids = None,
- attention_mask = None,
- position_ids = None,
- output_attentions = None,
- output_hidden_states = None,
- return_dict = None,
- embedding_manager = None,
+ input_ids=None,
+ attention_mask=None,
+ position_ids=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ embedding_manager=None,
):
return self.text_model(
input_ids=input_ids,
@@ -310,11 +416,12 @@ class FrozenCLIPEmbedder(AbstractEncoder):
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
- embedding_manager = embedding_manager
+ embedding_manager=embedding_manager,
)
- self.transformer.forward = transformer_forward.__get__(self.transformer)
-
+ self.transformer.forward = transformer_forward.__get__(
+ self.transformer
+ )
def freeze(self):
self.transformer = self.transformer.eval()
@@ -322,9 +429,16 @@ class FrozenCLIPEmbedder(AbstractEncoder):
param.requires_grad = False
def forward(self, text, **kwargs):
- batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
- return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
- tokens = batch_encoding["input_ids"].to(self.device)
+ batch_encoding = self.tokenizer(
+ text,
+ truncation=True,
+ max_length=self.max_length,
+ return_length=True,
+ return_overflowing_tokens=False,
+ padding='max_length',
+ return_tensors='pt',
+ )
+ tokens = batch_encoding['input_ids'].to(self.device)
z = self.transformer(input_ids=tokens, **kwargs)
return z
@@ -337,9 +451,17 @@ class FrozenCLIPTextEmbedder(nn.Module):
"""
Uses the CLIP transformer encoder for text.
"""
- def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
+
+ def __init__(
+ self,
+ version='ViT-L/14',
+ device='cuda',
+ max_length=77,
+ n_repeat=1,
+ normalize=True,
+ ):
super().__init__()
- self.model, _ = clip.load(version, jit=False, device="cpu")
+ self.model, _ = clip.load(version, jit=False, device='cpu')
self.device = device
self.max_length = max_length
self.n_repeat = n_repeat
@@ -359,7 +481,7 @@ class FrozenCLIPTextEmbedder(nn.Module):
def encode(self, text):
z = self(text)
- if z.ndim==2:
+ if z.ndim == 2:
z = z[:, None, :]
z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
return z
@@ -367,29 +489,42 @@ class FrozenCLIPTextEmbedder(nn.Module):
class FrozenClipImageEmbedder(nn.Module):
"""
- Uses the CLIP image encoder.
- """
+ Uses the CLIP image encoder.
+ """
+
def __init__(
- self,
- model,
- jit=False,
- device='cuda' if torch.cuda.is_available() else 'cpu',
- antialias=False,
- ):
+ self,
+ model,
+ jit=False,
+ device='cuda' if torch.cuda.is_available() else 'cpu',
+ antialias=False,
+ ):
super().__init__()
self.model, _ = clip.load(name=model, device=device, jit=jit)
self.antialias = antialias
- self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
- self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+ self.register_buffer(
+ 'mean',
+ torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
+ persistent=False,
+ )
+ self.register_buffer(
+ 'std',
+ torch.Tensor([0.26862954, 0.26130258, 0.27577711]),
+ persistent=False,
+ )
def preprocess(self, x):
# normalize to [0,1]
- x = kornia.geometry.resize(x, (224, 224),
- interpolation='bicubic',align_corners=True,
- antialias=self.antialias)
- x = (x + 1.) / 2.
+ x = kornia.geometry.resize(
+ x,
+ (224, 224),
+ interpolation='bicubic',
+ align_corners=True,
+ antialias=self.antialias,
+ )
+ x = (x + 1.0) / 2.0
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
@@ -399,7 +534,8 @@ class FrozenClipImageEmbedder(nn.Module):
return self.model.encode_image(self.preprocess(x))
-if __name__ == "__main__":
+if __name__ == '__main__':
from ldm.util import count_params
+
model = FrozenCLIPEmbedder()
count_params(model, verbose=True)
diff --git a/ldm/modules/image_degradation/__init__.py b/ldm/modules/image_degradation/__init__.py
index 7836cada81..c6b3b62ea8 100644
--- a/ldm/modules/image_degradation/__init__.py
+++ b/ldm/modules/image_degradation/__init__.py
@@ -1,2 +1,6 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
+from ldm.modules.image_degradation.bsrgan import (
+ degradation_bsrgan_variant as degradation_fn_bsr,
+)
+from ldm.modules.image_degradation.bsrgan_light import (
+ degradation_bsrgan_variant as degradation_fn_bsr_light,
+)
diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py
index 32ef561699..b51217bd48 100644
--- a/ldm/modules/image_degradation/bsrgan.py
+++ b/ldm/modules/image_degradation/bsrgan.py
@@ -27,16 +27,16 @@ import ldm.modules.image_degradation.utils_image as util
def modcrop_np(img, sf):
- '''
+ """
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
- '''
+ """
w, h = img.shape[:2]
im = np.copy(img)
- return im[:w - w % sf, :h - h % sf, ...]
+ return im[: w - w % sf, : h - h % sf, ...]
"""
@@ -54,7 +54,9 @@ def analytic_kernel(k):
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
- big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+ big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += (
+ k[r, c] * k
+ )
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
@@ -63,7 +65,7 @@ def analytic_kernel(k):
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
- """ generate an anisotropic Gaussian kernel
+ """generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
@@ -74,7 +76,12 @@ def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
k : kernel
"""
- v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+ v = np.dot(
+ np.array(
+ [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+ ),
+ np.array([1.0, 0.0]),
+ )
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
@@ -126,24 +133,32 @@ def shift_pixel(x, sf, upper_left=True):
def blur(x, k):
- '''
+ """
x: image, NxcxHxW
k: kernel, Nx1xhxw
- '''
+ """
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
- x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+ x = torch.nn.functional.conv2d(
+ x, k, bias=None, stride=1, padding=0, groups=n * c
+ )
x = x.view(n, c, x.shape[2], x.shape[3])
return x
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
- """"
+def gen_kernel(
+ k_size=np.array([15, 15]),
+ scale_factor=np.array([4, 4]),
+ min_var=0.6,
+ max_var=10.0,
+ noise_level=0,
+):
+ """ "
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
@@ -157,13 +172,16 @@ def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
- Q = np.array([[np.cos(theta), -np.sin(theta)],
- [np.sin(theta), np.cos(theta)]])
+ Q = np.array(
+ [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+ )
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
- MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
+ MU = k_size // 2 - 0.5 * (
+ scale_factor - 1
+ ) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
@@ -188,7 +206,9 @@ def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
- [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+ [x, y] = np.meshgrid(
+ np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)
+ )
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
@@ -208,10 +228,10 @@ def fspecial_laplacian(alpha):
def fspecial(filter_type, *args, **kwargs):
- '''
+ """
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
- '''
+ """
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
@@ -226,19 +246,19 @@ def fspecial(filter_type, *args, **kwargs):
def bicubic_degradation(x, sf=3):
- '''
+ """
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
- '''
+ """
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
- ''' blur + bicubic downsampling
+ """blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
@@ -253,14 +273,16 @@ def srmd_degradation(x, k, sf=3):
pages={3262--3271},
year={2018}
}
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
+ """
+ x = ndimage.filters.convolve(
+ x, np.expand_dims(k, axis=2), mode='wrap'
+ ) # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
- ''' bicubic downsampling + blur
+ """bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
@@ -275,21 +297,21 @@ def dpsr_degradation(x, k, sf=3):
pages={1671--1681},
year={2019}
}
- '''
+ """
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x
def classical_degradation(x, k, sf=3):
- ''' blur + downsampling
+ """blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
- '''
+ """
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
@@ -328,10 +350,19 @@ def add_blur(img, sf=4):
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
- k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+ k = anisotropic_Gaussian(
+ ksize=2 * random.randint(2, 11) + 3,
+ theta=random.random() * np.pi,
+ l1=l1,
+ l2=l2,
+ )
else:
- k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
- img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+ k = fspecial(
+ 'gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()
+ )
+ img = ndimage.filters.convolve(
+ img, np.expand_dims(k, axis=2), mode='mirror'
+ )
return img
@@ -344,7 +375,11 @@ def add_resize(img, sf=4):
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
- img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
img = np.clip(img, 0.0, 1.0)
return img
@@ -366,19 +401,26 @@ def add_resize(img, sf=4):
# img = np.clip(img, 0.0, 1.0)
# return img
+
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
+ np.float32
+ )
elif rnum < 0.4: # add grayscale Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ img = img + np.random.normal(
+ 0, noise_level / 255.0, (*img.shape[:2], 1)
+ ).astype(np.float32)
else: # add noise
- L = noise_level2 / 255.
+ L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
- img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = img + np.random.multivariate_normal(
+ [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+ ).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@@ -388,28 +430,37 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
- img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ img += img * np.random.normal(
+ 0, noise_level / 255.0, img.shape
+ ).astype(np.float32)
elif rnum < 0.4:
- img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ img += img * np.random.normal(
+ 0, noise_level / 255.0, (*img.shape[:2], 1)
+ ).astype(np.float32)
else:
- L = noise_level2 / 255.
+ L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
- img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img += img * np.random.multivariate_normal(
+ [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+ ).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
- img = np.clip((img * 255.0).round(), 0, 255) / 255.
+ img = np.clip((img * 255.0).round(), 0, 255) / 255.0
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
- img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
- noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+ img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
+ noise_gray = (
+ np.random.poisson(img_gray * vals).astype(np.float32) / vals
+ - img_gray
+ )
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
@@ -418,7 +469,9 @@ def add_Poisson_noise(img):
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
- result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+ result, encimg = cv2.imencode(
+ '.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
+ )
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
@@ -428,10 +481,14 @@ def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
- lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+ lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
- hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+ hq = hq[
+ rnd_h_H : rnd_h_H + lq_patchsize * sf,
+ rnd_w_H : rnd_w_H + lq_patchsize * sf,
+ :,
+ ]
return lq, hq
@@ -452,7 +509,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
sf_ori = sf
h1, w1 = img.shape[:2]
- img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
@@ -462,8 +519,11 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
- img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
@@ -472,7 +532,10 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+ shuffle_order[idx1], shuffle_order[idx2] = (
+ shuffle_order[idx2],
+ shuffle_order[idx1],
+ )
for i in shuffle_order:
@@ -487,19 +550,30 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
- img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ k_shifted = (
+ k_shifted / k_shifted.sum()
+ ) # blur with shifted kernel
+ img = ndimage.filters.convolve(
+ img, np.expand_dims(k_shifted, axis=2), mode='mirror'
+ )
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
- img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(1 / sf * a), int(1 / sf * b)),
+ interpolation=random.choice([1, 2, 3]),
+ )
img = np.clip(img, 0.0, 1.0)
elif i == 4:
@@ -544,15 +618,18 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
sf_ori = sf
h1, w1 = image.shape[:2]
- image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
- image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ image = cv2.resize(
+ image,
+ (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
@@ -561,7 +638,10 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+ shuffle_order[idx1], shuffle_order[idx2] = (
+ shuffle_order[idx2],
+ shuffle_order[idx1],
+ )
for i in shuffle_order:
@@ -576,19 +656,33 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
- image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ image = cv2.resize(
+ image,
+ (
+ int(1 / sf1 * image.shape[1]),
+ int(1 / sf1 * image.shape[0]),
+ ),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ k_shifted = (
+ k_shifted / k_shifted.sum()
+ ) # blur with shifted kernel
+ image = ndimage.filters.convolve(
+ image, np.expand_dims(k_shifted, axis=2), mode='mirror'
+ )
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
- image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ image = cv2.resize(
+ image,
+ (int(1 / sf * a), int(1 / sf * b)),
+ interpolation=random.choice([1, 2, 3]),
+ )
image = np.clip(image, 0.0, 1.0)
elif i == 4:
@@ -609,12 +703,19 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
- example = {"image":image}
+ example = {'image': image}
return example
# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
+def degradation_bsrgan_plus(
+ img,
+ sf=4,
+ shuffle_prob=0.5,
+ use_sharp=True,
+ lq_patchsize=64,
+ isp_model=None,
+):
"""
This is an extended degradation model by combining
the degradation models of BSRGAN and Real-ESRGAN
@@ -630,7 +731,7 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
"""
h1, w1 = img.shape[:2]
- img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
@@ -645,8 +746,12 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
else:
shuffle_order = list(range(13))
# local shuffle for noise, JPEG is always the last one
- shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
- shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
+ shuffle_order[2:6] = random.sample(
+ shuffle_order[2:6], len(range(2, 6))
+ )
+ shuffle_order[9:13] = random.sample(
+ shuffle_order[9:13], len(range(9, 13))
+ )
poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
@@ -689,8 +794,11 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
print('check the shuffle!')
# resize to desired size
- img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
# add final JPEG compression noise
img = add_JPEG_noise(img)
@@ -702,29 +810,37 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
if __name__ == '__main__':
- print("hey")
- img = util.imread_uint('utils/test.png', 3)
- print(img)
- img = util.uint2single(img)
- print(img)
- img = img[:448, :448]
- h = img.shape[0] // 4
- print("resizing to", h)
- sf = 4
- deg_fn = partial(degradation_bsrgan_variant, sf=sf)
- for i in range(20):
- print(i)
- img_lq = deg_fn(img)
- print(img_lq)
- img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
- print(img_lq.shape)
- print("bicubic", img_lq_bicubic.shape)
- print(img_hq.shape)
- lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
- util.imsave(img_concat, str(i) + '.png')
-
-
+ print('hey')
+ img = util.imread_uint('utils/test.png', 3)
+ print(img)
+ img = util.uint2single(img)
+ print(img)
+ img = img[:448, :448]
+ h = img.shape[0] // 4
+ print('resizing to', h)
+ sf = 4
+ deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+ for i in range(20):
+ print(i)
+ img_lq = deg_fn(img)
+ print(img_lq)
+ img_lq_bicubic = albumentations.SmallestMaxSize(
+ max_size=h, interpolation=cv2.INTER_CUBIC
+ )(image=img)['image']
+ print(img_lq.shape)
+ print('bicubic', img_lq_bicubic.shape)
+ print(img_hq.shape)
+ lq_nearest = cv2.resize(
+ util.single2uint(img_lq),
+ (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0,
+ )
+ lq_bicubic_nearest = cv2.resize(
+ util.single2uint(img_lq_bicubic),
+ (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0,
+ )
+ img_concat = np.concatenate(
+ [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
+ )
+ util.imsave(img_concat, str(i) + '.png')
diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py
index 9e1f823996..3500ef7316 100644
--- a/ldm/modules/image_degradation/bsrgan_light.py
+++ b/ldm/modules/image_degradation/bsrgan_light.py
@@ -27,16 +27,16 @@ import ldm.modules.image_degradation.utils_image as util
def modcrop_np(img, sf):
- '''
+ """
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
- '''
+ """
w, h = img.shape[:2]
im = np.copy(img)
- return im[:w - w % sf, :h - h % sf, ...]
+ return im[: w - w % sf, : h - h % sf, ...]
"""
@@ -54,7 +54,9 @@ def analytic_kernel(k):
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
- big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+ big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += (
+ k[r, c] * k
+ )
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
@@ -63,7 +65,7 @@ def analytic_kernel(k):
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
- """ generate an anisotropic Gaussian kernel
+ """generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
@@ -74,7 +76,12 @@ def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
k : kernel
"""
- v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+ v = np.dot(
+ np.array(
+ [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+ ),
+ np.array([1.0, 0.0]),
+ )
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
D = np.array([[l1, 0], [0, l2]])
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
@@ -126,24 +133,32 @@ def shift_pixel(x, sf, upper_left=True):
def blur(x, k):
- '''
+ """
x: image, NxcxHxW
k: kernel, Nx1xhxw
- '''
+ """
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
- x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+ x = torch.nn.functional.conv2d(
+ x, k, bias=None, stride=1, padding=0, groups=n * c
+ )
x = x.view(n, c, x.shape[2], x.shape[3])
return x
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
- """"
+def gen_kernel(
+ k_size=np.array([15, 15]),
+ scale_factor=np.array([4, 4]),
+ min_var=0.6,
+ max_var=10.0,
+ noise_level=0,
+):
+ """ "
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
@@ -157,13 +172,16 @@ def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
- Q = np.array([[np.cos(theta), -np.sin(theta)],
- [np.sin(theta), np.cos(theta)]])
+ Q = np.array(
+ [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+ )
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
- MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
+ MU = k_size // 2 - 0.5 * (
+ scale_factor - 1
+ ) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
@@ -188,7 +206,9 @@ def fspecial_gaussian(hsize, sigma):
hsize = [hsize, hsize]
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
std = sigma
- [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+ [x, y] = np.meshgrid(
+ np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)
+ )
arg = -(x * x + y * y) / (2 * std * std)
h = np.exp(arg)
h[h < scipy.finfo(float).eps * h.max()] = 0
@@ -208,10 +228,10 @@ def fspecial_laplacian(alpha):
def fspecial(filter_type, *args, **kwargs):
- '''
+ """
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
- '''
+ """
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
@@ -226,19 +246,19 @@ def fspecial(filter_type, *args, **kwargs):
def bicubic_degradation(x, sf=3):
- '''
+ """
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
- '''
+ """
x = util.imresize_np(x, scale=1 / sf)
return x
def srmd_degradation(x, k, sf=3):
- ''' blur + bicubic downsampling
+ """blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
@@ -253,14 +273,16 @@ def srmd_degradation(x, k, sf=3):
pages={3262--3271},
year={2018}
}
- '''
- x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
+ """
+ x = ndimage.filters.convolve(
+ x, np.expand_dims(k, axis=2), mode='wrap'
+ ) # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
def dpsr_degradation(x, k, sf=3):
- ''' bicubic downsampling + blur
+ """bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
@@ -275,21 +297,21 @@ def dpsr_degradation(x, k, sf=3):
pages={1671--1681},
year={2019}
}
- '''
+ """
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x
def classical_degradation(x, k, sf=3):
- ''' blur + downsampling
+ """blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
- '''
+ """
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
@@ -326,16 +348,25 @@ def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
- wd2 = wd2/4
- wd = wd/4
+ wd2 = wd2 / 4
+ wd = wd / 4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
- k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+ k = anisotropic_Gaussian(
+ ksize=random.randint(2, 11) + 3,
+ theta=random.random() * np.pi,
+ l1=l1,
+ l2=l2,
+ )
else:
- k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
- img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+ k = fspecial(
+ 'gaussian', random.randint(2, 4) + 3, wd * random.random()
+ )
+ img = ndimage.filters.convolve(
+ img, np.expand_dims(k, axis=2), mode='mirror'
+ )
return img
@@ -348,7 +379,11 @@ def add_resize(img, sf=4):
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
- img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
img = np.clip(img, 0.0, 1.0)
return img
@@ -370,19 +405,26 @@ def add_resize(img, sf=4):
# img = np.clip(img, 0.0, 1.0)
# return img
+
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
+ np.float32
+ )
elif rnum < 0.4: # add grayscale Gaussian noise
- img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ img = img + np.random.normal(
+ 0, noise_level / 255.0, (*img.shape[:2], 1)
+ ).astype(np.float32)
else: # add noise
- L = noise_level2 / 255.
+ L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
- img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = img + np.random.multivariate_normal(
+ [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+ ).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@@ -392,28 +434,37 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
- img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ img += img * np.random.normal(
+ 0, noise_level / 255.0, img.shape
+ ).astype(np.float32)
elif rnum < 0.4:
- img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ img += img * np.random.normal(
+ 0, noise_level / 255.0, (*img.shape[:2], 1)
+ ).astype(np.float32)
else:
- L = noise_level2 / 255.
+ L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
- img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img += img * np.random.multivariate_normal(
+ [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+ ).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
- img = np.clip((img * 255.0).round(), 0, 255) / 255.
