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Merge branch 'development' of github.com:lstein/stable-diffusion into development

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Lincoln Stein 2022-09-14 07:07:20 -04:00
commit 6ec7eab85a
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204
ldm/generate.py
View File

@ -17,7 +17,7 @@ import transformers
from omegaconf import OmegaConf from omegaconf import OmegaConf
from PIL import Image, ImageOps from PIL import Image, ImageOps
from torch import nn 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.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.ddim import DDIMSampler
@ -35,7 +35,7 @@ Example Usage:
from ldm.generate import Generate from ldm.generate import Generate
# Create an object with default values # Create an object with default values
gr = Generate() gr = Generate('stable-diffusion-1.4')
# do the slow model initialization # do the slow model initialization
gr.load_model() gr.load_model()
@ -79,16 +79,17 @@ still work.
The full list of arguments to Generate() are: The full list of arguments to Generate() are:
gr = Generate( 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') weights = path to model weights ('models/ldm/stable-diffusion-v1/model.ckpt')
config = path to model configuraiton ('configs/stable-diffusion/v1-inference.yaml') 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)
) )
""" """
@ -101,66 +102,62 @@ class Generate:
def __init__( def __init__(
self, self,
iterations = 1, model = 'stable-diffusion-1.4',
steps = 50, conf = 'configs/models.yaml',
cfg_scale = 7.5, embedding_path = None,
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', sampler_name = 'k_lms',
ddim_eta = 0.0, # deterministic ddim_eta = 0.0, # deterministic
full_precision = False, full_precision = False,
strength = 0.75, # default in scripts/img2img.py # these are deprecated; if present they override values in the conf file
seamless = False, weights = None,
embedding_path = None, config = None,
device_type = 'cuda',
ignore_ctrl_c = False,
): ):
self.iterations = iterations models = OmegaConf.load(conf)
self.width = width mconfig = models[model]
self.height = height self.weights = mconfig.weights if weights is None else weights
self.steps = steps self.config = mconfig.config if config is None else config
self.cfg_scale = cfg_scale self.height = mconfig.height
self.weights = weights self.width = mconfig.width
self.config = config self.iterations = 1
self.sampler_name = sampler_name self.steps = 50
self.grid = grid self.cfg_scale = 7.5
self.ddim_eta = ddim_eta self.sampler_name = sampler_name
self.full_precision = True if choose_torch_device() == 'mps' else full_precision self.ddim_eta = 0.0 # same seed always produces same image
self.strength = strength self.full_precision = True if choose_torch_device() == 'mps' else full_precision
self.seamless = seamless self.strength = 0.75
self.embedding_path = embedding_path self.seamless = False
self.device_type = device_type self.embedding_path = embedding_path
self.ignore_ctrl_c = ignore_ctrl_c # note, this logic probably doesn't belong here... self.model = None # empty for now
self.model = None # empty for now self.sampler = None
self.sampler = None self.device = None
self.device = None self.session_peakmem = None
self.generators = {} self.generators = {}
self.base_generator = None self.base_generator = None
self.seed = 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_type = choose_torch_device() # device to Generate(). However the device was then ignored, so
print(">> cuda not available, using device", device_type) # it wasn't actually doing anything. This logic could be reinstated.
device_type = choose_torch_device()
self.device = torch.device(device_type) self.device = torch.device(device_type)
# for VRAM usage statistics # for VRAM usage statistics
device_type = choose_torch_device() self.session_peakmem = torch.cuda.max_memory_allocated() if self._has_cuda else None
self.session_peakmem = torch.cuda.max_memory_allocated() if device_type == 'cuda' else None
transformers.logging.set_verbosity_error() 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): def prompt2png(self, prompt, outdir, **kwargs):
""" """
Takes a prompt and an output directory, writes out the requested number Takes a prompt and an output directory, writes out the requested number
of PNG files, and returns an array of [[filename,seed],[filename,seed]...] 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() Optional named arguments are the same as those passed to Generate and prompt2image()
""" """
results = self.prompt2image(prompt, **kwargs) results = self.prompt2image(prompt, **kwargs)
pngwriter = PngWriter(outdir) pngwriter = PngWriter(outdir)
prefix = pngwriter.unique_prefix() prefix = pngwriter.unique_prefix()
outputs = [] outputs = []
for image, seed in results: for image, seed in results:
name = f'{prefix}.{seed}.png' name = f'{prefix}.{seed}.png'
path = pngwriter.save_image_and_prompt_to_png( path = pngwriter.save_image_and_prompt_to_png(
@ -183,33 +180,35 @@ class Generate:
self, self,
# these are common # these are common
prompt, prompt,
iterations = None, iterations = None,
steps = None, steps = None,
seed = None, seed = None,
cfg_scale = None, cfg_scale = None,
ddim_eta = None, ddim_eta = None,
skip_normalize = False, skip_normalize = False,
image_callback = None, image_callback = None,
step_callback = None, step_callback = None,
width = None, width = None,
height = None, height = None,
sampler_name = None, sampler_name = None,
seamless = False, seamless = False,
log_tokenization= False, log_tokenization = False,
with_variations = None, with_variations = None,
