stablehlo
SHARK
stablehlo | SHARK | |
---|---|---|
5 | 84 | |
333 | 1,385 | |
4.2% | 1.4% | |
9.8 | 9.4 | |
about 15 hours ago | about 10 hours ago | |
MLIR | Python | |
Apache License 2.0 | Apache License 2.0 |
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stablehlo
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Nvidia H200 Tensor Core GPU
I am going to paste a cousin comment:
StableHLO[1] is an interesting project that might help AMD here:
> Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).
From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend.
[1] https://github.com/openxla/stablehlo
[2] https://github.com/openxla/iree
[3] https://www.tensorflow.org/xla
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Chrome Ships WebGPU
Also see the recently introduced StableHLO and its serialization format: https://github.com/openxla/stablehlo/blob/main/docs/bytecode...
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OpenXLA Is Available Now
If you mean StableHLO, then it has an MLIR dialect: https://github.com/openxla/stablehlo/blob/main/stablehlo/dia....
In the StableHLO spec, we are talking about this in more abstract terms - "StableHLO opset" - to be able to unambiguously reason about the semantics of StableHLO programs. However, in practice the StableHLO dialect is the primary implementation of the opset at the moment.
I wrote "primary implementation" because e.g. there is also ongoing work on adding StableHLO support to the TFLite flatbuffer schema: https://github.com/tensorflow/tensorflow/blob/master/tensorf.... Having an abstract notion of the StableHLO opset enables us to have a source of truth that all the implementations correspond to.
SHARK
- Llama 2 on ONNX runs locally
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[D] Confusion over AMD GPU Ai benchmarking
https://github.com/AUTOMATIC1111/stable-diffusion-webui, https://github.com/nod-ai/SHARK, those are the repos for the open source tools mentioned. u/CeFurkan has really nice tutorial videos on YouTube for stable diffusion. Automatic1111 is the most popular open source stable diffusion ui and has the biggest open source plug-in ecosystem currently. Nvidia’s compute driver is separate from normal driver and called cuda. Amd’s compute driver is called rocm. Most windows programs like games use apis like directx, Vulkan,metal, web gpu and not cuda. Most ml code was originally intended to run in on scientific computing systems that were Linux. Today the traditional windows gpu apis are tying to get better at gpu ml supports. Amd has no official windows ml code support and is Hoping that other developers figure it out for them but amd made their ml driver open source but no support for consumer graphics cards. Nvidia is proprietary ml driver but guaranteed support across all cards including consumer
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Amd Gpu not utilised
I got it working using SHARK with an AMD RX 480 on Windows 10.
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New to SD - Slow working
Here the link for shark, faster (uses vulkan) than automatic1111 with directml but has less functions https://github.com/nod-ai/SHARK
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7900 XTX Stable Diffusion Shark Nod Ai performance on Windows 10. Seem to have gotten a bump with the latest prerelease drivers 23.10.01.41
I would recommend trying out Nod AI's Shark (That is the link for the most recent 786.exe release), and see how it works for you. From others I've read, it does 512x512 pics at around 3 it/s, which I know isn't mind blowing, but it's good enough to do a pic in about 30 seconds.
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New here
Problem solve, i had it to work i simply put this nod's ai shark exe in my stabble diffusion folder and launch it instead of Webui-user -> Release nod.ai SHARK 20230623.786 · nod-ai/SHARK (github.com)
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I built the easiest-to-use desktop application for running Stable Diffusion on your PC - and it's free for all of you
How does it compare with Shark SD (I am not affiliated with it in any way)? (https://github.com/nod-ai/SHARK)
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after changing GPU from RX 470 4gb to RTX 3060 12GB, I decided to make a few cozy houses, and these are a few of them
you should if you want to run SD on your card https://github.com/nod-ai/SHARK
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20 minute load time per image on high end pc?
Forgive me for not reading you whole comment. I suspect you're version of the SD eb UI doesn't recognize the AMD GPU., so you're using the CPU. AMD GPUs only work with a few web UIs. Try Nod.ai's Shark variant
- AMD support for Microsoft® DirectML optimization of Stable Diffusion
What are some alternatives?
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
stable-diffusion-webui - Stable Diffusion web UI
SHA256-WebGPU - Implementation of sha256 in WGSL
stable-diffusion-webui-directml - Stable Diffusion web UI
wgpu-mm
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
glare-core - C++ code used in various Glare Tech Ltd products
AMD-Stable-Diffusion-ONNX-FP16 - Example code and documentation on how to get FP16 models running with ONNX on AMD GPUs [Moved to: https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16]
mach - zig game engine & graphics toolkit
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.