Instructions to use stabilityai/stable-video-diffusion-img2vid-xt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stabilityai/stable-video-diffusion-img2vid-xt 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("stabilityai/stable-video-diffusion-img2vid-xt", 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
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# Stable Video Diffusion Image-to-Video Model Card
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## How to Get Started with the Model
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Check out https://github.com/Stability-AI/generative-models
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license: apache-2.0
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datasets:
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- togethercomputer/RedPajama-Data-V2
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- fka/awesome-chatgpt-prompts
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- HuggingFaceH4/ultrachat_200k
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- laion/dalle-3-dataset
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- vivym/midjourney-messages
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- THUDM/AgentInstruct
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- stingning/ultrachat
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language:
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- ko
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metrics:
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- character
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- cer
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library_name: adapter-transformers
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pipeline_tag: text-classification
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tags:
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- art
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- music
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# Stable Video Diffusion Image-to-Video Model Card
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## How to Get Started with the Model
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Check out https://github.com/Stability-AI/generative-models
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