variation_amount = 0.0, variation_amount = 0.0,
# these are specific to img2img and inpaint # these are specific to img2img and inpaint
init_img = None, init_img = None,
init_mask = None, init_mask = None,
fit = False, fit = False,
strength = None, strength = None,
# these are specific to embiggen (which also relies on img2img args) # these are specific to embiggen (which also relies on img2img args)
embiggen = None, embiggen = None,
embiggen_tiles = None, embiggen_tiles = None,
# these are specific to GFPGAN/ESRGAN # these are specific to GFPGAN/ESRGAN
gfpgan_strength= 0, gfpgan_strength = 0,
save_original = False, save_original = False,
upscale = None, upscale = None,
# Set this True to handle KeyboardInterrupt internally
catch_interrupts = False,
**args, **args,
): # eat up additional cruft ): # eat up additional cruft
""" """
@ -262,10 +261,9 @@ class Generate:
self.log_tokenization = log_tokenization self.log_tokenization = log_tokenization
with_variations = [] if with_variations is None else with_variations with_variations = [] if with_variations is None else with_variations
model = ( # will instantiate the model or return it from cache
self.load_model() model = self.load_model()
) # will instantiate the model or return it from cache
for m in model.modules(): for m in model.modules():
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
m.padding_mode = 'circular' if seamless else m._orig_padding_mode m.padding_mode = 'circular' if seamless else m._orig_padding_mode
@ -281,7 +279,6 @@ class Generate:
(embiggen == None and embiggen_tiles == None) or ((embiggen != None or embiggen_tiles != None) and init_img != None) (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' ), '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: if len(with_variations) > 0 or variation_amount > 1.0:
assert seed is not None,\ assert seed is not None,\
'seed must be specified when using with_variations' 'seed must be specified when using with_variations'
@ -298,7 +295,7 @@ class Generate:
self._set_sampler() self._set_sampler()
tic = time.time() tic = time.time()
if torch.cuda.is_available(): if self._has_cuda():
torch.cuda.reset_peak_memory_stats() torch.cuda.reset_peak_memory_stats()
results = list() results = list()
@ -307,9 +304,9 @@ class Generate:
try: try:
uc, c = get_uc_and_c( uc, c = get_uc_and_c(
prompt, model=self.model, prompt, model =self.model,
skip_normalize=skip_normalize, 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) (init_image,mask_image) = self._make_images(init_img,init_mask, width, height, fit)
@ -352,27 +349,25 @@ class Generate:
save_original = save_original, save_original = save_original,
image_callback = image_callback) 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: except RuntimeError as e:
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
print('>> Could not generate image.') print('>> Could not generate image.')
except KeyboardInterrupt:
if catch_interrupts:
print('**Interrupted** Partial results will be returned.')
else:
raise KeyboardInterrupt
toc = time.time() toc = time.time()
print('>> Usage stats:') print('>> Usage stats:')
print( print(
f'>> {len(results)} image(s) generated in', '%4.2fs' % (toc - tic) 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( print(
f'>> Max VRAM used for this generation:', f'>> Max VRAM used for this generation:',
'%4.2fG.' % (torch.cuda.max_memory_allocated() / 1e9), '%4.2fG.' % (torch.cuda.max_memory_allocated() / 1e9),
'Current VRAM utilization:' 'Current VRAM utilization:',
'%4.2fG' % (torch.cuda.memory_allocated() / 1e9), '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
) )
@ -439,8 +434,7 @@ class Generate:
if self.model is None: if self.model is None:
seed_everything(random.randrange(0, np.iinfo(np.uint32).max)) seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
try: try:
config = OmegaConf.load(self.config) model = self._load_model_from_config(self.config, self.weights)
model = self._load_model_from_config(config, self.weights)
if self.embedding_path is not None: if self.embedding_path is not None:
model.embedding_manager.load( model.embedding_manager.load(
self.embedding_path, self.full_precision self.embedding_path, self.full_precision
@ -541,8 +535,11 @@ class Generate:
print(msg) print(msg)
def _load_model_from_config(self, config, ckpt): # Be warned: config is the path to the model config file, not the dream conf file!
print(f'>> Loading model from {ckpt}') # 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 # for usage statistics
device_type = choose_torch_device() device_type = choose_torch_device()
@ -551,10 +548,11 @@ class Generate:
tic = time.time() tic = time.time()
# this does the work # this does the work
pl_sd = torch.load(ckpt, map_location='cpu') c = OmegaConf.load(config)
sd = pl_sd['state_dict'] pl_sd = torch.load(weights, map_location='cpu')
model = instantiate_from_config(config.model) sd = pl_sd['state_dict']
m, u = model.load_state_dict(sd, strict=False) model = instantiate_from_config(c.model)
m, u = model.load_state_dict(sd, strict=False)
if self.full_precision: if self.full_precision:
print( print(
@ -573,7 +571,7 @@ class Generate:
print( print(
f'>> Model loaded in', '%4.2fs' % (toc - tic) f'>> Model loaded in', '%4.2fs' % (toc - tic)
) )
if device_type == 'cuda': if self._has_cuda():
print( print(
'>> Max VRAM used to load the model:', '>> Max VRAM used to load the model:',
'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9), '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
@ -710,3 +708,5 @@ class Generate:
return width, height, resize_needed return width, height, resize_needed
def _has_cuda(self):
return self.device.type == 'cuda'

