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
#1
by nielsr HF Staff - opened
This 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.
Thanks !
linwf changed pull request status to merged