DiffCollage: Parallel Generation of Large Content with Diffusion Models
This addresses the challenge of scalable content generation for applications in computer vision and graphics, offering a more efficient alternative to autoregressive methods.
The paper tackles the problem of generating large content like images and motion sequences by proposing DiffCollage, a compositional diffusion model that aggregates outputs from models trained on smaller pieces, enabling parallel generation without autoregressive steps. Experimental results show it outperforms strong autoregressive baselines in tasks such as infinite image generation and panorama creation.
We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content. Our approach is based on a factor graph representation where each factor node represents a portion of the content and a variable node represents their overlap. This representation allows us to aggregate intermediate outputs from diffusion models defined on individual nodes to generate content of arbitrary size and shape in parallel without resorting to an autoregressive generation procedure. We apply DiffCollage to various tasks, including infinite image generation, panorama image generation, and long-duration text-guided motion generation. Extensive experimental results with a comparison to strong autoregressive baselines verify the effectiveness of our approach.