+ img = np.clip((img * 255.0).round(), 0, 255) / 255.0
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
- img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
- noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+ img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
+ noise_gray = (
+ np.random.poisson(img_gray * vals).astype(np.float32) / vals
+ - img_gray
+ )
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
@@ -422,7 +473,9 @@ def add_Poisson_noise(img):
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
- result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+ result, encimg = cv2.imencode(
+ '.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
+ )
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
@@ -432,10 +485,14 @@ def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
- lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+ lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
- hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+ hq = hq[
+ rnd_h_H : rnd_h_H + lq_patchsize * sf,
+ rnd_w_H : rnd_w_H + lq_patchsize * sf,
+ :,
+ ]
return lq, hq
@@ -456,7 +513,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
sf_ori = sf
h1, w1 = img.shape[:2]
- img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
@@ -466,8 +523,11 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
- img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
@@ -476,7 +536,10 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+ shuffle_order[idx1], shuffle_order[idx2] = (
+ shuffle_order[idx2],
+ shuffle_order[idx1],
+ )
for i in shuffle_order:
@@ -491,19 +554,30 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
- img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ k_shifted = (
+ k_shifted / k_shifted.sum()
+ ) # blur with shifted kernel
+ img = ndimage.filters.convolve(
+ img, np.expand_dims(k_shifted, axis=2), mode='mirror'
+ )
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
- img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ img = cv2.resize(
+ img,
+ (int(1 / sf * a), int(1 / sf * b)),
+ interpolation=random.choice([1, 2, 3]),
+ )
img = np.clip(img, 0.0, 1.0)
elif i == 4:
@@ -548,15 +622,18 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
sf_ori = sf
h1, w1 = image.shape[:2]
- image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
- image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ image = cv2.resize(
+ image,
+ (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
@@ -565,7 +642,10 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
- shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+ shuffle_order[idx1], shuffle_order[idx2] = (
+ shuffle_order[idx2],
+ shuffle_order[idx1],
+ )
for i in shuffle_order:
@@ -583,20 +663,34 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
# downsample2
if random.random() < 0.8:
sf1 = random.uniform(1, 2 * sf)
- image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
- interpolation=random.choice([1, 2, 3]))
+ image = cv2.resize(
+ image,
+ (
+ int(1 / sf1 * image.shape[1]),
+ int(1 / sf1 * image.shape[0]),
+ ),
+ interpolation=random.choice([1, 2, 3]),
+ )
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
- k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
- image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ k_shifted = (
+ k_shifted / k_shifted.sum()
+ ) # blur with shifted kernel
+ image = ndimage.filters.convolve(
+ image, np.expand_dims(k_shifted, axis=2), mode='mirror'
+ )
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
- image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ image = cv2.resize(
+ image,
+ (int(1 / sf * a), int(1 / sf * b)),
+ interpolation=random.choice([1, 2, 3]),
+ )
image = np.clip(image, 0.0, 1.0)
elif i == 4:
@@ -617,34 +711,41 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
- example = {"image": image}
+ example = {'image': image}
return example
-
-
if __name__ == '__main__':
- print("hey")
+ print('hey')
img = util.imread_uint('utils/test.png', 3)
img = img[:448, :448]
h = img.shape[0] // 4
- print("resizing to", h)
+ print('resizing to', h)
sf = 4
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
for i in range(20):
print(i)
img_hq = img
- img_lq = deg_fn(img)["image"]
+ img_lq = deg_fn(img)['image']
img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
print(img_lq)
- img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
+ img_lq_bicubic = albumentations.SmallestMaxSize(
+ max_size=h, interpolation=cv2.INTER_CUBIC
+ )(image=img_hq)['image']
print(img_lq.shape)
- print("bicubic", img_lq_bicubic.shape)
+ print('bicubic', img_lq_bicubic.shape)
print(img_hq.shape)
- lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
- (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
- interpolation=0)
- img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+ lq_nearest = cv2.resize(
+ util.single2uint(img_lq),
+ (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0,
+ )
+ lq_bicubic_nearest = cv2.resize(
+ util.single2uint(img_lq_bicubic),
+ (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0,
+ )
+ img_concat = np.concatenate(
+ [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
+ )
util.imsave(img_concat, str(i) + '.png')
diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py
index 0175f155ad..4b6e64658a 100644
--- a/ldm/modules/image_degradation/utils_image.py
+++ b/ldm/modules/image_degradation/utils_image.py
@@ -6,13 +6,14 @@ import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
-#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
+
+# import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
-'''
+"""
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
@@ -20,10 +21,22 @@ os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
# https://github.com/twhui/SRGAN-pyTorch
# https://github.com/xinntao/BasicSR
# --------------------------------------------
-'''
+"""
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
+IMG_EXTENSIONS = [
+ '.jpg',
+ '.JPG',
+ '.jpeg',
+ '.JPEG',
+ '.png',
+ '.PNG',
+ '.ppm',
+ '.PPM',
+ '.bmp',
+ '.BMP',
+ '.tif',
+]
def is_image_file(filename):
@@ -49,19 +62,19 @@ def surf(Z, cmap='rainbow', figsize=None):
ax3 = plt.axes(projection='3d')
w, h = Z.shape[:2]
- xx = np.arange(0,w,1)
- yy = np.arange(0,h,1)
+ xx = np.arange(0, w, 1)
+ yy = np.arange(0, h, 1)
X, Y = np.meshgrid(xx, yy)
- ax3.plot_surface(X,Y,Z,cmap=cmap)
- #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
+ ax3.plot_surface(X, Y, Z, cmap=cmap)
+ # ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
plt.show()
-'''
+"""
# --------------------------------------------
# get image pathes
# --------------------------------------------
-'''
+"""
def get_image_paths(dataroot):
@@ -83,26 +96,26 @@ def _get_paths_from_images(path):
return images
-'''
+"""
# --------------------------------------------
# split large images into small images
# --------------------------------------------
-'''
+"""
def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
w, h = img.shape[:2]
patches = []
if w > p_max and h > p_max:
- w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
- h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
- w1.append(w-p_size)
- h1.append(h-p_size)
-# print(w1)
-# print(h1)
+ w1 = list(np.arange(0, w - p_size, p_size - p_overlap, dtype=np.int))
+ h1 = list(np.arange(0, h - p_size, p_size - p_overlap, dtype=np.int))
+ w1.append(w - p_size)
+ h1.append(h - p_size)
+ # print(w1)
+ # print(h1)
for i in w1:
for j in h1:
- patches.append(img[i:i+p_size, j:j+p_size,:])
+ patches.append(img[i : i + p_size, j : j + p_size, :])
else:
patches.append(img)
@@ -118,11 +131,21 @@ def imssave(imgs, img_path):
for i, img in enumerate(imgs):
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
- new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
+ new_path = os.path.join(
+ os.path.dirname(img_path),
+ img_name + str('_s{:04d}'.format(i)) + '.png',
+ )
cv2.imwrite(new_path, img)
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
+def split_imageset(
+ original_dataroot,
+ taget_dataroot,
+ n_channels=3,
+ p_size=800,
+ p_overlap=96,
+ p_max=1000,
+):
"""
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
@@ -139,15 +162,18 @@ def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800,
# img_name, ext = os.path.splitext(os.path.basename(img_path))
img = imread_uint(img_path, n_channels=n_channels)
patches = patches_from_image(img, p_size, p_overlap, p_max)
- imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
- #if original_dataroot == taget_dataroot:
- #del img_path
+ imssave(
+ patches, os.path.join(taget_dataroot, os.path.basename(img_path))
+ )
+ # if original_dataroot == taget_dataroot:
+ # del img_path
-'''
+
+"""
# --------------------------------------------
# makedir
# --------------------------------------------
-'''
+"""
def mkdir(path):
@@ -171,12 +197,12 @@ def mkdir_and_rename(path):
os.makedirs(path)
-'''
+"""
# --------------------------------------------
# read image from path
# opencv is fast, but read BGR numpy image
# --------------------------------------------
-'''
+"""
# --------------------------------------------
@@ -206,6 +232,7 @@ def imsave(img, img_path):
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
+
def imwrite(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
@@ -213,7 +240,6 @@ def imwrite(img, img_path):
cv2.imwrite(img_path, img)
-
# --------------------------------------------
# get single image of size HxWxn_channles (BGR)
# --------------------------------------------
@@ -221,7 +247,7 @@ def read_img(path):
# read image by cv2
# return: Numpy float32, HWC, BGR, [0,1]
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
- img = img.astype(np.float32) / 255.
+ img = img.astype(np.float32) / 255.0
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# some images have 4 channels
@@ -230,7 +256,7 @@ def read_img(path):
return img
-'''
+"""
# --------------------------------------------
# image format conversion
# --------------------------------------------
@@ -238,7 +264,7 @@ def read_img(path):
# numpy(single) <---> tensor
# numpy(unit) <---> tensor
# --------------------------------------------
-'''
+"""
# --------------------------------------------
@@ -248,22 +274,22 @@ def read_img(path):
def uint2single(img):
- return np.float32(img/255.)
+ return np.float32(img / 255.0)
def single2uint(img):
- return np.uint8((img.clip(0, 1)*255.).round())
+ return np.uint8((img.clip(0, 1) * 255.0).round())
def uint162single(img):
- return np.float32(img/65535.)
+ return np.float32(img / 65535.0)
def single2uint16(img):
- return np.uint16((img.clip(0, 1)*65535.).round())
+ return np.uint16((img.clip(0, 1) * 65535.0).round())
# --------------------------------------------
@@ -275,14 +301,25 @@ def single2uint16(img):
def uint2tensor4(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
+ return (
+ torch.from_numpy(np.ascontiguousarray(img))
+ .permute(2, 0, 1)
+ .float()
+ .div(255.0)
+ .unsqueeze(0)
+ )
# convert uint to 3-dimensional torch tensor
def uint2tensor3(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
+ return (
+ torch.from_numpy(np.ascontiguousarray(img))
+ .permute(2, 0, 1)
+ .float()
+ .div(255.0)
+ )
# convert 2/3/4-dimensional torch tensor to uint
@@ -290,7 +327,7 @@ def tensor2uint(img):
img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
- return np.uint8((img*255.0).round())
+ return np.uint8((img * 255.0).round())
# --------------------------------------------
@@ -305,7 +342,12 @@ def single2tensor3(img):
# convert single (HxWxC) to 4-dimensional torch tensor
def single2tensor4(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
+ return (
+ torch.from_numpy(np.ascontiguousarray(img))
+ .permute(2, 0, 1)
+ .float()
+ .unsqueeze(0)
+ )
# convert torch tensor to single
@@ -316,6 +358,7 @@ def tensor2single(img):
return img
+
# convert torch tensor to single
def tensor2single3(img):
img = img.data.squeeze().float().cpu().numpy()
@@ -327,30 +370,48 @@ def tensor2single3(img):
def single2tensor5(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
+ return (
+ torch.from_numpy(np.ascontiguousarray(img))
+ .permute(2, 0, 1, 3)
+ .float()
+ .unsqueeze(0)
+ )
def single32tensor5(img):
- return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
+ return (
+ torch.from_numpy(np.ascontiguousarray(img))
+ .float()
+ .unsqueeze(0)
+ .unsqueeze(0)
+ )
def single42tensor4(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+ return (
+ torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+ )
# from skimage.io import imread, imsave
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
- '''
+ """
Converts a torch Tensor into an image Numpy array of BGR channel order
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
- '''
- tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
- tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
+ """
+ tensor = (
+ tensor.squeeze().float().cpu().clamp_(*min_max)
+ ) # squeeze first, then clamp
+ tensor = (tensor - min_max[0]) / (
+ min_max[1] - min_max[0]
+ ) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
- img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
+ img_np = make_grid(
+ tensor, nrow=int(math.sqrt(n_img)), normalize=False
+ ).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
@@ -359,14 +420,17 @@ def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
img_np = tensor.numpy()
else:
raise TypeError(
- 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
+ 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(
+ n_dim
+ )
+ )
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
return img_np.astype(out_type)
-'''
+"""
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
@@ -374,12 +438,11 @@ def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------
-'''
+"""
def augment_img(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
+ """Kai Zhang (github: https://github.com/cszn)"""
if mode == 0:
return img
elif mode == 1:
@@ -399,8 +462,7 @@ def augment_img(img, mode=0):
def augment_img_tensor4(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
+ """Kai Zhang (github: https://github.com/cszn)"""
if mode == 0:
return img
elif mode == 1:
@@ -420,8 +482,7 @@ def augment_img_tensor4(img, mode=0):
def augment_img_tensor(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
+ """Kai Zhang (github: https://github.com/cszn)"""
img_size = img.size()
img_np = img.data.cpu().numpy()
if len(img_size) == 3:
@@ -484,11 +545,11 @@ def augment_imgs(img_list, hflip=True, rot=True):
return [_augment(img) for img in img_list]
-'''
+"""
# --------------------------------------------
# modcrop and shave
# --------------------------------------------
-'''
+"""
def modcrop(img_in, scale):
@@ -497,11 +558,11 @@ def modcrop(img_in, scale):
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
- img = img[:H - H_r, :W - W_r]
+ img = img[: H - H_r, : W - W_r]
elif img.ndim == 3:
H, W, C = img.shape
H_r, W_r = H % scale, W % scale
- img = img[:H - H_r, :W - W_r, :]
+ img = img[: H - H_r, : W - W_r, :]
else:
raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
return img
@@ -511,11 +572,11 @@ def shave(img_in, border=0):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
h, w = img.shape[:2]
- img = img[border:h-border, border:w-border]
+ img = img[border : h - border, border : w - border]
return img
-'''
+"""
# --------------------------------------------
# image processing process on numpy image
# channel_convert(in_c, tar_type, img_list):
@@ -523,74 +584,92 @@ def shave(img_in, border=0):
# bgr2ycbcr(img, only_y=True):
# ycbcr2rgb(img):
# --------------------------------------------
-'''
+"""
def rgb2ycbcr(img, only_y=True):
- '''same as matlab rgb2ycbcr
+ """same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
- '''
+ """
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
- img *= 255.
+ img *= 255.0
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
- rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
- [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
+ rlt = np.matmul(
+ img,
+ [
+ [65.481, -37.797, 112.0],
+ [128.553, -74.203, -93.786],
+ [24.966, 112.0, -18.214],
+ ],
+ ) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
- rlt /= 255.
+ rlt /= 255.0
return rlt.astype(in_img_type)
def ycbcr2rgb(img):
- '''same as matlab ycbcr2rgb
+ """same as matlab ycbcr2rgb
Input:
uint8, [0, 255]
float, [0, 1]
- '''
+ """
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
- img *= 255.
+ img *= 255.0
# convert
- rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
- [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
+ rlt = np.matmul(
+ img,
+ [
+ [0.00456621, 0.00456621, 0.00456621],
+ [0, -0.00153632, 0.00791071],
+ [0.00625893, -0.00318811, 0],
+ ],
+ ) * 255.0 + [-222.921, 135.576, -276.836]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
- rlt /= 255.
+ rlt /= 255.0
return rlt.astype(in_img_type)
def bgr2ycbcr(img, only_y=True):
- '''bgr version of rgb2ycbcr
+ """bgr version of rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
- '''
+ """
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
- img *= 255.
+ img *= 255.0
# convert
if only_y:
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
else:
- rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
- [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
+ rlt = np.matmul(
+ img,
+ [
+ [24.966, 112.0, -18.214],
+ [128.553, -74.203, -93.786],
+ [65.481, -37.797, 112.0],
+ ],
+ ) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
- rlt /= 255.
+ rlt /= 255.0
return rlt.astype(in_img_type)
@@ -608,11 +687,11 @@ def channel_convert(in_c, tar_type, img_list):
return img_list
-'''
+"""
# --------------------------------------------
# metric, PSNR and SSIM
# --------------------------------------------
-'''
+"""
# --------------------------------------------
@@ -620,17 +699,17 @@ def channel_convert(in_c, tar_type, img_list):
# --------------------------------------------
def calculate_psnr(img1, img2, border=0):
# img1 and img2 have range [0, 255]
- #img1 = img1.squeeze()
- #img2 = img2.squeeze()
+ # img1 = img1.squeeze()
+ # img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
- img1 = img1[border:h-border, border:w-border]
- img2 = img2[border:h-border, border:w-border]
+ img1 = img1[border : h - border, border : w - border]
+ img2 = img2[border : h - border, border : w - border]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
- mse = np.mean((img1 - img2)**2)
+ mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float('inf')
return 20 * math.log10(255.0 / math.sqrt(mse))
@@ -640,17 +719,17 @@ def calculate_psnr(img1, img2, border=0):
# SSIM
# --------------------------------------------
def calculate_ssim(img1, img2, border=0):
- '''calculate SSIM
+ """calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
- '''
- #img1 = img1.squeeze()
- #img2 = img2.squeeze()
+ """
+ # img1 = img1.squeeze()
+ # img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
- img1 = img1[border:h-border, border:w-border]
- img2 = img2[border:h-border, border:w-border]
+ img1 = img1[border : h - border, border : w - border]
+ img2 = img2[border : h - border, border : w - border]
if img1.ndim == 2:
return ssim(img1, img2)
@@ -658,7 +737,7 @@ def calculate_ssim(img1, img2, border=0):
if img1.shape[2] == 3:
ssims = []
for i in range(3):
- ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
+ ssims.append(ssim(img1[:, :, i], img2[:, :, i]))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
@@ -667,8 +746,8 @@ def calculate_ssim(img1, img2, border=0):
def ssim(img1, img2):
- C1 = (0.01 * 255)**2
- C2 = (0.03 * 255)**2
+ C1 = (0.01 * 255) ** 2
+ C2 = (0.03 * 255) ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
@@ -684,16 +763,17 @@ def ssim(img1, img2):
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
- ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
- (sigma1_sq + sigma2_sq + C2))
+ ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
+ (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
+ )
return ssim_map.mean()
-'''
+"""
# --------------------------------------------
# matlab's bicubic imresize (numpy and torch) [0, 1]
# --------------------------------------------
-'''
+"""
# matlab 'imresize' function, now only support 'bicubic'
@@ -701,11 +781,14 @@ def cubic(x):
absx = torch.abs(x)
absx2 = absx**2
absx3 = absx**3
- return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
- (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
+ return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + (
+ -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
+ ) * (((absx > 1) * (absx <= 2)).type_as(absx))
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
+def calculate_weights_indices(
+ in_length, out_length, scale, kernel, kernel_width, antialiasing
+):
if (scale < 1) and (antialiasing):
# Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
kernel_width = kernel_width / scale
@@ -729,8 +812,9 @@ def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width
# The indices of the input pixels involved in computing the k-th output
# pixel are in row k of the indices matrix.
- indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
- 1, P).expand(out_length, P)
+ indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(
+ 0, P - 1, P
+ ).view(1, P).expand(out_length, P)
