mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'development' into development
This commit is contained in:
Peter Baylies
GitHub
commit
80579a30e5
@ -36,7 +36,7 @@ Outputs:
|
||||
|
||||
The one with seed 3357757885 looks nice:
|
||||
|
||||
<img src="assets/variation_walkthru/000001.3357757885.png"/>
|
||||
<img src="../assets/variation_walkthru/000001.3357757885.png"/>
|
||||
|
||||
---
|
||||
|
||||
@ -65,8 +65,8 @@ variation amount used to generate it.
|
||||
|
||||
This gives us a series of closely-related variations, including the two shown here.
|
||||
|
||||
<img src="assets/variation_walkthru/000002.3647897225.png">
|
||||
<img src="assets/variation_walkthru/000002.1614299449.png">
|
||||
<img src="../assets/variation_walkthru/000002.3647897225.png">
|
||||
<img src="../assets/variation_walkthru/000002.1614299449.png">
|
||||
|
||||
I like the expression on Xena's face in the first one (subseed 3647897225), and the armor on her shoulder in the second one (subseed 1614299449). Can we combine them to get the best of both worlds?
|
||||
|
||||
@ -81,7 +81,7 @@ Outputs:
|
||||
|
||||
Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The resulting image is close, but not exactly what I wanted:
|
||||
|
||||
<img src="assets/variation_walkthru/000003.1614299449.png">
|
||||
<img src="../assets/variation_walkthru/000003.1614299449.png">
|
||||
|
||||
We could either try combining the images with different weights, or we can generate more variations around the almost-but-not-quite image. We do the latter, using both the `-V` (combining) and `-v` (variation strength) options. Note that we use `-n6` to generate 6 variations:
|
||||
|
||||
@ -98,7 +98,7 @@ Outputs:
|
||||
|
||||
This produces six images, all slight variations on the combination of the chosen two images. Here's the one I like best:
|
||||
|
||||
<img src="assets/variation_walkthru/000004.3747154981.png">
|
||||
<img src="../assets/variation_walkthru/000004.3747154981.png">
|
||||
|
||||
As you can see, this is a very powerful tool, which when combined with subprompt weighting, gives you great control over the content and
|
||||
quality of your generated images.
|
||||
|
||||
@ -152,21 +152,27 @@ You might also need to install Rust (I mention this again below).
|
||||
|
||||
### How many snakes are living in your computer?
|
||||
|
||||
Here's the reason why you have to specify which python to use.
|
||||
There are several versions of python on macOS and the computer is
|
||||
picking the wrong one. More specifically, preload_models.py and dream.py says to
|
||||
find the first `python3` in the path environment variable. You can see which one
|
||||
it is picking with `which python3`. These are the mostly likely paths you'll see.
|
||||
You might have multiple Python installations on your system, in which case it's
|
||||
important to be explicit and consistent about which one to use for a given project.
|
||||
This is because virtual environments are coupled to the Python that created it (and all
|
||||
the associated 'system-level' modules).
|
||||
|
||||
When you run `python` or `python3`, your shell searches the colon-delimited locations
|
||||
in the `PATH` environment variable (`echo $PATH` to see that list) in that order - first match wins.
|
||||
You can ask for the location of the first `python3` found in your `PATH` with the `which` command like this:
|
||||
|
||||
% which python3
|
||||
/usr/bin/python3
|
||||
|
||||
The above path is part of the OS. However, that path is a stub that asks you if
|
||||
you want to install Xcode. If you have Xcode installed already,
|
||||
/usr/bin/python3 will execute /Library/Developer/CommandLineTools/usr/bin/python3 or
|
||||
/Applications/Xcode.app/Contents/Developer/usr/bin/python3 (depending on which
|
||||
Anything in `/usr/bin` is [part of the OS](https://developer.apple.com/library/archive/documentation/FileManagement/Conceptual/FileSystemProgrammingGuide/FileSystemOverview/FileSystemOverview.html#//apple_ref/doc/uid/TP40010672-CH2-SW6). However, `/usr/bin/python3` is not actually python3, but
|
||||
rather a stub that offers to install Xcode (which includes python 3). If you have Xcode installed already,
|
||||
`/usr/bin/python3` will execute `/Library/Developer/CommandLineTools/usr/bin/python3` or
|
||||
`/Applications/Xcode.app/Contents/Developer/usr/bin/python3` (depending on which
|
||||
Xcode you've selected with `xcode-select`).
|
||||
|
||||
Note that `/usr/bin/python` is an entirely different python - specifically, python 2. Note: starting in
|
||||
macOS 12.3, `/usr/bin/python` no longer exists.
|
||||
|
||||
% which python3
|
||||
/opt/homebrew/bin/python3
|
||||
|
||||
@ -176,17 +182,21 @@ for Homebrew binaries before system ones, you'll see the above path.
|
||||
% which python
|
||||
/opt/anaconda3/bin/python
|
||||
|
||||
If you drop the "3" you get an entirely different python. Note: starting in
|
||||
macOS 12.3, /usr/bin/python no longer exists (it was python 2 anyway).
|
||||
|
||||
If you have Anaconda installed, this is what you'll see. There is a
|
||||
/opt/anaconda3/bin/python3 also.
|
||||
If you have Anaconda installed, you will see the above path. There is a
|
||||
`/opt/anaconda3/bin/python3` also. We expect that `/opt/anaconda3/bin/python`
|
||||
and `/opt/anaconda3/bin/python3` should actually be the *same python*, which you can
|
||||
verify by comparing the output of `python3 -V` and `python -V`.
|
||||
|
||||
(ldm) % which python
|
||||
/Users/name/miniforge3/envs/ldm/bin/python
|
||||
|
||||
This is what you'll see if you have miniforge and you've correctly activated
|
||||
the ldm environment. This is the goal.
|
||||
The above is what you'll see if you have miniforge and you've correctly activated
|
||||
the ldm environment, and you used option 2 in the setup instructions above ("no pyenv").