2
ldm/models/diffusion/ddim.py
View File

@ -225,7 +225,7 @@ class DDIMSampler(object):
total_steps = ( total_steps = (
timesteps if ddim_use_original_steps else timesteps.shape[0] 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( iterator = tqdm(
time_range, time_range,

68
notebooks/Stable-Diffusion-local-Windows.ipynb
View File

@ -40,57 +40,9 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"%%cmd\n", "%%cmd\n",
"git clone https://github.com/lstein/stable-diffusion.git" "git clone https://github.com/lstein/stable-diffusion.git\n",
] "cd /content/stable-diffusion/\n",
}, "git checkout --quiet development"
{
"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"
] ]
}, },
{ {
@ -100,14 +52,14 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"%%cmd\n", "%%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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Switch the notebook kernel to the new 'ldm' environment!\n", "# Switch the notebook kernel to the new 'stable-diffusion' environment!\n",
"\n", "\n",
"## VSCode: restart VSCode and come back to this cell\n", "## VSCode: restart VSCode and come back to this cell\n",
"\n", "\n",
@ -115,7 +67,7 @@
"1. Type \"Select Interpreter\" and select \"Jupyter: Select Interpreter to Start Jupyter Server\"\n", "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. 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. Once the install is finished, do 1 & 2 again\n",
"1. Pick 'ldm'\n", "1. Pick 'stable-diffusion'\n",
"1. Run the following cell" "1. Run the following cell"
] ]
}, },
@ -136,7 +88,7 @@
"## Jupyter/JupyterLab\n", "## Jupyter/JupyterLab\n",
"\n", "\n",
"1. Run the cell below\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": [ "source": [
"# DO NOT RUN THIS CELL IF YOU ARE USING VSCODE!!\n", "# DO NOT RUN THIS CELL IF YOU ARE USING VSCODE!!\n",
"%%cmd\n", "%%cmd\n",
"pew workon ldm\n", "pew workon stable-diffusion\n",
"pip3 install ipykernel\n", "pip3 install ipykernel\n",
"python -m ipykernel install --name=ldm" "python -m ipykernel install --name=stable-diffusion"
] ]
}, },
{ {
@ -231,7 +183,7 @@
"Now:\n", "Now:\n",
"\n", "\n",
"1. `cd` to wherever the 'stable-diffusion' directory is\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. Run `winpty python scripts\\dream.py`"
] ]
} }

