Image-to-Video
Diffusers
Safetensors
ti2v
world-model
video-generation
camera-control
interactive-generation
diffusion
transformer
Instructions to use GD-ML/DreamX-World-5B-Cam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GD-ML/DreamX-World-5B-Cam 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("GD-ML/DreamX-World-5B-Cam", 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
Upload README.md with huggingface_hub
Browse files
README.md
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**Contact**: 📧 ally.sl@alibaba-inc.com | hongxi.wjh@alibaba-inc.com
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## License
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This model is released under the [MIT License](LICENSE).
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## License
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This model is released under the [MIT License](LICENSE).
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