|
||||
|
||||
(anaconda3-2022.05) % which python
|
||||
/Users/name/.pyenv/shims/python
|
||||
|
||||
... and the above is what you'll see if you used option 1 ("Alongside pyenv").
|
||||
|
||||
It's all a mess and you should know [how to modify the path environment variable](https://support.apple.com/guide/terminal/use-environment-variables-apd382cc5fa-4f58-4449-b20a-41c53c006f8f/mac)
|
||||
if you want to fix it. Here's a brief hint of all the ways you can modify it
|
||||
@ -201,6 +211,13 @@ if you want to fix it. Here's a brief hint of all the ways you can modify it
|
||||
Which one you use will depend on what you have installed except putting a file
|
||||
in /etc/paths.d is what I prefer to do.
|
||||
|
||||
Finally, to answer the question posed by this section's title, it may help to list
|
||||
all of the `python` / `python3` things found in `$PATH` instead of just the one that
|
||||
will be executed by default. To do that, add the `-a` switch to `which`:
|
||||
|
||||
% which -a python3
|
||||
...
|
||||
|
||||
### Debugging?
|
||||
|
||||
Tired of waiting for your renders to finish before you can see if it
|
||||
|
||||
@ -105,10 +105,14 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
self.end_headers()
|
||||
self.wfile.write(bytes('{}', 'utf8'))
|
||||
else:
|
||||
path = "." + self.path
|
||||
cwd = os.path.realpath(os.getcwd())
|
||||
is_in_cwd = os.path.commonprefix((os.path.realpath(path), cwd)) == cwd
|
||||
if not (is_in_cwd and os.path.exists(path)):
|
||||
path_dir = os.path.dirname(self.path)
|
||||
out_dir = os.path.realpath(self.outdir.rstrip('/'))
|
||||
if self.path.startswith('/static/dream_web/'):
|
||||
path = '.' + self.path
|
||||
elif out_dir.endswith(path_dir):
|
||||
file = os.path.basename(self.path)
|
||||
path = os.path.join(self.outdir,file)
|
||||
else:
|
||||
self.send_response(404)
|
||||
return
|
||||
mime_type = mimetypes.guess_type(path)[0]
|
||||
@ -116,7 +120,7 @@ class DreamServer(BaseHTTPRequestHandler):
|
||||
self.send_response(200)
|
||||
self.send_header("Content-type", mime_type)
|
||||
self.end_headers()
|
||||
with open("." + self.path, "rb") as content:
|
||||
with open(path, "rb") as content:
|
||||
self.wfile.write(content.read())
|
||||
else:
|
||||
self.send_response(404)
|
||||
|
||||
130
ldm/generate.py
130
ldm/generate.py
@ -17,7 +17,7 @@ import transformers
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image, ImageOps
|
||||
from torch import nn
|
||||
from pytorch_lightning import seed_everything
|
||||
from pytorch_lightning import seed_everything, logging
|
||||
|
||||
from ldm.util import instantiate_from_config
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
@ -35,7 +35,7 @@ Example Usage:
|
||||
from ldm.generate import Generate
|
||||
|
||||
# Create an object with default values
|
||||
gr = Generate()
|
||||
gr = Generate('stable-diffusion-1.4')
|
||||
|
||||
# do the slow model initialization
|
||||
gr.load_model()
|
||||
@ -79,16 +79,17 @@ still work.
|
||||
|
||||
The full list of arguments to Generate() are:
|
||||
gr = Generate(
|
||||
# these values are set once and shouldn't be changed
|
||||
conf = path to configuration file ('configs/models.yaml')
|
||||
model = symbolic name of the model in the configuration file
|
||||
full_precision = False
|
||||
|
||||
# this value is sticky and maintained between generation calls
|
||||
sampler_name = ['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'] // k_lms
|
||||
|
||||
# these are deprecated - use conf and model instead
|
||||
weights = path to model weights ('models/ldm/stable-diffusion-v1/model.ckpt')
|
||||
config = path to model configuraiton ('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)
|
||||
)
|
||||
|
||||
"""
|
||||
@ -101,56 +102,52 @@ class Generate:
|
||||
|
||||
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,
|
||||
model = 'stable-diffusion-1.4',
|
||||
conf = 'configs/models.yaml',
|
||||
embedding_path = None,
|
||||
sampler_name = 'k_lms',
|
||||
ddim_eta = 0.0, # deterministic
|
||||
full_precision = False,
|
||||
strength = 0.75, # default in scripts/img2img.py
|
||||
seamless = False,
|
||||
embedding_path = None,
|
||||
device_type = 'cuda',
|
||||
ignore_ctrl_c = False,
|
||||
# these are deprecated; if present they override values in the conf file
|
||||
weights = None,
|
||||
config = None,
|
||||
):
|
||||
self.iterations = iterations
|
||||
self.width = width
|
||||
self.height = height
|
||||
self.steps = steps
|
||||
self.cfg_scale = cfg_scale
|
||||
self.weights = weights
|
||||
self.config = config
|
||||
models = OmegaConf.load(conf)
|
||||
mconfig = models[model]
|
||||
self.weights = mconfig.weights if weights is None else weights
|
||||
self.config = mconfig.config if config is None else config
|
||||
self.height = mconfig.height
|
||||
self.width = mconfig.width
|
||||
self.iterations = 1
|
||||
self.steps = 50
|
||||
self.cfg_scale = 7.5
|
||||
self.sampler_name = sampler_name
|
||||
self.grid = grid
|
||||
self.ddim_eta = ddim_eta
|
||||
self.ddim_eta = 0.0 # same seed always produces same image
|
||||
self.full_precision = True if choose_torch_device() == 'mps' else full_precision
|
||||
self.strength = strength
|
||||
self.seamless = seamless
|
||||
self.strength = 0.75
|
||||
self.seamless = False
|
||||
self.embedding_path = embedding_path
|
||||
self.device_type = device_type
|
||||
self.ignore_ctrl_c = ignore_ctrl_c # note, this logic probably doesn't belong here...
