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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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338 lines
16 KiB
Markdown
338 lines
16 KiB
Markdown
# Stable Diffusion web UI
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A browser interface based on Gradio library for Stable Diffusion.
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Original script with Gradio UI was written by a kind anonymous user. This is a modification.
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## Installing and running
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You need [python](https://www.python.org/downloads/windows/) and [git](https://git-scm.com/download/win)
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installed to run this, and an NVidia videocard.
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I tested the installation to work Windows with Python 3.8.10, and with Python 3.10.6. You may be able
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to have success with different versions.
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You need `model.ckpt`, Stable Diffusion model checkpoint, a big file containing the neural network weights. You
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can obtain it from the following places:
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- [official download](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
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- [file storage](https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl)
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- magnet:?xt=urn:btih:3a4a612d75ed088ea542acac52f9f45987488d1c&dn=sd-v1-4.ckpt&tr=udp%3a%2f%2ftracker.openbittorrent.com%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337
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You optionally can use GPFGAN to improve faces, then you'll need to download the model from [here](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth).
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### Automatic installation/launch
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- install [Python 3.10.6](https://www.python.org/downloads/windows/)
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- install [git](https://git-scm.com/download/win)
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- install [CUDA 11.3](https://developer.nvidia.com/cuda-11.3.0-download-archive?target_os=Windows&target_arch=x86_64)
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- place `model.ckpt` into webui directory, next to `webui.bat`.
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- _*(optional)*_ place `GFPGANv1.3.pth` into webui directory, next to `webui.bat`.
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- run `webui.bat` from Windows Explorer.
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#### Troublehooting:
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- if you get out of memory errors and your videocard has low amount of VRAM (4GB), edit `webui.bat`, change line 5 to from `set COMMANDLINE_ARGS=` to `set COMMANDLINE_ARGS=--medvram` (see below for other possible options)
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- installer creates python virtual environment, so none of installed modules will affect your system installation of python if you had one prior to installing this.
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- to prevent the creation of virtual environment and use your system python, edit `webui.bat` replacing `set VENV_DIR=venv` with `set VENV_DIR=`.
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- webui.bat installs requirements from files `requirements_versions.txt`, which lists versions for modules specifically compatible with Python 3.10.6. If you choose to install for a different version of python, editing `webui.bat` to have `set REQS_FILE=requirements.txt` instead of `set REQS_FILE=requirements_versions.txt` may help (but I still reccomend you to just use the recommended version of python).
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- if your version of Python is not in PATH, edit the line `set PYTHON=python` to say the full path to your python executable: `B:\soft\Python310\python.exe`. You can do this for python, but not for git.
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### Manual instructions
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Alternatively, if you don't want to run webui.bat, here are instructions for installing
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everything by hand:
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```commandline
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:: crate a directory somewhere for stable diffusion and open cmd in it;
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:: make sure you are in the right directory; the command must output the directory you chose
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echo %cd%
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:: install torch with CUDA support. See https://pytorch.org/get-started/locally/ for more instructions if this fails.
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pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
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:: check if torch supports GPU; this must output "True". You need CUDA 11. installed for this. You might be able to use
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:: a different version, but this is what I tested.
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python -c "import torch; print(torch.cuda.is_available())"
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:: clone Stable Diffusion repositories
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git clone https://github.com/CompVis/stable-diffusion.git
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git clone https://github.com/CompVis/taming-transformers
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:: install requirements of Stable Diffusion
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pip install transformers==4.19.2 diffusers invisible-watermark
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:: install k-diffusion
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pip install git+https://github.com/crowsonkb/k-diffusion.git
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:: (optional) install GFPGAN to fix faces
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pip install git+https://github.com/TencentARC/GFPGAN.git
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:: go into stable diffusion's repo directory
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cd stable-diffusion
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:: clone web ui
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git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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:: install requirements of web ui
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pip install -r stable-diffusion-webui/requirements.txt
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:: update numpy to latest version
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pip install -U numpy
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:: (outside of command line) put stable diffusion model into models/ldm/stable-diffusion-v1/model.ckpt; you'll have
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:: to create one missing directory;
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:: the command below must output something like: 1 File(s) 4,265,380,512 bytes
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dir models\ldm\stable-diffusion-v1\model.ckpt
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:: (outside of command line) put the GFPGAN model into same directory as webui script
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:: the command below must output something like: 1 File(s) 348,632,874 bytes
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dir stable-diffusion-webui\GFPGANv1.3.pth
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```
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After that the installation is finished.
