Instructions to use kandinskylab/Kandinsky-S2V-1.0-Pro-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kandinskylab/Kandinsky-S2V-1.0-Pro-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kandinskylab/Kandinsky-S2V-1.0-Pro-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Remove the Benchmarks section from the model card
Browse files
README.md
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**Kandinsky S2V 1.0 is not part of a released Diffusers version yet**; it lands as a modular pipeline.
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## Benchmarks
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EMTD, the standard semi-body benchmark; ↑ higher is better, ↓ lower is better,
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best in bold. The paper has more details and more benchmarks.
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| Model | Sync-C ↑ | Sync-D ↓ | CSIM ↑ | IQA ↑ | ASE ↑ | Min-max ↑ | Mean rank ↓ |
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| **Kandinsky S2V 1.0 Pro SD (ours)** | **8.03** | **7.65** | **0.751** | 4.72 | 3.52 | **0.876** | **1.8** |
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| MultiTalk | 7.38 | 8.04 | 0.630 | 4.52 | 3.33 | 0.423 | 4.8 |
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| InfiniteTalk | 7.24 | 8.03 | 0.733 | 4.58 | 3.41 | 0.574 | 3.8 |
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| LongCat 1.5 | 7.21 | 8.11 | 0.734 | 4.73 | **3.62** | 0.766 | 2.8 |
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| OmniAvatar | 6.82 | 8.60 | 0.502 | 4.56 | 3.24 | 0.153 | 6.0 |
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| HunyuanAvatar | 6.61 | 8.64 | 0.745 | 4.69 | 3.45 | 0.515 | 4.4 |
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| Wan-S2V | 6.06 | 8.92 | 0.724 | **4.83** | 3.60 | 0.568 | 4.4 |
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## Notes
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Euler flow matching, 50 steps, classifier-free guidance 3.0.
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**Kandinsky S2V 1.0 is not part of a released Diffusers version yet**; it lands as a modular pipeline.
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## Notes
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Euler flow matching, 50 steps, classifier-free guidance 3.0.
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