|
||||
self.model = None # empty for now
|
||||
self.sampler = None
|
||||
self.device = None
|
||||
self.session_peakmem = None
|
||||
self.generators = {}
|
||||
self.base_generator = None
|
||||
self.seed = None
|
||||
|
||||
if device_type == 'cuda' and not torch.cuda.is_available():
|
||||
# Note that in previous versions, there was an option to pass the
|
||||
# device to Generate(). However the device was then ignored, so
|
||||
# it wasn't actually doing anything. This logic could be reinstated.
|
||||
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
|
||||
self.session_peakmem = torch.cuda.max_memory_allocated() if self._has_cuda else None
|
||||
transformers.logging.set_verbosity_error()
|
||||
|
||||
# gets rid of annoying messages about random seed
|
||||
logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
|
||||
|
||||
def prompt2png(self, prompt, outdir, **kwargs):
|
||||
"""
|
||||
Takes a prompt and an output directory, writes out the requested number
|
||||
@ -195,7 +192,7 @@ class Generate:
|
||||
height = None,
|
||||
sampler_name = None,
|
||||
seamless = False,
|
||||
log_tokenization= False,
|
||||
log_tokenization = False,
|
||||
with_variations = None,
|
||||
variation_amount = 0.0,
|
||||
threshold = 0.0,
|
||||
@ -209,9 +206,11 @@ class Generate:
|
||||
embiggen = None,
|
||||
embiggen_tiles = None,
|
||||
# these are specific to GFPGAN/ESRGAN
|
||||
gfpgan_strength= 0,
|
||||
gfpgan_strength = 0,
|
||||
save_original = False,
|
||||
upscale = None,
|
||||
# Set this True to handle KeyboardInterrupt internally
|
||||
catch_interrupts = False,
|
||||
**args,
|
||||
): # eat up additional cruft
|
||||
"""
|
||||
@ -266,9 +265,8 @@ class Generate:
|
||||
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
|
||||
# will instantiate the model or return it from cache
|
||||
model = self.load_model()
|
||||
|
||||
for m in model.modules():
|
||||
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
|
||||
@ -289,7 +287,6 @@ class Generate:
|
||||
(embiggen == None and embiggen_tiles == None) or ((embiggen != None or embiggen_tiles != None) and init_img != None)
|
||||
), 'Embiggen requires an init/input image to be specified'
|
||||
|
||||
# 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'
|
||||
@ -306,7 +303,7 @@ class Generate:
|
||||
self._set_sampler()
|
||||
|
||||
tic = time.time()
|
||||
if torch.cuda.is_available():
|
||||
if self._has_cuda():
|
||||
torch.cuda.reset_peak_memory_stats()
|
||||
|
||||
results = list()
|
||||
@ -315,9 +312,9 @@ class Generate:
|
||||
|
||||
try:
|
||||
uc, c = get_uc_and_c(
|
||||
prompt, model=self.model,
|
||||
prompt, model =self.model,
|
||||
skip_normalize=skip_normalize,
|
||||
log_tokens=self.log_tokenization
|
||||
log_tokens =self.log_tokenization
|
||||
)
|
||||
|
||||
(init_image,mask_image) = self._make_images(init_img,init_mask, width, height, fit)
|
||||
@ -362,27 +359,25 @@ class Generate:
|
||||
save_original = save_original,
|
||||
image_callback = image_callback)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print('*interrupted*')
|
||||
if not self.ignore_ctrl_c:
|
||||
raise KeyboardInterrupt
|
||||
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('>> Could not generate image.')
|
||||
except KeyboardInterrupt:
|
||||
if catch_interrupts:
|
||||
print('**Interrupted** Partial results will be returned.')
|
||||
else:
|
||||
raise KeyboardInterrupt
|
||||
|
||||
toc = time.time()
|
||||
print('>> Usage stats:')
|
||||
print(
|
||||
f'>> {len(results)} image(s) generated in', '%4.2fs' % (toc - tic)
|
||||
)
|
||||
if torch.cuda.is_available() and self.device.type == 'cuda':
|
||||
if self._has_cuda():
|
||||
print(
|
||||
f'>> Max VRAM used for this generation:',
|
||||
'%4.2fG.' % (torch.cuda.max_memory_allocated() / 1e9),
|
||||
'Current VRAM utilization:'
|
||||
'Current VRAM utilization:',
|
||||
'%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
|
||||
)
|
||||
|
||||
@ -449,8 +444,7 @@ class Generate:
|
||||
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)
|
||||
model = self._load_model_from_config(self.config, self.weights)
|
||||
if self.embedding_path is not None:
|
||||
model.embedding_manager.load(
|
||||
self.embedding_path, self.full_precision
|
||||
@ -551,8 +545,11 @@ class Generate:
|
||||
|
||||
print(msg)
|
||||
|
||||
def _load_model_from_config(self, config, ckpt):
|
||||
print(f'>> Loading model from {ckpt}')
