stable-diffusion-rocm
3d-ken-burns
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stable-diffusion-rocm | 3d-ken-burns | |
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5 | 5 | |
57 | 1,496 | |
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0.0 | 3.8 | |
about 1 year ago | about 2 months ago | |
Dockerfile | Python | |
- | 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
3d-ken-burns
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Making a trailer for my book with midjourney. What do you think?
I guess you could use a free video editor (Davinci resolve is free with less features than payed version). And then try to use this open source script: https://github.com/sniklaus/3d-ken-burns, but it would definitely be harder.
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It's time to upscale FSR 2 even further: Meet FSR 2.1
installing ROCm is bit of a pain (there is little packaging, so you have to rebuild it yourself)
Search who's running Stable Diffusion on Nvidia and who's running on AMD: if you are using AMD, you are kind of on your own.
Finally, you have model with custom CUDA code (e.g. https://github.com/sniklaus/3d-ken-burns )
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Are there any websites that create an .mp4 of simple parallax effect movement from a jpg using machine learning?
There's a great Github with a script that I run on Google Colab that does a decent parallax effect in about 30 seconds through ML... but it just takes a while to get the instance spun up, and I've yet to figure out how to push out a 4k .mp4 from it. Surely someone has coding chops and can do this for parallax and monetize it like the Dall-E bot?
- How are people taking still photos and making these stereoscopic videos out of them? [READ COMMENTS]
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Battle Round 1: 3D Ken Burns Effect using PyTorch
Making use of: https://github.com/sniklaus/3d-ken-burns
What are some alternatives?
stable-diffusion
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
stable_diffusion.openvino
cupy - NumPy & SciPy for GPU
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
XNOR-popcount-GEMM-PyTorch-CPU-CUDA - A PyTorch implemenation of real XNOR-popcount (1-bit op) GEMM Linear PyTorch extension support both CPU and CUDA
stable-diffusion
pyhpc-benchmarks - A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:
invisible-watermark - python library for invisible image watermark (blind image watermark)
NewsMTSC - Target-dependent sentiment classification in news articles reporting on political events. Includes a high-quality data set of over 11k sentences and a state-of-the-art classification model.