Authors: Yoad Tewel, Omri Kaduri, Rinon Gal, Yoni Kasten, Lior Wolf, Gal Chechik, Yuval Atzmon

Published on: February 05, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.03286

Summary

• What is new: ConsiStory introduces a training-free approach for consistent subject generation in text-to-image models, emphasizing subject consistency while promoting layout diversity.
• Why this is important: Existing text-to-image models struggle with consistently portraying the same subject across diverse prompts and handling multiple subjects.
• What the research proposes: The proposed ConsiStory approach employs a subject-driven shared attention block and correspondence-based feature injection for maintaining subject consistency and supports layout diversity.
• Results: ConsiStory demonstrates state-of-the-art performance in ensuring subject consistency and text alignment across images without any optimization steps, effectively extending to multi-subject scenarios.

Technical Details

Technological frameworks used: ConsiStory, subject-driven shared attention block, correspondence-based feature injection

Models used: Pretrained text-to-image models

Data used: Not specified

Potential Impact

Creative industries (e.g., digital art, advertising), AI-based content creation tools, and personalized merchandise companies could benefit from or be disrupted by these insights.

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