Description:
onnxweb 0.12.0
onnx-web
onnx-web is a tool for running Stable Diffusion and other ONNX models with hardware acceleration,
on both AMD and Nvidia GPUs and with a CPU software fallback.
The GUI is hosted on Github Pages 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.
The API runs on both Linux and Windows and provides a REST API to run many of the pipelines from diffusers
, 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.
Please check out the setup guide to get started and the user guide for more
details.
Features
This is an incomplete list of new and interesting features, with links to the user guide:
supports SDXL and SDXL Turbo
wide variety of schedulers: DDIM, DEIS, DPM SDE, Euler Ancestral, LCM, UniPC, and more
hardware acceleration on both AMD and Nvidia
tested on CUDA, DirectML, and ROCm
half-precision support for low-memory GPUs on both
AMD and Nvidia
software fallback for CPU-only systems
web app to generate and view images
hosted on Github Pages, from your CDN, or locally
persists your recent images and progress as you change tabs
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
txt2img
img2img
inpainting, with mask drawing and upload
panorama, for both SD v1.5 and SDXL
upscaling, with ONNX acceleration
add and use your own models
convert models from diffusers and SD checkpoints
download models from HuggingFace hub, Civitai, and HTTPS sources
blend in additional networks
permanent and prompt-based blending
supports LoRA and LyCORIS weights
supports Textual Inversion concepts and embeddings
each layer of the embeddings can be controlled and used individually
ControlNet
image filters for edge detection and other methods
with ONNX acceleration
highres mode
runs img2img on the results of the other pipelines
multiple iterations can produce 8k images and larger
multi-stage and region prompts
seamlessly combine multiple prompts in the same image
provide prompts for different areas in the image and blend them together
change the prompt for highres mode and refine details without recursion
infinite prompt length
with long prompt weighting
image blending mode
combine images from history
upscaling and correction
upscaling with Real ESRGAN, SwinIR, and Stable Diffusion
face correction with CodeFormer and GFPGAN
API server can be run remotely
REST API can be served over HTTPS or HTTP
background processing for all image pipelines
polling for image status, plays nice with load balancers
OCI containers provided
for all supported hardware accelerators
includes both the API and GUI bundle in a single container
runs well on RunPod, Vast.ai, and other GPU container hosting services
Contents
onnx-web
Features
Contents
Setup
Adding your own models
Usage
Known errors and solutions
Running the containers
Credits
Setup
There are a few ways to run onnx-web:
cross-platform:
clone this repository, create a virtual environment, and run pip install
pulling and running the OCI containers
on Windows:
clone this repository and run one of the setup-*.bat scripts
download and run the experimental 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.
The extended setup docs have been moved to the setup guide.
Adding your own models
You can add your own models by downloading them from the HuggingFace Hub
or Civitai or by converting them from local files, without making any code changes. You can also download and blend in
additional networks, such as LoRAs and Textual Inversions, using tokens in the
prompt.
Usage
Known errors and solutions
Please see the Known Errors section of the user guide.
Running the containers
This has been moved to the server admin guide.
Credits
Some of the conversion and pipeline code was copied or derived from code in:
Amblyopius/Stable-Diffusion-ONNX-FP16
GPL v3: https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16/blob/main/LICENSE
https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16/blob/main/pipeline_onnx_stable_diffusion_controlnet.py
https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16/blob/main/pipeline_onnx_stable_diffusion_instruct_pix2pix.py
d8ahazard/sd_dreambooth_extension
Non-commerical license: https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/license.md
https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/dreambooth/sd_to_diff.py
huggingface/diffusers
Apache v2: https://github.com/huggingface/diffusers/blob/main/LICENSE
https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py
uchuusen/onnx_stable_diffusion_controlnet
GPL v3: https://github.com/uchuusen/onnx_stable_diffusion_controlnet/blob/main/LICENSE
uchuusen/pipeline_onnx_stable_diffusion_instruct_pix2pix
Apache v2: https://github.com/uchuusen/pipeline_onnx_stable_diffusion_instruct_pix2pix/blob/main/LICENSE
Those parts have their own licenses with additional restrictions on commercial usage, modification, and redistribution.
The rest of the project is provided under the MIT license, and I am working to isolate these components into a library.
There are many other good options for using Stable Diffusion with hardware acceleration, including:
https://github.com/Amblyopius/AMD-Stable-Diffusion-ONNX-FP16
https://github.com/azuritecoin/OnnxDiffusersUI
https://github.com/ForserX/StableDiffusionUI
https://github.com/pingzing/stable-diffusion-playground
https://github.com/quickwick/stable-diffusion-win-amd-ui
Getting this set up and running on AMD would not have been possible without guides by:
https://gist.github.com/harishanand95/75f4515e6187a6aa3261af6ac6f61269
https://gist.github.com/averad/256c507baa3dcc9464203dc14610d674
https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs
https://www.travelneil.com/stable-diffusion-updates.html
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
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