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Diffusion-Fast

Diffusion fast is a simple and efficient pytorch-native way of optimizing Stable Diffusion XL (SDXL).

It features:

  • Running with the bfloat16 precision
  • scaled_dot_product_attention (SDPA)
  • torch.compile
  • Combining q,k,v projections for attention computation
  • Dynamic int8 quantization

Details about the optimizations and various results can be found in this blog. The example has been tested on A10, A100 as well as H100.

Pre-requisites

cd to the example folder examples/image_generation/diffusion_fast

Install dependencies and upgrade torch to nightly build (currently required)

git clone https://github.com/huggingface/diffusion-fast.git
pip install accelerate transformers diffusers peft
pip install --no-cache-dir git+https://github.com/pytorch-labs/ao@54bcd5a10d0abbe7b0c045052029257099f83fd9
pip install pandas matplotlib seaborn

Step 1: Download the Stable diffusion model

python Download_model.py

This saves the model in Base_Diffusion_model

Step 1: Generate model archive

At this stage we're creating the model archive which includes the configuration of our model in model_config.yaml. It's also the point where we need to decide if we want to deploy our model on a single or multiple GPUs. For the single GPU case we can use the default configuration that can be found in model_config.yaml.

torch-model-archiver --model-name diffusion_fast --version 1.0 --handler diffusion_fast_handler.py --config-file model_config.yaml --extra-files "diffusion-fast/utils/pipeline_utils.py" --archive-format no-archive
mv Base_Diffusion_model diffusion_fast/

Step 2: Add the model archive to model store

mkdir model_store
mv diffusion_fast model_store

Step 3: Start torchserve

torchserve --start --ts-config config.properties --model-store model_store --models diffusion_fast

Step 4: Run inference

python query.py --url "http://localhost:8080/predictions/diffusion_fast" --prompt "a photo of an astronaut riding a horse on mars"

The image generated will be written to a file output-<>.jpg