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720e5cd651
InvokeAI/ldm/generate.py
Lincoln Stein 720e5cd651
Refactoring simplet2i (#387) 
* start refactoring -not yet functional

* first phase of refactor done - not sure weighted prompts working

* Second phase of refactoring. Everything mostly working.
* The refactoring has moved all the hard-core inference work into
ldm.dream.generator.*, where there are submodules for txt2img and
img2img. inpaint will go in there as well.
* Some additional refactoring will be done soon, but relatively
minor work.

* fix -save_orig flag to actually work

* add @neonsecret attention.py memory optimization

* remove unneeded imports

* move token logging into conditioning.py

* add placeholder version of inpaint; porting in progress

* fix crash in img2img

* inpainting working; not tested on variations

* fix crashes in img2img

* ported attention.py memory optimization #117 from basujindal branch

* added @torch_no_grad() decorators to img2img, txt2img, inpaint closures

* Final commit prior to PR against development
* fixup crash when generating intermediate images in web UI
* rename ldm.simplet2i to ldm.generate
* add backward-compatibility simplet2i shell with deprecation warning

* add back in mps exception, addresses @vargol comment in #354

* replaced Conditioning class with exported functions

* fix wrong type of with_variations attribute during intialization

* changed "image_iterator()" to "get_make_image()"

