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README.md
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# onnx-web
onnx-web is a tool for running Stable Diffusion and other [ONNX models](https://onnx.ai/) with hardware acceleration,
on both AMD and Nvidia GPUs and with a CPU software fallback.
onnx-web is designed to simplify the process of running Stable Diffusion and other [ONNX models](https://onnx.ai) so you
can focus on making high quality, high resolution art. With the efficiency of hardware acceleration on both AMD and
Nvidia GPUs, and offering a reliable CPU software fallback, it offers the full feature set on desktop, laptops, and
multi-GPU servers with a seamless user experience.
The GUI is [hosted on Github Pages](https://ssube.github.io/onnx-web/) and runs in all major browsers, including on
mobile devices. It allows you to select the model and accelerator being used for each image pipeline. Image parameters
are shown for each of the major modes, and you can either upload or paint the mask for inpainting and outpainting. The
last few output images are shown below the image controls, making it easy to refer back to previous parameters or save
an image from earlier.
You can navigate through the user-friendly web UI, hosted on Github Pages and accessible across all major browsers,
including your go-to mobile device. Here, you have the flexibility to choose diffusion models and accelerators for each
image pipeline, with easy access to the image parameters that define each modes. Whether you're uploading images or
expressing your artistic touch through inpainting and outpainting, onnx-web provides an environment that's as
user-friendly as it is powerful. Recent output images are neatly presented beneath the controls, serving as a handy
visual reference to revisit previous parameters or remix your earlier outputs.
The API runs on both Linux and Windows and provides a REST API to run many of the pipelines from [`diffusers`
](https://huggingface.co/docs/diffusers/main/en/index), along with metadata about the available models and accelerators,
and the output of previous runs. Hardware acceleration is supported on both AMD and Nvidia for both Linux and Windows,
with a CPU fallback capable of running on laptop-class machines.
Dive deeper into the onnx-web experience with its API, compatible with both Linux and Windows. This RESTful interface
seamlessly integrates various pipelines from the [HuggingFace diffusers](https://huggingface.co/diffusers/main/en/index)
library, offering valuable metadata on models and accelerators, along with detailed outputs from your creative runs.
Please check out [the setup guide to get started](docs/setup-guide.md) and [the user guide for more
details](https://github.com/ssube/onnx-web/blob/main/docs/user-guide.md).
Embark on your generative art journey with onnx-web, and explore its capabilities through our detailed documentation
site. Find a comprehensive getting started guide, setup guide, and user guide waiting to empower your creative
endeavors!
Please [check out the documentation site](https://www.onnx-web.ai/docs/) for more info:
- [getting started guide](https://www.onnx-web.ai/docs/getting-started/)
- [setup guide](https://www.onnx-web.ai/docs/setup-guide/)
- [user guide](https://www.onnx-web.ai/docs/user-guide/)
![preview of txt2img tab using SDXL to generate ghostly astronauts eating weird hamburgers on an abandoned space station](./docs/readme-sdxl.png)
## Features
This is an incomplete list of new and interesting features, with links to the user guide:
This is an incomplete list of new and interesting features:
- supports SDXL and SDXL Turbo
- wide variety of schedulers: DDIM, DEIS, DPM SDE, Euler Ancestral, LCM, UniPC, and more

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docs/index.md
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# onnx-web
onnx-web is a tool for running Stable Diffusion and other [ONNX models](https://onnx.ai/) with hardware acceleration,
on both AMD and Nvidia GPUs and with a CPU software fallback.
onnx-web is designed to simplify the process of running Stable Diffusion and other [ONNX models](https://onnx.ai) so you
can focus on making high quality, high resolution art. With the efficiency of hardware acceleration on both AMD and
Nvidia GPUs, and offering a reliable CPU software fallback, it offers the full feature set on desktop, laptops, and
servers with a seamless user experience.
The GUI is [hosted on Github Pages](https://ssube.github.io/onnx-web/) and runs in all major browsers, including on
mobile devices. It allows you to select the model and accelerator being used for each image pipeline. Image parameters
are shown for each of the major modes, and you can either upload or paint the mask for inpainting and outpainting. The
last few output images are shown below the image controls, making it easy to refer back to previous parameters or save
an image from earlier.
You can navigate through the user-friendly web UI, hosted on Github Pages and accessible across all major browsers,
including your go-to mobile device. Here, you have the flexibility to choose diffusion models and accelerators for each
image pipeline, with easy access to the image parameters that define each modes. Whether you're uploading images or
expressing your artistic touch through inpainting and outpainting, onnx-web provides an environment that's as
user-friendly as it is powerful. Recent output images are neatly presented beneath the controls, serving as a handy
visual reference to revisit previous parameters or remix your earlier outputs.
The API runs on both Linux and Windows and provides a REST API to run many of the pipelines from [`diffusers`
](https://huggingface.co/./diffusers/main/en/index), along with metadata about the available models and accelerators,
and the output of previous runs. Hardware acceleration is supported on both AMD and Nvidia for both Linux and Windows,
with a CPU fallback capable of running on laptop-class machines.
Dive deeper into the onnx-web experience with its API, compatible with both Linux and Windows. This RESTful interface
seamlessly integrates various pipelines from the [HuggingFace diffusers](https://huggingface.co/diffusers/main/en/index)
library, offering valuable metadata on models and accelerators, along with detailed outputs from your creative runs.
Please check out [the setup guide to get started](./setup-guide.md) and [the user guide for more
details](https://github.com/ssube/onnx-web/blob/main/./user-guide.md).
Embark on your generative art journey with onnx-web, and explore its capabilities through our detailed documentation
site. Find a comprehensive getting started guide, setup guide, and user guide waiting to empower your creative
endeavors!
Please [check out the documentation site](https://www.onnx-web.ai/docs/) for more info:
- [getting started guide](https://www.onnx-web.ai/docs/getting-started/)
- [setup guide](https://www.onnx-web.ai/docs/setup-guide/)
- [user guide](https://www.onnx-web.ai/docs/user-guide/)
![preview of txt2img tab using SDXL to generate ghostly astronauts eating weird hamburgers on an abandoned space station](./readme-sdxl.png)
## Features
This is an incomplete list of new and interesting features, with links to the user guide:
This is an incomplete list of new and interesting features:
- supports SDXL and SDXL Turbo
- wide variety of schedulers: DDIM, DEIS, DPM SDE, Euler Ancestral, LCM, UniPC, and more
@ -35,7 +44,7 @@ This is an incomplete list of new and interesting features, with links to the us
- [persists your recent images and progress as you change tabs](./user-guide.md#image-history)
- queue up multiple images and retry errors
- translations available for English, French, German, and Spanish (please open an issue for more)
- supports many `diffusers` pipelines
- many pipelines, from `diffusers` and beyond
- [txt2img](./user-guide.md#txt2img-tab)
- [img2img](./user-guide.md#img2img-tab)
- [inpainting](./user-guide.md#inpaint-tab), with mask drawing and upload
@ -95,8 +104,8 @@ There are a few ways to run onnx-web:
- [clone this repository, create a virtual environment, and run `pip install`](./setup-guide.md#cross-platform-method)
- [pulling and running the OCI containers](./server-admin.md#running-the-containers)
- on Windows:
- [download and run the all-in-one bundle](./setup-guide.md#windows-all-in-one-bundle)
- [clone this repository and run one of the `setup-*.bat` scripts](./setup-guide.md#windows-python-installer)
- [download and run the experimental all-in-one bundle](./setup-guide.md#windows-all-in-one-bundle)
You only need to run the server and should not need to compile anything. The client GUI is hosted on Github Pages and
is included with the Windows all-in-one bundle.