mirror of
https://github.com/Sygil-Dev/sygil-webui.git
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167 lines
8.1 KiB
Markdown
167 lines
8.1 KiB
Markdown
Features:
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* Gradio GUI: Idiot-proof, fully featured frontend for both txt2img and img2img generation
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* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders
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* :fire::fire: Mask and crop :fire::fire:
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* Textual inversion 🔥: [info](https://textual-inversion.github.io/) - requires enabling, see [here](https://github.com/hlky/sd-enable-textual-inversion), script works as usual without it enabled
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* Mask painting (NEW) 🖌️: Powerful tool for re-generating only specific parts of an image you want to change
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* Loopback (NEW) ➿: Automatically feed the last generated sample back into img2img
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* Prompt Weighting (NEW) 🏋️: Adjust the strength of different terms in your prompt
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* GFPGAN Face Correction 🔥: Automatically correct distorted faces with a built-in GFPGAN option, fixes them in less than half a second
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* More k_diffusion samplers 🔥🔥 : Far greater quality outputs than the default sampler, less distortion and more accurate
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* CFG: Classifier free guidance scale, a feature for fine-tuning your output
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* Memory Monitoring 🔥: Shows Vram usage and generation time after outputting.
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* Word Seeds 🔥: Use words instead of seed numbers
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* Launcher Automatic 👑🔥 shortcut to load the model, no more typing in Conda
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* Lighter on Vram: 512x512 img2img & txt2img tested working on 6gb
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* and ????
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# 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 anonymopus user. This is a modification.
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## Installing and running
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### Stable Diffusion
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This script assumes that you already have main Stable Diffusion sutff installed, assumed to be in directory `/sd`.
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If you don't have it installed, follow the guide:
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- https://rentry.org/kretard
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This repository's `webgui.py` is a replacement for `kdiff.py` from the guide.
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Particularly, following files must exist:
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- `/sd/configs/stable-diffusion/v1-inference.yaml`
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- `/sd/models/ldm/stable-diffusion-v1/model.ckpt`
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- `/sd/ldm/util.py`
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- `/sd/k_diffusion/__init__.py`
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### GFPGAN
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If you want to use GFPGAN to improve generated faces, you need to install it separately.
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Follow instructions from https://github.com/TencentARC/GFPGAN, but when cloning it, do so into Stable Diffusion main directory, `/sd`.
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After that download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and put it
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into the `/sd/GFPGAN/experiments/pretrained_models` directory. If you're getting troubles with GFPGAN support, follow instructions
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from the GFPGAN's repository until `inference_gfpgan.py` script works.
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The following files must exist:
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- `/sd/GFPGAN/inference_gfpgan.py`
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- `/sd/GFPGAN/experiments/pretrained_models/GFPGANv1.3.pth`
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If the GFPGAN directory does not exist, you will not get the option to use GFPGAN in the UI. If it does exist, you will either be able
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to use it, or there will be a message in console with an error related to GFPGAN.
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### Web UI
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Run the script as:
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`python webui.py`
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When running the script, you must be in the main Stable Diffusion directory, `/sd`. If you cloned this repository into a subdirectory
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of `/sd`, say, the `stable-diffusion-webui` directory, you will run it as:
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`python stable-diffusion-webui/webui.py`
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When launching, you may get a very long warning message related to some weights not being used. You may freely ignore it.
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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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## 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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### 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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### Sampling method selection
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Pick out of three sampling methods for txt2img: DDIM, PLMS, k-diffusion:
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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 paramerters 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 iamge 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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