# The weights used to compute the k-th output pixel are in row k of the
# weights matrix.
@@ -771,7 +855,11 @@ def imresize(img, scale, antialiasing=True):
if need_squeeze:
img.unsqueeze_(0)
in_C, in_H, in_W = img.size()
- out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+ out_C, out_H, out_W = (
+ in_C,
+ math.ceil(in_H * scale),
+ math.ceil(in_W * scale),
+ )
kernel_width = 4
kernel = 'cubic'
@@ -782,9 +870,11 @@ def imresize(img, scale, antialiasing=True):
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
- in_H, out_H, scale, kernel, kernel_width, antialiasing)
+ in_H, out_H, scale, kernel, kernel_width, antialiasing
+ )
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
- in_W, out_W, scale, kernel, kernel_width, antialiasing)
+ in_W, out_W, scale, kernel, kernel_width, antialiasing
+ )
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
@@ -805,7 +895,11 @@ def imresize(img, scale, antialiasing=True):
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
- out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
+ out_1[j, i, :] = (
+ img_aug[j, idx : idx + kernel_width, :]
+ .transpose(0, 1)
+ .mv(weights_H[i])
+ )
# process W dimension
# symmetric copying
@@ -827,7 +921,9 @@ def imresize(img, scale, antialiasing=True):
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
- out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
+ out_2[j, :, i] = out_1_aug[j, :, idx : idx + kernel_width].mv(
+ weights_W[i]
+ )
if need_squeeze:
out_2.squeeze_()
return out_2
@@ -846,7 +942,11 @@ def imresize_np(img, scale, antialiasing=True):
img.unsqueeze_(2)
in_H, in_W, in_C = img.size()
- out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+ out_C, out_H, out_W = (
+ in_C,
+ math.ceil(in_H * scale),
+ math.ceil(in_W * scale),
+ )
kernel_width = 4
kernel = 'cubic'
@@ -857,9 +957,11 @@ def imresize_np(img, scale, antialiasing=True):
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
- in_H, out_H, scale, kernel, kernel_width, antialiasing)
+ in_H, out_H, scale, kernel, kernel_width, antialiasing
+ )
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
- in_W, out_W, scale, kernel, kernel_width, antialiasing)
+ in_W, out_W, scale, kernel, kernel_width, antialiasing
+ )
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
@@ -880,7 +982,11 @@ def imresize_np(img, scale, antialiasing=True):
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
- out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
+ out_1[i, :, j] = (
+ img_aug[idx : idx + kernel_width, :, j]
+ .transpose(0, 1)
+ .mv(weights_H[i])
+ )
# process W dimension
# symmetric copying
@@ -902,7 +1008,9 @@ def imresize_np(img, scale, antialiasing=True):
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
- out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
+ out_2[:, i, j] = out_1_aug[:, idx : idx + kernel_width, j].mv(
+ weights_W[i]
+ )
if need_squeeze:
out_2.squeeze_()
@@ -913,4 +1021,4 @@ if __name__ == '__main__':
print('---')
# img = imread_uint('test.bmp', 3)
# img = uint2single(img)
-# img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
+# img_bicubic = imresize_np(img, 1/4)
diff --git a/ldm/modules/losses/__init__.py b/ldm/modules/losses/__init__.py
index 876d7c5bd6..d86294210c 100644
--- a/ldm/modules/losses/__init__.py
+++ b/ldm/modules/losses/__init__.py
@@ -1 +1 @@
-from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
\ No newline at end of file
+from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
diff --git a/ldm/modules/losses/contperceptual.py b/ldm/modules/losses/contperceptual.py
index 672c1e32a1..7fa4124346 100644
--- a/ldm/modules/losses/contperceptual.py
+++ b/ldm/modules/losses/contperceptual.py
@@ -5,13 +5,24 @@ from taming.modules.losses.vqperceptual import * # TODO: taming dependency yes/
class LPIPSWithDiscriminator(nn.Module):
- def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
- disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
- perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
- disc_loss="hinge"):
+ def __init__(
+ self,
+ disc_start,
+ logvar_init=0.0,
+ kl_weight=1.0,
+ pixelloss_weight=1.0,
+ disc_num_layers=3,
+ disc_in_channels=3,
+ disc_factor=1.0,
+ disc_weight=1.0,
+ perceptual_weight=1.0,
+ use_actnorm=False,
+ disc_conditional=False,
+ disc_loss='hinge',
+ ):
super().__init__()
- assert disc_loss in ["hinge", "vanilla"]
+ assert disc_loss in ['hinge', 'vanilla']
self.kl_weight = kl_weight
self.pixel_weight = pixelloss_weight
self.perceptual_loss = LPIPS().eval()
@@ -19,42 +30,68 @@ class LPIPSWithDiscriminator(nn.Module):
# output log variance
self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
- self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
- n_layers=disc_num_layers,
- use_actnorm=use_actnorm
- ).apply(weights_init)
+ self.discriminator = NLayerDiscriminator(
+ input_nc=disc_in_channels,
+ n_layers=disc_num_layers,
+ use_actnorm=use_actnorm,
+ ).apply(weights_init)
self.discriminator_iter_start = disc_start
- self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+ self.disc_loss = (
+ hinge_d_loss if disc_loss == 'hinge' else vanilla_d_loss
+ )
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
if last_layer is not None:
- nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
- g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+ nll_grads = torch.autograd.grad(
+ nll_loss, last_layer, retain_graph=True
+ )[0]
+ g_grads = torch.autograd.grad(
+ g_loss, last_layer, retain_graph=True
+ )[0]
else:
- nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
- g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+ nll_grads = torch.autograd.grad(
+ nll_loss, self.last_layer[0], retain_graph=True
+ )[0]
+ g_grads = torch.autograd.grad(
+ g_loss, self.last_layer[0], retain_graph=True
+ )[0]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
- def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
- global_step, last_layer=None, cond=None, split="train",
- weights=None):
- rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+ def forward(
+ self,
+ inputs,
+ reconstructions,
+ posteriors,
+ optimizer_idx,
+ global_step,
+ last_layer=None,
+ cond=None,
+ split='train',
+ weights=None,
+ ):
+ rec_loss = torch.abs(
+ inputs.contiguous() - reconstructions.contiguous()
+ )
if self.perceptual_weight > 0:
- p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+ p_loss = self.perceptual_loss(
+ inputs.contiguous(), reconstructions.contiguous()
+ )
rec_loss = rec_loss + self.perceptual_weight * p_loss
nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
weighted_nll_loss = nll_loss
if weights is not None:
- weighted_nll_loss = weights*nll_loss
- weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+ weighted_nll_loss = weights * nll_loss
+ weighted_nll_loss = (
+ torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+ )
nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
kl_loss = posteriors.kl()
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
@@ -67,45 +104,72 @@ class LPIPSWithDiscriminator(nn.Module):
logits_fake = self.discriminator(reconstructions.contiguous())
else:
assert self.disc_conditional
- logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+ logits_fake = self.discriminator(
+ torch.cat((reconstructions.contiguous(), cond), dim=1)
+ )
g_loss = -torch.mean(logits_fake)
if self.disc_factor > 0.0:
try:
- d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+ d_weight = self.calculate_adaptive_weight(
+ nll_loss, g_loss, last_layer=last_layer
+ )
except RuntimeError:
assert not self.training
d_weight = torch.tensor(0.0)
else:
d_weight = torch.tensor(0.0)
- disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
- loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
+ disc_factor = adopt_weight(
+ self.disc_factor,
+ global_step,
+ threshold=self.discriminator_iter_start,
+ )
+ loss = (
+ weighted_nll_loss
+ + self.kl_weight * kl_loss
+ + d_weight * disc_factor * g_loss
+ )
- log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
- "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
- "{}/rec_loss".format(split): rec_loss.detach().mean(),
- "{}/d_weight".format(split): d_weight.detach(),
- "{}/disc_factor".format(split): torch.tensor(disc_factor),
- "{}/g_loss".format(split): g_loss.detach().mean(),
- }
+ log = {
+ '{}/total_loss'.format(split): loss.clone().detach().mean(),
+ '{}/logvar'.format(split): self.logvar.detach(),
+ '{}/kl_loss'.format(split): kl_loss.detach().mean(),
+ '{}/nll_loss'.format(split): nll_loss.detach().mean(),
+ '{}/rec_loss'.format(split): rec_loss.detach().mean(),
+ '{}/d_weight'.format(split): d_weight.detach(),
+ '{}/disc_factor'.format(split): torch.tensor(disc_factor),
+ '{}/g_loss'.format(split): g_loss.detach().mean(),
+ }
return loss, log
if optimizer_idx == 1:
# second pass for discriminator update
if cond is None:
logits_real = self.discriminator(inputs.contiguous().detach())
- logits_fake = self.discriminator(reconstructions.contiguous().detach())
+ logits_fake = self.discriminator(
+ reconstructions.contiguous().detach()
+ )
else:
- logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
- logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+ logits_real = self.discriminator(
+ torch.cat((inputs.contiguous().detach(), cond), dim=1)
+ )
+ logits_fake = self.discriminator(
+ torch.cat(
+ (reconstructions.contiguous().detach(), cond), dim=1
+ )
+ )
- disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+ disc_factor = adopt_weight(
+ self.disc_factor,
+ global_step,
+ threshold=self.discriminator_iter_start,
+ )
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
- log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
- "{}/logits_real".format(split): logits_real.detach().mean(),
- "{}/logits_fake".format(split): logits_fake.detach().mean()
- }
+ log = {
+ '{}/disc_loss'.format(split): d_loss.clone().detach().mean(),
+ '{}/logits_real'.format(split): logits_real.detach().mean(),
+ '{}/logits_fake'.format(split): logits_fake.detach().mean(),
+ }
return d_loss, log
-
diff --git a/ldm/modules/losses/vqperceptual.py b/ldm/modules/losses/vqperceptual.py
index f69981769e..2f94bf5281 100644
--- a/ldm/modules/losses/vqperceptual.py
+++ b/ldm/modules/losses/vqperceptual.py
@@ -3,21 +3,25 @@ from torch import nn
import torch.nn.functional as F
from einops import repeat
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
+from taming.modules.discriminator.model import (
+ NLayerDiscriminator,
+ weights_init,
+)
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
- loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
- loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
+ loss_real = torch.mean(F.relu(1.0 - logits_real), dim=[1, 2, 3])
+ loss_fake = torch.mean(F.relu(1.0 + logits_fake), dim=[1, 2, 3])
loss_real = (weights * loss_real).sum() / weights.sum()
loss_fake = (weights * loss_fake).sum() / weights.sum()
d_loss = 0.5 * (loss_real + loss_fake)
return d_loss
-def adopt_weight(weight, global_step, threshold=0, value=0.):
+
+def adopt_weight(weight, global_step, threshold=0, value=0.0):
if global_step < threshold:
weight = value
return weight
@@ -26,57 +30,76 @@ def adopt_weight(weight, global_step, threshold=0, value=0.):
def measure_perplexity(predicted_indices, n_embed):
# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
- encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
+ encodings = (
+ F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
+ )
avg_probs = encodings.mean(0)
perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
cluster_use = torch.sum(avg_probs > 0)
return perplexity, cluster_use
+
def l1(x, y):
- return torch.abs(x-y)
+ return torch.abs(x - y)
def l2(x, y):
- return torch.pow((x-y), 2)
+ return torch.pow((x - y), 2)
class VQLPIPSWithDiscriminator(nn.Module):
- def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
- disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
- perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
- disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
- pixel_loss="l1"):
+ def __init__(
+ self,
+ disc_start,
+ codebook_weight=1.0,
+ pixelloss_weight=1.0,
+ disc_num_layers=3,
+ disc_in_channels=3,
+ disc_factor=1.0,
+ disc_weight=1.0,
+ perceptual_weight=1.0,
+ use_actnorm=False,
+ disc_conditional=False,
+ disc_ndf=64,
+ disc_loss='hinge',
+ n_classes=None,
+ perceptual_loss='lpips',
+ pixel_loss='l1',
+ ):
super().__init__()
- assert disc_loss in ["hinge", "vanilla"]
- assert perceptual_loss in ["lpips", "clips", "dists"]
- assert pixel_loss in ["l1", "l2"]
+ assert disc_loss in ['hinge', 'vanilla']
+ assert perceptual_loss in ['lpips', 'clips', 'dists']
+ assert pixel_loss in ['l1', 'l2']
self.codebook_weight = codebook_weight
self.pixel_weight = pixelloss_weight
- if perceptual_loss == "lpips":
- print(f"{self.__class__.__name__}: Running with LPIPS.")
+ if perceptual_loss == 'lpips':
+ print(f'{self.__class__.__name__}: Running with LPIPS.')
self.perceptual_loss = LPIPS().eval()
else:
- raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
+ raise ValueError(
+ f'Unknown perceptual loss: >> {perceptual_loss} <<'
+ )
self.perceptual_weight = perceptual_weight
- if pixel_loss == "l1":
+ if pixel_loss == 'l1':
self.pixel_loss = l1
else:
self.pixel_loss = l2
- self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
- n_layers=disc_num_layers,
- use_actnorm=use_actnorm,
- ndf=disc_ndf
- ).apply(weights_init)
+ self.discriminator = NLayerDiscriminator(
+ input_nc=disc_in_channels,
+ n_layers=disc_num_layers,
+ use_actnorm=use_actnorm,
+ ndf=disc_ndf,
+ ).apply(weights_init)
self.discriminator_iter_start = disc_start
- if disc_loss == "hinge":
+ if disc_loss == 'hinge':
self.disc_loss = hinge_d_loss
- elif disc_loss == "vanilla":
+ elif disc_loss == 'vanilla':
self.disc_loss = vanilla_d_loss
else:
raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
- print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
+ print(f'VQLPIPSWithDiscriminator running with {disc_loss} loss.')
self.disc_factor = disc_factor
self.discriminator_weight = disc_weight
self.disc_conditional = disc_conditional
@@ -84,31 +107,53 @@ class VQLPIPSWithDiscriminator(nn.Module):
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
if last_layer is not None:
- nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
- g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+ nll_grads = torch.autograd.grad(
+ nll_loss, last_layer, retain_graph=True
+ )[0]
+ g_grads = torch.autograd.grad(
+ g_loss, last_layer, retain_graph=True
+ )[0]
else:
- nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
- g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+ nll_grads = torch.autograd.grad(
+ nll_loss, self.last_layer[0], retain_graph=True
+ )[0]
+ g_grads = torch.autograd.grad(
+ g_loss, self.last_layer[0], retain_graph=True
+ )[0]
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
d_weight = d_weight * self.discriminator_weight
return d_weight
- def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
- global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
+ def forward(
+ self,
+ codebook_loss,
+ inputs,
+ reconstructions,
+ optimizer_idx,
+ global_step,
+ last_layer=None,
+ cond=None,
+ split='train',
+ predicted_indices=None,
+ ):
if not exists(codebook_loss):
- codebook_loss = torch.tensor([0.]).to(inputs.device)
- #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
- rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
+ codebook_loss = torch.tensor([0.0]).to(inputs.device)
+ # rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+ rec_loss = self.pixel_loss(
+ inputs.contiguous(), reconstructions.contiguous()
+ )
if self.perceptual_weight > 0:
- p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+ p_loss = self.perceptual_loss(
+ inputs.contiguous(), reconstructions.contiguous()
+ )
rec_loss = rec_loss + self.perceptual_weight * p_loss
else:
p_loss = torch.tensor([0.0])
nll_loss = rec_loss
- #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+ # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
nll_loss = torch.mean(nll_loss)
# now the GAN part
@@ -119,49 +164,77 @@ class VQLPIPSWithDiscriminator(nn.Module):
logits_fake = self.discriminator(reconstructions.contiguous())
else:
assert self.disc_conditional
- logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+ logits_fake = self.discriminator(
+ torch.cat((reconstructions.contiguous(), cond), dim=1)
+ )
g_loss = -torch.mean(logits_fake)
try:
- d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+ d_weight = self.calculate_adaptive_weight(
+ nll_loss, g_loss, last_layer=last_layer
+ )
except RuntimeError:
assert not self.training
d_weight = torch.tensor(0.0)
- disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
- loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
+ disc_factor = adopt_weight(
+ self.disc_factor,
+ global_step,
+ threshold=self.discriminator_iter_start,
+ )
+ loss = (
+ nll_loss
+ + d_weight * disc_factor * g_loss
+ + self.codebook_weight * codebook_loss.mean()
+ )
- log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
- "{}/quant_loss".format(split): codebook_loss.detach().mean(),
- "{}/nll_loss".format(split): nll_loss.detach().mean(),
- "{}/rec_loss".format(split): rec_loss.detach().mean(),
- "{}/p_loss".format(split): p_loss.detach().mean(),
- "{}/d_weight".format(split): d_weight.detach(),
- "{}/disc_factor".format(split): torch.tensor(disc_factor),
- "{}/g_loss".format(split): g_loss.detach().mean(),
- }
+ log = {
+ '{}/total_loss'.format(split): loss.clone().detach().mean(),
+ '{}/quant_loss'.format(split): codebook_loss.detach().mean(),
+ '{}/nll_loss'.format(split): nll_loss.detach().mean(),
+ '{}/rec_loss'.format(split): rec_loss.detach().mean(),
+ '{}/p_loss'.format(split): p_loss.detach().mean(),
+ '{}/d_weight'.format(split): d_weight.detach(),
+ '{}/disc_factor'.format(split): torch.tensor(disc_factor),
+ '{}/g_loss'.format(split): g_loss.detach().mean(),
+ }
if predicted_indices is not None:
assert self.n_classes is not None
with torch.no_grad():
- perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
- log[f"{split}/perplexity"] = perplexity
- log[f"{split}/cluster_usage"] = cluster_usage
+ perplexity, cluster_usage = measure_perplexity(
+ predicted_indices, self.n_classes
+ )
+ log[f'{split}/perplexity'] = perplexity
+ log[f'{split}/cluster_usage'] = cluster_usage
return loss, log
if optimizer_idx == 1:
# second pass for discriminator update
if cond is None:
logits_real = self.discriminator(inputs.contiguous().detach())
- logits_fake = self.discriminator(reconstructions.contiguous().detach())
+ logits_fake = self.discriminator(
+ reconstructions.contiguous().detach()
+ )
else:
- logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
- logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+ logits_real = self.discriminator(
+ torch.cat((inputs.contiguous().detach(), cond), dim=1)
+ )
+ logits_fake = self.discriminator(
+ torch.cat(
+ (reconstructions.contiguous().detach(), cond), dim=1
+ )
+ )
- disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+ disc_factor = adopt_weight(
+ self.disc_factor,
+ global_step,
+ threshold=self.discriminator_iter_start,
+ )
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
- log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
- "{}/logits_real".format(split): logits_real.detach().mean(),
- "{}/logits_fake".format(split): logits_fake.detach().mean()
- }
+ log = {
+ '{}/disc_loss'.format(split): d_loss.clone().detach().mean(),
+ '{}/logits_real'.format(split): logits_real.detach().mean(),
+ '{}/logits_fake'.format(split): logits_fake.detach().mean(),
+ }
return d_loss, log
diff --git a/ldm/modules/x_transformer.py b/ldm/modules/x_transformer.py
index 1316dbd505..d6c4cc6881 100644
--- a/ldm/modules/x_transformer.py
+++ b/ldm/modules/x_transformer.py
@@ -11,15 +11,13 @@ from einops import rearrange, repeat, reduce
DEFAULT_DIM_HEAD = 64
-Intermediates = namedtuple('Intermediates', [
- 'pre_softmax_attn',
- 'post_softmax_attn'
-])
+Intermediates = namedtuple(
+ 'Intermediates', ['pre_softmax_attn', 'post_softmax_attn']
+)
-LayerIntermediates = namedtuple('Intermediates', [
- 'hiddens',
- 'attn_intermediates'
-])
+LayerIntermediates = namedtuple(
+ 'Intermediates', ['hiddens', 'attn_intermediates']
+)
class AbsolutePositionalEmbedding(nn.Module):
@@ -39,11 +37,16 @@ class AbsolutePositionalEmbedding(nn.Module):
class FixedPositionalEmbedding(nn.Module):
def __init__(self, dim):
super().__init__()
- inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+ inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
def forward(self, x, seq_dim=1, offset=0):
- t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+ t = (
+ torch.arange(x.shape[seq_dim], device=x.device).type_as(
+ self.inv_freq
+ )
+ + offset
+ )
sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
return emb[None, :, :]
@@ -51,6 +54,7 @@ class FixedPositionalEmbedding(nn.Module):
# helpers
+
def exists(val):
return val is not None
@@ -64,18 +68,21 @@ def default(val, d):
def always(val):
def inner(*args, **kwargs):
return val
+
return inner
def not_equals(val):
def inner(x):
return x != val
+
return inner
def equals(val):
def inner(x):
return x == val
+
return inner
@@ -85,6 +92,7 @@ def max_neg_value(tensor):
# keyword argument helpers
+
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values))
@@ -108,8 +116,15 @@ def group_by_key_prefix(prefix, d):
def groupby_prefix_and_trim(prefix, d):
- kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
- kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+ kwargs_with_prefix, kwargs = group_dict_by_key(
+ partial(string_begins_with, prefix), d
+ )
+ kwargs_without_prefix = dict(
+ map(
+ lambda x: (x[0][len(prefix) :], x[1]),
+ tuple(kwargs_with_prefix.items()),
+ )
+ )
return kwargs_without_prefix, kwargs
@@ -139,7 +154,7 @@ class Rezero(nn.Module):
class ScaleNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
- self.scale = dim ** -0.5
+ self.scale = dim**-0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(1))
@@ -151,7 +166,7 @@ class ScaleNorm(nn.Module):
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-8):
super().__init__()
- self.scale = dim ** -0.5
+ self.scale = dim**-0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
@@ -173,7 +188,7 @@ class GRUGating(nn.Module):
def forward(self, x, residual):
gated_output = self.gru(
rearrange(x, 'b n d -> (b n) d'),
- rearrange(residual, 'b n d -> (b n) d')
+ rearrange(residual, 'b n d -> (b n) d'),
)
return gated_output.reshape_as(x)
@@ -181,6 +196,7 @@ class GRUGating(nn.Module):
# feedforward
+
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
@@ -192,19 +208,18 @@ class GEGLU(nn.Module):
class FeedForward(nn.Module):
- def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
- project_in = nn.Sequential(
- nn.Linear(dim, inner_dim),
- nn.GELU()
- ) if not glu else GEGLU(dim, inner_dim)
+ project_in = (
+ nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+ if not glu
+ else GEGLU(dim, inner_dim)
+ )
self.net = nn.Sequential(
- project_in,
- nn.Dropout(dropout),
- nn.Linear(inner_dim, dim_out)
+ project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
@@ -214,23 +229,25 @@ class FeedForward(nn.Module):
# attention.
class Attention(nn.Module):
def __init__(
- self,
- dim,
- dim_head=DEFAULT_DIM_HEAD,
- heads=8,
- causal=False,
- mask=None,
- talking_heads=False,
- sparse_topk=None,
- use_entmax15=False,
- num_mem_kv=0,
- dropout=0.,
- on_attn=False
+ self,
+ dim,
+ dim_head=DEFAULT_DIM_HEAD,
+ heads=8,
+ causal=False,
+ mask=None,
+ talking_heads=False,
+ sparse_topk=None,
+ use_entmax15=False,
+ num_mem_kv=0,
+ dropout=0.0,
+ on_attn=False,
):
super().__init__()
if use_entmax15:
- raise NotImplementedError("Check out entmax activation instead of softmax activation!")
