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Prompt-tuning latent diffusion models for inverse problems

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arxiv 2310.01110 v1 pith:3JAGJ5XL submitted 2023-10-02 cs.LG cs.AIcs.CVstat.ML

Hyungjin Chung , Jong Chul Ye , Peyman Milanfar , Mauricio Delbracio This is my paper · ORCID
classification cs.LGcs.AIcs.CVstat.ML
keywords diffusionlatentmodelsinversemethodproblemsproblempropose
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors. Existing methods using latent diffusion models for inverse problems typically rely on simple null text prompts, which can lead to suboptimal performance. To address this limitation, we introduce a method for prompt tuning, which jointly optimizes the text embedding on-the-fly while running the reverse diffusion process. This allows us to generate images that are more faithful to the diffusion prior. In addition, we propose a method to keep the evolution of latent variables within the range space of the encoder, by projection. This helps to reduce image artifacts, a major problem when using latent diffusion models instead of pixel-based diffusion models. Our combined method, called P2L, outperforms both image- and latent-diffusion model-based inverse problem solvers on a variety of tasks, such as super-resolution, deblurring, and inpainting.

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Cited by 5 Pith papers

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