|
||||
# Be warned: config is the path to the model config file, not the dream conf file!
|
||||
# Also note that we can get config and weights from self, so why do we need to
|
||||
# pass them as args?
|
||||
def _load_model_from_config(self, config, weights):
|
||||
print(f'>> Loading model from {weights}')
|
||||
|
||||
# for usage statistics
|
||||
device_type = choose_torch_device()
|
||||
@ -561,9 +558,10 @@ class Generate:
|
||||
tic = time.time()
|
||||
|
||||
# this does the work
|
||||
pl_sd = torch.load(ckpt, map_location='cpu')
|
||||
c = OmegaConf.load(config)
|
||||
pl_sd = torch.load(weights, map_location='cpu')
|
||||
sd = pl_sd['state_dict']
|
||||
model = instantiate_from_config(config.model)
|
||||
model = instantiate_from_config(c.model)
|
||||
m, u = model.load_state_dict(sd, strict=False)
|
||||
|
||||
if self.full_precision:
|
||||
@ -583,7 +581,7 @@ class Generate:
|
||||
print(
|
||||
f'>> Model loaded in', '%4.2fs' % (toc - tic)
|
||||
)
|
||||
if device_type == 'cuda':
|
||||
if self._has_cuda():
|
||||
print(
|
||||
'>> Max VRAM used to load the model:',
|
||||
'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
|
||||
@ -720,3 +718,5 @@ class Generate:
|
||||
return width, height, resize_needed
|
||||
|
||||
|
||||
def _has_cuda(self):
|
||||
return self.device.type == 'cuda'
|
||||
|
||||
@ -225,7 +225,7 @@ class DDIMSampler(object):
|
||||
total_steps = (
|
||||
timesteps if ddim_use_original_steps else timesteps.shape[0]
|
||||
)
|
||||
print(f'Running DDIM Sampling with {total_steps} timesteps')
|
||||
print(f'\nRunning DDIM Sampling with {total_steps} timesteps')
|
||||
|
||||
iterator = tqdm(
|
||||
time_range,
|
||||
|
||||
@ -40,57 +40,9 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%cmd\n",
|
||||
"git clone https://github.com/lstein/stable-diffusion.git"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%cd stable-diffusion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile requirements.txt\n",
|
||||
"albumentations==0.4.3\n",
|
||||
"einops==0.3.0\n",
|
||||
"huggingface-hub==0.8.1\n",
|
||||
"imageio-ffmpeg==0.4.2\n",
|
||||
"imageio==2.9.0\n",
|
||||
"kornia==0.6.0\n",
|
||||
"# pip will resolve the version which matches torch\n",
|
||||
"numpy\n",
|
||||
"omegaconf==2.1.1\n",
|
||||
"opencv-python==4.6.0.66\n",
|
||||
"pillow==9.2.0\n",
|
||||
"pip>=22\n",
|
||||
"pudb==2019.2\n",
|
||||
"pytorch-lightning==1.4.2\n",
|
||||
"streamlit==1.12.0\n",
|
||||
"# \"CompVis/taming-transformers\" doesn't work\n",
|
||||
"# ldm\\models\\autoencoder.py\", line 6, in <module>\n",
|
||||
"# from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer\n",
|
||||
"# ModuleNotFoundError\n",
|
||||
"taming-transformers-rom1504==0.0.6\n",
|
||||
"test-tube>=0.7.5\n",
|
||||
"torch-fidelity==0.3.0\n",
|
||||
"torchmetrics==0.6.0\n",
|
||||
"transformers==4.19.2\n",
|
||||
"git+https://github.com/openai/CLIP.git@main#egg=clip\n",
|
||||
"git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion\n",
|
||||
"# No CUDA in PyPi builds\n",
|
||||
"--extra-index-url https://download.pytorch.org/whl/cu113 --trusted-host https://download.pytorch.org\n",
|
||||
"torch==1.11.0\n",
|
||||
"# Same as numpy - let pip do its thing\n",
|
||||
"torchvision\n",
|
||||
"-e .\n"
|
||||
"git clone https://github.com/lstein/stable-diffusion.git\n",
|
||||
"cd /content/stable-diffusion/\n",
|
||||
"git checkout --quiet development"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -100,14 +52,14 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%cmd\n",
|
||||
"pew new --python 3.10 -r requirements.txt --dont-activate ldm"
|
||||
"pew new --python 3.10 -r requirements-lin-win-colab-CUDA.txt --dont-activate stable-diffusion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Switch the notebook kernel to the new 'ldm' environment!\n",
|
||||
"# Switch the notebook kernel to the new 'stable-diffusion' environment!\n",
|
||||
"\n",
|
||||
"## VSCode: restart VSCode and come back to this cell\n",
|
||||
"\n",
|
||||
@ -115,7 +67,7 @@
|
||||
"1. Type \"Select Interpreter\" and select \"Jupyter: Select Interpreter to Start Jupyter Server\"\n",
|
||||
"1. VSCode will say that it needs to install packages. Click the \"Install\" button.\n",
|
||||
"1. Once the install is finished, do 1 & 2 again\n",
|
||||
"1. Pick 'ldm'\n",
|
||||
"1. Pick 'stable-diffusion'\n",
|
||||
"1. Run the following cell"
|
||||
]
|
||||
},
|
||||
@ -136,7 +88,7 @@
|
||||
"## Jupyter/JupyterLab\n",
|
||||
"\n",
|
||||
"1. Run the cell below\n",
|
||||
"1. Click on the toolbar where it says \"(ipyknel)\" ↗️. You should get a pop-up asking you to \"Select Kernel\". Pick 'ldm' from the drop-down.\n"
|
||||
"1. Click on the toolbar where it says \"(ipyknel)\" ↗️. You should get a pop-up asking you to \"Select Kernel\". Pick 'stable-diffusion' from the drop-down.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -154,9 +106,9 @@
|
||||
"source": [
|
||||
"# DO NOT RUN THIS CELL IF YOU ARE USING VSCODE!!\n",
|
||||
"%%cmd\n",
|
||||
"pew workon ldm\n",
|
||||
"pew workon stable-diffusion\n",
|
||||
"pip3 install ipykernel\n",
|
||||
"python -m ipykernel install --name=ldm"
|
||||
"python -m ipykernel install --name=stable-diffusion"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -231,7 +183,7 @@
|
||||
"Now:\n",
|
||||
"\n",
|
||||
"1. `cd` to wherever the 'stable-diffusion' directory is\n",
|
||||
"1. Run `pew workon ldm`\n",
|
||||
"1. Run `pew workon stable-diffusion`\n",
|
||||
