rocm-gfx803
stable-diffusion
rocm-gfx803 | stable-diffusion | |
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7 | 20 | |
167 | 338 | |
- | - | |
1.1 | 0.0 | |
about 1 year ago | over 1 year ago | |
Jupyter Notebook | ||
- | GNU General Public License v3.0 or later |
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rocm-gfx803
- ROCm gfx803 archlinux
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My brother is giving away a PC he built with 8 AMD Radeon RX Vega x64 GPUs (8GB ram). I've only ever done ML on Nvidia cards. Is there anything I can do with these?
That specific card has current support for rocm and that is supported by at least tensorflow and torch, plus many other less known/used libraries like cupy, although you are correct in the fact that support sucks in the long run, I have a GPU that is known to be useful and that has continued COMMUNITY support because AMD cut the support with rocm 4.0, thanks to Xuhuisheng for the patch to make the rx580 work with current rocm despite AMD lack of support, what open source can accomplish https://github.com/xuhuisheng/rocm-gfx803
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Automatic111 - Torch is not able to use GPU. Help!
You'll also need to compile pytorch and torchvision for gfx803, although I recommend you install the whl files from here inside your venv because it's a massive pain to compile them on non-Ubuntu (I tried)
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Image Creation Time for each GPU.
I followed the guide from here: https://github.com/xuhuisheng/rocm-gfx803
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I *think* it's impossible to run SD on an RX 570 (and probably below?)
There is an unofficial build of ROCm 5.2.0 + pytorch + torchvision with GFX8 support added back in. I have no idea if it works. Perhaps someone who knows Docker/Conda could get SD working with those files.
- Run Stable Diffusion on Intel CPUs
stable-diffusion
- [Machine Learning] [P] Exécutez une diffusion stable sur le GPU de votre M1 Mac
- High-performance image generation using Stable Diffusion in KerasCV
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Charl-e: “Stable Diffusion on your Mac in 1 click”
SD on an Intel mac with Vega graphics runs pretty well though — I think it ran at something like ~3-5 iterations/s for me, which is decent. I ran either https://github.com/magnusviri/stable-diffusion or https://github.com/lstein/stable-diffusion which have MPS support
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Stable Diffusion PR optimizes VRAM, generate 576x1280 images with 6 GB VRAM
https://github.com/magnusviri/stable-diffusion/commit/d0b168...
Copying this change fixed seeds on M1 for me.
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Intel Mac User, How do I start?
You should be able to run it on a CPU. Maybe try this version. If MPS is supported on your Mac you can check this out.
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[P] Run Stable Diffusion on your M1 Mac’s GPU
A group of open source hackers forked Stable Diffusion on GitHub and optimized the model to run on Apple's M1 chip, enabling images to be generated in ~ 15 seconds (512x512 pixels, 50 diffusion steps).
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Run Stable Diffusion on Your M1 Mac’s GPU
Magnusviro [0], the original author of the SD M1 repo credited in this article, has merged his fork into the Lstein Stable Diffusion repo [1], and you can now run Lstein fork with M1 as of a few hours ago.
This adds a ton of functionality - GUI, Upscaling & Facial improvements, weighted subprompts etc.
This has been a big undertaking over the last few days, and I highly recommend checking it out.
[0] https://github.com/magnusviri/stable-diffusion
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How are Mac people using Windows for A.I. stuff?
You can run it on an M1. Using a macbook M1 pro max with 32Gb I get 512x512 in about 50 seconds. use this branch https://github.com/magnusviri/stable-diffusion/tree/apple-mps-support
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ResolvePackageNotFound
I had this error too, and I tried a ton of things to get cudatoolkit to install, without any luck. This fork has an environment-mac.yml file that actually got it working on my M1 Max: https://github.com/magnusviri/stable-diffusion/tree/apple-silicon-mps-support
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If I set a seed value and re-run using the exact same settings, should I get the same image back each time?
But when I run it (locally, using the Mac M1 port), every time I run it creates a different image.
What are some alternatives?
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
AITemplate - AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.
stable-diffusion-cpu
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
DeepSpeed-MII - MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.
rocm-build - build scripts for ROCm
stable_diffusion.openvino
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]