Instructions to use ByteDance/ContentV-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/ContentV-8B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/ContentV-8B", 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
Add library name and pipeline tag to ContentV model card
Browse filesThis PR adds the `pipeline_tag` and `library_name` to the YAML metadata, improving discoverability and clarity. The `pipeline_tag` is set to `text-to-video` reflecting the model's functionality, and `library_name` is set to `diffusers` based on the code examples.
README.md
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license: apache-2.0
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# ContentV: Efficient Training of Video Generation Models with Limited Compute
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license: apache-2.0
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pipeline_tag: text-to-video
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library_name: diffusers
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# ContentV: Efficient Training of Video Generation Models with Limited Compute
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