"1. Run `winpty python scripts\\dream.py`"
|
||||
]
|
||||
}
|
||||
|
||||
@ -1,27 +1,12 @@
|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": [],
|
||||
"collapsed_sections": [],
|
||||
"private_outputs": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
},
|
||||
"accelerator": "GPU",
|
||||
"gpuClass": "standard"
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ycYWcsEKc6w7"
|
||||
},
|
||||
"source": [
|
||||
"# Stable Diffusion AI Notebook (Release 1.13)\n",
|
||||
"# Stable Diffusion AI Notebook (Release 1.14)\n",
|
||||
"\n",
|
||||
"<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
|
||||
"#### Instructions:\n",
|
||||
@ -35,33 +20,30 @@
|
||||
"<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
|
||||
"##### For more details visit Github repository: [lstein/stable-diffusion](https://github.com/lstein/stable-diffusion)\n",
|
||||
"---\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ycYWcsEKc6w7"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## ◢ Installation"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "dr32VLxlnouf"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Installation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title 1. Check current GPU assigned\n",
|
||||
"!nvidia-smi -L\n",
|
||||
"!nvidia-smi"
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "a2Z5Qu_o8VtQ"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 1. Check current GPU assigned\n",
|
||||
"!nvidia-smi -L\n",
|
||||
"!nvidia-smi"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@ -75,90 +57,91 @@
|
||||
"#@title 2. Download stable-diffusion Repository\n",
|
||||
"from os.path import exists\n",
|
||||
"\n",
|
||||
"if exists(\"/content/stable-diffusion/\")==True:\n",
|
||||
" %cd /content/stable-diffusion/\n",
|
||||
" print(\"Already downloaded repo\")\n",
|
||||
"else:\n",
|
||||
" !git clone --quiet https://github.com/lstein/stable-diffusion.git # Original repo\n",
|
||||
" %cd /content/stable-diffusion/\n",
|
||||
" !git checkout --quiet tags/release-1.13"
|
||||
"!git clone --quiet https://github.com/lstein/stable-diffusion.git # Original repo\n",
|
||||
"%cd /content/stable-diffusion/\n",
|
||||
"!git checkout --quiet tags/release-1.14.1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title 3. Install dependencies\n",
|
||||
"import gc\n",
|
||||
"\n",
|
||||
"if exists(\"/content/stable-diffusion/requirements-colab.txt\")==True:\n",
|
||||
" %cd /content/stable-diffusion/\n",
|
||||
" print(\"Already downloaded requirements file\")\n",
|
||||
"else:\n",
|
||||
" !wget https://raw.githubusercontent.com/lstein/stable-diffusion/development/requirements-colab.txt\n",
|
||||
"!pip install colab-xterm\n",
|
||||
"!pip install -r requirements-colab.txt\n",
|
||||
"gc.collect()"
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "QbXcGXYEFSNB"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 4. Load small ML models required\n",
|
||||
"%cd /content/stable-diffusion/\n",
|
||||
"!python scripts/preload_models.py\n",
|
||||
"#@title 3. Install dependencies\n",
|
||||
"import gc\n",
|
||||
"\n",
|
||||
"!wget https://raw.githubusercontent.com/lstein/stable-diffusion/development/requirements.txt\n",
|
||||
"!wget https://raw.githubusercontent.com/lstein/stable-diffusion/development/requirements-lin-win-colab-CUDA.txt\n",
|
||||
"!pip install colab-xterm\n",
|
||||
"!pip install -r requirements-lin-win-colab-CUDA.txt\n",
|
||||
"!pip install clean-fid torchtext\n",
|
||||
"gc.collect()"
|
||||
],
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ChIDWxLVHGGJ"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title 5. Restart Runtime\n",
|
||||
"exit()"
|
||||
],
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "8rSMhgnAttQa"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"## ◢ Configuration"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "795x1tMoo8b1"
|
||||
}
|
||||
"#@title 4. Restart Runtime\n",
|
||||
"exit()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ChIDWxLVHGGJ"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 6. Mount google Drive\n",
|
||||
"from google.colab import drive\n",
|
||||
"drive.mount('/content/drive')"
|
||||
],
|
||||
"#@title 5. Load small ML models required\n",
|
||||
"import gc\n",
|
||||
"%cd /content/stable-diffusion/\n",
|
||||
"!python scripts/preload_models.py\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "795x1tMoo8b1"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Configuration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "YEWPV-sF1RDM"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 6. Mount google Drive\n",
|
||||
"from google.colab import drive\n",
|
||||
"drive.mount('/content/drive')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "zRTJeZ461WGu"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 7. Drive Path to model\n",
|
||||
"#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
|
||||
@ -167,20 +150,20 @@
|
||||
"from os.path import exists\n",
|
||||
"\n",
|
||||
"model_path = \"\" #@param {type:\"string\"}\n",
|
||||
"if exists(model_path)==True:\n",
|
||||
"if exists(model_path):\n",
|
||||
" print(\"✅ Valid directory\")\n",
|
||||
"else: \n",
|
||||
" print(\"❌ File doesn't exist\")"
|
||||
],
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "zRTJeZ461WGu"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "UY-NNz4I8_aG"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 8. Symlink to model\n",
|
||||
"\n",
|
||||