- self.scale = dim_head ** -0.5
+ raise NotImplementedError(
+ 'Check out entmax activation instead of softmax activation!'
+ )
+ self.scale = dim_head**-0.5
self.heads = heads
self.causal = causal
self.mask = mask
@@ -252,7 +269,7 @@ class Attention(nn.Module):
self.sparse_topk = sparse_topk
# entmax
- #self.attn_fn = entmax15 if use_entmax15 else F.softmax
+ # self.attn_fn = entmax15 if use_entmax15 else F.softmax
self.attn_fn = F.softmax
# add memory key / values
@@ -263,20 +280,29 @@ class Attention(nn.Module):
# attention on attention
self.attn_on_attn = on_attn
- self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
+ self.to_out = (
+ nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU())
+ if on_attn
+ else nn.Linear(inner_dim, dim)
+ )
def forward(
- self,
- x,
- context=None,
- mask=None,
- context_mask=None,
- rel_pos=None,
- sinusoidal_emb=None,
- prev_attn=None,
- mem=None
+ self,
+ x,
+ context=None,
+ mask=None,
+ context_mask=None,
+ rel_pos=None,
+ sinusoidal_emb=None,
+ prev_attn=None,
+ mem=None,
):
- b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
+ b, n, _, h, talking_heads, device = (
+ *x.shape,
+ self.heads,
+ self.talking_heads,
+ x.device,
+ )
kv_input = default(context, x)
q_input = x
@@ -297,23 +323,35 @@ class Attention(nn.Module):
k = self.to_k(k_input)
v = self.to_v(v_input)
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+ q, k, v = map(
+ lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)
+ )
input_mask = None
if any(map(exists, (mask, context_mask))):
- q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+ q_mask = default(
+ mask, lambda: torch.ones((b, n), device=device).bool()
+ )
k_mask = q_mask if not exists(context) else context_mask
- k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+ k_mask = default(
+ k_mask,
+ lambda: torch.ones((b, k.shape[-2]), device=device).bool(),
+ )
q_mask = rearrange(q_mask, 'b i -> b () i ()')
k_mask = rearrange(k_mask, 'b j -> b () () j')
input_mask = q_mask * k_mask
if self.num_mem_kv > 0:
- mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+ mem_k, mem_v = map(
+ lambda t: repeat(t, 'h n d -> b h n d', b=b),
+ (self.mem_k, self.mem_v),
+ )
k = torch.cat((mem_k, k), dim=-2)
v = torch.cat((mem_v, v), dim=-2)
if exists(input_mask):
- input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+ input_mask = F.pad(
+ input_mask, (self.num_mem_kv, 0), value=True
+ )
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
mask_value = max_neg_value(dots)
@@ -324,7 +362,9 @@ class Attention(nn.Module):
pre_softmax_attn = dots
if talking_heads:
- dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+ dots = einsum(
+ 'b h i j, h k -> b k i j', dots, self.pre_softmax_proj
+ ).contiguous()
if exists(rel_pos):
dots = rel_pos(dots)
@@ -336,7 +376,9 @@ class Attention(nn.Module):
if self.causal:
i, j = dots.shape[-2:]
r = torch.arange(i, device=device)
- mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+ mask = rearrange(r, 'i -> () () i ()') < rearrange(
+ r, 'j -> () () () j'
+ )
mask = F.pad(mask, (j - i, 0), value=False)
dots.masked_fill_(mask, mask_value)
del mask
@@ -354,14 +396,16 @@ class Attention(nn.Module):
attn = self.dropout(attn)
if talking_heads:
- attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+ attn = einsum(
+ 'b h i j, h k -> b k i j', attn, self.post_softmax_proj
+ ).contiguous()
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
intermediates = Intermediates(
pre_softmax_attn=pre_softmax_attn,
- post_softmax_attn=post_softmax_attn
+ post_softmax_attn=post_softmax_attn,
)
return self.to_out(out), intermediates
@@ -369,28 +413,28 @@ class Attention(nn.Module):
class AttentionLayers(nn.Module):
def __init__(
- self,
- dim,
- depth,
- heads=8,
- causal=False,
- cross_attend=False,
- only_cross=False,
- use_scalenorm=False,
- use_rmsnorm=False,
- use_rezero=False,
- rel_pos_num_buckets=32,
- rel_pos_max_distance=128,
- position_infused_attn=False,
- custom_layers=None,
- sandwich_coef=None,
- par_ratio=None,
- residual_attn=False,
- cross_residual_attn=False,
- macaron=False,
- pre_norm=True,
- gate_residual=False,
- **kwargs
+ self,
+ dim,
+ depth,
+ heads=8,
+ causal=False,
+ cross_attend=False,
+ only_cross=False,
+ use_scalenorm=False,
+ use_rmsnorm=False,
+ use_rezero=False,
+ rel_pos_num_buckets=32,
+ rel_pos_max_distance=128,
+ position_infused_attn=False,
+ custom_layers=None,
+ sandwich_coef=None,
+ par_ratio=None,
+ residual_attn=False,
+ cross_residual_attn=False,
+ macaron=False,
+ pre_norm=True,
+ gate_residual=False,
+ **kwargs,
):
super().__init__()
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
@@ -403,10 +447,14 @@ class AttentionLayers(nn.Module):
self.layers = nn.ModuleList([])
self.has_pos_emb = position_infused_attn
- self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+ self.pia_pos_emb = (
+ FixedPositionalEmbedding(dim) if position_infused_attn else None
+ )
self.rotary_pos_emb = always(None)
- assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+ assert (
+ rel_pos_num_buckets <= rel_pos_max_distance
+ ), 'number of relative position buckets must be less than the relative position max distance'
self.rel_pos = None
self.pre_norm = pre_norm
@@ -438,15 +486,27 @@ class AttentionLayers(nn.Module):
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
default_block = tuple(filter(not_equals('f'), default_block))
par_attn = par_depth // par_ratio
- depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
+ depth_cut = (
+ par_depth * 2 // 3
+ ) # 2 / 3 attention layer cutoff suggested by PAR paper
par_width = (depth_cut + depth_cut // par_attn) // par_attn
- assert len(default_block) <= par_width, 'default block is too large for par_ratio'
- par_block = default_block + ('f',) * (par_width - len(default_block))
+ assert (
+ len(default_block) <= par_width
+ ), 'default block is too large for par_ratio'
+ par_block = default_block + ('f',) * (
+ par_width - len(default_block)
+ )
par_head = par_block * par_attn
layer_types = par_head + ('f',) * (par_depth - len(par_head))
elif exists(sandwich_coef):
- assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
- layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+ assert (
+ sandwich_coef > 0 and sandwich_coef <= depth
+ ), 'sandwich coefficient should be less than the depth'
+ layer_types = (
+ ('a',) * sandwich_coef
+ + default_block * (depth - sandwich_coef)
+ + ('f',) * sandwich_coef
+ )
else:
layer_types = default_block * depth
@@ -455,7 +515,9 @@ class AttentionLayers(nn.Module):
for layer_type in self.layer_types:
if layer_type == 'a':
- layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+ layer = Attention(
+ dim, heads=heads, causal=causal, **attn_kwargs
+ )
elif layer_type == 'c':
layer = Attention(dim, heads=heads, **attn_kwargs)
elif layer_type == 'f':
@@ -472,21 +534,17 @@ class AttentionLayers(nn.Module):
else:
residual_fn = Residual()
- self.layers.append(nn.ModuleList([
- norm_fn(),
- layer,
- residual_fn
- ]))
+ self.layers.append(nn.ModuleList([norm_fn(), layer, residual_fn]))
def forward(
- self,
- x,
- context=None,
- mask=None,
- context_mask=None,
- mems=None,
- return_hiddens=False,
- **kwargs
+ self,
+ x,
+ context=None,
+ mask=None,
+ context_mask=None,
+ mems=None,
+ return_hiddens=False,
+ **kwargs,
):
hiddens = []
intermediates = []
@@ -495,7 +553,9 @@ class AttentionLayers(nn.Module):
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
- for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+ for ind, (layer_type, (norm, block, residual_fn)) in enumerate(
+ zip(self.layer_types, self.layers)
+ ):
is_last = ind == (len(self.layers) - 1)
if layer_type == 'a':
@@ -508,10 +568,22 @@ class AttentionLayers(nn.Module):
x = norm(x)
if layer_type == 'a':
- out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
- prev_attn=prev_attn, mem=layer_mem)
+ out, inter = block(
+ x,
+ mask=mask,
+ sinusoidal_emb=self.pia_pos_emb,
+ rel_pos=self.rel_pos,
+ prev_attn=prev_attn,
+ mem=layer_mem,
+ )
elif layer_type == 'c':
- out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
+ out, inter = block(
+ x,
+ context=context,
+ mask=mask,
+ context_mask=context_mask,
+ prev_attn=prev_cross_attn,
+ )
elif layer_type == 'f':
out = block(x)
@@ -530,8 +602,7 @@ class AttentionLayers(nn.Module):
if return_hiddens:
intermediates = LayerIntermediates(
- hiddens=hiddens,
- attn_intermediates=intermediates
+ hiddens=hiddens, attn_intermediates=intermediates
)
return x, intermediates
@@ -545,23 +616,24 @@ class Encoder(AttentionLayers):
super().__init__(causal=False, **kwargs)
-
class TransformerWrapper(nn.Module):
def __init__(
- self,
- *,
- num_tokens,
- max_seq_len,
- attn_layers,
- emb_dim=None,
- max_mem_len=0.,
- emb_dropout=0.,
- num_memory_tokens=None,
- tie_embedding=False,
- use_pos_emb=True
+ self,
+ *,
+ num_tokens,
+ max_seq_len,
+ attn_layers,
+ emb_dim=None,
+ max_mem_len=0.0,
+ emb_dropout=0.0,
+ num_memory_tokens=None,
+ tie_embedding=False,
+ use_pos_emb=True,
):
super().__init__()
- assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+ assert isinstance(
+ attn_layers, AttentionLayers
+ ), 'attention layers must be one of Encoder or Decoder'
dim = attn_layers.dim
emb_dim = default(emb_dim, dim)
@@ -571,23 +643,34 @@ class TransformerWrapper(nn.Module):
self.num_tokens = num_tokens
self.token_emb = nn.Embedding(num_tokens, emb_dim)
- self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
- use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+ self.pos_emb = (
+ AbsolutePositionalEmbedding(emb_dim, max_seq_len)
+ if (use_pos_emb and not attn_layers.has_pos_emb)
+ else always(0)
+ )
self.emb_dropout = nn.Dropout(emb_dropout)
- self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+ self.project_emb = (
+ nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+ )
self.attn_layers = attn_layers
self.norm = nn.LayerNorm(dim)
self.init_()
- self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+ self.to_logits = (
+ nn.Linear(dim, num_tokens)
+ if not tie_embedding
+ else lambda t: t @ self.token_emb.weight.t()
+ )
# memory tokens (like [cls]) from Memory Transformers paper
num_memory_tokens = default(num_memory_tokens, 0)
self.num_memory_tokens = num_memory_tokens
if num_memory_tokens > 0:
- self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+ self.memory_tokens = nn.Parameter(
+ torch.randn(num_memory_tokens, dim)
+ )
# let funnel encoder know number of memory tokens, if specified
if hasattr(attn_layers, 'num_memory_tokens'):
@@ -597,20 +680,20 @@ class TransformerWrapper(nn.Module):
nn.init.normal_(self.token_emb.weight, std=0.02)
def forward(
- self,
- x,
- return_embeddings=False,
- mask=None,
- return_mems=False,
- return_attn=False,
- mems=None,
- embedding_manager=None,
- **kwargs
+ self,
+ x,
+ return_embeddings=False,
+ mask=None,
+ return_mems=False,
+ return_attn=False,
+ mems=None,
+ embedding_manager=None,
+ **kwargs,
):
b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
embedded_x = self.token_emb(x)
-
+
if embedding_manager:
x = embedding_manager(x, embedded_x)
else:
@@ -629,7 +712,9 @@ class TransformerWrapper(nn.Module):
if exists(mask):
mask = F.pad(mask, (num_mem, 0), value=True)
- x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+ x, intermediates = self.attn_layers(
+ x, mask=mask, mems=mems, return_hiddens=True, **kwargs
+ )
x = self.norm(x)
mem, x = x[:, :num_mem], x[:, num_mem:]
@@ -638,13 +723,30 @@ class TransformerWrapper(nn.Module):
if return_mems:
hiddens = intermediates.hiddens
- new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
- new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
+ new_mems = (
+ list(
+ map(
+ lambda pair: torch.cat(pair, dim=-2),
+ zip(mems, hiddens),
+ )
+ )
+ if exists(mems)
+ else hiddens
+ )
+ new_mems = list(
+ map(
+ lambda t: t[..., -self.max_mem_len :, :].detach(), new_mems
+ )
+ )
return out, new_mems
if return_attn:
- attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+ attn_maps = list(
+ map(
+ lambda t: t.post_softmax_attn,
+ intermediates.attn_intermediates,
+ )
+ )
return out, attn_maps
return out
-
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index f1f88bba5e..8aad3557af 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -24,10 +24,10 @@ import re
import traceback
from ldm.util import instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
from ldm.models.diffusion.ksampler import KSampler
-from ldm.dream.pngwriter import PngWriter
+from ldm.dream.pngwriter import PngWriter
"""Simplified text to image API for stable diffusion/latent diffusion
@@ -93,67 +93,69 @@ still work.
class T2I:
"""T2I class
- Attributes
- ----------
- model
- config
- iterations
- batch_size
- steps
- seed
- sampler_name
- width
- height
- cfg_scale
- latent_channels
- downsampling_factor
- precision
- strength
- embedding_path
+ Attributes
+ ----------
+ model
+ config
+ iterations
+ batch_size
+ steps
+ seed
+ sampler_name
+ width
+ height
+ cfg_scale
+ latent_channels
+ downsampling_factor
+ precision
+ strength
+ embedding_path
-The vast majority of these arguments default to reasonable values.
+ The vast majority of these arguments default to reasonable values.
"""
- def __init__(self,
- batch_size=1,
- iterations = 1,
- steps=50,
- seed=None,
- cfg_scale=7.5,
- weights="models/ldm/stable-diffusion-v1/model.ckpt",
- config = "configs/stable-diffusion/v1-inference.yaml",
- width=512,
- height=512,
- sampler_name="klms",
- latent_channels=4,
- downsampling_factor=8,
- ddim_eta=0.0, # deterministic
- precision='autocast',
- full_precision=False,
- strength=0.75, # default in scripts/img2img.py
- embedding_path=None,
- latent_diffusion_weights=False, # just to keep track of this parameter when regenerating prompt
- device='cuda',
- gfpgan=None,
+
+ def __init__(
+ self,
+ batch_size=1,
+ iterations=1,
+ steps=50,
+ seed=None,
+ cfg_scale=7.5,
+ weights='models/ldm/stable-diffusion-v1/model.ckpt',
+ config='configs/stable-diffusion/v1-inference.yaml',
+ width=512,
+ height=512,
+ sampler_name='klms',
+ latent_channels=4,
+ downsampling_factor=8,
+ ddim_eta=0.0, # deterministic
+ precision='autocast',
+ full_precision=False,
+ strength=0.75, # default in scripts/img2img.py
+ embedding_path=None,
+ latent_diffusion_weights=False, # just to keep track of this parameter when regenerating prompt
+ device='cuda',
+ gfpgan=None,
):
- self.batch_size = batch_size
+ self.batch_size = batch_size
self.iterations = iterations
- self.width = width
- self.height = height
- self.steps = steps
- self.cfg_scale = cfg_scale
- self.weights = weights
- self.config = config
- self.sampler_name = sampler_name
- self.latent_channels = latent_channels
+ self.width = width
+ self.height = height
+ self.steps = steps
+ self.cfg_scale = cfg_scale
+ self.weights = weights
+ self.config = config
+ self.sampler_name = sampler_name
+ self.latent_channels = latent_channels
self.downsampling_factor = downsampling_factor
- self.ddim_eta = ddim_eta
- self.precision = precision
- self.full_precision = full_precision
- self.strength = strength
- self.embedding_path = embedding_path
- self.model = None # empty for now
- self.sampler = None
- self.latent_diffusion_weights=latent_diffusion_weights
+ self.ddim_eta = ddim_eta
+ self.precision = precision
+ self.full_precision = full_precision
+ self.strength = strength
+ self.embedding_path = embedding_path
+ self.model = None # empty for now
+ self.sampler = None
+ self.latent_diffusion_weights = latent_diffusion_weights
self.device = device
self.gfpgan = gfpgan
if seed is None:
@@ -162,49 +164,55 @@ The vast majority of these arguments default to reasonable values.
self.seed = seed
transformers.logging.set_verbosity_error()
- def prompt2png(self,prompt,outdir,**kwargs):
- '''
+ def prompt2png(self, prompt, outdir, **kwargs):
+ """
Takes a prompt and an output directory, writes out the requested number
of PNG files, and returns an array of [[filename,seed],[filename,seed]...]
