Reproduce “PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Junhyuk So, Jiwoong Shin, Chaeyeon Jang, Eunhyeok Park
The Picard Consistency Model (PCM) accelerates parallel sampling of diffusion models, reducing generation time by up to 2.71x while guaranteeing exact convergence to the original model's output. It achieves this by training a consistency model to predict the fixed-point solution of Picard iteration and employs an efficient parameter-space model switching mechanism via LoRA.
Key insights
- The principles of consistency training can be effectively applied to accelerate fixed-point iterations, enabling models to learn direct mappings to converged solutions.
- Maintaining exact convergence while accelerating a process requires a dynamic mechanism to switch between an accelerated, potentially approximated, model and the original, high-fidelity model.
- Parameter-space adaptation techniques like LoRA provide a computationally efficient method for implementing dynamic model switching, making accelerated sampling more practical.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.