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adjusted instructions for the released stable-diffusion-v1 weights

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Lincoln Stein 2022-08-22 15:33:27 -04:00
parent 9391eaff0e
commit a20827697c
4 changed files with 61 additions and 16 deletions
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49
README.md
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@ -146,17 +146,21 @@ After these steps, your command prompt will be prefixed by "(ldm)" as shown abov
7. Now you need to install the weights for the stable diffusion model. 7. Now you need to install the weights for the stable diffusion model.
For testing prior to the release of the real weights, you can use an older weight file that produces low-quality images. Create a directory within stable-diffusion named "models/ldm/text2img-large", and use the wget URL downloader tool to copy the weight file into it: For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
``` Use your credentials to log in, and then point browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
(ldm) ~/stable-diffusion$ mkdir -p models/ldm/text2img-large You may be asked to sign a license agreement at this point.
(ldm) ~/stable-diffusion$ wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt
``` Click on "Files and versions" near the top of the page, and then click on the file named "sd-v1-4.ckpt". You'll be taken
For testing with the released weighs, you will do something similar, but with a directory named "models/ldm/stable-diffusion-v1" to a page that prompts you to click the "download" link. Now save the file somewhere safe on your local machine.
Now run the following commands from within the stable-diffusion directory to point it to the weights file.
``` ```
(ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1 (ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
(ldm) ~/stable-diffusion$ wget -O models/ldm/stable-diffusion-v1/model.ckpt <ENTER URL HERE> (ldm) ~/stable-diffusion$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
``` ```
These weight files are ~5 GB in size, so downloading may take a while.
The weight file is >4 GB in size, so downloading may take a while.
8. Start generating images! 8. Start generating images!
``` ```
@ -213,15 +217,36 @@ This will install all python requirements and activate the "ldm" environment whi
``` ```
python scripts\preload_models.py python scripts\preload_models.py
``` ```
This installs two machine learning models that stable diffusion requires.
This installs several machine learning models that stable diffusion
requires. (Note that this step is required. I created it because some people
are using GPU systems that are behind a firewall and the models can't be
downloaded just-in-time)
9. Now you need to install the weights for the big stable diffusion model. 9. Now you need to install the weights for the big stable diffusion model.
For testing prior to the release of the real weights, create a directory within stable-diffusion named "models\ldm\text2img-large". For running with the released weights, you will first need to set up
an acount with Hugging Face (https://huggingface.co). Use your
credentials to log in, and then point browser at
https://huggingface.co/CompVis/stable-diffusion-v-1-4-original. You
may be asked to sign a license agreement at this point.
For testing with the released weights, create a directory within stable-diffusion named "models\ldm\stable-diffusion-v1". Click on "Files and versions" near the top of the page, and then click
on the file named "sd-v1-4.ckpt". You'll be taken to a page that
prompts you to click the "download" link. Now save the file somewhere
safe on your local machine. The weight file is >4 GB in size, so
downloading may take a while.
Then use a web browser to copy model.ckpt into the appropriate directory. For the text2img-large (pre-release) model, the weights are at https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt. Check back here later for the release URL. Now run the following commands from **within the stable-diffusion
directory** to point it to the weights file:
```
mkdir -p models/ldm/stable-diffusion-v1
copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
```
Instead of copying the file, you may instead create a shortcut within the
models\ldm\stable-diffusion-v1\ directory that points to it.
10. Start generating images! 10. Start generating images!
``` ```

4
ldm/modules/encoders/modules.py
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@ -146,8 +146,8 @@ class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)""" """Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77): def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
super().__init__() super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(version) self.tokenizer = CLIPTokenizer.from_pretrained(version,local_files_only=True)
self.transformer = CLIPTextModel.from_pretrained(version) self.transformer = CLIPTextModel.from_pretrained(version,local_files_only=True)
self.device = device self.device = device
self.max_length = max_length self.max_length = max_length
self.freeze() self.freeze()

4
scripts/dream.py
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@ -40,6 +40,10 @@ def main():
sys.path.append('.') sys.path.append('.')
from pytorch_lightning import logging from pytorch_lightning import logging
from ldm.simplet2i import T2I from ldm.simplet2i import T2I
# these two lines prevent a horrible warning message from appearing
# when the frozen CLIP tokenizer is imported
import transformers
transformers.logging.set_verbosity_error()
# creating a simple text2image object with a handful of # creating a simple text2image object with a handful of
# defaults passed on the command line. # defaults passed on the command line.

18
scripts/preload_models.py
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@ -2,6 +2,10 @@
# Before running stable-diffusion on an internet-isolated machine, # Before running stable-diffusion on an internet-isolated machine,
# run this script from one with internet connectivity. The # run this script from one with internet connectivity. The
# two machines must share a common .cache directory. # two machines must share a common .cache directory.
import sys
import transformers
transformers.logging.set_verbosity_error()
# this will preload the Bert tokenizer fles # this will preload the Bert tokenizer fles
print("preloading bert tokenizer...") print("preloading bert tokenizer...")
@ -10,7 +14,19 @@ tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
print("...success") print("...success")
# this will download requirements for Kornia # this will download requirements for Kornia
print("preloading Kornia requirements...") print("preloading Kornia requirements (ignore the warnings)...")
import kornia import kornia
print("...success") print("...success")
# doesn't work - probably wrong logger
# logging.getLogger('transformers.tokenization_utils').setLevel(logging.ERROR)
version='openai/clip-vit-large-patch14'
print('preloading CLIP model (Ignore the warnings)...')
sys.stdout.flush()
import clip
from transformers import CLIPTokenizer, CLIPTextModel
tokenizer =CLIPTokenizer.from_pretrained(version)
transformer=CLIPTextModel.from_pretrained(version)
print('\n\n...success')