@ -188,39 +171,39 @@
|
||||
"import os \n",
|
||||
"\n",
|
||||
"# Folder creation if it doesn't exist\n",
|
||||
"if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1\")==True:\n",
|
||||
"if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1\"):\n",
|
||||
" print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
|
||||
"else:\n",
|
||||
" %mkdir /content/stable-diffusion/models/ldm/stable-diffusion-v1\n",
|
||||
" print(\"✅ Dir stable-diffusion-v1 created\")\n",
|
||||
"\n",
|
||||
"# Symbolic link if it doesn't exist\n",
|
||||
"if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt\")==True:\n",
|
||||
"if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
|
||||
" print(\"❗ Symlink already created\")\n",
|
||||
"else: \n",
|
||||
" src = model_path\n",
|
||||
" dst = '/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt'\n",
|
||||
" os.symlink(src, dst) \n",
|
||||
" print(\"✅ Symbolic link created successfully\")"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "UY-NNz4I8_aG",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## ◢ Execution"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Mc28N0_NrCQH"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Execution"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ir4hCrMIuUpl"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 9. Run Terminal and Execute Dream bot\n",
|
||||
"#@markdown <font color=\"blue\">Steps:</font> <br>\n",
|
||||
@ -229,24 +212,21 @@
|
||||
"#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
|
||||
"#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
|
||||
"\n",
|
||||
"import gc\n",
|
||||
"%cd /content/stable-diffusion/\n",
|
||||
"%load_ext colabxterm\n",
|
||||
"%xterm\n",
|
||||
"gc.collect()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ir4hCrMIuUpl",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "qnLohSHmKoGk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 10. Show the last 15 generated images\n",
|
||||
"import gc\n",
|
||||
"import glob\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import matplotlib.image as mpimg\n",
|
||||
@ -269,13 +249,25 @@
|
||||
" plt.imshow(image)\n",
|
||||
" gc.collect()\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "qnLohSHmKoGk"
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"collapsed_sections": [],
|
||||
"private_outputs": true,
|
||||
"provenance": []
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
]
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
||||
@ -1,26 +0,0 @@
|
||||
albumentations==0.4.3
|
||||
clean-fid==0.1.29
|
||||
einops==0.3.0
|
||||
huggingface-hub==0.8.1
|
||||
imageio-ffmpeg==0.4.2
|
||||
imageio==2.9.0
|
||||
kornia==0.6.0
|
||||
numpy==1.21.6
|
||||
omegaconf==2.1.1
|
||||
opencv-python==4.6.0.66
|
||||
pillow==9.2.0
|
||||
pip>=22
|
||||
pudb==2019.2
|
||||
pytorch-lightning==1.4.2
|
||||
streamlit==1.12.0
|
||||
taming-transformers-rom1504==0.0.6
|
||||
test-tube>=0.7.5
|
||||
torch-fidelity==0.3.0
|
||||
torchmetrics==0.6.0
|
||||
torchtext==0.6.0
|
||||
transformers==4.19.2
|
||||
torch==1.12.1+cu113
|
||||
torchvision==0.13.1+cu113
|
||||
git+https://github.com/openai/CLIP.git@main#egg=clip
|
||||
git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
|
||||
-e .
|
||||
7
requirements-lin-AMD.txt
Normal file
7
requirements-lin-AMD.txt
Normal file
@ -0,0 +1,7 @@
|
||||
-r requirements.txt
|
||||
|
||||
# Get hardware-appropriate torch/torchvision
|
||||
--extra-index-url https://download.pytorch.org/whl/rocm5.1.1 --trusted-host https://download.pytorch.org
|
||||
torch
|
||||
torchvision
|
||||
-e .
|
||||
7
requirements-lin-win-colab-CUDA.txt
Normal file
7
requirements-lin-win-colab-CUDA.txt
Normal file
@ -0,0 +1,7 @@
|
||||
-r requirements.txt
|
||||
|
||||
# Get hardware-appropriate torch/torchvision
|
||||
--extra-index-url https://download.pytorch.org/whl/cu116 --trusted-host https://download.pytorch.org
|
||||
torch
|
||||
torchvision
|
||||
-e .
|
||||
@ -1,33 +0,0 @@
|
||||
albumentations==0.4.3
|
||||
einops==0.3.0
|
||||
huggingface-hub==0.8.1
|
||||
imageio-ffmpeg==0.4.2
|
||||
imageio==2.9.0
|
||||
kornia==0.6.0
|
||||
# pip will resolve the version which matches torch
|
||||
numpy
|
||||
omegaconf==2.1.1
|
||||
opencv-python==4.6.0.66
|
||||
pillow==9.2.0
|
||||
pip>=22
|
||||
pudb==2019.2
|
||||
pytorch-lightning==1.4.2
|
||||
streamlit==1.12.0
|
||||
# "CompVis/taming-transformers" doesn't work
|
||||
# ldm\models\autoencoder.py", line 6, in <module>
|
||||
# from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
|
||||
# ModuleNotFoundError
|
||||
taming-transformers-rom1504==0.0.6
|
||||
test-tube>=0.7.5
|
||||
torch-fidelity==0.3.0
|
||||
torchmetrics==0.6.0
|
||||
transformers==4.19.2
|
||||
git+https://github.com/openai/CLIP.git@main#egg=clip
|
||||
git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
|
||||
git+https://github.com/lstein/GFPGAN@fix-dark-cast-images#egg=gfpgan
|
||||
# No CUDA in PyPi builds
|
||||
--extra-index-url https://download.pytorch.org/whl/cu113 --trusted-host https://download.pytorch.org
|
||||
torch==1.11.0
|
||||
# Same as numpy - let pip do its thing
|
||||
torchvision
|
||||
-e .
|
||||
8
requirements-mac-MPS-CPU.txt
Normal file
8
requirements-mac-MPS-CPU.txt
Normal file
@ -0,0 +1,8 @@
|
||||
-r requirements.txt
|
||||
|
||||
--pre
|
||||
--extra-index-url https://download.pytorch.org/whl/nightly/cpu --trusted-host https://download.pytorch.org
|
||||
|
||||
torch
|
||||
torchvision
|
||||
-e .
