AITemplate
HIP-CPU
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| AITemplate | HIP-CPU | |
|---|---|---|
| 37 | 5 | |
| 4,455 | 104 | |
| 1.3% | 5.8% | |
| 8.7 | 7.2 | |
| about 20 hours ago | about 1 month ago | |
| Python | C++ | |
| Apache License 2.0 | MIT License |
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AITemplate
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Show HN: Shortbread, a web app that helps you create AI comics in minutes
VoltaML is a relatively vanilla diffusers-based backend, so its not a hairy monster to hack like you may have seen with SAI-based UIs.
The AITTemplate code is a lightly modified version of Facebook's example, code, to get rid of small issues like VRAM spikes: https://github.com/facebookincubator/AITemplate/tree/main/ex...
InvokeAI is also diffusers based, but they seem to mess with the pipeline a bit more.
And anyway, all that may be a better reference for interesting features rather than a backend to try and adopt.
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List of all the ways to improve performance for stable diffusion.
let me know if you discover any more ways to improve SD. I am currently looking into facebooks AITemplate : https://github.com/facebookincubator/AITemplate
- [R] AITemplate Python to AMD compiler {META}
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Nearly 2x speedup for SD rendering using AITemplate
Link to AITemplate itself: https://github.com/facebookincubator/AITemplate
- Render a neural network into CUDA/HIP code
- Render neural network into CUDA/HIP code
- AITemplate: 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.
- A1111 vs Olive vs AITemplate.
HIP-CPU
- HIP CPU
- [P] Pure C/C++ port of OpenAI's Whisper
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AMD publishes GPUFORT as Open Source to address CUDA’s dominance
If I'm reading this right, this is Fortran's equivalent of HIP, i.e. a way to (semi-)automatically convert CUDA-based solution to a more backend-independent one so that the same source can be run both on CUDA and ROCm GPUs (and potentially more; e.g. they also have an experimental CPU backend).
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Test Coverage with CUDA
So, I know that you asked about cuda, but this might actually be possible in hip, and you can convert your code to hip relatively easily. The path would be to use the CPU implementation (https://github.com/ROCm-Developer-Tools/HIP-CPU) and then run your code coverage on that.
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
nebuly - The user analytics platform for LLMs
libcudacxx - [ARCHIVED] The C++ Standard Library for your entire system. See https://github.com/NVIDIA/cccl
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
rocFFT - Next generation FFT implementation for ROCm
voltaML - ⚡VoltaML is a lightweight library to convert and run your ML/DL deep learning models in high performance inference runtimes like TensorRT, TorchScript, ONNX and TVM.
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
stable-diffusion-tensorflow - Stable Diffusion in TensorFlow / Keras
stdgpu - stdgpu: Efficient STL-like Data Structures on the GPU
rocm-gfx803
XNNPACK - High-efficiency floating-point neural network inference operators for mobile, server, and Web