268
notebooks/Stable_Diffusion_AI_Notebook.ipynb
View File

@ -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": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"id": "ycYWcsEKc6w7"
},
"source": [ "source": [
"# Stable Diffusion AI Notebook (Release 1.13)\n", "# Stable Diffusion AI Notebook (Release 1.14)\n",
"\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", "<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", "#### 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", "<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", "##### For more details visit Github repository: [lstein/stable-diffusion](https://github.com/lstein/stable-diffusion)\n",
"---\n" "---\n"
], ]
"metadata": {
"id": "ycYWcsEKc6w7"
}
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"source": [
"## ◢ Installation"
],
"metadata": { "metadata": {
"id": "dr32VLxlnouf" "id": "dr32VLxlnouf"
} },
"source": [
"## ◢ Installation"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"source": [ "execution_count": null,
"#@title 1. Check current GPU assigned\n",
"!nvidia-smi -L\n",
"!nvidia-smi"
],
"metadata": { "metadata": {
"cellView": "form", "cellView": "form",
"id": "a2Z5Qu_o8VtQ" "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", "cell_type": "code",
@ -75,90 +57,91 @@
"#@title 2. Download stable-diffusion Repository\n", "#@title 2. Download stable-diffusion Repository\n",
"from os.path import exists\n", "from os.path import exists\n",
"\n", "\n",
"if exists(\"/content/stable-diffusion/\")==True:\n", "!git clone --quiet https://github.com/lstein/stable-diffusion.git # Original repo\n",
" %cd /content/stable-diffusion/\n", "%cd /content/stable-diffusion/\n",
" print(\"Already downloaded repo\")\n", "!git checkout --quiet tags/release-1.14.1"
"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"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"source": [ "execution_count": null,
"#@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()"
],
"metadata": { "metadata": {
"cellView": "form", "cellView": "form",
"id": "QbXcGXYEFSNB" "id": "QbXcGXYEFSNB"
}, },
"execution_count": null, "outputs": [],
"outputs": []
},
{
"cell_type": "code",
"source": [ "source": [
"#@title 4. Load small ML models required\n", "#@title 3. Install dependencies\n",
"%cd /content/stable-diffusion/\n", "import gc\n",
"!python scripts/preload_models.py\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()" "gc.collect()"
], ]
"metadata": {
"cellView": "form",
"id": "ChIDWxLVHGGJ"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"source": [ "execution_count": null,
"#@title 5. Restart Runtime\n",
"exit()"
],
"metadata": { "metadata": {
"cellView": "form", "cellView": "form",
"id": "8rSMhgnAttQa" "id": "8rSMhgnAttQa"
}, },
"execution_count": null, "outputs": [],
"outputs": []
},
{
"cell_type": "markdown",
"source": [ "source": [
"## ◢ Configuration" "#@title 4. Restart Runtime\n",
], "exit()"
"metadata": { ]
"id": "795x1tMoo8b1"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "ChIDWxLVHGGJ"
},
"outputs": [],
"source": [ "source": [
"#@title 6. Mount google Drive\n", "#@title 5. Load small ML models required\n",
"from google.colab import drive\n", "import gc\n",
"drive.mount('/content/drive')" "%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": { "metadata": {
"cellView": "form", "cellView": "form",
"id": "YEWPV-sF1RDM" "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", "cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "zRTJeZ461WGu"
},
"outputs": [],
"source": [ "source": [
"#@title 7. Drive Path to model\n", "#@title 7. Drive Path to model\n",
"#@markdown Path should start with /content/drive/path-to-your-file <br>\n", "#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
