another UI for Stable Diffusion for Windows and AMD, now with LoRA and Textual Inversions

• onnx-web

  8 177 9.7 Python

  web UI for GPU-accelerated ONNX pipelines like Stable Diffusion, even on Windows and AMD

For a workaround: You can snag a copy from https://github.com/ssube/onnx-web/blob/main/api/extras.json. If you put that in the same directory as models/outputs/server and append --extras %ONNX_WEB_BASE_PATH%\extras.json to the line starting with server\onnx-web.exe --diffusion --correction ... in the batch script (onnx-web-half.bat and/or onnx-web-full.bat), it should work. A bunch of models I have tested, in no particular order: https://gist.github.com/ssube/bcb32781dc848d79657730e6ac540efd

• diffusers

  266 22,646 9.9 Python

  🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

  

Minutes vs hours, for the combination that originally drove me to build this, Windows and AMD. For Nvidia, it seems pretty comparable but ONNX can sometimes be faster? The best measurements I took for timing are in https://github.com/huggingface/diffusers/pull/2158, where upscaling a 128x128 image to 512x512 went from 2m28s on CPU to 42 seconds on Windows/DirectML and only 7 seconds on Linux/ROCm (which is really interesting). There are also some internal comparisons in https://github.com/ssube/onnx-web/blob/main/BENCHMARK.md, and https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/python/tools/transformers/models/stable_diffusion#stable-diffusion-cuda-optimization has a whole pile of numbers.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Stable-Diffusion-ONNX-FP16

  10 266 6.8 Python

  Example code and documentation on how to get Stable Diffusion running with ONNX FP16 models on DirectML. Can run accelerated on all DirectML supported cards including AMD and Intel.

Yes, the FP16 stuff in this latest release is a big part of that, and should tentatively support 8GB cards. I haven't pushed things quite as far as https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16 yet, but support for 4GB cards is possible and we've been discussing how it works: https://github.com/ssube/onnx-web/issues/241

Suggest a related project