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Results

• How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
• How does the model quality change? The quality of the model output might slightly vary compared to the base model.
• How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in model/smash_config.json and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
• What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.
• What is the naming convention for Pruna Huggingface models? We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
• How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.
• What are "first" metrics? Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.

Setup

1. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with nvcc --version and install with conda install nvidia/label/cuda-12.1.0::cuda.
2. Install the pruna-engine available here on Pypi. It might take up to 15 minutes to install.
   pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
3. Download the model files using one of these three options.
   • Option 1 - Use command line interface (CLI):
     bash

     1mkdir runwayml-stable-diffusion-v1-5-turbo-tiny-green-smashed 2huggingface-cli download PrunaAI/runwayml-stable-diffusion-v1-5-turbo-tiny-green-smashed --local-dir runwayml-stable-diffusion-v1-5-turbo-tiny-green-smashed --local-dir-use-symlinks False
   • Option 2 - Use Python:
     python

     1import subprocess 2repo_name = "runwayml-stable-diffusion-v1-5-turbo-tiny-green-smashed" 3subprocess.run(["mkdir", repo_name]) 4subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
   • Option 3 - Download them manually on the HuggingFace model page.
4. Load & run the model.
   python

   1from pruna_engine.PrunaModel import PrunaModel 2 3model_path = "runwayml-stable-diffusion-v1-5-turbo-tiny-green-smashed/model" # Specify the downloaded model path. 4smashed_model = PrunaModel.load_model(model_path) # Load the model. 5smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of.

Configurations

Credits & License

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