stable-diffusion-rocm
stable-diffusion
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stable-diffusion-rocm | stable-diffusion | |
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5 | 186 | |
57 | 3,146 | |
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0.0 | 0.0 | |
about 1 year ago | 7 months ago | |
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- | GNU General Public License v3.0 or later |
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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
stable-diffusion
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Possible to load Civitai models in basujindal optimizedSD fork?
I am using this repo: https://github.com/basujindal/stable-diffusion and it works fine with e.g. this model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
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40min to render 2x 256x256 pictures ..
That includes this optimized version : https://github.com/basujindal/stable-diffusion
- [Stable Diffusion] Coincé chez Unet: courir en mode EPS-Prédiction
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How to use safetensors locally (optimized-sd)?
Ah, I wasn't aware of that. I use this version, which was very easy to set up and use by CLI.
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[Stable Diffusion] stabile Diffusion 1.4 - CUDA-Speicherfehler
Used repo recommended in https://github.com/CompVis/stable-diffusion/issues/39 to use https://github.com/basujindal/stable-diffusion - same result.
- [Stable Diffusion] Aide avec Cuda hors de mémoire
- [Stable Diffusion] Comment créer notre propre modèle?
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Help installing optimisedSD please. Thank you so much!
As per the best solution I found, I have download this (https://github.com/basujindal/stable-diffusion) version and pasted the optimizedSD folder in the main (user>stable-diffusion-webui) folder as per site instruction.
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Stable Diffusion Web UI: Using Optimized SD Post-Installation
The git says you can simply grab the OptimizedSD folder and paste it into the installation path, which I did. However, I'm not sure how to call upon its functionality. Again, the reddit post says >Remember to call the optimized python script python optimizedSD/optimized_txt2img.py instead of standard scripts/txt2img. Though I'm not even sure where that script call is performed. Any ideas? Thanks in advance!
- [Stablediffusion] diffusion stable 1.4 - Erreur CUDA de mémoire insuffisante
What are some alternatives?
stable-diffusion
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.
stable_diffusion.openvino
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
diffusers-uncensored - Uncensored fork of diffusers
3d-ken-burns - an implementation of 3D Ken Burns Effect from a Single Image using PyTorch
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
stable-diffusion
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
invisible-watermark - python library for invisible image watermark (blind image watermark)
stable-diffusion-webui - Stable Diffusion web UI