|
||||
@ -1,24 +0,0 @@
|
||||
albumentations==0.4.3
|
||||
einops==0.3.0
|
||||
huggingface-hub==0.8.1
|
||||
imageio==2.9.0
|
||||
imageio-ffmpeg==0.4.2
|
||||
kornia==0.6.0
|
||||
numpy==1.23.1
|
||||
--pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
|
||||
omegaconf==2.1.1
|
||||
opencv-python==4.6.0.66
|
||||
pillow==9.2.0
|
||||
pudb==2019.2
|
||||
torch==1.12.1
|
||||
torchvision==0.13.0
|
||||
pytorch-lightning==1.4.2
|
||||
streamlit==1.12.0
|
||||
test-tube>=0.7.5
|
||||
torch-fidelity==0.3.0
|
||||
torchmetrics==0.6.0
|
||||
transformers==4.19.2
|
||||
-e git+https://github.com/openai/CLIP.git@main#egg=clip
|
||||
-e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
|
||||
-e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
|
||||
-e git+https://github.com/lstein/GFPGAN@fix-dark-cast-images#egg=gfpgan
|
||||
@ -1,33 +0,0 @@
|
||||
albumentations==0.4.3
|
||||
einops==0.3.0
|
||||
huggingface-hub==0.8.1
|
||||
imageio-ffmpeg==0.4.2
|
||||
imageio==2.9.0
|
||||
kornia==0.6.0
|
||||
# pip will resolve the version which matches torch
|
||||
numpy
|
||||
omegaconf==2.1.1
|
||||
opencv-python==4.6.0.66
|
||||
pillow==9.2.0
|
||||
pip>=22
|
||||
pudb==2019.2
|
||||
pytorch-lightning==1.4.2
|
||||
streamlit==1.12.0
|
||||
# "CompVis/taming-transformers" doesn't work
|
||||
# ldm\models\autoencoder.py", line 6, in <module>
|
||||
# from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
|
||||
# ModuleNotFoundError
|
||||
taming-transformers-rom1504==0.0.6
|
||||
test-tube>=0.7.5
|
||||
torch-fidelity==0.3.0
|
||||
torchmetrics==0.6.0
|
||||
transformers==4.19.2
|
||||
git+https://github.com/openai/CLIP.git@main#egg=clip
|
||||
git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
|
||||
git+https://github.com/lstein/GFPGAN@fix-dark-cast-images#egg=gfpgan
|
||||
# No CUDA in PyPi builds
|
||||
--extra-index-url https://download.pytorch.org/whl/cu113 --trusted-host https://download.pytorch.org
|
||||
torch==1.11.0
|
||||
# Same as numpy - let pip do its thing
|
||||
torchvision
|
||||
-e .
|
||||
27
requirements.txt
Normal file
27
requirements.txt
Normal file
@ -0,0 +1,27 @@
|
||||
--prefer-binary
|
||||
|
||||
albumentations
|
||||
einops
|
||||
huggingface-hub
|
||||
imageio-ffmpeg
|
||||
imageio
|
||||
kornia
|
||||
# pip will resolve the version which matches torch
|
||||
numpy
|
||||
omegaconf
|
||||
opencv-python
|
||||
pillow
|
||||
pip>=22
|
||||
pudb
|
||||
pytorch-lightning
|
||||
streamlit
|
||||
# "CompVis/taming-transformers" IS NOT INSTALLABLE
|
||||
# This is a drop-in replacement
|
||||
taming-transformers-rom1504
|
||||
test-tube
|
||||
torch-fidelity
|
||||
torchmetrics
|
||||
transformers
|
||||
git+https://github.com/openai/CLIP.git@main#egg=clip
|
||||
git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
|
||||
git+https://github.com/lstein/GFPGAN@fix-dark-cast-images#egg=gfpgan
|
||||
@ -33,53 +33,35 @@ def main():
|
||||
print('--weights argument has been deprecated. Please configure ./configs/models.yaml, and call it using --model instead.')
|
||||
sys.exit(-1)
|
||||
|
||||
try:
|
||||
models = OmegaConf.load(opt.config)
|
||||
width = models[opt.model].width
|
||||
height = models[opt.model].height
|
||||
config = models[opt.model].config
|
||||
weights = models[opt.model].weights
|
||||
except (FileNotFoundError, IOError, KeyError) as e:
|
||||
print(f'{e}. Aborting.')
|
||||
sys.exit(-1)
|
||||
|
||||
print('* Initializing, be patient...\n')
|
||||
sys.path.append('.')
|
||||
from pytorch_lightning import logging
|
||||
from ldm.generate import Generate
|
||||
|
||||
# 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
|
||||
# creating a simple Generate object with a handful of
|
||||
# defaults passed on the command line.
|
||||
# additional parameters will be added (or overriden) during
|
||||
# the user input loop
|
||||
t2i = Generate(
|
||||
width=width,
|
||||
height=height,
|
||||
sampler_name=opt.sampler_name,
|
||||
weights=weights,
|
||||
full_precision=opt.full_precision,
|
||||
config=config,
|
||||
grid=opt.grid,
|
||||
# this is solely for recreating the prompt
|
||||
seamless=opt.seamless,
|
||||
embedding_path=opt.embedding_path,
|
||||
device_type=opt.device,
|
||||
ignore_ctrl_c=opt.infile is None,
|
||||
try:
|
||||
gen = Generate(
|
||||
conf = opt.config,
|
||||
model = opt.model,
|
||||
sampler_name = opt.sampler_name,
|
||||
embedding_path = opt.embedding_path,
|
||||
full_precision = opt.full_precision,
|
||||
)
|
||||
except (FileNotFoundError, IOError, KeyError) as e:
|
||||
print(f'{e}. Aborting.')
|
||||
sys.exit(-1)
|
||||
|
||||
# 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)
|
||||
|
||||
# load the infile as a list of lines
|
||||
infile = None
|
||||
if opt.infile:
|
||||
@ -98,21 +80,23 @@ def main():
|
||||
print(">> changed to seamless tiling mode")
|
||||
|
||||
# preload the model
|
||||
t2i.load_model()
|
||||
gen.load_model()
|
||||
|
||||
if not infile:
|
||||
print(
|
||||
"\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
|
||||
)
|
||||
|
||||
cmd_parser = create_cmd_parser()
|
||||
# web server loops forever
|
||||
if opt.web:
|
||||
dream_server_loop(t2i, opt.host, opt.port, opt.outdir)
|
||||
else:
|
||||
main_loop(t2i, opt.outdir, opt.prompt_as_dir, cmd_parser, infile)
|
||||
dream_server_loop(gen, opt.host, opt.port, opt.outdir)
|
||||
sys.exit(0)
|
||||
|
||||
cmd_parser = create_cmd_parser()
|
||||
main_loop(gen, opt.outdir, opt.prompt_as_dir, cmd_parser, infile)
|
||||
|
||||
def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
|
||||
# TODO: main_loop() has gotten busy. Needs to be refactored.
|
||||
def main_loop(gen, outdir, prompt_as_dir, parser, infile):
|
||||
"""prompt/read/execute loop"""
|
||||
done = False
|
||||
path_filter = re.compile(r'[<>:"/\\|?*]')
|
||||
@ -132,9 +116,6 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
|
||||
except EOFError:
|
||||
done = True
|
||||
continue
|
||||
except KeyboardInterrupt:
|
||||
done = True
|
||||
continue
|
||||
|
||||
# skip empty lines
|
||||
if not command.strip():
|
||||
@ -184,6 +165,7 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
|
||||
if len(opt.prompt) == 0:
|
||||
print('Try again with a prompt!')
