DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback

Published: 09 Apr 2024, Last Modified: 09 Apr 2024SynData4CVEveryoneRevisionsBibTeXCC BY 4.0
Keywords: text-to-image synthesis, feedback, faithfulness, synthetic data
TL;DR: We introduce DreamSync, an approach to improve the faithfulness of text-to-image models, by training with synthetic data filtered by vision-language models.
Abstract: Despite their widespread success, Text-to-Image models (T2I) still struggle to produce images that are both aesthetically pleasing and faithful to the user's input text. We introduce DreamSync, a model-agnostic training algorithm by design that improves T2I models to be faithful to the text input. DreamSync builds off a recent insight from TIFA's evaluation framework—that large vision-language models (VLMs) can effectively identify the fine-grained discrepancies between generated images and the text inputs. DreamSync uses this insight to train T2I models without any labeled data; it improves T2I models using its own generations. First, it prompts the model to generate several candidate images for a given input text. Then, it uses two VLMs to select the best generation: a Visual Question Answering model that measures the alignment of generated images to the text, and another that measures the generation's aesthetic quality. After selection, we use LoRA to iteratively finetune the T2I model to guide its generation toward the selected best generations. DreamSync does not need any additional human annotation, model architecture changes, or reinforcement learning. Despite its simplicity, DreamSync improves both the semantic alignment and aesthetic appeal of two diffusion-based T2I models, evidenced by multiple benchmarks (+1.7% on TIFA, +2.9% on DSG1K, +3.4% on VILA aesthetic) and human evaluation.
Submission Number: 9