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Run the command to start web ui:
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```
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python stable-diffusion-webui/webui.py
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```
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If you have a 4GB video card, run the command with either `--lowvram` or `--medvram` argument:
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```
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python stable-diffusion-webui/webui.py --medvram
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```
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After a while, you will get a message like this:
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```
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Running on local URL: http://127.0.0.1:7860/
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```
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Open the URL in browser, and you are good to go.
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### What options to use for low VRAM videocardsd?
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- If you have 4GB VRAM and want to make 512x512 (or maybe up to 640x640) images, use `--medvram`.
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- If you have 4GB VRAM and want to make 512x512 images, but you get an out of memory error with `--medvram`, use `--lowvram --always-batch-cond-uncond` instead.
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- If you have 4GB VRAM and want to make images larger than you can with `--medvram`, use `--lowvram`.
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- If you have more VRAM and want to make larger images than you can usually make, use `--medvram`. You can use `--lowvram`
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also but the effect will likely be barely noticeable.
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- Otherwise, do not use any of those.
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Extra: if you get a green screen instead of generated pictures, you have a card that doesn't support half
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precision floating point numbers. You must use `--precision full --no-half` in addition to other flags,
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and the model will take much more space in VRAM.
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## Features
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The script creates a web UI for Stable Diffusion's txt2img and img2img scripts. Following are features added
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that are not in original script.
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### Extras tab
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Additional neural network image improvement methods unrelated to stable diffusion.
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#### GFPGAN
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Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and
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also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strongthe effect is.
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#### Real-ESRGAN
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Image upscaler. You can choose from multiple models by original author, and specify by how much the image should be upscaled.
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Requires `realesrgan` librarty:
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```commandline
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pip install realesrgan
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```
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### Sampling method selection
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Pick out of multiple sampling methods for txt2img:
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### Prompt matrix
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Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.
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For example, if you use `a busy city street in a modern city|illustration|cinematic lighting` prompt, there are four combinations possible (first part of prompt is always kept):
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- `a busy city street in a modern city`
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- `a busy city street in a modern city, illustration`
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- `a busy city street in a modern city, cinematic lighting`
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- `a busy city street in a modern city, illustration, cinematic lighting`
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Four images will be produced, in this order, all with same seed and each with corresponding prompt:
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Another example, this time with 5 prompts and 16 variations:
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If you use this feature, batch count will be ignored, because the number of pictures to produce
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depends on your prompts, but batch size will still work (generating multiple pictures at the
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same time for a small speed boost).
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### Flagging
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Click the Flag button under the output section, and generated images will be saved to `log/images` directory, and generation parameters
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will be appended to a csv file `log/log.csv` in the `/sd` directory.
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> but every image is saved, why would I need this?
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If you're like me, you experiment a lot with prompts and settings, and only few images are worth saving. You can
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just save them using right click in browser, but then you won't be able to reproduce them later because you will not
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know what exact prompt created the image. If you use the flag button, generation parameters will be written to csv file,
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and you can easily find parameters for an image by searching for its filename.
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### Copy-paste generation parameters
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A text output provides generation parameters in an easy to copy-paste form for easy sharing.
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If you generate multiple pictures, the displayed seed will be the seed of the first one.
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### Correct seeds for batches
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If you use a seed of 1000 to generate two batches of two images each, four generated images will have seeds: `1000, 1001, 1002, 1003`.
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Previous versions of the UI would produce `1000, x, 1001, x`, where x is an image that can't be generated by any seed.
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### Resizing
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There are three options for resizing input images in img2img mode:
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- Just resize - simply resizes source image to target resolution, resulting in incorrect aspect ratio
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- Crop and resize - resize source image preserving aspect ratio so that entirety of target resolution is occupied by it, and crop parts that stick out
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- Resize and fill - resize source image preserving aspect ratio so that it entirely fits target resolution, and fill empty space by rows/columns from source image
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Example:
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### Loading
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Gradio's loading graphic has a very negative effect on the processing speed of the neural network.
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My RTX 3090 makes images about 10% faster when the tab with gradio is not active. By default, the UI
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now hides loading progress animation and replaces it with static "Loading..." text, which achieves
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the same effect. Use the `--no-progressbar-hiding` commandline option to revert this and show loading animations.
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### Prompt validation
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Stable Diffusion has a limit for input text length. If your prompt is too long, you will get a
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warning in the text output field, showing which parts of your text were truncated and ignored by the model.