@ -167,20 +150,20 @@
"from os.path import exists\n", "from os.path import exists\n",
"\n", "\n",
"model_path = \"\" #@param {type:\"string\"}\n", "model_path = \"\" #@param {type:\"string\"}\n",
"if exists(model_path)==True:\n", "if exists(model_path):\n",
" print(\"✅ Valid directory\")\n", " print(\"✅ Valid directory\")\n",
"else: \n", "else: \n",
" print(\"❌ File doesn't exist\")" " print(\"❌ File doesn't exist\")"
], ]
"metadata": {
"cellView": "form",
"id": "zRTJeZ461WGu"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "UY-NNz4I8_aG"
},
"outputs": [],
"source": [ "source": [
"#@title 8. Symlink to model\n", "#@title 8. Symlink to model\n",
"\n", "\n",
@ -188,39 +171,39 @@
"import os \n", "import os \n",
"\n", "\n",
"# Folder creation if it doesn't exist\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", " print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
"else:\n", "else:\n",
" %mkdir /content/stable-diffusion/models/ldm/stable-diffusion-v1\n", " %mkdir /content/stable-diffusion/models/ldm/stable-diffusion-v1\n",
" print(\"✅ Dir stable-diffusion-v1 created\")\n", " print(\"✅ Dir stable-diffusion-v1 created\")\n",
"\n", "\n",
"# Symbolic link if it doesn't exist\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", " print(\"❗ Symlink already created\")\n",
"else: \n", "else: \n",
" src = model_path\n", " src = model_path\n",
" dst = '/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt'\n", " dst = '/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt'\n",
" os.symlink(src, dst) \n", " os.symlink(src, dst) \n",
" print(\"✅ Symbolic link created successfully\")" " print(\"✅ Symbolic link created successfully\")"
], ]
"metadata": {
"id": "UY-NNz4I8_aG",
"cellView": "form"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"source": [
"## ◢ Execution"
],
"metadata": { "metadata": {
"id": "Mc28N0_NrCQH" "id": "Mc28N0_NrCQH"
} },
"source": [
"## ◢ Execution"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "ir4hCrMIuUpl"
},
"outputs": [],
"source": [ "source": [
"#@title 9. Run Terminal and Execute Dream bot\n", "#@title 9. Run Terminal and Execute Dream bot\n",
"#@markdown <font color=\"blue\">Steps:</font> <br>\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 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
"#@markdown 4. To quit Dream bot use: `q` command.<br>\n", "#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
"\n", "\n",
"import gc\n",
"%cd /content/stable-diffusion/\n",
"%load_ext colabxterm\n", "%load_ext colabxterm\n",
"%xterm\n", "%xterm\n",
"gc.collect()" "gc.collect()"
], ]
"metadata": {
"id": "ir4hCrMIuUpl",
"cellView": "form"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "qnLohSHmKoGk"
},
"outputs": [],
"source": [ "source": [
"#@title 10. Show the last 15 generated images\n", "#@title 10. Show the last 15 generated images\n",
"import gc\n",
"import glob\n", "import glob\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n", "import matplotlib.image as mpimg\n",
@ -269,13 +249,25 @@
" plt.imshow(image)\n", " plt.imshow(image)\n",
" gc.collect()\n", " gc.collect()\n",
"\n" "\n"
], ]
"metadata": {
"cellView": "form",
"id": "qnLohSHmKoGk"
},
"execution_count": null,
"outputs": []
} }
] ],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"private_outputs": true,
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} }

26
requirements-colab.txt
View File

@ -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
View 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
View 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 .

33
requirements-lin.txt
View File

@ -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
View 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 .