* raise NotImplementedError for calling get_make_image() in parent class

* Update ldm/generate.py

better error message

Co-authored-by: Kevin Gibbons <bakkot@gmail.com>

* minor stylistic fixes and assertion checks from code review

* moved get_noise() method into img2img class

* break get_noise() into two methods, one for txt2img and the other for img2img

* inpainting works on non-square images now

* make get_noise() an abstract method in base class

* much improved inpainting

Co-authored-by: Kevin Gibbons <bakkot@gmail.com>
2022-09-05 20:40:10 -04:00

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# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
# Derived from source code carrying the following copyrights
# Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
# Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
import torch
import numpy as np
import random
import os
import time
import re
import sys
import traceback
import transformers
from omegaconf import OmegaConf
from PIL import Image, ImageOps
from torch import nn
from pytorch_lightning import seed_everything
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.pngwriter import PngWriter
from ldm.dream.image_util import InitImageResizer
from ldm.dream.devices import choose_torch_device
from ldm.dream.conditioning import get_uc_and_c
"""Simplified text to image API for stable diffusion/latent diffusion
Example Usage:
from ldm.generate import Generate
# Create an object with default values
gr = Generate(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)
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> // condition-free guidance scale (7.5)
)
# do the slow model initialization
gr.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 = gr.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 = gr.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 = gr.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 Generate:
"""Generate class
Stores default values for multiple configuration items
"""
def __init__(
self,
iterations = 1,
steps = 50,
cfg_scale = 7.5,
weights = 'models/ldm/stable-diffusion-v1/model.ckpt',
config = 'configs/stable-diffusion/v1-inference.yaml',
grid = False,
width = 512,
height = 512,
sampler_name = 'k_lms',
ddim_eta = 0.0, # deterministic
precision = 'autocast',
full_precision = False,
strength = 0.75, # default in scripts/img2img.py
seamless = False,
embedding_path = None,
device_type = 'cuda',
):
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.grid = grid
self.ddim_eta = ddim_eta
self.precision = precision
self.full_precision = True if choose_torch_device() == 'mps' else full_precision
self.strength = strength
self.seamless = seamless
self.embedding_path = embedding_path
self.device_type = device_type
self.model = None # empty for now
self.sampler = None
self.device = None
self.generators = {}
self.base_generator = None
self.seed = None
if device_type == 'cuda' and not torch.cuda.is_available():
device_type = choose_torch_device()
print(">> cuda not available, using device", device_type)
self.device = torch.device(device_type)
# for VRAM usage statistics
device_type = choose_torch_device()
self.session_peakmem = torch.cuda.max_memory_allocated() if device_type == 'cuda' else None
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 Generate and prompt2image()
"""
results = self.prompt2image(prompt, **kwargs)
pngwriter = PngWriter(outdir)
prefix = pngwriter.unique_prefix()
outputs = []
for image, seed in results:
name = f'{prefix}.{seed}.png'
path = pngwriter.save_image_and_prompt_to_png(
image, f'{prompt} -S{seed}', name)
outputs.append([path, seed])
return outputs
def txt2img(self, prompt, **kwargs):
outdir = kwargs.pop('outdir', 'outputs/img-samples')
return self.prompt2png(prompt, outdir, **kwargs)
def img2img(self, prompt, **kwargs):
outdir = kwargs.pop('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,
iterations = None,
steps = None,
seed = None,
cfg_scale = None,
ddim_eta = None,
skip_normalize = False,
image_callback = None,
step_callback = None,
width = None,
height = None,
sampler_name = None,
seamless = False,
log_tokenization= False,
with_variations = None,
variation_amount = 0.0,
# these are specific to img2img
init_img = None,
mask = None,
invert_mask = False,
fit = False,
strength = None,
# these are specific to GFPGAN/ESRGAN
gfpgan_strength= 0,
save_original = False,
upscale = 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)
iterations // iterations (1); image count=iterations
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)
seamless // whether the generated image should tile
init_img // path to an initial image
mask // path to an initial image mask for inpainting
invert_mask // paint over opaque areas, retain transparent areas
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)
step_callback // a function or method that will be called each step
image_callback // a function or method that will be called each time an image is generated
with_variations // a weighted list [(seed_1, weight_1), (seed_2, weight_2), ...] of variations which should be applied before doing any generation
variation_amount // optional 0-1 value to slerp from -S noise to random noise (allows variations on an image)
To use the step callback, define a function that receives two arguments:
- Image GPU data
- The step number
To use the image 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.
"""
# TODO: convert this into a getattr() loop
steps = steps or self.steps
width = width or self.width
