Contextualized Diffusion Models for Text-Guided Image and Video Generation

TL;DR

This paper introduces ContextDiff, a novel diffusion model that incorporates cross-modal context into both forward and reverse processes, significantly improving text-guided image and video generation quality and semantic alignment.

Contribution

The paper proposes a general contextualized diffusion framework that integrates text-visual interactions into all diffusion process steps, enhancing semantic consistency in generated visuals.

Findings

Achieves state-of-the-art results in text-to-image generation.

Enhances semantic alignment between text and generated visuals.

Effective in text-to-video editing tasks.

Abstract

Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion models primarily focus on incorporating text-visual relationships exclusively into the reverse process, often disregarding their relevance in the forward process. This inconsistency between forward and reverse processes may limit the precise conveyance of textual semantics in visual synthesis results. To address this issue, we propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample into forward and reverse processes. We propagate this context to all timesteps in the two processes to adapt their trajectories, thereby facilitating cross-modal conditional modeling. We generalize our contextualized diffusion to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing. In each task, our ContextDiff achieves new state-of-the-art performance, significantly enhancing the semantic alignment between text condition and generated samples, as evidenced by quantitative and qualitative evaluations. Our code is available at https://github.com/YangLing0818/ContextDiff