stable-diffusion-rocm
tvm
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stable-diffusion-rocm | tvm | |
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5 | 15 | |
57 | 11,186 | |
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0.0 | 9.9 | |
about 1 year ago | about 15 hours ago | |
Dockerfile | Python | |
- | Apache License 2.0 |
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stable-diffusion-rocm
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[D] About the current state of ROCm
Re: stable diffusion https://github.com/AshleyYakeley/stable-diffusion-rocm
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It's time to upscale FSR 2 even further: Meet FSR 2.1
Very easy actually. This is not officially documented, but with a recent enough kernel you don't have to install anything. You can grab the official rocm container and it'll just work. For example for Stable Diffusion see https://github.com/AshleyYakeley/stable-diffusion-rocm/blob/...
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Running Stable Diffusion on Your GPU with Less Than 10Gb of VRAM
I had good luck with these directions, which let you run inside a docker container:
https://github.com/AshleyYakeley/stable-diffusion-rocm
I had to make the one line change suggested in issue #3 to get it to run under 8GB.
radeontop suggests 4GB might work.
I also had to add this environment variable to make it work on my unsupported radeon 6600xt:
HSA_OVERRIDE_GFX_VERSION=10.3.0
It takes under two minutes per batch of 5 images with the --turbo option.
(Base OS is manjaro; using the distro's version of docker; not the flatpack docker package.)
If you don't have a GPU, paperspace will rent you an appropriate VM.
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Run Stable Diffusion on Your M1 Mac’s GPU
I have it working on an RX 6800, used the scripts from this repo[0] to build a docker image that has ROCm drivers and PyTorch installed.
I'm running Ubuntu 22.04 LTS as the host OS, didn't have to touch anything beyond the basic Docker install. Next step is build a new Dockerfile that adds in the Stable Diffusion WebUI.[1]
[0] https://github.com/AshleyYakeley/stable-diffusion-rocm
- Dockerfile for easy use on an AMD GPU
tvm
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Making AMD GPUs competitive for LLM inference
Yes, this is coming! Myself and others at OctoML and in the TVM community are actively working on multi-gpu support in the compiler and runtime. Here are some of the merged and active PRs on the multi-GPU (multi-device) roadmap:
Support in TVM’s graph IR (Relax) - https://github.com/apache/tvm/pull/15447
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VSL; Vlang's Scientific Library
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
- Apache TVM
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MLC LLM - "MLC LLM is a universal solution that allows any language model to be deployed natively on a diverse set of hardware backends and native applications, plus a productive framework for everyone to further optimize model performance for their own use cases."
I have tried the iPhone app. It's fast. They're using Apache TVM which should allow better use of native accelerators on different devices. Like using metal on Apple and Vulcan or CUDA or whatever instead of just running the thing on the CPU like llama.cpp.
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ONNX Runtime merges WebGPU back end
I was going to answer the same, I find the approach of machine learning compilers that directly compile models to host and device code better than having to bring a huge runtime. There are exciting projects in this area like TVM Unity, IREE [2], or torch.export [3]
[1] https://github.com/apache/tvm/tree/unity
[2] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
[3] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
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Esp32 tensorflow lite
Apache TVM home page: https://tvm.apache.org/
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Decompiling x86 Deep Neural Network Executables
It's pretty clear its referring to the output of Apache TVM and Meta's Glow
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Run Stable Diffusion on Your M1 Mac’s GPU
As mentioned in sibling comments, Torch is indeed the glue in this implementation. Other glues are TVM[0] and ONNX[1]
These just cover the neural net though, and there is lots of surrounding code and pre-/post-processing that isn't covered by these systems.
For models on Replicate, we use Docker, packaged with Cog for this stuff.[2] Unfortunately Docker doesn't run natively on Mac, so if we want to use the Mac's GPU, we can't use Docker.
I wish there was a good container system for Mac. Even better if it were something that spanned both Mac and Linux. (Not as far-fetched as it seems... I used to work at Docker and spent a bit of time looking into this...)
[0] https://tvm.apache.org/
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How to get started with machine learning.
Or use TVM, the idea is to compile your model into code that you can load at runtime. Similar to onnxruntime, it only does DNN inference; so you need domain-specific code.
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An open-source library for optimizing deep learning inference. (1) You select the target optimization, (2) nebullvm searches for the best optimization techniques for your model-hardware configuration, and then (3) serves an optimized model that runs much faster in inference
Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.
What are some alternatives?
stable-diffusion
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
stable_diffusion.openvino
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
3d-ken-burns - an implementation of 3D Ken Burns Effect from a Single Image using PyTorch
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
stable-diffusion
invisible-watermark - python library for invisible image watermark (blind image watermark)
nebuly - The user analytics platform for LLMs