Instructions to use adamdad/videocrafterv2_diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use adamdad/videocrafterv2_diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("adamdad/videocrafterv2_diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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license: apache-2.0
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language:
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- en
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tags:
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- code
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---
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# Videocrafterv2 Diffusers
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We convert the original VideoCraft2 checkpoint (available at https://github.com/AILab-CVC/VideoCrafter) to the diffusers format, which offers a more flexible inference pipeline and improved memory efficiency.
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