24
requirements-mac.txt
View File

@ -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

33
requirements-win.txt
View File

@ -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
View 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

97
scripts/dream.py
View File

@ -33,53 +33,35 @@ def main():
print('--weights argument has been deprecated. Please configure ./configs/models.yaml, and call it using --model instead.') print('--weights argument has been deprecated. Please configure ./configs/models.yaml, and call it using --model instead.')
sys.exit(-1) 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') print('* Initializing, be patient...\n')
sys.path.append('.') sys.path.append('.')
from pytorch_lightning import logging
from ldm.generate import Generate from ldm.generate import Generate
# these two lines prevent a horrible warning message from appearing # these two lines prevent a horrible warning message from appearing
# when the frozen CLIP tokenizer is imported # when the frozen CLIP tokenizer is imported
import transformers import transformers
transformers.logging.set_verbosity_error() 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. # defaults passed on the command line.
# additional parameters will be added (or overriden) during # additional parameters will be added (or overriden) during
# the user input loop # the user input loop
t2i = Generate( try:
width=width, gen = Generate(
height=height, conf = opt.config,
sampler_name=opt.sampler_name, model = opt.model,
weights=weights, sampler_name = opt.sampler_name,
full_precision=opt.full_precision, embedding_path = opt.embedding_path,
config=config, full_precision = opt.full_precision,
grid=opt.grid, )
# this is solely for recreating the prompt except (FileNotFoundError, IOError, KeyError) as e:
seamless=opt.seamless, print(f'{e}. Aborting.')
embedding_path=opt.embedding_path, sys.exit(-1)
device_type=opt.device,
ignore_ctrl_c=opt.infile is None,
)
# make sure the output directory exists # make sure the output directory exists
if not os.path.exists(opt.outdir): if not os.path.exists(opt.outdir):
os.makedirs(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 # load the infile as a list of lines
infile = None infile = None
if opt.infile: if opt.infile:
@ -98,21 +80,23 @@ def main():
print(">> changed to seamless tiling mode") print(">> changed to seamless tiling mode")
# preload the model # preload the model
t2i.load_model() gen.load_model()
if not infile: if not infile:
print( print(
"\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)" "\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
) )
cmd_parser = create_cmd_parser() # web server loops forever
if opt.web: if opt.web:
dream_server_loop(t2i, opt.host, opt.port, opt.outdir) dream_server_loop(gen, opt.host, opt.port, opt.outdir)
else: sys.exit(0)
main_loop(t2i, opt.outdir, opt.prompt_as_dir, cmd_parser, infile)
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""" """prompt/read/execute loop"""
done = False done = False
path_filter = re.compile(r'[<>:"/\\|?*]') path_filter = re.compile(r'[<>:"/\\|?*]')
@ -132,9 +116,6 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
except EOFError: except EOFError:
done = True done = True
continue continue
except KeyboardInterrupt:
done = True
continue
# skip empty lines # skip empty lines
if not command.strip(): if not command.strip():
@ -184,6 +165,7 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
if len(opt.prompt) == 0: if len(opt.prompt) == 0:
print('Try again with a prompt!') print('Try again with a prompt!')
continue continue
# retrieve previous value! # retrieve previous value!
if opt.init_img is not None and re.match('^-\\d+$', opt.init_img): if opt.init_img is not None and re.match('^-\\d+$', opt.init_img):
try: try:
@ -204,8 +186,6 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
opt.seed = None opt.seed = None
continue continue
do_grid = opt.grid or t2i.grid
if opt.with_variations is not None: if opt.with_variations is not None:
# shotgun parsing, woo # shotgun parsing, woo
parts = [] parts = []
@ -258,11 +238,11 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
file_writer = PngWriter(current_outdir) file_writer = PngWriter(current_outdir)
prefix = file_writer.unique_prefix() prefix = file_writer.unique_prefix()
