Summary of Lost in Translation: Latent Concept Misalignment in Text-to-image Diffusion Models, by Juntu Zhao et al.
Lost in Translation: Latent Concept Misalignment in Text-to-Image Diffusion Models
by Juntu Zhao, Junyu Deng, Yixin Ye, Chongxuan Li, Zhijie Deng, Dequan Wang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Advancements in text-to-image diffusion models have enabled extensive downstream applications, but these models often encounter misalignment issues between text and image. For instance, when given the prompt “a tea cup of iced coke”, existing models typically generate a glass cup of iced coke due to co-occurrence patterns during training. This phenomenon is attributed to confusion in the latent semantic space, referred to as Latent Concept Misalignment (LC-Mis). To investigate this issue and develop an automated pipeline for aligning diffusion model latent semantics with text prompts, we leverage large language models (LLMs). Empirical assessments demonstrate the effectiveness of our approach, substantially reducing LC-Mis errors and enhancing the robustness and versatility of text-to-image diffusion models. Our code and dataset are available at https://github.com/RossoneriZhao/iced_coke. We utilize VQ-VAE and LLMs to tackle this challenge, with evaluations on Cifar10, Imagenet, and COCO datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about improving text-to-image models that can generate images from text descriptions. Right now, these models often get confused when the description doesn’t match what they’ve learned during training. For example, if you ask a model to draw “a tea cup of iced coke”, it might instead draw a glass cup because it’s seen those two things together before. The researchers found that this problem is caused by how the models understand words and images being mixed together in their internal workings. They developed a new way to fix this issue using large language models and tested it on different datasets, showing that it makes the models better at generating accurate images. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Prompt » Semantics
