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https://github.com/Auto1111SDK/Auto1111SDK
An SDK/Python library for Automatic 1111 to run state-of-the-art diffusion models
https://github.com/Auto1111SDK/Auto1111SDK
ai ai-art api automatic1111 deep-learning diffusers diffusion image-generation image-to-image img2img python pytorch stable-diffusion stable-diffusion-webui text-to-image torch txt2img unstable upscaling web
Last synced: about 1 month ago
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An SDK/Python library for Automatic 1111 to run state-of-the-art diffusion models
- Host: GitHub
- URL: https://github.com/Auto1111SDK/Auto1111SDK
- Owner: Auto1111SDK
- License: agpl-3.0
- Created: 2024-01-28T09:20:30.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-03-26T08:56:20.000Z (3 months ago)
- Last Synced: 2024-03-26T09:49:57.600Z (3 months ago)
- Topics: ai, ai-art, api, automatic1111, deep-learning, diffusers, diffusion, image-generation, image-to-image, img2img, python, pytorch, stable-diffusion, stable-diffusion-webui, text-to-image, torch, txt2img, unstable, upscaling, web
- Language: Python
- Homepage:
- Size: 10.6 MB
- Stars: 332
- Watchers: 7
- Forks: 11
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-oss - Auto 1111 SDK - SDK for editing and generating images<br/> (Uncategorized / Uncategorized)
README
# Auto 1111 SDK: Stable Diffusion Python library
Auto 1111 SDK is a lightweight Python library for using Stable Diffusion generating images, upscaling images, and editing images with diffusion models. It is designed to be a modular, light-weight Python client that encapsulates all the main features of the [Automatic 1111 Stable Diffusion Web Ui](https://github.com/AUTOMATIC1111/stable-diffusion-webui). Auto 1111 SDK offers 3 main core features currently:
- Text-to-Image, Image-to-Image, Inpainting, and Outpainting pipelines. Our pipelines support the exact same parameters as the [Stable Diffusion Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui), so you can easily replicate creations from the Web UI on the SDK.
- Upscaling Pipelines that can run inference for any Esrgan or Real Esrgan upscaler in a few lines of code.
- An integration with Civit AI to directly download models from the website.
Join our [Discord!!](https://discord.gg/S7wRQqt6QV)
## Demo
We have a colab demo where you can run many of the operations of Auto 1111 SDK. Check it out [here!!](https://colab.research.google.com/drive/1SekiJ-mdB2V8ogWbyRyF_yDnoMuDGWTl?usp=sharing)
## Installation
We recommend installing Auto 1111 SDK in a virtual environment from PyPI. Right now, we do not have support for conda environments yet.
```bash
pip3 install auto1111sdk
```
To install the latest version of Auto 1111 SDK, run:
```bash
pip3 install git+https://github.com/saketh12/Auto1111SDK.git
```
## Quickstart
Generating images with Auto 1111 SDK is super easy. To run inference for Text-to-Image, Image-to-Image, Inpainting, Outpainting, or Stable Diffusion Upscale, we have 1 pipeline that can support all these operations. This saves a lot of RAM from having to create multiple pipeline objects with other solutions.
```python
from auto1111sdk import StableDiffusionPipeline
pipe = StableDiffusionPipeline("")
prompt = "a picture of a brown dog"
output = pipe.generate_txt2img(prompt = prompt, height = 1024, width = 768, steps = 10)
output[0].save("image.png")
```
## Running on Windows
Find the instructions [here.](https://github.com/saketh12/Auto1111SDK/blob/main/automatic1111sdk_on_windows_w_gpu.md) Contributed by by Marco Guardigli, [email protected]
## Documentation
We have more detailed examples/documentation of how you can use Auto 1111 SDK [here](https://flush-ai.gitbook.io/automatic-1111-sdk/).
For a detailed comparison between us and Huggingface diffusers, you can read [this](https://flush-ai.gitbook.io/automatic-1111-sdk/auto-1111-sdk-vs-huggingface-diffusers).
For a detailed guide on how to use SDXL, we recommend reading [this](https://flush-ai.gitbook.io/automatic-1111-sdk/pipelines/stable-diffusion-xl)
## Features
- Original txt2img and img2img modes
- Real ESRGAN upscale and Esrgan Upscale (compatible with any pth file)
- Outpainting
- Inpainting
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- a man in a `((tuxedo))` - will pay more attention to tuxedo
- a man in a `(tuxedo:1.21)` - alternative syntax
- select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
- Composable Diffusion: a way to use multiple prompts at once
- separate prompts using uppercase AND
- also supports weights for prompts: a cat :1.2 AND a dog AND a penguin :2.2
- Works with a variety of samplers
- Download models directly from Civit AI and RealEsrgan checkpoints
- Set custom VAE: works for any model including SDXL
- Support for SDXL with Stable Diffusion XL Pipelines
- Pass in custom arguments to the models
- No 77 prompt token limit (unlike Huggingface Diffusers, which has this limit)
## Roadmap
- Adding support Hires Fix and Refiner parameters for inference.
- Adding support for Lora's
- Adding support for Face restoration
- Adding support for Dreambooth training script.
- Adding support for custom extensions like Controlnet.
We will be adding support for these features very soon. We also accept any contributions to work on these issues!
## Contributing
Auto1111 SDK is continuously evolving, and we appreciate community involvement. We welcome all forms of contributions - bug reports, feature requests, and code contributions.
Report bugs and request features by opening an issue on Github.
Contribute to the project by forking/cloning the repository and submitting a pull request with your changes.
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Automatic 1111 Stable Diffusion Web UI - https://github.com/AUTOMATIC1111/stable-diffusion-webui
- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- ESRGAN - https://github.com/xinntao/ESRGAN
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)