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Welcome to x-stable-diffusion by Stochastic!

This project is a compilation of acceleration techniques for the Stable Diffusion model to help you generate images faster and more efficiently, saving you both time and money.

With example images and a comprehensive benchmark, you can easily choose the best technique for your needs. When you're ready to deploy, our CLI called stochasticx makes it easy to get started on your local machine. Try x-stable-diffusion and see the difference it can make for your image generation performance and cost savings.

Make sure you have Python and Docker installed on your system

1. Install the latest version of stochasticx library.

pip install stochasticx

2. Deploy the Stable Diffusion model

stochasticx stable-diffusion deploy --type aitemplate

If you don't have a Stochastic account, then the CLI will prompt you to quickly create one. It is free and just takes 1 minute Sign up →

Alternatively, you can deploy stable diffusion without our CLI by checking the steps here.

3. To perform inference with this deployed model:

stochasticx stable-diffusion inference --prompt "Riding a horse"

Check all the options of the inference command:

stochasticx stable-diffusion inference --help

4. You can get the logs of the deployment executing the following command:

stochasticx stable-diffusion logs

5. Stop and remove the deployment with this command:

stochasticx stable-diffusion stop

Change the num_inference_steps to 30. With this, you can get an image generated in 0.88 seconds.

{
  'max_seq_length': 64,
  'num_inference_steps': 30, 
  'image_size': (512, 512) 
}

You can also experiment with reducing the image_size.

• Try PyTorch - FP16 in Colab ->
• Try TensorRT in Colab ->

In each folder, we will provide a Google Colab notebook with which you can test the full flow and inference on a T4 GPU

Check the README.md of the following directories:

• AITemplate
• FlashAttention
• nvFuser
• PyTorch
• TensorRT

• AITemplate: Latest optimization framework of Meta
• TensorRT: NVIDIA TensorRT framework
• nvFuser: nvFuser with Pytorch
• FlashAttention: FlashAttention intergration in Xformers

For hardware, we used 1x40GB A100 GPU with CUDA 11.6 and the results are reported by averaging 50 runs.

The following arguments were used for image generation for all the benchmarks:

{
  'max_seq_length': 64,
  'num_inference_steps': 50, 
  'image_size': (512, 512) 
}

For batch_size 1, these are the latency results:

Note: AITemplate might not support T4 GPU yet. Check support here

The following results were obtained by varying batch_size from 1 to 24.

Note: TensorRT fails to convert UNet model from ONNX to TensorRT due to memory issues.

Click here to view the complete list of generated images

Optimization \ Prompt	Super Mario learning to fly in an airport, Painting by Leonardo Da Vinci	The Easter bunny riding a motorcycle in New York City	Drone flythrough of a tropical jungle convered in snow
PyTorch fp16
nvFuser fp16
FlashAttention fp16
TensorRT fp16
AITemplate fp16

• HuggingFace Diffusers
• AITemplate

• Discord - https://discord.gg/TgHXuSJEk6

x-stable-diffusion is a community-driven project with several AI systems engineers and researchers contributing to it.

It is currently maintained by: Toan Do, Marcos Rivera, Sarthak Langde, Subhash GN, Riccardo Romagnoli, Roman Ageev and Glenn Ko

Stochastic was founded with a vision to make deep learning optimization and deployment effortless. With our cloud platform, you can easily optimize and deploy your deep learning models with confidence, knowing that you are getting the best performance possible. Our platform automatically optimizes your models, benchmarking them on various evaluation metrics to ensure they are running at their peak.

And when it comes time to deploy, Stochastic has you covered with auto-scaling accelerated inference for models like BLOOM 176B, Stable Diffusion, and GPT-J. Plus, our platform is cloud agnostic, supporting AWS, GCP, Azure, and Kubernetes clusters

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