Optional named arguments are the same as those passed to T2I and prompt2image()
- '''
- results = self.prompt2image(prompt,**kwargs)
- pngwriter = PngWriter(outdir,prompt,kwargs.get('batch_size',self.batch_size))
+ """
+ results = self.prompt2image(prompt, **kwargs)
+ pngwriter = PngWriter(
+ outdir, prompt, kwargs.get('batch_size', self.batch_size)
+ )
for r in results:
metadata_str = f'prompt2png("{prompt}" {kwargs} seed={r[1]}' # gets written into the PNG
- pngwriter.write_image(r[0],r[1])
+ pngwriter.write_image(r[0], r[1])
return pngwriter.files_written
- def txt2img(self,prompt,**kwargs):
- outdir = kwargs.get('outdir','outputs/img-samples')
- return self.prompt2png(prompt,outdir,**kwargs)
+ def txt2img(self, prompt, **kwargs):
+ outdir = kwargs.get('outdir', 'outputs/img-samples')
+ return self.prompt2png(prompt, outdir, **kwargs)
- def img2img(self,prompt,**kwargs):
- outdir = kwargs.get('outdir','outputs/img-samples')
- assert 'init_img' in kwargs,'call to img2img() must include the init_img argument'
- return self.prompt2png(prompt,outdir,**kwargs)
+ def img2img(self, prompt, **kwargs):
+ outdir = kwargs.get('outdir', 'outputs/img-samples')
+ assert (
+ 'init_img' in kwargs
+ ), 'call to img2img() must include the init_img argument'
+ return self.prompt2png(prompt, outdir, **kwargs)
- def prompt2image(self,
- # these are common
- prompt,
- batch_size=None,
- iterations=None,
- steps=None,
- seed=None,
- cfg_scale=None,
- ddim_eta=None,
- skip_normalize=False,
- image_callback=None,
- # these are specific to txt2img
- width=None,
- height=None,
- # these are specific to img2img
- init_img=None,
- strength=None,
- gfpgan_strength=None,
- variants=None,
- **args): # eat up additional cruft
- '''
+ def prompt2image(
+ self,
+ # these are common
+ prompt,
+ batch_size=None,
+ iterations=None,
+ steps=None,
+ seed=None,
+ cfg_scale=None,
+ ddim_eta=None,
+ skip_normalize=False,
+ image_callback=None,
+ # these are specific to txt2img
+ width=None,
+ height=None,
+ # these are specific to img2img
+ init_img=None,
+ strength=None,
+ gfpgan_strength=None,
+ variants=None,
+ **args,
+ ): # eat up additional cruft
+ """
ldm.prompt2image() is the common entry point for txt2img() and img2img()
It takes the following arguments:
prompt // prompt string (no default)
@@ -232,118 +240,157 @@ The vast majority of these arguments default to reasonable values.
The callback used by the prompt2png() can be found in ldm/dream_util.py. It contains code
to create the requested output directory, select a unique informative name for each image, and
write the prompt into the PNG metadata.
- '''
- steps = steps or self.steps
- seed = seed or self.seed
- width = width or self.width
- height = height or self.height
- cfg_scale = cfg_scale or self.cfg_scale
- ddim_eta = ddim_eta or self.ddim_eta
+ """
+ steps = steps or self.steps
+ seed = seed or self.seed
+ width = width or self.width
+ height = height or self.height
+ cfg_scale = cfg_scale or self.cfg_scale
+ ddim_eta = ddim_eta or self.ddim_eta
batch_size = batch_size or self.batch_size
iterations = iterations or self.iterations
- strength = strength or self.strength
+ strength = strength or self.strength
- model = self.load_model() # will instantiate the model or return it from cache
- assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
- assert 0. <= strength <= 1., 'can only work with strength in [0.0, 1.0]'
- w = int(width/64) * 64
- h = int(height/64) * 64
+ model = (
+ self.load_model()
+ ) # will instantiate the model or return it from cache
+ assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0'
+ assert (
+ 0.0 <= strength <= 1.0
+ ), 'can only work with strength in [0.0, 1.0]'
+ w = int(width / 64) * 64
+ h = int(height / 64) * 64
if h != height or w != width:
- print(f'Height and width must be multiples of 64. Resizing to {h}x{w}')
+ print(
+ f'Height and width must be multiples of 64. Resizing to {h}x{w}'
+ )
height = h
- width = w
+ width = w
- scope = autocast if self.precision=="autocast" else nullcontext
+ scope = autocast if self.precision == 'autocast' else nullcontext
tic = time.time()
results = list()
try:
if init_img:
- assert os.path.exists(init_img),f'{init_img}: File not found'
- images_iterator = self._img2img(prompt,
- precision_scope=scope,
- batch_size=batch_size,
- steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
- skip_normalize=skip_normalize,
- init_img=init_img,strength=strength)
+ assert os.path.exists(init_img), f'{init_img}: File not found'
+ images_iterator = self._img2img(
+ prompt,
+ precision_scope=scope,
+ batch_size=batch_size,
+ steps=steps,
+ cfg_scale=cfg_scale,
+ ddim_eta=ddim_eta,
+ skip_normalize=skip_normalize,
+ init_img=init_img,
+ strength=strength,
+ )
else:
- images_iterator = self._txt2img(prompt,
- precision_scope=scope,
- batch_size=batch_size,
- steps=steps,cfg_scale=cfg_scale,ddim_eta=ddim_eta,
- skip_normalize=skip_normalize,
- width=width,height=height)
+ images_iterator = self._txt2img(
+ prompt,
+ precision_scope=scope,
+ batch_size=batch_size,
+ steps=steps,
+ cfg_scale=cfg_scale,
+ ddim_eta=ddim_eta,
+ skip_normalize=skip_normalize,
+ width=width,
+ height=height,
+ )
with scope(self.device.type), self.model.ema_scope():
- for n in trange(iterations, desc="Sampling"):
+ for n in trange(iterations, desc='Sampling'):
seed_everything(seed)
iter_images = next(images_iterator)
for image in iter_images:
try:
if gfpgan_strength > 0:
- image = self._run_gfpgan(image, gfpgan_strength)
+ image = self._run_gfpgan(
+ image, gfpgan_strength
+ )
except Exception as e:
- print(f"Error running GFPGAN - Your image was not enhanced.\n{e}")
+ print(
+ f'Error running GFPGAN - Your image was not enhanced.\n{e}'
+ )
results.append([image, seed])
if image_callback is not None:
- image_callback(image,seed)
+ image_callback(image, seed)
seed = self._new_seed()
except KeyboardInterrupt:
print('*interrupted*')
- print('Partial results will be returned; if --grid was requested, nothing will be returned.')
+ print(
+ 'Partial results will be returned; if --grid was requested, nothing will be returned.'
+ )
except RuntimeError as e:
print(str(e))
print('Are you sure your system has an adequate NVIDIA GPU?')
- toc = time.time()
- print(f'{len(results)} images generated in',"%4.2fs"% (toc-tic))
+ toc = time.time()
+ print(f'{len(results)} images generated in', '%4.2fs' % (toc - tic))
return results
@torch.no_grad()
- def _txt2img(self,
- prompt,
- precision_scope,
- batch_size,
- steps,cfg_scale,ddim_eta,
- skip_normalize,
- width,height):
+ def _txt2img(
+ self,
+ prompt,
+ precision_scope,
+ batch_size,
+ steps,
+ cfg_scale,
+ ddim_eta,
+ skip_normalize,
+ width,
+ height,
+ ):
"""
An infinite iterator of images from the prompt.
"""
-
sampler = self.sampler
while True:
uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
- shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
- samples, _ = sampler.sample(S=steps,
- conditioning=c,
- batch_size=batch_size,
- shape=shape,
- verbose=False,
- unconditional_guidance_scale=cfg_scale,
- unconditional_conditioning=uc,
- eta=ddim_eta)
+ shape = [
+ self.latent_channels,
+ height // self.downsampling_factor,
+ width // self.downsampling_factor,
+ ]
+ samples, _ = sampler.sample(
+ S=steps,
+ conditioning=c,
+ batch_size=batch_size,
+ shape=shape,
+ verbose=False,
+ unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,
+ eta=ddim_eta,
+ )
yield self._samples_to_images(samples)
@torch.no_grad()
- def _img2img(self,
- prompt,
- precision_scope,
- batch_size,
- steps,cfg_scale,ddim_eta,
- skip_normalize,
- init_img,strength):
+ def _img2img(
+ self,
+ prompt,
+ precision_scope,
+ batch_size,
+ steps,
+ cfg_scale,
+ ddim_eta,
+ skip_normalize,
+ init_img,
+ strength,
+ ):
"""
An infinite iterator of images from the prompt and the initial image
"""
# PLMS sampler not supported yet, so ignore previous sampler
- if self.sampler_name!='ddim':
- print(f"sampler '{self.sampler_name}' is not yet supported. Using DDM sampler")
+ if self.sampler_name != 'ddim':
+ print(
+ f"sampler '{self.sampler_name}' is not yet supported. Using DDM sampler"
+ )
sampler = DDIMSampler(self.model, device=self.device)
else:
sampler = self.sampler
@@ -351,9 +398,13 @@ The vast majority of these arguments default to reasonable values.
init_image = self._load_img(init_img).to(self.device)
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
with precision_scope(self.device.type):
- init_latent = self.model.get_first_stage_encoding(self.model.encode_first_stage(init_image)) # move to latent space
+ init_latent = self.model.get_first_stage_encoding(
+ self.model.encode_first_stage(init_image)
+ ) # move to latent space
- sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
+ sampler.make_schedule(
+ ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+ )
t_enc = int(strength * steps)
# print(f"target t_enc is {t_enc} steps")
@@ -362,31 +413,44 @@ The vast majority of these arguments default to reasonable values.
uc, c = self._get_uc_and_c(prompt, batch_size, skip_normalize)
# encode (scaled latent)
- z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
+ z_enc = sampler.stochastic_encode(
+ init_latent, torch.tensor([t_enc] * batch_size).to(self.device)
+ )
# decode it
- samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
- unconditional_conditioning=uc,)
+ samples = sampler.decode(
+ z_enc,
+ c,
+ t_enc,
+ unconditional_guidance_scale=cfg_scale,
+ unconditional_conditioning=uc,
+ )
yield self._samples_to_images(samples)
# TODO: does this actually need to run every loop? does anything in it vary by random seed?
def _get_uc_and_c(self, prompt, batch_size, skip_normalize):
- uc = self.model.get_learned_conditioning(batch_size * [""])
+ uc = self.model.get_learned_conditioning(batch_size * [''])
# weighted sub-prompts
- subprompts,weights = T2I._split_weighted_subprompts(prompt)
+ subprompts, weights = T2I._split_weighted_subprompts(prompt)
if len(subprompts) > 1:
# i dont know if this is correct.. but it works
c = torch.zeros_like(uc)
# get total weight for normalizing
totalWeight = sum(weights)
# normalize each "sub prompt" and add it
- for i in range(0,len(subprompts)):
+ for i in range(0, len(subprompts)):
weight = weights[i]
if not skip_normalize:
weight = weight / totalWeight
- c = torch.add(c, self.model.get_learned_conditioning(batch_size * [subprompts[i]]), alpha=weight)
- else: # just standard 1 prompt
+ c = torch.add(
+ c,
+ self.model.get_learned_conditioning(
+ batch_size * [subprompts[i]]
+ ),
+ alpha=weight,
+ )
+ else: # just standard 1 prompt
c = self.model.get_learned_conditioning(batch_size * [prompt])
return (uc, c)
@@ -395,23 +459,29 @@ The vast majority of these arguments default to reasonable values.
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
images = list()
for x_sample in x_samples:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+ x_sample = 255.0 * rearrange(
+ x_sample.cpu().numpy(), 'c h w -> h w c'
+ )
image = Image.fromarray(x_sample.astype(np.uint8))
images.append(image)
return images
def _new_seed(self):
- self.seed = random.randrange(0,np.iinfo(np.uint32).max)
+ self.seed = random.randrange(0, np.iinfo(np.uint32).max)
return self.seed
def load_model(self):
- """ Load and initialize the model from configuration variables passed at object creation time """
+ """Load and initialize the model from configuration variables passed at object creation time"""
if self.model is None:
seed_everything(self.seed)
try:
config = OmegaConf.load(self.config)
- self.device = torch.device(self.device) if torch.cuda.is_available() else torch.device("cpu")
- model = self._load_model_from_config(config,self.weights)
+ self.device = (
+ torch.device(self.device)
+ if torch.cuda.is_available()
+ else torch.device('cpu')
+ )
+ model = self._load_model_from_config(config, self.weights)
if self.embedding_path is not None:
model.embedding_manager.load(self.embedding_path)
self.model = model.to(self.device)
@@ -421,18 +491,26 @@ The vast majority of these arguments default to reasonable values.
raise SystemExit
msg = f'setting sampler to {self.sampler_name}'
- if self.sampler_name=='plms':
+ if self.sampler_name == 'plms':
self.sampler = PLMSSampler(self.model, device=self.device)
elif self.sampler_name == 'ddim':
self.sampler = DDIMSampler(self.model, device=self.device)
elif self.sampler_name == 'k_dpm_2_a':
- self.sampler = KSampler(self.model, 'dpm_2_ancestral', device=self.device)
+ self.sampler = KSampler(
+ self.model, 'dpm_2_ancestral', device=self.device
+ )
elif self.sampler_name == 'k_dpm_2':
- self.sampler = KSampler(self.model, 'dpm_2', device=self.device)
+ self.sampler = KSampler(
+ self.model, 'dpm_2', device=self.device
+ )
elif self.sampler_name == 'k_euler_a':
- self.sampler = KSampler(self.model, 'euler_ancestral', device=self.device)
+ self.sampler = KSampler(
+ self.model, 'euler_ancestral', device=self.device
+ )
elif self.sampler_name == 'k_euler':
- self.sampler = KSampler(self.model, 'euler', device=self.device)
+ self.sampler = KSampler(
+ self.model, 'euler', device=self.device
+ )
elif self.sampler_name == 'k_heun':
self.sampler = KSampler(self.model, 'heun', device=self.device)
elif self.sampler_name == 'k_lms':
@@ -446,32 +524,38 @@ The vast majority of these arguments default to reasonable values.
return self.model
def _load_model_from_config(self, config, ckpt):
- print(f"Loading model from {ckpt}")
- pl_sd = torch.load(ckpt, map_location="cpu")
-# if "global_step" in pl_sd:
-# print(f"Global Step: {pl_sd['global_step']}")
- sd = pl_sd["state_dict"]
+ print(f'Loading model from {ckpt}')
+ pl_sd = torch.load(ckpt, map_location='cpu')
+ # if "global_step" in pl_sd:
+ # print(f"Global Step: {pl_sd['global_step']}")
+ sd = pl_sd['state_dict']
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
model.to(self.device)
model.eval()
if self.full_precision:
- print('Using slower but more accurate full-precision math (--full_precision)')
+ print(
+ 'Using slower but more accurate full-precision math (--full_precision)'
+ )
else:
- print('Using half precision math. Call with --full_precision to use slower but more accurate full precision.')
+ print(
+ 'Using half precision math. Call with --full_precision to use slower but more accurate full precision.'
+ )
model.half()
return model
- def _load_img(self,path):
- image = Image.open(path).convert("RGB")
+ def _load_img(self, path):
+ image = Image.open(path).convert('RGB')
w, h = image.size
- print(f"loaded input image of size ({w}, {h}) from {path}")
- w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
+ print(f'loaded input image of size ({w}, {h}) from {path}')
+ w, h = map(
+ lambda x: x - x % 32, (w, h)
+ ) # resize to integer multiple of 32
image = image.resize((w, h), resample=Image.Resampling.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
- return 2.*image - 1.
+ return 2.0 * image - 1.0
def _split_weighted_subprompts(text):
"""
@@ -484,34 +568,36 @@ The vast majority of these arguments default to reasonable values.
prompts = []
weights = []
while remaining > 0:
- if ":" in text:
- idx = text.index(":") # first occurrence from start
+ if ':' in text:
+ idx = text.index(':') # first occurrence from start
# grab up to index as sub-prompt
prompt = text[:idx]
remaining -= idx
# remove from main text
- text = text[idx+1:]
+ text = text[idx + 1 :]
# find value for weight
- if " " in text:
- idx = text.index(" ") # first occurence
- else: # no space, read to end
+ if ' ' in text:
+ idx = text.index(' ') # first occurence
+ else: # no space, read to end
idx = len(text)
if idx != 0:
try:
weight = float(text[:idx])
- except: # couldn't treat as float
- print(f"Warning: '{text[:idx]}' is not a value, are you missing a space?")
+ except: # couldn't treat as float
+ print(
+ f"Warning: '{text[:idx]}' is not a value, are you missing a space?"
+ )
weight = 1.0
- else: # no value found
+ else: # no value found
weight = 1.0
# remove from main text
remaining -= idx
- text = text[idx+1:]
+ text = text[idx + 1 :]
# append the sub-prompt and its weight
prompts.append(prompt)
weights.append(weight)
- else: # no : found
- if len(text) > 0: # there is still text though
+ else: # no : found
+ if len(text) > 0: # there is still text though
# take remainder as weight 1
prompts.append(text)
weights.append(1.0)
@@ -519,13 +605,20 @@ The vast majority of these arguments default to reasonable values.
return prompts, weights
def _run_gfpgan(self, image, strength):
- if (self.gfpgan is None):
- print(f"GFPGAN not initialized, it must be loaded via the --gfpgan argument")
+ if self.gfpgan is None:
+ print(
+ f'GFPGAN not initialized, it must be loaded via the --gfpgan argument'
+ )
return image
-
- image = image.convert("RGB")
- cropped_faces, restored_faces, restored_img = self.gfpgan.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
+ image = image.convert('RGB')
+
+ cropped_faces, restored_faces, restored_img = self.gfpgan.enhance(
+ np.array(image, dtype=np.uint8),
+ has_aligned=False,
+ only_center_face=False,
+ paste_back=True,
+ )
res = Image.fromarray(restored_img)
if strength < 1.0:
diff --git a/ldm/util.py b/ldm/util.py
index 3affd249de..d1379cae2b 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -13,22 +13,25 @@ from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
+
def log_txt_as_img(wh, xc, size=10):
# wh a tuple of (width, height)
# xc a list of captions to plot
b = len(xc)
txts = list()
for bi in range(b):
- txt = Image.new("RGB", wh, color="white")
+ txt = Image.new('RGB', wh, color='white')
draw = ImageDraw.Draw(txt)
font = ImageFont.load_default()
nc = int(40 * (wh[0] / 256))
- lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+ lines = '\n'.join(
+ xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
+ )
try:
- draw.text((0, 0), lines, fill="black", font=font)
+ draw.text((0, 0), lines, fill='black', font=font)
except UnicodeEncodeError:
- print("Cant encode string for logging. Skipping.")
+ print('Cant encode string for logging. Skipping.')
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
@@ -70,22 +73,26 @@ def mean_flat(tensor):
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
- print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
+ print(
+ f'{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'
+ )
return total_params
def instantiate_from_config(config, **kwargs):
- if not "target" in config:
+ if not 'target' in config:
if config == '__is_first_stage__':
return None
- elif config == "__is_unconditional__":
+ elif config == '__is_unconditional__':
return None
- raise KeyError("Expected key `target` to instantiate.")
- return get_obj_from_str(config["target"])(**config.get("params", dict()), **kwargs)
+ raise KeyError('Expected key `target` to instantiate.')
+ return get_obj_from_str(config['target'])(
+ **config.get('params', dict()), **kwargs
+ )
def get_obj_from_str(string, reload=False):
- module, cls = string.rsplit(".", 1)
+ module, cls = string.rsplit('.', 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
@@ -101,31 +108,36 @@ def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
else:
res = func(data)
Q.put([idx, res])
- Q.put("Done")
+ Q.put('Done')
def parallel_data_prefetch(
- func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
+ func: callable,
+ data,
+ n_proc,
+ target_data_type='ndarray',
+ cpu_intensive=True,
+ use_worker_id=False,
):
# if target_data_type not in ["ndarray", "list"]:
# raise ValueError(
# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
# )
- if isinstance(data, np.ndarray) and target_data_type == "list":
- raise ValueError("list expected but function got ndarray.")
+ if isinstance(data, np.ndarray) and target_data_type == 'list':
+ raise ValueError('list expected but function got ndarray.')
elif isinstance(data, abc.Iterable):
if isinstance(data, dict):
print(
f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
)
data = list(data.values())
- if target_data_type == "ndarray":
+ if target_data_type == 'ndarray':
data = np.asarray(data)
else:
data = list(data)
else:
raise TypeError(
- f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
+ f'The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}.'
)
if cpu_intensive:
@@ -135,7 +147,7 @@ def parallel_data_prefetch(
Q = Queue(1000)
proc = Thread
# spawn processes
- if target_data_type == "ndarray":
+ if target_data_type == 'ndarray':
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(np.array_split(data, n_proc))
@@ -149,7 +161,7 @@ def parallel_data_prefetch(
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(
- [data[i: i + step] for i in range(0, len(data), step)]
+ [data[i : i + step] for i in range(0, len(data), step)]
)
]
processes = []
@@ -158,7 +170,7 @@ def parallel_data_prefetch(
processes += [p]
# start processes
- print(f"Start prefetching...")
