auto1111sdk 0.0.3

SDK for Automatic 1111.

Auto 1111 SDK/Python Client

Auto 1111 SDK is a light-weight Python library for 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. Auto 1111 SDK offers 3 main core features currently:

• State of the Art Diffusion Pipelines that can run inference for in just a few lines of code. Our pipelines can currently run Text-to-Image, Image-to-Image, Inpainting, Outpainting, and Stable Diffusion Upscale. Our pipelines support the exact same parameters as the Stable Diffusion Web UI, 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.

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Installation

We recommend installing Auto 1111 SDK in a virtual environment from PyPI or Conda.

pip3 install auto1111sdk

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.

from auto1111sdk import StableDiffusionPipeline

pipe = StableDiffusionPipeline("<Path to your local safetensors or checkpoint file>")

prompt = "a picture of a brown dog"
output = pipe.generate_txt2img(prompt = prompt, height = 1024, width = 768, steps = 10)

output[0].save("image.png")

Documentation

We have more detailed examples/documentation of how you can use Auto 1111 SDK here.. For a detailed comparison between us and Huggingface diffusers, you can read this..

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

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)