Summarize into no more than 20 words:

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Abstract: Large text-to-image diffusion models have exhibited impressive proficiency in\ngenerating high-quality images. However, when applying these models to video\ndomain, ensuring temporal consistency across video frames remains a formidable\nchallenge. This paper proposes a novel zero-shot text-guided video-to-video\ntranslation framework to adapt image models to videos. The framework includes\ntwo parts: key frame translation and full video translation. The first part\nuses an adapted diffusion model to generate key frames, with hierarchical\ncross-frame constraints applied to enforce coherence in shapes, textures and\ncolors. The second part propagates the key frames to other frames with\ntemporal-aware patch matching and frame blending. Our framework achieves global\nstyle and local texture temporal consistency at a low cost (without re-training\nor optimization). The adaptation is compatible with existing image diffusion\ntechniques, allowing our framework to take advantage of them, such as\ncustomizing a specific subject with LoRA, and introducing extra spatial\nguidance with ControlNet. Extensive experimental results demonstrate the\neffectiveness of our proposed framework over existing methods in rendering\nhigh-quality and temporally-coherent videos.
``` Please reply with exactly 7 paragraphs and separate each paragraph with two new lines. The very last paragraph must start with the word "credit".
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