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README.md

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----
-license: mit
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+---
+license: mit
+---
+# VidToMe: Video Token Merging for Zero-Shot Video Editing
+
+Edit videos instantly with just a prompt! 🎥
+
+Diffusers Implementation of VidToMe is a diffusion-based pipeline for zero-shot video editing that enhances temporal consistency and reduces memory usage by merging self-attention tokens across video frames. 
+This approach allows for a harmonious video generation and editing without needing to fine-tune the model. 
+By aligning and compressing redundant tokens across frames, VidToMe ensures smooth transitions and coherent video output, improving over traditional video editing methods.
+It follows by [this paper](https://arxiv.org/abs/2312.10656).
+
+## Usage
+
+```python
+from diffusers import DiffusionPipeline
+
+# load the pretrained model
+pipeline = DiffusionPipeline.from_pretrained("jadechoghari/VidToMe", trust_remote_code=True, custom_pipeline="jadechoghari/VidToMe", sd_version="depth", device="cuda", float_precision="fp16")
+
+# Edit a video with prompts
+pipeline(
+    video_path="path/to/video.mp4", 
+    video_prompt="A serene beach scene", 
+    edit_prompt="Make the sunset more vibrant", 
+    control_type="depth", 
+    n_timesteps=50
+)
+```
+
+## Applications:
+- Zero-shot video editing for content creators
+- Video transformation using natural language prompts
+- Memory-optimized video generation for longer or complex sequences
+
+**Model Authors:**  
+- Xirui Li  
+- Chao Ma  
+- Xiaokang Yang  
+- Ming-Hsuan Yang
+
+For more check the [Github Repo](https://github.com/lixirui142/VidToMe).