HIPIFY
stable-diffusion
HIPIFY | stable-diffusion | |
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11 | 142 | |
318 | 2,438 | |
- | - | |
0.0 | 9.8 | |
5 months ago | over 1 year ago | |
C++ | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
HIPIFY
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AMD Hip SDK: Making CUDA Applications Run Across Consumer, Pro GPUs and APUs
Right. I can't speak to its correctness/completeness as I've only done a quick installation and smoke test of the ROCm/HIP/MIOpen stack, but there's even a tool that automates the translation [1].
[1] https://github.com/ROCm-Developer-Tools/HIPIFY
- How to run Llama 13B with a 6GB graphics card
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How Nvidia’s CUDA Monopoly in Machine Learning Is Breaking
From https://news.ycombinator.com/item?id=32904285 re: AMD Rocm, HIPIFY, :
>> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :
>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.
> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :
>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.
> AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...
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Stable Diffusion on AMD RDNA3
> Thus, the idea is that through typically negligible effort porting to HiP, your code becomes vendor-independent.
Here, the big AMD mistake was to rename those function prefixes in the first place. It's a mistake that they could have avoided...
What a lot of SW codebases did to support AMD (see PyTorch code notably): codebase is still CUDA, have the conversion pass to HIP done at build time.
See https://github.com/ROCm-Developer-Tools/HIPIFY/blob/amd-stag... for the Perl script to do it.
Then comes the problem of AMD not supporting ROCm HIP on most of their hardware or user base.
On Windows, the ROCm HIP SDK is private and only available under NDA. This means that while you can use Blender w/ HIP on Windows, the Blender builds that you compile yourself will not be able to use ROCm HIP.
On Linux, the supported GPUs are few and far between, Vega20 onwards are supported today. APUs, RDNA1, and lower end RDNA2 w/o unsupported hacks (6700 XT and below) are excluded.
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AI Seamless Texture Generator Built-In to Blender
https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...
RadeonOpenCompute/ROCm_Documentation: https://github.com/RadeonOpenCompute/ROCm_Documentation
ROCm-Developer-Tools/HIPIFYhttps://github.com/ROCm-Developer-Tools/HIPIFY :
> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.
ROCmSoftwarePlatform/gpufort: https://github.com/ROCmSoftwarePlatform/gpufort :
> GPUFORT: S2S translation tool for CUDA Fortran and Fortran+X in the spirit of hipify
ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP:
> HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code. [...] Key features include:
> - HIP is very thin and has little or no performance impact over coding directly in CUDA mode.
> - HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.
> - HIP allows developers to use the "best" development environment and tools on each target platform.
> - The [HIPIFY] tools automatically convert source from CUDA to HIP.
> - * Developers can specialize for the platform (CUDA or AMD) to tune for performance or handle tricky cases.*
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单位要求五一之后上缴旧电脑,统一换国产新电脑、新系统,由于不兼容windows软件,所以还要装个windows模拟器,导致办公效率倒退10年。主任吐槽说,这不是用落后代替先进么,我心说连他都看出来了。
并且有一个自动转换工具 https://github.com/ROCm-Developer-Tools/HIPIFY https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-porting-guide.html
- Hipify: Convert CUDA to Portable C++ Code
- Hipify: Convert CUDA to Portable Hip C++ Code
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Deep Learning options on Radeon RX 6800
It might be worth checking out HIPIFY, which lets you automatically convert CUDA code to vendor neutral code that can be run on any GPU. Disclaimer, I have never used it and have no idea how it works.
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Will NVIDIA's cryptocurrency limiter interfere with nouveau drivers?
CUDA zu AMD HIP conversion: https://github.com/ROCm-Developer-Tools/HIPIFY
stable-diffusion
- [Stable Diffusion] Aide nécessaire à l'augmentation de la taille du fichier maximum sur l'installation locale
- [Machine Learning] [P] Exécutez une diffusion stable sur le GPU de votre M1 Mac
- Its time!
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Anybody running SD on a Macbook Pro? What are you using and how did you install it?
Yes, you can install it with Python! https://github.com/lstein/stable-diffusion works with macOS, and you can control all the common parameter via their WebUI or CLI :)
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How do I save the arguments for images I create when using the terminal? (Apple M1 Pro)
I'm using lstein fork ("dream") and when I create an image from the terminal, it also writes back to the terminal like this:
- I Resurrected “Ugly Sonic” with Stable Diffusion Textual Inversion
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AI Seamless Texture Generator Built-In to Blender
> Whenever I ask for something like ‘seamless tiling xxxxxx’ it kinda sorta gets the idea, but the resulting texture doesn’t quite tile right.
Getting seamless tiling requires more than just have "seamless tiling" in the prompt. It also depends on if the fork you're using has that feature at all.
https://github.com/lstein/stable-diffusion has the feature, but you need to pass it outside the prompt. So if you use the `dream.py` prompt cli, you can pass it `"Hats on the ground" --seamless` and it should be perfectly tilable.
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Auto SD Workflow - Update 0.2.0 - "Collections", Password Protection, Brand new UI + more
From https://github.com/lstein/stable-diffusion
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Stable Diffusion GUIs for Apple Silicon
Stable Diffusion Dream Script: This is the original site/script for supporting macOS. I found this soon after Stable Diffusion was publicly released and it was the site which inspired me to try out using Stable Diffusion on a mac. They have a web-based UI (as well as command-line scripts) and a lot of documentation on how to get things working.
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Still can't believe this technology is real. My talentless 2 minute sketch on the left.
I’m pretty sure it works for M2 as well - basically the newer ARM-based Macs. The instructions to get it working are detailed! https://github.com/lstein/stable-diffusion
What are some alternatives?
ZLUDA - CUDA on AMD GPUs
waifu-diffusion - stable diffusion finetuned on weeb stuff
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
stable-diffusion-webui - Stable Diffusion web UI
llama-cpp-python - Python bindings for llama.cpp
diffusers-uncensored - Uncensored fork of diffusers
rocm-build - build scripts for ROCm
txt2imghd - A port of GOBIG for Stable Diffusion
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
dream-textures - Stable Diffusion built-in to Blender