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### Loopback
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A checkbox for img2img allowing to automatically feed output image as input for the next batch. Equivalent to
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saving output image, and replacing input image with it. Batch count setting controls how many iterations of
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this you get.
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Usually, when doing this, you would choose one of many images for the next iteration yourself, so the usefulness
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of this feature may be questionable, but I've managed to get some very nice outputs with it that I wasn't abble
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to get otherwise.
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Example: (cherrypicked result; original picture by anon)
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### Png info
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Adds information about generation parameters to PNG as a text chunk. You
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can view this information later using any software that supports viewing
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PNG chunk info, for example: https://www.nayuki.io/page/png-file-chunk-inspector
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### Textual Inversion
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Allows you to use pretrained textual inversion embeddings.
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See original site for details: https://textual-inversion.github.io/.
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I used lstein's repo for training embdedding: https://github.com/lstein/stable-diffusion; if
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you want to train your own, I recommend following the guide on his site.
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No additional libraries/repositories are required to use pretrained embeddings.
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To make use of pretrained embeddings, create `embeddings` directory in the root dir of Stable
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Diffusion and put your embeddings into it. They must be .pt files about 5Kb in size, each with only
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one trained embedding, and the filename (without .pt) will be the term you'd use in prompt
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to get that embedding.
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As an example, I trained one for about 5000 steps: https://files.catbox.moe/e2ui6r.pt; it does
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not produce very good results, but it does work. Download and rename it to `Usada Pekora.pt`,
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and put it into `embeddings` dir and use Usada Pekora in prompt.
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### Settings
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A tab with settings, allowing you to use UI to edit more than half of parameters that previously
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were commandline. Settings are saved to config.js file. Settings that remain as commandline
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options are ones that are required at startup.
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### Attention
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Using `()` in prompt increases model's attention to enclosed words, and `[]` decreases it. You can combine
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multiple modifiers:
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### SD upscale
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Upscale image using RealESRGAN and then go through tiles of the result, improving them with img2img.
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Original idea by: https://github.com/jquesnelle/txt2imghd. This is an independent implementation.
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To use this feature, tick a checkbox in the img2img interface. Original
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image will be upscaled to twice the original width and height, while width and height sliders
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will specify the size of individual tiles. At the moment this method does not support batch size.
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Rcommended parameters for upscaling:
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- Sampling method: Euler a
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- Denoising strength: 0.2, can go up to 0.4 if you feel adventureous
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### User scripts
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If the program is launched with `--allow-code` option, an extra text input field for script code
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is available in txt2img interface. It allows you to input python code that will do the work with
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image. If this field is not empty, the processing that would happen normally is skipped.
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In code, access parameters from web UI using the `p` variable, and provide outputs for web UI
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using the `display(images, seed, info)` function. All globals from script are also accessible.
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As an example, here is a script that draws a chart seen below (and also saves it as `test/gnomeplot/gnome5.png`):
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```python
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steps = [4, 8,12,16, 20]
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cfg_scales = [5.0,10.0,15.0]
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def cell(x, y, p=p):
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p.steps = x
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p.cfg_scale = y
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return process_images(p).images[0]
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images = [draw_xy_grid(
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xs = steps,
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ys = cfg_scales,
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x_label = lambda x: f'Steps = {x}',
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y_label = lambda y: f'CFG = {y}',
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cell = cell
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)]
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save_image(images[0], 'test/gnomeplot', 'gnome5')
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display(images)
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```
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A more simple script that would just process the image and output it normally:
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```python
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processed = process_images(p)
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print("Seed was: " + str(processed.seed))
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display(processed.images, processed.seed, processed.info)
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```
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### 4GB videocard support
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Optimizations for GPUs with low VRAM. This should make it possible to generate 512x512 images on videocards with 4GB memory.
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`--lowvram` is a reimplementation of optimization idea from by [basujindal](https://github.com/basujindal/stable-diffusion).
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Model is separated into modules, and only one module is kept in GPU memory; when another module needs to run, the previous
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is removed from GPU memory. The nature of this optimization makes the processing run slower -- about 10 times slower
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compared to normal operation on my RTX 3090.
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`--medvram` is another optimization that should reduce VRAM usage significantly by not processing conditional and
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unconditional denoising in a same batch.
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This implementation of optimization does not require any modification to original Stable Diffusion code.
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### Inpainting
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In img2img tab, draw a mask over a part of image, and that part will be in-painted.
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