height = height or self.height
seamless = seamless or self.seamless
cfg_scale = cfg_scale or self.cfg_scale
ddim_eta = ddim_eta or self.ddim_eta
iterations = iterations or self.iterations
strength = strength or self.strength
self.seed = seed
self.log_tokenization = log_tokenization
with_variations = [] if with_variations is None else with_variations
model = (
self.load_model()
) # will instantiate the model or return it from cache
for m in model.modules():
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
m.padding_mode = 'circular' if seamless else m._orig_padding_mode
assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0'
assert (
0.0 < strength < 1.0
), 'img2img and inpaint strength can only work with 0.0 < strength < 1.0'
assert (
0.0 <= variation_amount <= 1.0
), '-v --variation_amount must be in [0.0, 1.0]'
# check this logic - doesn't look right
if len(with_variations) > 0 or variation_amount > 1.0:
assert seed is not None,\
'seed must be specified when using with_variations'
if variation_amount == 0.0:
assert iterations == 1,\
'when using --with_variations, multiple iterations are only possible when using --variation_amount'
assert all(0 <= weight <= 1 for _, weight in with_variations),\
f'variation weights must be in [0.0, 1.0]: got {[weight for _, weight in with_variations]}'
width, height, _ = self._resolution_check(width, height, log=True)
if sampler_name and (sampler_name != self.sampler_name):
self.sampler_name = sampler_name
self._set_sampler()
tic = time.time()
if torch.cuda.is_available():
torch.cuda.reset_peak_memory_stats()
results = list()
init_image = None
init_mask_image = None
try:
uc, c = get_uc_and_c(
prompt, model=self.model,
skip_normalize=skip_normalize,
log_tokens=self.log_tokenization
)
if mask and not init_img:
raise AssertionError('If mask path is provided, initial image path should be provided as well')
if mask and init_img:
init_image,size1 = self._load_img(init_img, width, height,fit=fit)
init_image.to(self.device)
init_mask_image,size2 = self._load_img_mask(mask, width, height,fit=fit, invert=invert_mask)
init_mask_image.to(self.device)
assert size1==size2,f"for inpainting, the initial image and its mask must be identical sizes, instead got {size1} vs {size2}"
generator = self._make_inpaint()
elif init_img: # little bit of repeated code here, but makes logic clearer
init_image,_ = self._load_img(init_img, width, height, fit=fit)
init_image.to(self.device)
generator = self._make_img2img()
else:
generator = self._make_txt2img()
generator.set_variation(self.seed, variation_amount, with_variations)
results = generator.generate(
prompt,
iterations = iterations,
seed = self.seed,
sampler = self.sampler,
steps = steps,
cfg_scale = cfg_scale,
conditioning = (uc,c),
ddim_eta = ddim_eta,
image_callback = image_callback, # called after the final image is generated
step_callback = step_callback, # called after each intermediate image is generated
width = width,
height = height,
init_image = init_image, # notice that init_image is different from init_img
init_mask = init_mask_image,
strength = strength
)
if upscale is not None or gfpgan_strength > 0:
self.upscale_and_reconstruct(results,
upscale = upscale,
strength = gfpgan_strength,
save_original = save_original,
image_callback = image_callback)
except KeyboardInterrupt:
print('*interrupted*')
print(
'>> Partial results will be returned; if --grid was requested, nothing will be returned.'
)
except RuntimeError as e:
print(traceback.format_exc(), file=sys.stderr)
print('>> Are you sure your system has an adequate GPU?')
toc = time.time()
print('>> Usage stats:')
print(
f'>> {len(results)} image(s) generated in', '%4.2fs' % (toc - tic)
)
print(
f'>> Max VRAM used for this generation:',
'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
)
if self.session_peakmem:
self.session_peakmem = max(
self.session_peakmem, torch.cuda.max_memory_allocated()
)
print(
f'>> Max VRAM used since script start: ',
'%4.2fG' % (self.session_peakmem / 1e9),
)
return results
def _make_img2img(self):
if not self.generators.get('img2img'):
from ldm.dream.generator.img2img import Img2Img
self.generators['img2img'] = Img2Img(self.model)
return self.generators['img2img']
def _make_txt2img(self):
if not self.generators.get('txt2img'):
from ldm.dream.generator.txt2img import Txt2Img
self.generators['txt2img'] = Txt2Img(self.model)
return self.generators['txt2img']
def _make_inpaint(self):
if not self.generators.get('inpaint'):
from ldm.dream.generator.inpaint import Inpaint
self.generators['inpaint'] = Inpaint(self.model)
return self.generators['inpaint']
def load_model(self):
"""Load and initialize the model from configuration variables passed at object creation time"""
if self.model is None:
seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
try:
config = OmegaConf.load(self.config)
model = self._load_model_from_config(config, self.weights)
if self.embedding_path is not None:
model.embedding_manager.load(
self.embedding_path, self.full_precision
)
self.model = model.to(self.device)
# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
self.model.cond_stage_model.device = self.device
except AttributeError as e:
print(f'>> Error loading model. {str(e)}', file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
raise SystemExit from e
self._set_sampler()
for m in self.model.modules():
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
m._orig_padding_mode = m.padding_mode
return self.model
def upscale_and_reconstruct(self,
image_list,
