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About this paper
Merve Kocabas, Gege Gao, Bernhard Schölkopf, Andreas Geiger
A framework structures image synthesis as a coarse-to-fine process, exposing visually interpretable and editable intermediate states. This approach allows users to interactively guide image creation from abstract structure to fine details, achieving competitive image quality with only 4 network function evaluations per image.
Key insights
- Pretrained visual representations like DINOv2 can serve as a foundational substrate for constructing semantically meaningful and visually decodable hierarchical generative trajectories.
- By combining one-step flow matching with level-wise conditioning, efficient (L-NFE) multi-stage generation is achievable, maintaining high image quality with fine-grained control.
- Making intermediate generative states explicitly editable enables a shift towards 'trajectory-aware image synthesis,' fostering greater alignment with human-centric iterative creative processes.
Summary by alphaXiv.
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