Papers › LeftRefill: Filling Right Canvas based on Left Reference through Generalized...

LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion Model

19 May 2023CVPR 2024 1arXiv:2305.11577archive 2025-07-28

Chenjie Cao, Yunuo Cai, Qiaole Dong, Yikai Wang, Yanwei Fu

This paper introduces LeftRefill, an innovative approach to efficiently harness large Text-to-Image (T2I) diffusion models for reference-guided image synthesis. As the name implies, LeftRefill horizontally stitches reference and target views together as a whole input. The reference image occupies the left side, while the target canvas is positioned on the right. Then, LeftRefill paints the right-side target canvas based on the left-side reference and specific task instructions. Such a task formulation shares some similarities with contextual inpainting, akin to the actions of a human painter. This novel formulation efficiently learns both structural and textured correspondence between reference and target without other image encoders or adapters. We inject task and view information through cross-attention modules in T2I models, and further exhibit multi-view reference ability via the re-arranged self-attention modules. These enable LeftRefill to perform consistent generation as a generalized model without requiring test-time fine-tuning or model modifications. Thus, LeftRefill can be seen as a simple yet unified framework to address reference-guided synthesis. As an exemplar, we leverage LeftRefill to address two different challenges: reference-guided inpainting and novel view synthesis, based on the pre-trained StableDiffusion. Codes and models are released at https://github.com/ewrfcas/LeftRefill.

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ewrfcas/arci officialmentioned in papermentioned on GitHubpytorch report
ewrfcas/leftrefill officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
ewrfcas/pgic_inpainting officialmentioned in papermentioned on GitHubpytorch report

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Image GenerationImage InpaintingImage ManipulationNovel View SynthesisSuper-Resolution

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DiffusionInpainting

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