upscale = None,
strength = 0.0,
save_original = False,
image_callback = None):
try:
if upscale is not None:
from ldm.gfpgan.gfpgan_tools import real_esrgan_upscale
if strength > 0:
from ldm.gfpgan.gfpgan_tools import run_gfpgan
except (ModuleNotFoundError, ImportError):
print(traceback.format_exc(), file=sys.stderr)
print('>> You may need to install the ESRGAN and/or GFPGAN modules')
return
for r in image_list:
image, seed = r
try:
if upscale is not None:
if len(upscale) < 2:
upscale.append(0.75)
image = real_esrgan_upscale(
image,
upscale[1],
int(upscale[0]),
seed,
)
if strength > 0:
image = run_gfpgan(
image, strength, seed, 1
)
except Exception as e:
print(
f'>> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}'
)
if image_callback is not None:
image_callback(image, seed, upscaled=True)
else:
r[0] = image
# to help WebGUI - front end to generator util function
def sample_to_image(self,samples):
return self._sample_to_image(samples)
def _sample_to_image(self,samples):
if not self.base_generator:
from ldm.dream.generator import Generator
self.base_generator = Generator(self.model)
return self.base_generator.sample_to_image(samples)
def _set_sampler(self):
msg = f'>> Setting Sampler to {self.sampler_name}'
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
)
elif self.sampler_name == 'k_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', device=self.device
)
elif self.sampler_name == 'k_euler':
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':
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)
print(msg)
def _load_model_from_config(self, config, ckpt):
print(f'>> Loading model from {ckpt}')
pl_sd = torch.load(ckpt, map_location='cpu')
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)'
)
else:
print(
'>> Using half precision math. Call with --full_precision to use more accurate but VRAM-intensive full precision.'
)
model.half()
return model
def _load_img(self, path, width, height, fit=False):
assert os.path.exists(path), f'>> {path}: File not found'
with Image.open(path) as img:
image = img.convert('RGB')
print(
f'>> loaded input image of size {image.width}x{image.height} from {path}'
)
if fit:
image = self._fit_image(image,(width,height))
else:
image = self._squeeze_image(image)
size = image.size
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
image = 2.0 * image - 1.0
return image.to(self.device),size
def _load_img_mask(self, path, width, height, fit=False, invert=False):
assert os.path.exists(path), f'>> {path}: File not found'
image = Image.open(path)
print(
f'>> loaded input mask of size {image.width}x{image.height} from {path}'
)
if fit:
image = self._fit_image(image,(width,height))
else:
image = self._squeeze_image(image)
# convert into a black/white mask
image = self._mask_to_image(image,invert)
image = image.convert('RGB')
size = image.size
# not quite sure what's going on here. It is copied from basunjindal's implementation
# image = image.resize((64, 64), resample=Image.Resampling.LANCZOS)
# BUG: We need to use the model's downsample factor rather than hardcoding "8"
from ldm.dream.generator.base import downsampling
image = image.resize((size[0]//downsampling, size[1]//downsampling), resample=Image.Resampling.LANCZOS)
image = np.array(image)
image = image.astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image.to(self.device),size
# The mask is expected to have the region to be inpainted
# with alpha transparency. It converts it into a black/white
# image with the transparent part black.
def _mask_to_image(self, init_mask, invert=False) -> Image:
if self._has_transparency(init_mask):
# Obtain the mask from the transparency channel
mask = Image.new(mode="L", size=init_mask.size, color=255)
mask.putdata(init_mask.getdata(band=3))
if invert:
mask = ImageOps.invert(mask)
return mask
else:
print(f'>> No transparent pixels in this image. Will paint across entire image.')
return Image.new(mode="L", size=mask.size, color=0)
def _has_transparency(self,image):
if image.info.get("transparency", None) is not None:
return True
if image.mode == "P":
transparent = image.info.get("transparency", -1)
for _, index in image.getcolors():
if index == transparent:
return True
elif image.mode == "RGBA":
extrema = image.getextrema()
if extrema[3][0] < 255:
return True
return False
def _squeeze_image(self,image):
x,y,resize_needed = self._resolution_check(image.width,image.height)
if resize_needed:
return InitImageResizer(image).resize(x,y)
return image
def _fit_image(self,image,max_dimensions):
w,h = max_dimensions
print(
f'>> image will be resized to fit inside a box {w}x{h} in size.'
)
if image.width > image.height:
h = None # by setting h to none, we tell InitImageResizer to fit into the width and calculate height
elif image.height > image.width:
w = None # ditto for w
else:
pass
image = InitImageResizer(image).resize(w,h) # note that InitImageResizer does the multiple of 64 truncation internally
print(
f'>> after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}'
)
return image
def _resolution_check(self, width, height, log=False):
resize_needed = False
w, h = map(
lambda x: x - x % 64, (width, height)
) # resize to integer multiple of 64
if h != height or w != width:
if log:
print(
f'>> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}'
)
height = h
width = w
resize_needed = True
if (width * height) > (self.width * self.height):
print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
return width, height, resize_needed
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