stable-diffusion
rocm-build
stable-diffusion | rocm-build | |
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20 | 7 | |
338 | 168 | |
- | - | |
0.0 | 0.0 | |
over 1 year ago | 4 months ago | |
Jupyter Notebook | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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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.
rocm-build
- AMD's Hidden $100 Stable Diffusion Beast!
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AMD GPU driver not installed correctly
Scripts to help with building rocm and hip. It will also help work out dependencies. You will need to modify the scripts for them to work and not all are required. https://github.com/xuhuisheng/rocm-build
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Stable Diffusion on AMD RDNA3
Short answer no. Long answer "in theory" yes. I tried this [1] but gave up as building rocm + deps takes up to 6h :/ Official statement [2]
[1] https://github.com/xuhuisheng/rocm-build
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Show HN: InvokeAI, an open source Stable Diffusion toolkit and WebUI
I am in the same boat with a gfx03 card. What patch did you use? The ones here? https://github.com/xuhuisheng/rocm-build
I also tried to compile pytorch with its Vulkan backend, but ended throwing the towel as LDFLAGS are a mess to get right (I successfully compiled it, but that was only part of the build chain, and decided I had better things to spend time on). I wonder how that would perform; ncnn works pretty decently.
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How do I run Stable Diffusion and sharing FAQs
Unofficial black magic is available: https://github.com/xuhuisheng/rocm-build/tree/master/navi10 (pytorch 1.12.0 is outdated but can run SD)
- Deep Learning options on Radeon RX 6800
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Which version of ROCm and Tensorflow should I use?
also have an RX570, currently running latest Tensorflow and ROCm 4.1. had to recompile some parts of ROCm 4.1 libraries to get tensorflow to work. mostly followed this guide: https://github.com/xuhuisheng/rocm-build/tree/master/gfx803
What are some alternatives?
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
stable-diffusion-webui - Stable Diffusion web UI
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
tensorflow-upstream - TensorFlow ROCm port
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 - Optimized Stable Diffusion modified to run on lower GPU VRAM
sd-akashic - A compendium of informations regarding Stable Diffusion (SD)