Summary of Contextualized Diffusion Models For Text-guided Image and Video Generation, by Ling Yang et al.
Contextualized Diffusion Models for Text-Guided Image and Video Generation
by Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Conditional diffusion models have shown exceptional performance in high-fidelity text-guided visual generation and editing. However, existing text-guided visual diffusion models primarily focus on incorporating text-visual relationships into the reverse process, neglecting their relevance in the forward process. This inconsistency may limit the precise conveyance of textual semantics in visual synthesis results. To address this issue, we propose ContextDiff, a novel contextualized diffusion model that incorporates cross-modal context encompassing interactions and alignments between text condition and visual sample into both forward and reverse processes. We demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing. Our ContextDiff achieves new state-of-the-art performance in each task, significantly enhancing semantic alignment between text condition and generated samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make computer-generated images using text prompts. Right now, these models are really good at making realistic pictures from text descriptions. But they’re not perfect because they don’t fully understand what the text is saying. The new model, called ContextDiff, fixes this problem by looking at how the text relates to the image as it’s being generated. This makes the images much better and more like what you would see in real life. |
Keywords
* Artificial intelligence * Alignment * Diffusion * Diffusion model * Image generation * Semantics