|
||||
continue
|
||||
|
||||
# retrieve previous value!
|
||||
if opt.init_img is not None and re.match('^-\\d+$', opt.init_img):
|
||||
try:
|
||||
@ -204,8 +186,6 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
|
||||
opt.seed = None
|
||||
continue
|
||||
|
||||
do_grid = opt.grid or t2i.grid
|
||||
|
||||
if opt.with_variations is not None:
|
||||
# shotgun parsing, woo
|
||||
parts = []
|
||||
@ -258,11 +238,11 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
|
||||
file_writer = PngWriter(current_outdir)
|
||||
prefix = file_writer.unique_prefix()
|
||||
results = [] # list of filename, prompt pairs
|
||||
grid_images = dict() # seed -> Image, only used if `do_grid`
|
||||
grid_images = dict() # seed -> Image, only used if `opt.grid`
|
||||
|
||||
def image_writer(image, seed, upscaled=False):
|
||||
path = None
|
||||
if do_grid:
|
||||
if opt.grid:
|
||||
grid_images[seed] = image
|
||||
else:
|
||||
if upscaled and opt.save_original:
|
||||
@ -278,16 +258,16 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
|
||||
iter_opt.with_variations = opt.with_variations + this_variation
|
||||
iter_opt.variation_amount = 0
|
||||
normalized_prompt = PromptFormatter(
|
||||
t2i, iter_opt).normalize_prompt()
|
||||
gen, iter_opt).normalize_prompt()
|
||||
metadata_prompt = f'{normalized_prompt} -S{iter_opt.seed}'
|
||||
elif opt.with_variations is not None:
|
||||
normalized_prompt = PromptFormatter(
|
||||
t2i, opt).normalize_prompt()
|
||||
gen, opt).normalize_prompt()
|
||||
# use the original seed - the per-iteration value is the last variation-seed
|
||||
metadata_prompt = f'{normalized_prompt} -S{opt.seed}'
|
||||
else:
|
||||
normalized_prompt = PromptFormatter(
|
||||
t2i, opt).normalize_prompt()
|
||||
gen, opt).normalize_prompt()
|
||||
metadata_prompt = f'{normalized_prompt} -S{seed}'
|
||||
path = file_writer.save_image_and_prompt_to_png(
|
||||
image, metadata_prompt, filename)
|
||||
@ -296,16 +276,21 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
|
||||
results.append([path, metadata_prompt])
|
||||
last_results.append([path, seed])
|
||||
|
||||
t2i.prompt2image(image_callback=image_writer, **vars(opt))
|
||||
catch_ctrl_c = infile is None # if running interactively, we catch keyboard interrupts
|
||||
gen.prompt2image(
|
||||
image_callback=image_writer,
|
||||
catch_interrupts=catch_ctrl_c,
|
||||
**vars(opt)
|
||||
)
|
||||
|
||||
if do_grid and len(grid_images) > 0:
|
||||
if opt.grid and len(grid_images) > 0:
|
||||
grid_img = make_grid(list(grid_images.values()))
|
||||
grid_seeds = list(grid_images.keys())
|
||||
first_seed = last_results[0][1]
|
||||
filename = f'{prefix}.{first_seed}.png'
|
||||
# TODO better metadata for grid images
|
||||
normalized_prompt = PromptFormatter(
|
||||
t2i, opt).normalize_prompt()
|
||||
gen, opt).normalize_prompt()
|
||||
metadata_prompt = f'{normalized_prompt} -S{first_seed} --grid -n{len(grid_images)} # {grid_seeds}'
|
||||
path = file_writer.save_image_and_prompt_to_png(
|
||||
grid_img, metadata_prompt, filename
|
||||
@ -337,11 +322,12 @@ def get_next_command(infile=None) -> str: # command string
|
||||
raise EOFError
|
||||
else:
|
||||
command = command.strip()
|
||||
if len(command)>0:
|
||||
print(f'#{command}')
|
||||
return command
|
||||
|
||||
|
||||
def dream_server_loop(t2i, host, port, outdir):
|
||||
def dream_server_loop(gen, host, port, outdir):
|
||||
print('\n* --web was specified, starting web server...')
|
||||
# Change working directory to the stable-diffusion directory
|
||||
os.chdir(
|
||||
@ -349,7 +335,7 @@ def dream_server_loop(t2i, host, port, outdir):
|
||||
)
|
||||
|
||||
# Start server
|
||||
DreamServer.model = t2i
|
||||
DreamServer.model = gen # misnomer in DreamServer - this is not the model you are looking for
|
||||
DreamServer.outdir = outdir
|
||||
dream_server = ThreadingDreamServer((host, port))
|
||||
print(">> Started Stable Diffusion dream server!")
|
||||
@ -519,13 +505,6 @@ def create_argv_parser():
|
||||
default='model',
|
||||
help='Indicates the Stable Diffusion model to use.',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--device',
|
||||
'-d',
|
||||
type=str,
|
||||
default='cuda',
|
||||
help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if available"
|
||||
)
|
||||
parser.add_argument(
|
||||
'--model',
|
||||
default='stable-diffusion-1.4',
|
||||
|
||||
Loading…
Reference in New Issue
Block a user