diffusionq 0.1.0

quantizing your diffusion models efficiently and accurately

diffusionq

diffusionq is a Python package designed for quantizing pre-trained diffusion models. By using diffusionq, you can efficiently quantize your models to save memory and accelerate inference, making it easier to deploy these models in resource-constrained environments.

Installation

To install diffusionq, simply run the following command:

pip install diffusionq

Usage

Using diffusionq is straightforward. Once you have a pre-trained diffusion model, you can quantize it by following these steps:

from diffusionq import QuantModel

# Assuming 'model' is your pre-trained diffusion model
quant_model = QuantModel(model)

# Now 'quant_model' is the quantized version of your original model
# You can use 'quant_model' for your inference tasks

Features

Easy Integration: Seamlessly quantize pre-trained diffusion models with just one line of code. Memory Efficiency: Reduce the memory footprint of your models significantly. Faster Inference: Enjoy faster model inference, especially beneficial for deploying models on edge devices.

Contributing

Contributions to diffusionq are welcome! If you have suggestions for improvements or encounter any issues, please feel free to open an issue or submit a pull request.

Contact

For any queries or further assistance with diffusionq, please reach out to Your Name.