results = [] # list of filename, prompt pairs 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): def image_writer(image, seed, upscaled=False):
path = None path = None
if do_grid: if opt.grid:
grid_images[seed] = image grid_images[seed] = image
else: else:
if upscaled and opt.save_original: 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.with_variations = opt.with_variations + this_variation
iter_opt.variation_amount = 0 iter_opt.variation_amount = 0
normalized_prompt = PromptFormatter( normalized_prompt = PromptFormatter(
t2i, iter_opt).normalize_prompt() gen, iter_opt).normalize_prompt()
metadata_prompt = f'{normalized_prompt} -S{iter_opt.seed}' metadata_prompt = f'{normalized_prompt} -S{iter_opt.seed}'
elif opt.with_variations is not None: elif opt.with_variations is not None:
normalized_prompt = PromptFormatter( normalized_prompt = PromptFormatter(
t2i, opt).normalize_prompt() gen, opt).normalize_prompt()
# use the original seed - the per-iteration value is the last variation-seed # use the original seed - the per-iteration value is the last variation-seed
metadata_prompt = f'{normalized_prompt} -S{opt.seed}' metadata_prompt = f'{normalized_prompt} -S{opt.seed}'
else: else:
normalized_prompt = PromptFormatter( normalized_prompt = PromptFormatter(
t2i, opt).normalize_prompt() gen, opt).normalize_prompt()
metadata_prompt = f'{normalized_prompt} -S{seed}' metadata_prompt = f'{normalized_prompt} -S{seed}'
path = file_writer.save_image_and_prompt_to_png( path = file_writer.save_image_and_prompt_to_png(
image, metadata_prompt, filename) image, metadata_prompt, filename)
@ -296,16 +276,21 @@ def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
results.append([path, metadata_prompt]) results.append([path, metadata_prompt])
last_results.append([path, seed]) 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_img = make_grid(list(grid_images.values()))
grid_seeds = list(grid_images.keys()) grid_seeds = list(grid_images.keys())
first_seed = last_results[0][1] first_seed = last_results[0][1]
filename = f'{prefix}.{first_seed}.png' filename = f'{prefix}.{first_seed}.png'
# TODO better metadata for grid images # TODO better metadata for grid images
normalized_prompt = PromptFormatter( 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}' metadata_prompt = f'{normalized_prompt} -S{first_seed} --grid -n{len(grid_images)} # {grid_seeds}'
path = file_writer.save_image_and_prompt_to_png( path = file_writer.save_image_and_prompt_to_png(
grid_img, metadata_prompt, filename grid_img, metadata_prompt, filename
@ -337,11 +322,12 @@ def get_next_command(infile=None) -> str: # command string
raise EOFError raise EOFError
else: else:
command = command.strip() command = command.strip()
print(f'#{command}') if len(command)>0:
print(f'#{command}')
return 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...') print('\n* --web was specified, starting web server...')
# Change working directory to the stable-diffusion directory # Change working directory to the stable-diffusion directory
os.chdir( os.chdir(
@ -349,7 +335,7 @@ def dream_server_loop(t2i, host, port, outdir):
) )
# Start server # Start server
DreamServer.model = t2i DreamServer.model = gen # misnomer in DreamServer - this is not the model you are looking for
DreamServer.outdir = outdir DreamServer.outdir = outdir
dream_server = ThreadingDreamServer((host, port)) dream_server = ThreadingDreamServer((host, port))
print(">> Started Stable Diffusion dream server!") print(">> Started Stable Diffusion dream server!")
@ -519,13 +505,6 @@ def create_argv_parser():
default='model', default='model',
help='Indicates the Stable Diffusion model to use.', 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( parser.add_argument(
'--model', '--model',
default='stable-diffusion-1.4', default='stable-diffusion-1.4',

0
scripts/inpaint.py → scripts/orig_scripts/inpaint.py
View File

4
setup.py
View File

@ -1,8 +1,8 @@
from setuptools import setup, find_packages from setuptools import setup, find_packages
setup( setup(
name='latent-diffusion', name='stable-diffusion',
version='0.0.1', version='1.15.0-dev',
description='', description='',
packages=find_packages(), packages=find_packages(),
install_requires=[ install_requires=[