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>>>>>>> upstream/main
<<<<<<< HEAD This is a fork of lstein/stable-diffusion, which based on the wonderful open source text-to-image generator stable-diffusion but addding supports for M1/M2 chips and much more:
upstream/main
This is a fork of CompVis/stable-diffusion, the open source text-to-image generator. It provides a streamlined process with various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM.
Note: This fork is rapidly evolving. Please use the Issues tab to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
<<<<<<< HEAD 4. A notebook for running the code on Google Colab.
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Upscaling and face fixing using the optional ESRGAN and GFPGAN packages.
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Weighted subprompts for prompt tuning.
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Image variations which allow you to systematically generate variations of an image you like and combine two or more images together to combine the best features of both.
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Textual inversion for customization of the prompt language and images.
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...and more!
This fork is rapidly evolving, so use the Issues panel to report bugs and make feature requests, and check back periodically for improvements and bug fixes.
upstream/main
This fork is supported across multiple platforms. You can find individual installation instructions below.
System
You wil need one of the following:
- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
- An Apple computer with an M1 chip.
Memory
- At least 12 GB Main Memory RAM.
Disk
- At least 6 GB of free disk space for the machine learning model, Python, and all its dependencies.
Note
If you are have a Nvidia 10xx series card (e.g. the 1080ti), please run the dream script in full-precision mode as shown below.
Similarly, specify full-precision mode on Apple M1 hardware.
To run in full-precision mode, start dream.py with the
--full_precision flag:
(ldm) ~/stable-diffusion$ python scripts/dream.py --full_precision
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WINDOWS: As the default backend is not available on Windows, if you're using that platform, set the environment variable PL_TORCH_DISTRIBUTED_BACKEND=gloo
(ldm) ~/stable-diffusion$ python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
-t \
--actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
-n my_cat \
--gpus 0, \
--data_root D:/textual-inversion/my_cat \
--init_word 'cat'
=======
upstream/main
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v1.14 (11 September 2022)
- Memory optimizations for small-RAM cards. 512x512 now possible on 4 GB GPUs.
- Full support for Apple hardware with M1 or M2 chips.
- Add "seamless mode" for circular tiling of image. Generates beautiful effects. (prixt).
- Inpainting support.
- Improved web server GUI.
- Lots of code and documentation cleanups.
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v1.13 (3 September 2022
- Support image variations (see VARIATIONS (Kevin Gibbons and many contributors and reviewers)
- Supports a Google Colab notebook for a standalone server running on Google hardware Arturo Mendivil
- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling Kevin Gibbons
- WebUI supports incremental display of in-progress images during generation Kevin Gibbons
- A new configuration file scheme that allows new models (including upcoming stable-diffusion-v1.5) to be added without altering the code. (David Wager)
- Can specify --grid on dream.py command line as the default.
- Miscellaneous internal bug and stability fixes.
- Works on M1 Apple hardware.
- Multiple bug fixes.
<<<<<<< HEAD For older changelogs, please visit CHANGELOGS.
There are separate installation walkthroughs for Linux, Windows and Macintosh
- You will need to install the following prerequisites if they are not already available. Use your operating system's preferred installer
- Python (version 3.8.5 recommended; higher may work)
- git
- Install the Python Anaconda environment manager.
~$ wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
~$ chmod +x Anaconda3-2022.05-Linux-x86_64.sh
~$ ./Anaconda3-2022.05-Linux-x86_64.sh
After installing anaconda, you should log out of your system and log back in. If the installation worked, your command prompt will be prefixed by the name of the current anaconda environment, "(base)".
- Copy the stable-diffusion source code from GitHub:
(base) ~$ git clone https://github.com/lstein/stable-diffusion.git
This will create stable-diffusion folder where you will follow the rest of the steps.
- Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
(base) ~$ cd stable-diffusion
(base) ~/stable-diffusion$
- Use anaconda to copy necessary python packages, create a new python environment named "ldm", and activate the environment.
(base) ~/stable-diffusion$ conda env create -f environment.yaml
(base) ~/stable-diffusion$ conda activate ldm
(ldm) ~/stable-diffusion$
After these steps, your command prompt will be prefixed by "(ldm)" as shown above.
- Load a couple of small machine-learning models required by stable diffusion:
(ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
Note that this step is necessary because I modified the original just-in-time model loading scheme to allow the script to work on GPU machines that are not internet connected. See Workaround for machines with limited internet connectivity
- Now you need to install the weights for the stable diffusion model.
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 your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original. You may be asked to sign a license agreement at this point.
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. Save the file somewhere safe on your local machine.
Now run the following commands from within the stable-diffusion directory. This will create a symbolic link from the stable-diffusion model.ckpt file, to the true location of the sd-v1-4.ckpt file.
(ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
(ldm) ~/stable-diffusion$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
- Start generating images!
