rocm-gfx803
stable-diffusion-webui
rocm-gfx803 | stable-diffusion-webui | |
---|---|---|
7 | 2,808 | |
167 | 131,121 | |
- | - | |
1.1 | 9.9 | |
about 1 year ago | about 4 hours ago | |
Python | ||
- | MIT |
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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-webui
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Show HN: I made an app to use local AI as daily driver
* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.
- https://diffusionbee.com/
- https://github.com/comfyanonymous/ComfyUI
- https://github.com/AUTOMATIC1111/stable-diffusion-webui
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
I would love to be able to have a native stable diffusion experience, my rx 580 takes 30s to generate a single image. But it does work after following https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...
I got this up and running on my windows machine in short order and I don't even know what stable diffusion is.
But again, it would be nice to have first class support to locally participate in the fun.
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Ask HN: What is the state of the art in AI photo enhancement?
In Auto1111, that just uses Image.blend. :)
https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob...
- How To Increase Performance Time on MacOS
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Can anyone suggest an AI model that can help me enhance a poorly drawn logo?
I used SDXL in automatic1111 webui for both images. Now that I think about it, the procedure I described was how I made this one, but the one that looks like an illustration was done in two steps. I used the canny ControlNet as I said for the outer part of the logo to preserve the shape of the fonts, but I had to turn it off for the boot to give SDXL leeway to add detail and make it look more like a boot.
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Seeking out an experienced and empathetic coding buddy.
That said, please do learn coding and don't get discouraged when somebody says to learn PyTorch or recommends using a Jupiter notebook with no further information on how to translate the skill into images. I would highly recommend some short term goals. Get your feet wet by taking apart the UIs. The comfy API documentation is here and the A1111 API documentation is here. There is a difference in completeness, welcome to programming. Writing nodes or plugins is also a good way to jump into this world. Custom wildcard logic might be very attractive to you if you aren't the type that want to deal with a nested file structure to simulate logic.
- can't get it working with an AMD gpu
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SD extension that allows for setting override
Possibly Unprompted? https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8094
- Need to write an application to use Stable Diffusion on my desktop PC - which resource should I learn to use?
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4090 Speed Decrease on each Generation/Iteration
version: v1.6.1 • python: 3.10.13 • torch: 2.0.1+cu118 • xformers: 0.0.20 • gradio: 3.41.2 • checkpoint: 6e8d4871f8
What are some alternatives?
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
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.
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
stable-diffusion-cpu
SHARK - SHARK - High Performance Machine Learning Distribution
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
stable-diffusion - Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it.
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
DeepSpeed-MII - MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.
safetensors - Simple, safe way to store and distribute tensors