D M D2

Introduction

DMD2 (Improved Distribution Matching Distillation) is a model designed for fast image synthesis using a diffusion process. The project is a collaboration involving researchers from MIT and other institutions. It aims to improve image generation efficiency through advanced diffusion techniques.

Architecture

The architecture uses a diffusion pipeline with models such as UNet2DConditionModel and LCMScheduler. It supports multiple generation configurations, including four-step and one-step models, leveraging the Stable Diffusion framework.

Training

The training process involves the use of a standard diffuser pipeline. For training, different timesteps are utilized than those used for inference. The model benefits from a variety of advanced techniques and neural network configurations to enhance image synthesis.

Guide: Running Locally

Basic Steps

1. Install Dependencies: Ensure you have Python installed and set up a virtual environment if necessary. Install required libraries like torch, diffusers, and huggingface_hub.
2. Load Models: Use the provided Python scripts to load models from the Hugging Face Hub with pre-trained weights.
3. Generate Images: Run the scripts with a given prompt to generate images. Adjust parameters like guidance_scale and timesteps as needed.
4. Save Outputs: The generated images can be saved locally for further use.

Cloud GPUs

For optimal performance, especially with large models and datasets, consider utilizing cloud GPUs from providers like AWS EC2, Google Cloud, or Azure.

License

The DMD2 project is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. This restricts commercial use and requires attribution for any derived works. More information can be found at Creative Commons License.