+ print(f'Start prefetching...')
import time
start = time.time()
@@ -171,13 +183,13 @@ def parallel_data_prefetch(
while k < n_proc:
# get result
res = Q.get()
- if res == "Done":
+ if res == 'Done':
k += 1
else:
gather_res[res[0]] = res[1]
except Exception as e:
- print("Exception: ", e)
+ print('Exception: ', e)
for p in processes:
p.terminate()
@@ -185,7 +197,7 @@ def parallel_data_prefetch(
finally:
for p in processes:
p.join()
- print(f"Prefetching complete. [{time.time() - start} sec.]")
+ print(f'Prefetching complete. [{time.time() - start} sec.]')
if target_data_type == 'ndarray':
if not isinstance(gather_res[0], np.ndarray):
diff --git a/main.py b/main.py
index 5653cf5e06..8c36c270b1 100644
--- a/main.py
+++ b/main.py
@@ -14,145 +14,171 @@ from PIL import Image
from pytorch_lightning import seed_everything
from pytorch_lightning.trainer import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
+from pytorch_lightning.callbacks import (
+ ModelCheckpoint,
+ Callback,
+ LearningRateMonitor,
+)
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities import rank_zero_info
from ldm.data.base import Txt2ImgIterableBaseDataset
from ldm.util import instantiate_from_config
+
def load_model_from_config(config, ckpt, verbose=False):
- print(f"Loading model from {ckpt}")
- pl_sd = torch.load(ckpt, map_location="cpu")
- sd = pl_sd["state_dict"]
+ print(f'Loading model from {ckpt}')
+ pl_sd = torch.load(ckpt, map_location='cpu')
+ sd = pl_sd['state_dict']
config.model.params.ckpt_path = ckpt
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
- print("missing keys:")
+ print('missing keys:')
print(m)
if len(u) > 0 and verbose:
- print("unexpected keys:")
+ print('unexpected keys:')
print(u)
model.cuda()
return model
+
def get_parser(**parser_kwargs):
def str2bool(v):
if isinstance(v, bool):
return v
- if v.lower() in ("yes", "true", "t", "y", "1"):
+ if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
- elif v.lower() in ("no", "false", "f", "n", "0"):
+ elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
- raise argparse.ArgumentTypeError("Boolean value expected.")
+ raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser(**parser_kwargs)
parser.add_argument(
- "-n",
- "--name",
+ '-n',
+ '--name',
type=str,
const=True,
- default="",
- nargs="?",
- help="postfix for logdir",
+ default='',
+ nargs='?',
+ help='postfix for logdir',
)
parser.add_argument(
- "-r",
- "--resume",
+ '-r',
+ '--resume',
type=str,
const=True,
- default="",
- nargs="?",
- help="resume from logdir or checkpoint in logdir",
+ default='',
+ nargs='?',
+ help='resume from logdir or checkpoint in logdir',
)
parser.add_argument(
- "-b",
- "--base",
- nargs="*",
- metavar="base_config.yaml",
- help="paths to base configs. Loaded from left-to-right. "
- "Parameters can be overwritten or added with command-line options of the form `--key value`.",
+ '-b',
+ '--base',
+ nargs='*',
+ metavar='base_config.yaml',
+ help='paths to base configs. Loaded from left-to-right. '
+ 'Parameters can be overwritten or added with command-line options of the form `--key value`.',
default=list(),
)
parser.add_argument(
- "-t",
- "--train",
+ '-t',
+ '--train',
type=str2bool,
const=True,
default=False,
- nargs="?",
- help="train",
+ nargs='?',
+ help='train',
)
parser.add_argument(
- "--no-test",
+ '--no-test',
type=str2bool,
const=True,
default=False,
- nargs="?",
- help="disable test",
+ nargs='?',
+ help='disable test',
)
parser.add_argument(
- "-p",
- "--project",
- help="name of new or path to existing project"
+ '-p', '--project', help='name of new or path to existing project'
)
parser.add_argument(
- "-d",
- "--debug",
+ '-d',
+ '--debug',
type=str2bool,
- nargs="?",
+ nargs='?',
const=True,
default=False,
- help="enable post-mortem debugging",
+ help='enable post-mortem debugging',
)
parser.add_argument(
- "-s",
- "--seed",
+ '-s',
+ '--seed',
type=int,
default=23,
- help="seed for seed_everything",
+ help='seed for seed_everything',
)
parser.add_argument(
- "-f",
- "--postfix",
+ '-f',
+ '--postfix',
type=str,
- default="",
- help="post-postfix for default name",
+ default='',
+ help='post-postfix for default name',
)
parser.add_argument(
- "-l",
- "--logdir",
+ '-l',
+ '--logdir',
type=str,
- default="logs",
- help="directory for logging dat shit",
+ default='logs',
+ help='directory for logging dat shit',
)
parser.add_argument(
- "--scale_lr",
+ '--scale_lr',
type=str2bool,
- nargs="?",
+ nargs='?',
const=True,
default=True,
- help="scale base-lr by ngpu * batch_size * n_accumulate",
+ help='scale base-lr by ngpu * batch_size * n_accumulate',
)
parser.add_argument(
- "--datadir_in_name",
- type=str2bool,
- nargs="?",
- const=True,
- default=True,
- help="Prepend the final directory in the data_root to the output directory name")
+ '--datadir_in_name',
+ type=str2bool,
+ nargs='?',
+ const=True,
+ default=True,
+ help='Prepend the final directory in the data_root to the output directory name',
+ )
- parser.add_argument("--actual_resume", type=str, default="", help="Path to model to actually resume from")
- parser.add_argument("--data_root", type=str, required=True, help="Path to directory with training images")
+ parser.add_argument(
+ '--actual_resume',
+ type=str,
+ default='',
+ help='Path to model to actually resume from',
+ )
+ parser.add_argument(
+ '--data_root',
+ type=str,
+ required=True,
+ help='Path to directory with training images',
+ )
- parser.add_argument("--embedding_manager_ckpt", type=str, default="", help="Initialize embedding manager from a checkpoint")
- parser.add_argument("--placeholder_tokens", type=str, nargs="+", default=["*"])
+ parser.add_argument(
+ '--embedding_manager_ckpt',
+ type=str,
+ default='',
+ help='Initialize embedding manager from a checkpoint',
+ )
+ parser.add_argument(
+ '--placeholder_tokens', type=str, nargs='+', default=['*']
+ )
- parser.add_argument("--init_word", type=str, help="Word to use as source for initial token embedding.")
+ parser.add_argument(
+ '--init_word',
+ type=str,
+ help='Word to use as source for initial token embedding.',
+ )
return parser
@@ -186,7 +212,9 @@ def worker_init_fn(_):
if isinstance(dataset, Txt2ImgIterableBaseDataset):
split_size = dataset.num_records // worker_info.num_workers
# reset num_records to the true number to retain reliable length information
- dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
+ dataset.sample_ids = dataset.valid_ids[
+ worker_id * split_size : (worker_id + 1) * split_size
+ ]
current_id = np.random.choice(len(np.random.get_state()[1]), 1)
return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
else:
@@ -194,25 +222,41 @@ def worker_init_fn(_):
class DataModuleFromConfig(pl.LightningDataModule):
- def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
- wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
- shuffle_val_dataloader=False):
+ def __init__(
+ self,
+ batch_size,
+ train=None,
+ validation=None,
+ test=None,
+ predict=None,
+ wrap=False,
+ num_workers=None,
+ shuffle_test_loader=False,
+ use_worker_init_fn=False,
+ shuffle_val_dataloader=False,
+ ):
super().__init__()
self.batch_size = batch_size
self.dataset_configs = dict()
- self.num_workers = num_workers if num_workers is not None else batch_size * 2
+ self.num_workers = (
+ num_workers if num_workers is not None else batch_size * 2
+ )
self.use_worker_init_fn = use_worker_init_fn
if train is not None:
- self.dataset_configs["train"] = train
+ self.dataset_configs['train'] = train
self.train_dataloader = self._train_dataloader
if validation is not None:
- self.dataset_configs["validation"] = validation
- self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
+ self.dataset_configs['validation'] = validation
+ self.val_dataloader = partial(
+ self._val_dataloader, shuffle=shuffle_val_dataloader
+ )
if test is not None:
- self.dataset_configs["test"] = test
- self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
+ self.dataset_configs['test'] = test
+ self.test_dataloader = partial(
+ self._test_dataloader, shuffle=shuffle_test_loader
+ )
if predict is not None:
- self.dataset_configs["predict"] = predict
+ self.dataset_configs['predict'] = predict
self.predict_dataloader = self._predict_dataloader
self.wrap = wrap
@@ -223,34 +267,48 @@ class DataModuleFromConfig(pl.LightningDataModule):
def setup(self, stage=None):
self.datasets = dict(
(k, instantiate_from_config(self.dataset_configs[k]))
- for k in self.dataset_configs)
+ for k in self.dataset_configs
+ )
if self.wrap:
for k in self.datasets:
self.datasets[k] = WrappedDataset(self.datasets[k])
def _train_dataloader(self):
- is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
+ is_iterable_dataset = isinstance(
+ self.datasets['train'], Txt2ImgIterableBaseDataset
+ )
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
init_fn = None
- return DataLoader(self.datasets["train"], batch_size=self.batch_size,
- num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True,
- worker_init_fn=init_fn)
+ return DataLoader(
+ self.datasets['train'],
+ batch_size=self.batch_size,
+ num_workers=self.num_workers,
+ shuffle=False if is_iterable_dataset else True,
+ worker_init_fn=init_fn,
+ )
def _val_dataloader(self, shuffle=False):
- if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
+ if (
+ isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset)
+ or self.use_worker_init_fn
+ ):
init_fn = worker_init_fn
else:
init_fn = None
- return DataLoader(self.datasets["validation"],
- batch_size=self.batch_size,
- num_workers=self.num_workers,
- worker_init_fn=init_fn,
- shuffle=shuffle)
+ return DataLoader(
+ self.datasets['validation'],
+ batch_size=self.batch_size,
+ num_workers=self.num_workers,
+ worker_init_fn=init_fn,
+ shuffle=shuffle,
+ )
def _test_dataloader(self, shuffle=False):
- is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
+ is_iterable_dataset = isinstance(
+ self.datasets['train'], Txt2ImgIterableBaseDataset
+ )
if is_iterable_dataset or self.use_worker_init_fn:
init_fn = worker_init_fn
else:
@@ -259,20 +317,34 @@ class DataModuleFromConfig(pl.LightningDataModule):
# do not shuffle dataloader for iterable dataset
shuffle = shuffle and (not is_iterable_dataset)
- return DataLoader(self.datasets["test"], batch_size=self.batch_size,
- num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)
+ return DataLoader(
+ self.datasets['test'],
+ batch_size=self.batch_size,
+ num_workers=self.num_workers,
+ worker_init_fn=init_fn,
+ shuffle=shuffle,
+ )
def _predict_dataloader(self, shuffle=False):
- if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
+ if (
+ isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset)
+ or self.use_worker_init_fn
+ ):
init_fn = worker_init_fn
else:
init_fn = None
- return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
- num_workers=self.num_workers, worker_init_fn=init_fn)
+ return DataLoader(
+ self.datasets['predict'],
+ batch_size=self.batch_size,
+ num_workers=self.num_workers,
+ worker_init_fn=init_fn,
+ )
class SetupCallback(Callback):
- def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
+ def __init__(
+ self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config
+ ):
super().__init__()
self.resume = resume
self.now = now
@@ -284,8 +356,8 @@ class SetupCallback(Callback):
def on_keyboard_interrupt(self, trainer, pl_module):
if trainer.global_rank == 0:
- print("Summoning checkpoint.")
- ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
+ print('Summoning checkpoint.')
+ ckpt_path = os.path.join(self.ckptdir, 'last.ckpt')
trainer.save_checkpoint(ckpt_path)
def on_pretrain_routine_start(self, trainer, pl_module):
@@ -295,24 +367,36 @@ class SetupCallback(Callback):
os.makedirs(self.ckptdir, exist_ok=True)
os.makedirs(self.cfgdir, exist_ok=True)
- if "callbacks" in self.lightning_config:
- if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
- os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
- print("Project config")
+ if 'callbacks' in self.lightning_config:
+ if (
+ 'metrics_over_trainsteps_checkpoint'
+ in self.lightning_config['callbacks']
+ ):
+ os.makedirs(
+ os.path.join(self.ckptdir, 'trainstep_checkpoints'),
+ exist_ok=True,
+ )
+ print('Project config')
print(OmegaConf.to_yaml(self.config))
- OmegaConf.save(self.config,
- os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
+ OmegaConf.save(
+ self.config,
+ os.path.join(self.cfgdir, '{}-project.yaml'.format(self.now)),
+ )
- print("Lightning config")
+ print('Lightning config')
print(OmegaConf.to_yaml(self.lightning_config))
- OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
- os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
+ OmegaConf.save(
+ OmegaConf.create({'lightning': self.lightning_config}),
+ os.path.join(
+ self.cfgdir, '{}-lightning.yaml'.format(self.now)
+ ),
+ )
else:
# ModelCheckpoint callback created log directory --- remove it
if not self.resume and os.path.exists(self.logdir):
dst, name = os.path.split(self.logdir)
- dst = os.path.join(dst, "child_runs", name)
+ dst = os.path.join(dst, 'child_runs', name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
try:
os.rename(self.logdir, dst)
@@ -321,9 +405,18 @@ class SetupCallback(Callback):
class ImageLogger(Callback):
- def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
- rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
- log_images_kwargs=None):
+ def __init__(
+ self,
+ batch_frequency,
+ max_images,
+ clamp=True,
+ increase_log_steps=True,
+ rescale=True,
+ disabled=False,
+ log_on_batch_idx=False,
+ log_first_step=False,
+ log_images_kwargs=None,
+ ):
super().__init__()
self.rescale = rescale
self.batch_freq = batch_frequency
@@ -331,7 +424,9 @@ class ImageLogger(Callback):
self.logger_log_images = {
pl.loggers.TestTubeLogger: self._testtube,
}
- self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
+ self.log_steps = [
+ 2**n for n in range(int(np.log2(self.batch_freq)) + 1)
+ ]
if not increase_log_steps:
self.log_steps = [self.batch_freq]
self.clamp = clamp
@@ -346,15 +441,16 @@ class ImageLogger(Callback):
grid = torchvision.utils.make_grid(images[k])
grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
- tag = f"{split}/{k}"
+ tag = f'{split}/{k}'
pl_module.logger.experiment.add_image(
- tag, grid,
- global_step=pl_module.global_step)
+ tag, grid, global_step=pl_module.global_step
+ )
@rank_zero_only
- def log_local(self, save_dir, split, images,
- global_step, current_epoch, batch_idx):
- root = os.path.join(save_dir, "images", split)
+ def log_local(
+ self, save_dir, split, images, global_step, current_epoch, batch_idx
+ ):
+ root = os.path.join(save_dir, 'images', split)
for k in images:
grid = torchvision.utils.make_grid(images[k], nrow=4)
if self.rescale:
@@ -362,21 +458,25 @@ class ImageLogger(Callback):
grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
grid = grid.numpy()
grid = (grid * 255).astype(np.uint8)
- filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
- k,
- global_step,
- current_epoch,
- batch_idx)
+ filename = '{}_gs-{:06}_e-{:06}_b-{:06}.png'.format(
+ k, global_step, current_epoch, batch_idx
+ )
path = os.path.join(root, filename)
os.makedirs(os.path.split(path)[0], exist_ok=True)
Image.fromarray(grid).save(path)
- def log_img(self, pl_module, batch, batch_idx, split="train"):
- check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
- if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
- hasattr(pl_module, "log_images") and
- callable(pl_module.log_images) and
- self.max_images > 0):
+ def log_img(self, pl_module, batch, batch_idx, split='train'):
+ check_idx = (
+ batch_idx if self.log_on_batch_idx else pl_module.global_step
+ )
+ if (
+ self.check_frequency(check_idx)
+ and hasattr( # batch_idx % self.batch_freq == 0
+ pl_module, 'log_images'
+ )
+ and callable(pl_module.log_images)
+ and self.max_images > 0
+ ):
logger = type(pl_module.logger)
is_train = pl_module.training
@@ -384,7 +484,9 @@ class ImageLogger(Callback):
pl_module.eval()
with torch.no_grad():
- images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
+ images = pl_module.log_images(
+ batch, split=split, **self.log_images_kwargs
+ )
for k in images:
N = min(images[k].shape[0], self.max_images)
@@ -392,20 +494,29 @@ class ImageLogger(Callback):
if isinstance(images[k], torch.Tensor):
images[k] = images[k].detach().cpu()
if self.clamp:
- images[k] = torch.clamp(images[k], -1., 1.)
+ images[k] = torch.clamp(images[k], -1.0, 1.0)
- self.log_local(pl_module.logger.save_dir, split, images,
- pl_module.global_step, pl_module.current_epoch, batch_idx)
+ self.log_local(
+ pl_module.logger.save_dir,
+ split,
+ images,
+ pl_module.global_step,
+ pl_module.current_epoch,
+ batch_idx,
+ )
- logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
+ logger_log_images = self.logger_log_images.get(
+ logger, lambda *args, **kwargs: None
+ )
logger_log_images(pl_module, images, pl_module.global_step, split)
if is_train:
pl_module.train()
def check_frequency(self, check_idx):
- if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
- check_idx > 0 or self.log_first_step):
+ if (
+ (check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)
+ ) and (check_idx > 0 or self.log_first_step):
try:
self.log_steps.pop(0)
except IndexError as e:
@@ -414,15 +525,23 @@ class ImageLogger(Callback):
return True
return False
- def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
- if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
- self.log_img(pl_module, batch, batch_idx, split="train")
+ def on_train_batch_end(
+ self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
+ ):
+ if not self.disabled and (
+ pl_module.global_step > 0 or self.log_first_step
+ ):
+ self.log_img(pl_module, batch, batch_idx, split='train')
- def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+ def on_validation_batch_end(
+ self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
+ ):
if not self.disabled and pl_module.global_step > 0:
- self.log_img(pl_module, batch, batch_idx, split="val")
+ self.log_img(pl_module, batch, batch_idx, split='val')
if hasattr(pl_module, 'calibrate_grad_norm'):
- if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
+ if (
+ pl_module.calibrate_grad_norm and batch_idx % 25 == 0
+ ) and batch_idx > 0:
self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
@@ -436,20 +555,22 @@ class CUDACallback(Callback):
def on_train_epoch_end(self, trainer, pl_module, outputs):
torch.cuda.synchronize(trainer.root_gpu)
- max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20
+ max_memory = (
+ torch.cuda.max_memory_allocated(trainer.root_gpu) / 2**20
+ )
epoch_time = time.time() - self.start_time
try:
max_memory = trainer.training_type_plugin.reduce(max_memory)
epoch_time = trainer.training_type_plugin.reduce(epoch_time)
- rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
- rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
+ rank_zero_info(f'Average Epoch time: {epoch_time:.2f} seconds')
+ rank_zero_info(f'Average Peak memory {max_memory:.2f}MiB')
except AttributeError:
pass
-if __name__ == "__main__":
+if __name__ == '__main__':
# custom parser to specify config files, train, test and debug mode,
# postfix, resume.
# `--key value` arguments are interpreted as arguments to the trainer.
@@ -491,7 +612,7 @@ if __name__ == "__main__":
# params:
# key: value
- now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+ now = datetime.datetime.now().strftime('%Y-%m-%dT%H-%M-%S')
# add cwd for convenience and to make classes in this file available when
# running as `python main.py`
@@ -504,47 +625,49 @@ if __name__ == "__main__":
opt, unknown = parser.parse_known_args()
if opt.name and opt.resume:
raise ValueError(
- "-n/--name and -r/--resume cannot be specified both."
- "If you want to resume training in a new log folder, "
- "use -n/--name in combination with --resume_from_checkpoint"
+ '-n/--name and -r/--resume cannot be specified both.'
