x-stable-diffusion
AITemplate
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| x-stable-diffusion | AITemplate | |
|---|---|---|
| 5 | 37 | |
| 548 | 4,441 | |
| 0.0% | 1.0% | |
| 4.5 | 9.1 | |
| 5 months ago | 5 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
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x-stable-diffusion
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[D] Is there an affordable way to host a diffusers Stable Diffusion model publicly on the Internet for "real-time"-inference? (CPU or Serverless GPU?)
Cheapest would be to deploy it on your own using: https://github.com/stochasticai/x-stable-diffusion. Let me if you need more help on real-time inference.
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[D]deploy stable diffusion
However, I suggest you "accelerate" your inference first. For example, you can use open-source inference engines (see: https://github.com/stochasticai/x-stable-diffusion) to easily accelerate your inference 2x or more. That means you can generates 2x more images / $ on public clouds.
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30% Faster than xformers? voltaML vs xformers stable diffusion - NVIDIA 4090
Brilliant, the x-stable-diffusion TensorRT/ AITemplate etc. sample image suggested they weren't consistent between the optimizations at all, unless they hadn't locked the seed which would have been foolish for the test.
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Upto 2.5X speed up of Stable-diffusion/Dreambooth using one line of code with voltaML.
I was looking at this three days ago, the problem is there seems to be a huge difference in what is being generated looking at the example spread on https://github.com/stochasticai/x-stable-diffusion , whereas copying model, params, seed should be giving a near identical image.
- Using Tensor Cores for Deep Learning.
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.
What are some alternatives?
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.
stable-diffusion-webui - Stable Diffusion web UI
sd_dreambooth_extension
nebuly - The user analytics platform for LLMs
jukebox - Code for the paper "Jukebox: A Generative Model for Music"
infery-examples - A collection of demo-apps and inference scripts for various deep learning frameworks using infery (Python).
xformers - Hackable and optimized Transformers building blocks, supporting a composable construction.
sdui - Local ImGui UI for Stable Diffusion. Features embedded PNG metadata, Apple M1 fixes, result caching, img2img, and more!
stable-diffusion-tensorflow - Stable Diffusion in TensorFlow / Keras
DeepSpeed-MII - MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.