stable-fast-colab
stable-fast
| stable-fast-colab | stable-fast | |
|---|---|---|
| 2 | 11 | |
| 5 | 1,018 | |
| - | - | |
| 6.8 | 9.3 | |
| 6 months ago | about 1 month ago | |
| Jupyter Notebook | Python | |
| - | MIT License |
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stable-fast-colab
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Has anyone managed to get TensorRT working in ComfyUI on Windows?
I found the [StableFast Github repo](https://github.com/chengzeyi/stable-fast), but it's not tested on windows and I'm unable to get it to work on windows. I'm stuck with the following error:
- Fastest SD Image Generation on Google Colab ever
stable-fast
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Has anyone managed to get TensorRT working in ComfyUI on Windows?
Download (https://github.com/chengzeyi/stable-fast/releases) and install stable-fast binary, compiled according to your system: pip install stable_fast-0.0.13.post3+torch210cu118-cp310-cp310-win_amd64.whl
- Optimum-NVIDIA - 28x faster inference in just 1 line of code !?
- stable-fast for SD inference: Faster than AITemplate, On par with TensorRT
- [N] stable-fast for SD inference: Faster than AITemplate, On par with TensorRT
- Stable-fast for SD inference: Faster than AITemplate, On par with TensorRT
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SDXL Turbo: A Real-Time Text-to-Image Generation Model
SDXL and ControlNet are already optimized, if thats what you mean: https://github.com/chengzeyi/stable-fast
(Note the links to various SD compilers).
But the whole field is moving so fast that people aren't even adopting the compilers at large.
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Getting sub 100ms refresh rate on LCMs
> already compiling
Hmm, well if you mean torch.compile, y'all should still check out stable-fast, which is claiming ~16ms/iter on a 4090:
https://github.com/chengzeyi/stable-fast#rtx-4090-512x512-ba...
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Generate images fast with SD 1.5 while typing on Gradio
Now combine this with an optimized SD implementation, like:
https://github.com/chengzeyi/stable-fast
Or AITemplate, and you are at 15FPS on a larger consumer GPU. 10 with a controlnet you can use for some motion consistency.
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S-LoRA: Serving Concurrent LoRA Adapters
Since I am sending you down the rabbit hole anyway, you should check out sfast:
https://github.com/chengzeyi/stable-fast
It's, the most promising "fast" and flexible stable diffusion implementation akin to this paper or vLLM that I know of. It doesn't have as many caveats as other implementations, like AITemplate (which is basically Turing+ and linux only) or torch.compile (basically no support for changing inputs/loras).
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🚀Announcing stable-fast v0.0.5: Speed Optimization for SDXL, Dynamic CUDA Graph
About 2 weeks ago, I released the stable-fast project, which is a lightweight inference performance optimization framework for HuggingFace Diffusers. It provides best performance while keeping the compilation dynamic and flexible, and supports ControlNet and LoRA seamlessly.
What are some alternatives?
Fooocus - Focus on prompting and generating
gpt-fast - Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
TensorRT-LLM - TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.