+ 'If you want to resume training in a new log folder, '
+ 'use -n/--name in combination with --resume_from_checkpoint'
)
if opt.resume:
if not os.path.exists(opt.resume):
- raise ValueError("Cannot find {}".format(opt.resume))
+ raise ValueError('Cannot find {}'.format(opt.resume))
if os.path.isfile(opt.resume):
- paths = opt.resume.split("/")
+ paths = opt.resume.split('/')
# idx = len(paths)-paths[::-1].index("logs")+1
# logdir = "/".join(paths[:idx])
- logdir = "/".join(paths[:-2])
+ logdir = '/'.join(paths[:-2])
ckpt = opt.resume
else:
assert os.path.isdir(opt.resume), opt.resume
- logdir = opt.resume.rstrip("/")
- ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
+ logdir = opt.resume.rstrip('/')
+ ckpt = os.path.join(logdir, 'checkpoints', 'last.ckpt')
opt.resume_from_checkpoint = ckpt
- base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
+ base_configs = sorted(
+ glob.glob(os.path.join(logdir, 'configs/*.yaml'))
+ )
opt.base = base_configs + opt.base
- _tmp = logdir.split("/")
+ _tmp = logdir.split('/')
nowname = _tmp[-1]
else:
if opt.name:
- name = "_" + opt.name
+ name = '_' + opt.name
elif opt.base:
cfg_fname = os.path.split(opt.base[0])[-1]
cfg_name = os.path.splitext(cfg_fname)[0]
- name = "_" + cfg_name
+ name = '_' + cfg_name
else:
- name = ""
+ name = ''
if opt.datadir_in_name:
now = os.path.basename(os.path.normpath(opt.data_root)) + now
-
+
nowname = now + name + opt.postfix
logdir = os.path.join(opt.logdir, nowname)
- ckptdir = os.path.join(logdir, "checkpoints")
- cfgdir = os.path.join(logdir, "configs")
+ ckptdir = os.path.join(logdir, 'checkpoints')
+ cfgdir = os.path.join(logdir, 'configs')
seed_everything(opt.seed)
try:
@@ -552,19 +675,19 @@ if __name__ == "__main__":
configs = [OmegaConf.load(cfg) for cfg in opt.base]
cli = OmegaConf.from_dotlist(unknown)
config = OmegaConf.merge(*configs, cli)
- lightning_config = config.pop("lightning", OmegaConf.create())
+ lightning_config = config.pop('lightning', OmegaConf.create())
# merge trainer cli with config
- trainer_config = lightning_config.get("trainer", OmegaConf.create())
+ trainer_config = lightning_config.get('trainer', OmegaConf.create())
# default to ddp
- trainer_config["accelerator"] = "ddp"
+ trainer_config['accelerator'] = 'ddp'
for k in nondefault_trainer_args(opt):
trainer_config[k] = getattr(opt, k)
- if not "gpus" in trainer_config:
- del trainer_config["accelerator"]
+ if not 'gpus' in trainer_config:
+ del trainer_config['accelerator']
cpu = True
else:
- gpuinfo = trainer_config["gpus"]
- print(f"Running on GPUs {gpuinfo}")
+ gpuinfo = trainer_config['gpus']
+ print(f'Running on GPUs {gpuinfo}')
cpu = False
trainer_opt = argparse.Namespace(**trainer_config)
lightning_config.trainer = trainer_config
@@ -572,11 +695,17 @@ if __name__ == "__main__":
# model
# config.model.params.personalization_config.params.init_word = opt.init_word
- config.model.params.personalization_config.params.embedding_manager_ckpt = opt.embedding_manager_ckpt
- config.model.params.personalization_config.params.placeholder_tokens = opt.placeholder_tokens
+ config.model.params.personalization_config.params.embedding_manager_ckpt = (
+ opt.embedding_manager_ckpt
+ )
+ config.model.params.personalization_config.params.placeholder_tokens = (
+ opt.placeholder_tokens
+ )
if opt.init_word:
- config.model.params.personalization_config.params.initializer_words[0] = opt.init_word
+ config.model.params.personalization_config.params.initializer_words[
+ 0
+ ] = opt.init_word
if opt.actual_resume:
model = load_model_from_config(config, opt.actual_resume)
@@ -588,123 +717,136 @@ if __name__ == "__main__":
# default logger configs
default_logger_cfgs = {
- "wandb": {
- "target": "pytorch_lightning.loggers.WandbLogger",
- "params": {
- "name": nowname,
- "save_dir": logdir,
- "offline": opt.debug,
- "id": nowname,
- }
+ 'wandb': {
+ 'target': 'pytorch_lightning.loggers.WandbLogger',
+ 'params': {
+ 'name': nowname,
+ 'save_dir': logdir,
+ 'offline': opt.debug,
+ 'id': nowname,
+ },
},
- "testtube": {
- "target": "pytorch_lightning.loggers.TestTubeLogger",
- "params": {
- "name": "testtube",
- "save_dir": logdir,
- }
+ 'testtube': {
+ 'target': 'pytorch_lightning.loggers.TestTubeLogger',
+ 'params': {
+ 'name': 'testtube',
+ 'save_dir': logdir,
+ },
},
}
- default_logger_cfg = default_logger_cfgs["testtube"]
- if "logger" in lightning_config:
+ default_logger_cfg = default_logger_cfgs['testtube']
+ if 'logger' in lightning_config:
logger_cfg = lightning_config.logger
else:
logger_cfg = OmegaConf.create()
logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
- trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
+ trainer_kwargs['logger'] = instantiate_from_config(logger_cfg)
# modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
# specify which metric is used to determine best models
default_modelckpt_cfg = {
- "target": "pytorch_lightning.callbacks.ModelCheckpoint",
- "params": {
- "dirpath": ckptdir,
- "filename": "{epoch:06}",
- "verbose": True,
- "save_last": True,
- }
+ 'target': 'pytorch_lightning.callbacks.ModelCheckpoint',
+ 'params': {
+ 'dirpath': ckptdir,
+ 'filename': '{epoch:06}',
+ 'verbose': True,
+ 'save_last': True,
+ },
}
- if hasattr(model, "monitor"):
- print(f"Monitoring {model.monitor} as checkpoint metric.")
- default_modelckpt_cfg["params"]["monitor"] = model.monitor
- default_modelckpt_cfg["params"]["save_top_k"] = 3
+ if hasattr(model, 'monitor'):
+ print(f'Monitoring {model.monitor} as checkpoint metric.')
+ default_modelckpt_cfg['params']['monitor'] = model.monitor
+ default_modelckpt_cfg['params']['save_top_k'] = 3
- if "modelcheckpoint" in lightning_config:
+ if 'modelcheckpoint' in lightning_config:
modelckpt_cfg = lightning_config.modelcheckpoint
else:
- modelckpt_cfg = OmegaConf.create()
+ modelckpt_cfg = OmegaConf.create()
modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
- print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
+ print(f'Merged modelckpt-cfg: \n{modelckpt_cfg}')
if version.parse(pl.__version__) < version.parse('1.4.0'):
- trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
+ trainer_kwargs['checkpoint_callback'] = instantiate_from_config(
+ modelckpt_cfg
+ )
# add callback which sets up log directory
default_callbacks_cfg = {
- "setup_callback": {
- "target": "main.SetupCallback",
- "params": {
- "resume": opt.resume,
- "now": now,
- "logdir": logdir,
- "ckptdir": ckptdir,
- "cfgdir": cfgdir,
- "config": config,
- "lightning_config": lightning_config,
- }
+ 'setup_callback': {
+ 'target': 'main.SetupCallback',
+ 'params': {
+ 'resume': opt.resume,
+ 'now': now,
+ 'logdir': logdir,
+ 'ckptdir': ckptdir,
+ 'cfgdir': cfgdir,
+ 'config': config,
+ 'lightning_config': lightning_config,
+ },
},
- "image_logger": {
- "target": "main.ImageLogger",
- "params": {
- "batch_frequency": 750,
- "max_images": 4,
- "clamp": True
- }
+ 'image_logger': {
+ 'target': 'main.ImageLogger',
+ 'params': {
+ 'batch_frequency': 750,
+ 'max_images': 4,
+ 'clamp': True,
+ },
},
- "learning_rate_logger": {
- "target": "main.LearningRateMonitor",
- "params": {
- "logging_interval": "step",
+ 'learning_rate_logger': {
+ 'target': 'main.LearningRateMonitor',
+ 'params': {
+ 'logging_interval': 'step',
# "log_momentum": True
- }
- },
- "cuda_callback": {
- "target": "main.CUDACallback"
+ },
},
+ 'cuda_callback': {'target': 'main.CUDACallback'},
}
if version.parse(pl.__version__) >= version.parse('1.4.0'):
- default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})
+ default_callbacks_cfg.update(
+ {'checkpoint_callback': modelckpt_cfg}
+ )
- if "callbacks" in lightning_config:
+ if 'callbacks' in lightning_config:
callbacks_cfg = lightning_config.callbacks
else:
callbacks_cfg = OmegaConf.create()
if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
print(
- 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
+ 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.'
+ )
default_metrics_over_trainsteps_ckpt_dict = {
- 'metrics_over_trainsteps_checkpoint':
- {"target": 'pytorch_lightning.callbacks.ModelCheckpoint',
- 'params': {
- "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
- "filename": "{epoch:06}-{step:09}",
- "verbose": True,
- 'save_top_k': -1,
- 'every_n_train_steps': 10000,
- 'save_weights_only': True
- }
- }
+ 'metrics_over_trainsteps_checkpoint': {
+ 'target': 'pytorch_lightning.callbacks.ModelCheckpoint',
+ 'params': {
+ 'dirpath': os.path.join(
+ ckptdir, 'trainstep_checkpoints'
+ ),
+ 'filename': '{epoch:06}-{step:09}',
+ 'verbose': True,
+ 'save_top_k': -1,
+ 'every_n_train_steps': 10000,
+ 'save_weights_only': True,
+ },
+ }
}
- default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
+ default_callbacks_cfg.update(
+ default_metrics_over_trainsteps_ckpt_dict
+ )
callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
- if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):
- callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint
+ if 'ignore_keys_callback' in callbacks_cfg and hasattr(
+ trainer_opt, 'resume_from_checkpoint'
+ ):
+ callbacks_cfg.ignore_keys_callback.params[
+ 'ckpt_path'
+ ] = trainer_opt.resume_from_checkpoint
elif 'ignore_keys_callback' in callbacks_cfg:
del callbacks_cfg['ignore_keys_callback']
- trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
- trainer_kwargs["max_steps"] = opt.max_steps
+ trainer_kwargs['callbacks'] = [
+ instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg
+ ]
+ trainer_kwargs['max_steps'] = opt.max_steps
trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
trainer.logdir = logdir ###
@@ -720,47 +862,60 @@ if __name__ == "__main__":
# lightning still takes care of proper multiprocessing though
data.prepare_data()
data.setup()
- print("#### Data #####")
+ print('#### Data #####')
for k in data.datasets:
- print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
+ print(
+ f'{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}'
+ )
# configure learning rate
- bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
+ bs, base_lr = (
+ config.data.params.batch_size,
+ config.model.base_learning_rate,
+ )
if not cpu:
- ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
+ ngpu = len(lightning_config.trainer.gpus.strip(',').split(','))
else:
ngpu = 1
if 'accumulate_grad_batches' in lightning_config.trainer:
- accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
+ accumulate_grad_batches = (
+ lightning_config.trainer.accumulate_grad_batches
+ )
else:
accumulate_grad_batches = 1
- print(f"accumulate_grad_batches = {accumulate_grad_batches}")
- lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
+ print(f'accumulate_grad_batches = {accumulate_grad_batches}')
+ lightning_config.trainer.accumulate_grad_batches = (
+ accumulate_grad_batches
+ )
if opt.scale_lr:
model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
print(
- "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
- model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
+ 'Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)'.format(
+ model.learning_rate,
+ accumulate_grad_batches,
+ ngpu,
+ bs,
+ base_lr,
+ )
+ )
else:
model.learning_rate = base_lr
- print("++++ NOT USING LR SCALING ++++")
- print(f"Setting learning rate to {model.learning_rate:.2e}")
-
+ print('++++ NOT USING LR SCALING ++++')
+ print(f'Setting learning rate to {model.learning_rate:.2e}')
# allow checkpointing via USR1
def melk(*args, **kwargs):
# run all checkpoint hooks
if trainer.global_rank == 0:
- print("Summoning checkpoint.")
- ckpt_path = os.path.join(ckptdir, "last.ckpt")
+ print('Summoning checkpoint.')
+ ckpt_path = os.path.join(ckptdir, 'last.ckpt')
trainer.save_checkpoint(ckpt_path)
-
def divein(*args, **kwargs):
if trainer.global_rank == 0:
- import pudb;
- pudb.set_trace()
+ import pudb
+ pudb.set_trace()
import signal
@@ -788,7 +943,7 @@ if __name__ == "__main__":
# move newly created debug project to debug_runs
if opt.debug and not opt.resume and trainer.global_rank == 0:
dst, name = os.path.split(logdir)
- dst = os.path.join(dst, "debug_runs", name)
+ dst = os.path.join(dst, 'debug_runs', name)
os.makedirs(os.path.split(dst)[0], exist_ok=True)
os.rename(logdir, dst)
# if trainer.global_rank == 0:
diff --git a/scripts/dream.py b/scripts/dream.py
index 6a17656593..f6feb10adc 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -8,62 +8,66 @@ import sys
import copy
import warnings
import ldm.dream.readline
-from ldm.dream.pngwriter import PngWriter,PromptFormatter
+from ldm.dream.pngwriter import PngWriter, PromptFormatter
debugging = False
+
def main():
- ''' Initialize command-line parsers and the diffusion model '''
+ """Initialize command-line parsers and the diffusion model"""
arg_parser = create_argv_parser()
- opt = arg_parser.parse_args()
+ opt = arg_parser.parse_args()
if opt.laion400m:
# defaults suitable to the older latent diffusion weights
- width = 256
- height = 256
- config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
- weights = "models/ldm/text2img-large/model.ckpt"
+ width = 256
+ height = 256
+ config = 'configs/latent-diffusion/txt2img-1p4B-eval.yaml'
+ weights = 'models/ldm/text2img-large/model.ckpt'
else:
# some defaults suitable for stable diffusion weights
- width = 512
- height = 512
- config = "configs/stable-diffusion/v1-inference.yaml"
- weights = "models/ldm/stable-diffusion-v1/model.ckpt"
+ width = 512
+ height = 512
+ config = 'configs/stable-diffusion/v1-inference.yaml'
+ weights = 'models/ldm/stable-diffusion-v1/model.ckpt'
- print("* Initializing, be patient...\n")
+ print('* Initializing, be patient...\n')
sys.path.append('.')
from pytorch_lightning import logging
from ldm.simplet2i import T2I
+
# these two lines prevent a horrible warning message from appearing
# when the frozen CLIP tokenizer is imported
import transformers
+
transformers.logging.set_verbosity_error()
-
+
# creating a simple text2image object with a handful of
# defaults passed on the command line.
# additional parameters will be added (or overriden) during
# the user input loop
- t2i = T2I(width=width,
- height=height,
- sampler_name=opt.sampler_name,
- weights=weights,
- full_precision=opt.full_precision,
- config=config,
- latent_diffusion_weights=opt.laion400m, # this is solely for recreating the prompt
- embedding_path=opt.embedding_path,
- device=opt.device
+ t2i = T2I(
+ width=width,
+ height=height,
+ sampler_name=opt.sampler_name,
+ weights=weights,
+ full_precision=opt.full_precision,
+ config=config,
+ latent_diffusion_weights=opt.laion400m, # this is solely for recreating the prompt
+ embedding_path=opt.embedding_path,
+ device=opt.device,
)
# make sure the output directory exists
if not os.path.exists(opt.outdir):
os.makedirs(opt.outdir)
-
+
# gets rid of annoying messages about random seed
- logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
+ logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
infile = None
try:
if opt.infile is not None:
- infile = open(opt.infile,'r')
+ infile = open(opt.infile, 'r')
except FileNotFoundError as e:
print(e)
exit(-1)
@@ -73,135 +77,156 @@ def main():
# load GFPGAN if requested
if opt.use_gfpgan:
- print("\n* --gfpgan was specified, loading gfpgan...")
+ print('\n* --gfpgan was specified, loading gfpgan...')
with warnings.catch_warnings():
- warnings.filterwarnings("ignore", category=DeprecationWarning)
+ warnings.filterwarnings('ignore', category=DeprecationWarning)
try:
- model_path = os.path.join(opt.gfpgan_dir, opt.gfpgan_model_path)
+ model_path = os.path.join(
+ opt.gfpgan_dir, opt.gfpgan_model_path
+ )
if not os.path.isfile(model_path):
- raise Exception("GFPGAN model not found at path "+model_path)
+ raise Exception(
+ 'GFPGAN model not found at path ' + model_path
+ )
sys.path.append(os.path.abspath(opt.gfpgan_dir))
from gfpgan import GFPGANer
- bg_upsampler = load_gfpgan_bg_upsampler(opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile)
+ bg_upsampler = load_gfpgan_bg_upsampler(
+ opt.gfpgan_bg_upsampler, opt.gfpgan_bg_tile
+ )
- t2i.gfpgan = GFPGANer(model_path=model_path, upscale=opt.gfpgan_upscale, arch='clean', channel_multiplier=2, bg_upsampler=bg_upsampler)
+ t2i.gfpgan = GFPGANer(
+ model_path=model_path,
+ upscale=opt.gfpgan_upscale,
+ arch='clean',
+ channel_multiplier=2,
+ bg_upsampler=bg_upsampler,
+ )
except Exception:
import traceback
- print("Error loading GFPGAN:", file=sys.stderr)
+
+ print('Error loading GFPGAN:', file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
- print("\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)...")
+ print(
+ "\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)..."
+ )
- log_path = os.path.join(opt.outdir,'dream_log.txt')
- with open(log_path,'a') as log:
+ log_path = os.path.join(opt.outdir, 'dream_log.txt')
+ with open(log_path, 'a') as log:
cmd_parser = create_cmd_parser()
- main_loop(t2i,opt.outdir,cmd_parser,log,infile)
+ main_loop(t2i, opt.outdir, cmd_parser, log, infile)
log.close()
if infile:
infile.close()
-def main_loop(t2i,outdir,parser,log,infile):
- ''' prompt/read/execute loop '''
- done = False
+def main_loop(t2i, outdir, parser, log, infile):
+ """prompt/read/execute loop"""
+ done = False
last_seeds = []
-
+
while not done:
try:
- command = infile.readline() if infile else input("dream> ")
+ command = infile.readline() if infile else input('dream> ')
except EOFError:
done = True
break
- if infile and len(command)==0:
+ if infile and len(command) == 0:
done = True
break
- if command.startswith(('#','//')):
+ if command.startswith(('#', '//')):
continue
# before splitting, escape single quotes so as not to mess
# up the parser
- command = command.replace("'","\\'")
+ command = command.replace("'", "\\'")
try:
elements = shlex.split(command)
except ValueError as e:
print(str(e))
continue
-
- if len(elements)==0:
+
+ if len(elements) == 0:
continue
- if elements[0]=='q':
+ if elements[0] == 'q':
done = True
break
- if elements[0]=='cd' and len(elements)>1:
+ if elements[0] == 'cd' and len(elements) > 1:
if os.path.exists(elements[1]):
- print(f"setting image output directory to {elements[1]}")
- outdir=elements[1]
+ print(f'setting image output directory to {elements[1]}')
+ outdir = elements[1]
else:
- print(f"directory {elements[1]} does not exist")
+ print(f'directory {elements[1]} does not exist')
continue
- if elements[0]=='pwd':
- print(f"current output directory is {outdir}")
+ if elements[0] == 'pwd':
+ print(f'current output directory is {outdir}')
continue
-
- if elements[0].startswith('!dream'): # in case a stored prompt still contains the !dream command
+
+ if elements[0].startswith(
+ '!dream'
+ ): # in case a stored prompt still contains the !dream command
elements.pop(0)
-
+
# rearrange the arguments to mimic how it works in the Dream bot.
switches = ['']
switches_started = False
for el in elements:
- if el[0]=='-' and not switches_started:
+ if el[0] == '-' and not switches_started:
switches_started = True
if switches_started:
switches.append(el)
else:
switches[0] += el
switches[0] += ' '
- switches[0] = switches[0][:len(switches[0])-1]
+ switches[0] = switches[0][: len(switches[0]) - 1]
try:
- opt = parser.parse_args(switches)
+ opt = parser.parse_args(switches)
except SystemExit:
parser.print_help()
continue
- if len(opt.prompt)==0:
- print("Try again with a prompt!")