# for the pre-release weights use the -l or --liaon400m switch
(ldm) ~/stable-diffusion$ python3 scripts/dream.py -l
# for the post-release weights do not use the switch
(ldm) ~/stable-diffusion$ python3 scripts/dream.py
# for additional configuration switches and arguments, use -h or --help
(ldm) ~/stable-diffusion$ python3 scripts/dream.py -h
- Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the "stable-diffusion" directory, and then launch the dream script (step 8). If you forget to activate the ldm environment, the script will fail with multiple ModuleNotFound errors.
This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
(ldm) ~/stable-diffusion$ git pull
This will bring your local copy into sync with the remote one.
We have a Jupyter notebook with cell-by-cell installation steps. It will download the code in this repo as one of the steps, so instead of cloning this repo, simply download the notebook from the link above and load it up in VSCode (with the appropriate extensions installed)/Jupyter/JupyterLab and start running the cells one-by-one.
Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehand - simplified step-by-step instructions are available in the wiki (you'll only need steps 1, 2, & 3 ).
See Easy-peasy Windows install in the wiki
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Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
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Install Git from here: https://git-scm.com/download/win
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Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
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Run the command:
git clone https://github.com/lstein/stable-diffusion.git
This will create stable-diffusion folder where you will follow the rest of the steps.
- Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
cd stable-diffusion
- Run the following two commands:
conda env create -f environment.yaml (step 6a)
conda activate ldm (step 6b)
This will install all python requirements and activate the "ldm" environment which sets PATH and other environment variables properly.
- Run the command:
python scripts\preload_models.py
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)
- Now you need to install the weights for the big stable diffusion model.
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 your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original. You may be asked to sign a license agreement at this point.
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.
Now run the following commands from within the stable-diffusion directory to copy the weights file to the right place:
mkdir -p models\ldm\stable-diffusion-v1
copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
Please replace "C:\path\to\sd-v1.4.ckpt" with the correct path to wherever you stashed this file. If you prefer not to copy or move the .ckpt file, you may instead create a shortcut to it from within "models\ldm\stable-diffusion-v1".
- Start generating images!
# for the pre-release weights
python scripts\dream.py -l
# for the post-release weights
python scripts\dream.py
- Subsequently, to relaunch the script, first activate the Anaconda command window (step 3), enter the stable-diffusion directory (step 5, "cd \path\to\stable-diffusion"), run "conda activate ldm" (step 6b), and then launch the dream script (step 9).
Note: Tildebyte has written an alternative "Easy peasy Windows install" which uses the Windows Powershell and pew. If you are having trouble with Anaconda on Windows, give this a try (or try it first!)
This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
git pull
This will bring your local copy into sync with the remote one.
See README-Mac-MPS for instructions.
For programmers who wish to incorporate stable-diffusion into other products, this repository includes a simplified API for text to image generation, which lets you create images from a prompt in just three lines of code:
from ldm.simplet2i import T2I
model = T2I()
outputs = model.txt2img("a unicorn in manhattan")
Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...] Please see ldm/simplet2i.py for more information. A set of example scripts is coming RSN.
My development machine is a GPU node in a high-performance compute cluster which has no connection to the internet. During model initialization, stable-diffusion tries to download the Bert tokenizer and a file needed by the kornia library. This obviously didn't work for me.
To work around this, I have modified ldm/modules/encoders/modules.py to look for locally cached Bert files rather than attempting to download them. For this to work, you must run "scripts/preload_models.py" once from an internet-connected machine prior to running the code on an isolated one. This assumes that both machines share a common network-mounted filesystem with a common .cache directory.
(ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py
preloading bert tokenizer...
Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
Downloading: 100%|██████████████████████████████████| 226k/226k [00:00<00:00, 2.79MB/s]
Downloading: 100%|██████████████████████████████████| 455k/455k [00:00<00:00, 4.36MB/s]
Downloading: 100%|██████████████████████████████████| 570/570 [00:00<00:00, 477kB/s]
...success
preloading kornia requirements...
Downloading: "https://github.com/DagnyT/hardnet/raw/master/pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth" to /u/lstein/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
100%|███████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 101MB/s]
...success
======= For older changelogs, please visit CHANGELOGS.
upstream/main
Please check out our Q&A to get solutions for common installation problems and other issues.
Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code cleanup, testing, or code reviews, is very much encouraged to do so. If you are unfamiliar with how to contribute to GitHub projects, here is a Getting Started Guide.
A full set of contribution guidelines, along with templates, are in progress, but for now the most important thing is to make your pull request against the "development" branch, and not against "main". This will help keep public breakage to a minimum and will allow you to propose more radical changes.
This fork is a combined effort of various people from across the world. Check out the list of all these amazing people. We thank them for their time, hard work and effort.
For support, please use this repository's GitHub Issues tracking service. Feel free to send me an email if you use and like the script.
Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
Please see the original README for more information on this software and underlying algorithm, located in the file README-CompViz.md.