Tuning-Free Noise Rectification for High Fidelity Image-to-Video Generation

### Model/Pipeline/Scheduler description

Applying pretrained Text-to-Video (T2V) Diffusion models to Image-to-video (I2V) generation tasks using SDEdit often results in low source image fidelity in open domains. This method achieves high source image fidelity in output videos through supplementing more precise source image information using noise interpolation during early denoising steps, resulting in a simple-to-implement, tuning-free, and plug-and-play implementation. The experimental results demonstrate the effectiveness in improving the source image fidelity of generated videos when applied to I2V generation using SDEdit with several different T2V models.

### Open source status

- [ ] The model implementation is available.
- [ ] The model weights are available (Only relevant if addition is not a scheduler).

### Provide useful links for the implementation

Website: https://noise-rectification.github.io/
Paper: https://arxiv.org/pdf/2403.02827.pdf

Start with the linked paper and project website to understand the noise-interpolation method and its required integration points. The issue names no files or tests, so first identify the relevant image-to-video SDEdit model, pipeline, or scheduler entry point in diffusers. Done should include the model implementation and, where applicable, the model weights.

Vom Indexierungsmodell aus dem Issue-Text verfasst.