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# [SV3D-diffusers](https://github.com/chenguolin/sv3d-diffusers) |
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 |
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This repo (https://github.com/chenguolin/sv3d-diffusers) provides scripts about: |
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1. Spatio-temporal UNet (`SV3DUNetSpatioTemporalConditionModel`) and pipeline (`StableVideo3DDiffusionPipeline`) modified from [SVD](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py) for [SV3D](https://sv3d.github.io) in the [diffusers](https://github.com/huggingface/diffusers) convention. |
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2. Converting the [Stability-AI](https://github.com/Stability-AI/generative-models)'s [SV3D-p UNet checkpoint](https://huggingface.co/stabilityai/sv3d) to the [diffusers](https://github.com/huggingface/diffusers) convention. |
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3. Infering the `SV3D-p` model with the [diffusers](https://github.com/huggingface/diffusers) library to synthesize a 21-frame orbital video around a 3D object from a single-view image (preprocessed by removing background and centering first). |
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Converted SV3D-p checkpoints have been uploaded to HuggingFace🤗 [chenguolin/sv3d-diffusers](https://huggingface.co/chenguolin/sv3d-diffusers). |
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## 🚀 Usage |
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```bash |
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git clone https://github.com/chenguolin/sv3d-diffusers.git |
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# Please install PyTorch first according to your CUDA version |
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pip3 install -r requirements.txt |
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# If you can't access to HuggingFace🤗, try: |
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# export HF_ENDPOINT=https://hf-mirror.com |
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python3 infer.py --output_dir out/ --image_path assets/images/sculpture.png --elevation 10 --half_precision --seed -1 |
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``` |
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The synthesized video will save at `out/` as a `.gif` file. |
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## 📸 Results |
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> Image preprocessing and random seed for different implementations are different, so the results are presented only for reference. |
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| Implementation | sculpture | bag | kunkun | |
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| :------------- | :------: | :----: | :----: | |
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| **SV3D-diffusers (Ours)** |  |  |  | |
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| **Official SV3D** |  |  |  | |
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## 📚 Citation |
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If you find this repo helpful, please consider giving this repository a star 🌟 and citing the original SV3D paper. |
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``` |
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@inproceedings{voleti2024sv3d, |
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author={Voleti, Vikram and Yao, Chun-Han and Boss, Mark and Letts, Adam and Pankratz, David and Tochilkin, Dmitrii and Laforte, Christian and Rombach, Robin and Jampani, Varun}, |
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title={{SV3D}: Novel Multi-view Synthesis and {3D} Generation from a Single Image using Latent Video Diffusion}, |
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booktitle={European Conference on Computer Vision (ECCV)}, |
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year={2024}, |
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} |
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``` |
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