+ if len(opt.prompt) == 0:
+ print('Try again with a prompt!')
continue
- if opt.seed is not None and opt.seed<0: # retrieve previous value!
+ if opt.seed is not None and opt.seed < 0: # retrieve previous value!
try:
opt.seed = last_seeds[opt.seed]
- print(f"reusing previous seed {opt.seed}")
+ print(f'reusing previous seed {opt.seed}')
except IndexError:
- print(f"No previous seed at position {opt.seed} found")
+ print(f'No previous seed at position {opt.seed} found')
opt.seed = None
-
- normalized_prompt = PromptFormatter(t2i,opt).normalize_prompt()
- individual_images = not opt.grid
+
+ normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
+ individual_images = not opt.grid
try:
- file_writer = PngWriter(outdir,normalized_prompt,opt.batch_size)
- callback = file_writer.write_image if individual_images else None
+ file_writer = PngWriter(outdir, normalized_prompt, opt.batch_size)
+ callback = file_writer.write_image if individual_images else None
- image_list = t2i.prompt2image(image_callback=callback,**vars(opt))
- results = file_writer.files_written if individual_images else image_list
+ image_list = t2i.prompt2image(image_callback=callback, **vars(opt))
+ results = (
+ file_writer.files_written if individual_images else image_list
+ )
if opt.grid and len(results) > 0:
grid_img = file_writer.make_grid([r[0] for r in results])
filename = file_writer.unique_filename(results[0][1])
- seeds = [a[1] for a in results]
- results = [[filename,seeds]]
- metadata_prompt = f'{normalized_prompt} -S{results[0][1]}'
- file_writer.save_image_and_prompt_to_png(grid_img,metadata_prompt,filename)
+ seeds = [a[1] for a in results]
+ results = [[filename, seeds]]
+ metadata_prompt = f'{normalized_prompt} -S{results[0][1]}'
+ file_writer.save_image_and_prompt_to_png(
+ grid_img, metadata_prompt, filename
+ )
last_seeds = [r[1] for r in results]
@@ -213,10 +238,11 @@ def main_loop(t2i,outdir,parser,log,infile):
print(e)
continue
- print("Outputs:")
- write_log_message(t2i,normalized_prompt,results,log)
+ print('Outputs:')
+ write_log_message(t2i, normalized_prompt, results, log)
+
+ print('goodbye!')
- print("goodbye!")
def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
import torch
@@ -224,13 +250,24 @@ def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
if bg_upsampler == 'realesrgan':
if not torch.cuda.is_available(): # CPU
import warnings
- warnings.warn('The unoptimized RealESRGAN is slow on CPU. We do not use it. '
- 'If you really want to use it, please modify the corresponding codes.')
+
+ warnings.warn(
+ 'The unoptimized RealESRGAN is slow on CPU. We do not use it. '
+ 'If you really want to use it, please modify the corresponding codes.'
+ )
bg_upsampler = None
else:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+
+ model = RRDBNet(
+ num_in_ch=3,
+ num_out_ch=3,
+ num_feat=64,
+ num_block=23,
+ num_grow_ch=32,
+ scale=2,
+ )
bg_upsampler = RealESRGANer(
scale=2,
model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
@@ -238,12 +275,14 @@ def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
tile=bg_tile,
tile_pad=10,
pre_pad=0,
- half=True) # need to set False in CPU mode
+ half=True,
+ ) # need to set False in CPU mode
else:
bg_upsampler = None
return bg_upsampler
+
# variant generation is going to be superseded by a generalized
# "prompt-morph" functionality
# def generate_variants(t2i,outdir,opt,previous_gens):
@@ -268,110 +307,209 @@ def load_gfpgan_bg_upsampler(bg_upsampler, bg_tile=400):
# continue
# print(f'{opt.variants} variants generated')
# return variants
-
-def write_log_message(t2i,prompt,results,logfile):
- ''' logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata'''
- last_seed = None
- img_num = 1
- seenit = {}
+
+def write_log_message(t2i, prompt, results, logfile):
+ """logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata"""
+ last_seed = None
+ img_num = 1
+ seenit = {}
for r in results:
seed = r[1]
- log_message = (f'{r[0]}: {prompt} -S{seed}')
+ log_message = f'{r[0]}: {prompt} -S{seed}'
print(log_message)
- logfile.write(log_message+"\n")
+ logfile.write(log_message + '\n')
logfile.flush()
+
def create_argv_parser():
- parser = argparse.ArgumentParser(description="Parse script's command line args")
- parser.add_argument("--laion400m",
- "--latent_diffusion",
- "-l",
- dest='laion400m',
- action='store_true',
- help="fallback to the latent diffusion (laion400m) weights and config")
- parser.add_argument("--from_file",
- dest='infile',
- type=str,
- help="if specified, load prompts from this file")
- parser.add_argument('-n','--iterations',
- type=int,
- default=1,
- help="number of images to generate")
- parser.add_argument('-F','--full_precision',
- dest='full_precision',
- action='store_true',
- help="use slower full precision math for calculations")
- parser.add_argument('--sampler','-m',
- dest="sampler_name",
- choices=['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'],
- default='k_lms',
- help="which sampler to use (k_lms) - can only be set on command line")
- parser.add_argument('--outdir',
- '-o',
- type=str,
- default="outputs/img-samples",
- help="directory in which to place generated images and a log of prompts and seeds (outputs/img-samples")
- parser.add_argument('--embedding_path',
- type=str,
- help="Path to a pre-trained embedding manager checkpoint - can only be set on command line")
- parser.add_argument('--device',
- '-d',
- type=str,
- default="cuda",
- help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if avalible")
+ parser = argparse.ArgumentParser(
+ description="Parse script's command line args"
+ )
+ parser.add_argument(
+ '--laion400m',
+ '--latent_diffusion',
+ '-l',
+ dest='laion400m',
+ action='store_true',
+ help='fallback to the latent diffusion (laion400m) weights and config',
+ )
+ parser.add_argument(
+ '--from_file',
+ dest='infile',
+ type=str,
+ help='if specified, load prompts from this file',
+ )
+ parser.add_argument(
+ '-n',
+ '--iterations',
+ type=int,
+ default=1,
+ help='number of images to generate',
+ )
+ parser.add_argument(
+ '-F',
+ '--full_precision',
+ dest='full_precision',
+ action='store_true',
+ help='use slower full precision math for calculations',
+ )
+ parser.add_argument(
+ '--sampler',
+ '-m',
+ dest='sampler_name',
+ choices=[
+ 'ddim',
+ 'k_dpm_2_a',
+ 'k_dpm_2',
+ 'k_euler_a',
+ 'k_euler',
+ 'k_heun',
+ 'k_lms',
+ 'plms',
+ ],
+ default='k_lms',
+ help='which sampler to use (k_lms) - can only be set on command line',
+ )
+ parser.add_argument(
+ '--outdir',
+ '-o',
+ type=str,
+ default='outputs/img-samples',
+ help='directory in which to place generated images and a log of prompts and seeds (outputs/img-samples',
+ )
+ parser.add_argument(
+ '--embedding_path',
+ type=str,
+ help='Path to a pre-trained embedding manager checkpoint - can only be set on command line',
+ )
+ parser.add_argument(
+ '--device',
+ '-d',
+ type=str,
+ default='cuda',
+ help='device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if avalible',
+ )
# GFPGAN related args
- parser.add_argument('--gfpgan',
- dest='use_gfpgan',
- action='store_true',
- help="load gfpgan for use in the dreambot. Note: Enabling GFPGAN will require more GPU memory")
- parser.add_argument("--gfpgan_upscale",
- type=int,
- default=2,
- help="The final upsampling scale of the image. Default: 2. Only used if --gfpgan is specified")
- parser.add_argument("--gfpgan_bg_upsampler",
- type=str,
- default='realesrgan',
- help="Background upsampler. Default: None. Options: realesrgan, none. Only used if --gfpgan is specified")
- parser.add_argument("--gfpgan_bg_tile",
- type=int,
- default=400,
- help="Tile size for background sampler, 0 for no tile during testing. Default: 400. Only used if --gfpgan is specified")
- parser.add_argument("--gfpgan_model_path",
- type=str,
- default='experiments/pretrained_models/GFPGANv1.3.pth',
- help="indicates the path to the GFPGAN model, relative to --gfpgan_dir. Only used if --gfpgan is specified")
- parser.add_argument("--gfpgan_dir",
- type=str,
- default='../GFPGAN',
- help="indicates the directory containing the GFPGAN code. Only used if --gfpgan is specified")
+ parser.add_argument(
+ '--gfpgan',
+ dest='use_gfpgan',
+ action='store_true',
+ help='load gfpgan for use in the dreambot. Note: Enabling GFPGAN will require more GPU memory',
+ )
+ parser.add_argument(
+ '--gfpgan_upscale',
+ type=int,
+ default=2,
+ help='The final upsampling scale of the image. Default: 2. Only used if --gfpgan is specified',
+ )
+ parser.add_argument(
+ '--gfpgan_bg_upsampler',
+ type=str,
+ default='realesrgan',
+ help='Background upsampler. Default: None. Options: realesrgan, none. Only used if --gfpgan is specified',
+ )
+ parser.add_argument(
+ '--gfpgan_bg_tile',
+ type=int,
+ default=400,
+ help='Tile size for background sampler, 0 for no tile during testing. Default: 400. Only used if --gfpgan is specified',
+ )
+ parser.add_argument(
+ '--gfpgan_model_path',
+ type=str,
+ default='experiments/pretrained_models/GFPGANv1.3.pth',
+ help='indicates the path to the GFPGAN model, relative to --gfpgan_dir. Only used if --gfpgan is specified',
+ )
+ parser.add_argument(
+ '--gfpgan_dir',
+ type=str,
+ default='../GFPGAN',
+ help='indicates the directory containing the GFPGAN code. Only used if --gfpgan is specified',
+ )
return parser
-
-
+
+
def create_cmd_parser():
- parser = argparse.ArgumentParser(description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12')
+ parser = argparse.ArgumentParser(
+ description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
+ )
parser.add_argument('prompt')
- parser.add_argument('-s','--steps',type=int,help="number of steps")
- parser.add_argument('-S','--seed',type=int,help="image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc")
- parser.add_argument('-n','--iterations',type=int,default=1,help="number of samplings to perform (slower, but will provide seeds for individual images)")
- parser.add_argument('-b','--batch_size',type=int,default=1,help="number of images to produce per sampling (will not provide seeds for individual images!)")
- parser.add_argument('-W','--width',type=int,help="image width, multiple of 64")
- parser.add_argument('-H','--height',type=int,help="image height, multiple of 64")
- parser.add_argument('-C','--cfg_scale',default=7.5,type=float,help="prompt configuration scale")
- parser.add_argument('-g','--grid',action='store_true',help="generate a grid")
- parser.add_argument('-i','--individual',action='store_true',help="generate individual files (default)")
- parser.add_argument('-I','--init_img',type=str,help="path to input image for img2img mode (supersedes width and height)")
- parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely")
- parser.add_argument('-G','--gfpgan_strength', default=0.5, type=float, help="The strength at which to apply the GFPGAN model to the result, in order to improve faces.")
-# variants is going to be superseded by a generalized "prompt-morph" function
-# parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
- parser.add_argument('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
+ parser.add_argument('-s', '--steps', type=int, help='number of steps')
+ parser.add_argument(
+ '-S',
+ '--seed',
+ type=int,
+ help='image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
+ )
+ parser.add_argument(
+ '-n',
+ '--iterations',
+ type=int,
+ default=1,
+ help='number of samplings to perform (slower, but will provide seeds for individual images)',
+ )
+ parser.add_argument(
+ '-b',
+ '--batch_size',
+ type=int,
+ default=1,
+ help='number of images to produce per sampling (will not provide seeds for individual images!)',
+ )
+ parser.add_argument(
+ '-W', '--width', type=int, help='image width, multiple of 64'
+ )
+ parser.add_argument(
+ '-H', '--height', type=int, help='image height, multiple of 64'
+ )
+ parser.add_argument(
+ '-C',
+ '--cfg_scale',
+ default=7.5,
+ type=float,
+ help='prompt configuration scale',
+ )
+ parser.add_argument(
+ '-g', '--grid', action='store_true', help='generate a grid'
+ )
+ parser.add_argument(
+ '-i',
+ '--individual',
+ action='store_true',
+ help='generate individual files (default)',
+ )
+ parser.add_argument(
+ '-I',
+ '--init_img',
+ type=str,
+ help='path to input image for img2img mode (supersedes width and height)',
+ )
+ parser.add_argument(
+ '-f',
+ '--strength',
+ default=0.75,
+ type=float,
+ help='strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
+ )
+ parser.add_argument(
+ '-G',
+ '--gfpgan_strength',
+ default=0.5,
+ type=float,
+ help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
+ )
+ # variants is going to be superseded by a generalized "prompt-morph" function
+ # parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
+ parser.add_argument(
+ '-x',
+ '--skip_normalize',
+ action='store_true',
+ help='skip subprompt weight normalization',
+ )
return parser
-
-if __name__ == "__main__":
+if __name__ == '__main__':
main()
-
diff --git a/scripts/preload_models.py b/scripts/preload_models.py
index d7538c82b8..624b61e48e 100755
--- a/scripts/preload_models.py
+++ b/scripts/preload_models.py
@@ -11,26 +11,28 @@ import warnings
transformers.logging.set_verbosity_error()
# this will preload the Bert tokenizer fles
-print("preloading bert tokenizer...")
+print('preloading bert tokenizer...')
from transformers import BertTokenizerFast
-tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-print("...success")
+
+tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+print('...success')
# this will download requirements for Kornia
-print("preloading Kornia requirements (ignore the deprecation warnings)...")
+print('preloading Kornia requirements (ignore the deprecation warnings)...')
with warnings.catch_warnings():
- warnings.filterwarnings("ignore", category=DeprecationWarning)
+ warnings.filterwarnings('ignore', category=DeprecationWarning)
import kornia
-print("...success")
+print('...success')
-version='openai/clip-vit-large-patch14'
+version = 'openai/clip-vit-large-patch14'
print('preloading CLIP model (Ignore the deprecation warnings)...')
sys.stdout.flush()
import clip
from transformers import CLIPTokenizer, CLIPTextModel
-tokenizer =CLIPTokenizer.from_pretrained(version)
-transformer=CLIPTextModel.from_pretrained(version)
+
+tokenizer = CLIPTokenizer.from_pretrained(version)
+transformer = CLIPTextModel.from_pretrained(version)
print('\n\n...success')
# In the event that the user has installed GFPGAN and also elected to use
@@ -38,23 +40,33 @@ print('\n\n...success')
gfpgan = False
try:
from realesrgan import RealESRGANer
+
gfpgan = True
except ModuleNotFoundError:
pass
if gfpgan:
- print("Loading models from RealESRGAN and facexlib")
+ print('Loading models from RealESRGAN and facexlib')
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
- RealESRGANer(scale=2,
- model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
- model=RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2))
- FaceRestoreHelper(1,det_model='retinaface_resnet50')
- print("...success")
+
+ RealESRGANer(
+ scale=2,
+ model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
+ model=RRDBNet(
+ num_in_ch=3,
+ num_out_ch=3,
+ num_feat=64,
+ num_block=23,
+ num_grow_ch=32,
+ scale=2,
+ ),
+ )
+ FaceRestoreHelper(1, det_model='retinaface_resnet50')
+ print('...success')
except Exception:
import traceback
- print("Error loading GFPGAN:")
+
+ print('Error loading GFPGAN:')
print(traceback.format_exc())
-
-
From eb9f0be91ada14e41df150127fdfba2b951ce893 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 03:53:55 -0400
Subject: [PATCH 54/58] Set default to none for gfpgan_strength
---
scripts/dream.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/scripts/dream.py b/scripts/dream.py
index f6feb10adc..5d5e8db4a5 100755
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -496,7 +496,7 @@ def create_cmd_parser():
parser.add_argument(
'-G',
'--gfpgan_strength',
- default=0.5,
+ default=None,
type=float,
help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
)
From 46a223f229c3458f0190813d48e1a52ed5a5ff5a Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 04:05:09 -0400
Subject: [PATCH 55/58] Double check for null and 0, and add a comment to
indicate intent
---
ldm/simplet2i.py | 9 ++++++---
1 file changed, 6 insertions(+), 3 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 8aad3557af..dd99f5e6b6 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -305,9 +305,12 @@ class T2I:
iter_images = next(images_iterator)
for image in iter_images:
try:
- if gfpgan_strength > 0:
- image = self._run_gfpgan(
- image, gfpgan_strength
+ # if gfpgan strength is not none or less than 0.0 then don't use GFPGAN
+ # if the user specified -G x, and the --gfpgan wasn't specified, then
+ # the net result is a message gets printed - nothing else happens
+ if gfpgan_strength is not None and gfpgan_strength > 0.0:
+ image = self.gfpgan.generate(
+ image, gfpgan_strength=gfpgan_strength
)
except Exception as e:
print(
From 4e5aa7e714a0230eeefb93dd45c31935e2064587 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 04:07:01 -0400
Subject: [PATCH 56/58] fix comment
---
ldm/simplet2i.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index dd99f5e6b6..5c3d08a5d1 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -305,7 +305,7 @@ class T2I:
iter_images = next(images_iterator)
for image in iter_images:
try:
- # if gfpgan strength is not none or less than 0.0 then don't use GFPGAN
+ # if gfpgan strength is none or less than 0.0 then don't use GFPGAN
# if the user specified -G x, and the --gfpgan wasn't specified, then
# the net result is a message gets printed - nothing else happens
if gfpgan_strength is not None and gfpgan_strength > 0.0:
From 7bf0bc52086ba0b9259202f90081566c3e91c74c Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 04:08:18 -0400
Subject: [PATCH 57/58] fix comment
---
ldm/simplet2i.py | 8 +++++---
1 file changed, 5 insertions(+), 3 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 5c3d08a5d1..93a33a6a5d 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -305,9 +305,11 @@ class T2I:
iter_images = next(images_iterator)
for image in iter_images:
try:
- # if gfpgan strength is none or less than 0.0 then don't use GFPGAN
- # if the user specified -G x, and the --gfpgan wasn't specified, then
- # the net result is a message gets printed - nothing else happens
+ # if gfpgan strength is none or less than or equal to 0.0 then
+ # don't even attempt to use GFPGAN.
+ # if the user specified a value of -G that satisifies the condition and
+ # --gfpgan wasn't specified, at startup then
+ # the net result is a message gets printed - nothing else happens.
if gfpgan_strength is not None and gfpgan_strength > 0.0:
image = self.gfpgan.generate(
image, gfpgan_strength=gfpgan_strength
From f3fad22cb6e24d8672f8548fe6601da91df9ecf6 Mon Sep 17 00:00:00 2001
From: Sean McLellan <sean@baristalabs.io>
Date: Fri, 26 Aug 2022 05:27:34 -0400
Subject: [PATCH 58/58] Fix
---
ldm/simplet2i.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/ldm/simplet2i.py b/ldm/simplet2i.py
index 93a33a6a5d..ffec5fda2b 100644
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -311,8 +311,8 @@ class T2I:
# --gfpgan wasn't specified, at startup then
# the net result is a message gets printed - nothing else happens.
if gfpgan_strength is not None and gfpgan_strength > 0.0:
- image = self.gfpgan.generate(
- image, gfpgan_strength=gfpgan_strength
+ image = self._run_gfpgan(
+ image, gfpgan_strength
)
except Exception as e:
print(