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README.md
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sdk_version: 4.5.0
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We have released the Gradio demo for **Hybrid (Trajectory + Landmark)** Controls [HERE](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid)!
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## Introduction
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This repo provides the inference Gradio demo for Trajectory Control of MOFA-Video.
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## Environment Setup
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`pip install -r requirements.txt`
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## Download checkpoints
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1. Download the pretrained checkpoints of [SVD_xt](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1) from huggingface to `./ckpts`.
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2. Download the checkpint of [MOFA-Adapter](https://huggingface.co/MyNiuuu/MOFA-Video-Traj) from huggingface to `./ckpts`.
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The final structure of checkpoints should be:
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```text
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./ckpts/
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|-- controlnet
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| |-- config.json
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| `-- diffusion_pytorch_model.safetensors
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|-- stable-video-diffusion-img2vid-xt-1-1
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| |-- feature_extractor
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| |-- ...
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| |-- image_encoder
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| |-- ...
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| |-- scheduler
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| |-- ...
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| |-- unet
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| |-- ...
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| |-- vae
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| |-- ...
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| |-- svd_xt_1_1.safetensors
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| `-- model_index.json
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```
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## Run Gradio Demo
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`python run_gradio.py`
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Please refer to the instructions on the gradio interface during the inference process.
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## Paper
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arxiv.org/abs/2405.20222
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title: Trajectory-based demo for MOFA-Video
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sdk_version: "4.5.0"
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app_file: app.py
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pinned: false
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