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This study introduces an efficient and effective method, MeDM, that utilizespre-trained image Diffusion Models for video-to-video translation withconsistent temporal flow. The proposed framework can render videos from sceneposition information, such as a normal G-buffer, or perform text-guided editingon videos captured in real-world scenarios. We employ explicit optical flows toconstruct a practical coding that enforces physical constraints on generatedframes and mediates independent frame-wise scores. By leveraging this coding,maintaining temporal consistency in the generated videos can be framed as anoptimization problem with a closed-form solution. To ensure compatibility withStable Diffusion, we also suggest a workaround for modifying observation-spacescores in latent Diffusion Models. Notably, MeDM does not require fine-tuningor test-time optimization of the Diffusion Models. Through extensivequalitative, quantitative, and subjective experiments on various benchmarks,the study demonstrates the effectiveness and superiority of the proposedapproach. Our project page can be found at https://medm2023.github.io

MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance