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
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README.md
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@@ -116,6 +116,7 @@ This code repository and part of the model weights are licensed under the [Apach
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@article{contentv2025,
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title = {ContentV: Efficient Training of Video Generation Models with Limited Compute},
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author = {Bytedance Douyin Content Team},
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}
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```
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@article{contentv2025,
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title = {ContentV: Efficient Training of Video Generation Models with Limited Compute},
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author = {Bytedance Douyin Content Team},
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journal = {arXiv preprint arXiv:2506.05343},
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year = {2025}
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}
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```
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