Instructions to use robin-courant/pulpmotion-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use robin-courant/pulpmotion-models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("robin-courant/pulpmotion-models", 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
Improve model card: add metadata, abstract, and setup instructions
#1
by nielsr HF Staff - opened
This PR enhances the model card for "Pulp Motion: Framing-aware multimodal camera and human motion generation" by:
- Adding
license: mit,pipeline_tag: text-to-video, andlibrary_name: diffusersto the YAML metadata for better discoverability and integration on the Hugging Face Hub. - Integrating the paper's abstract directly into the model card content.
- Linking the paper to the Hugging Face Papers page: Pulp Motion: Framing-aware multimodal camera and human motion generation.
- Incorporating the setup instructions from the Github README.
These changes ensure that users have a comprehensive overview of the model, its capabilities, and how it aligns with the Hugging Face ecosystem.
Thanks a lot!
robin-courant changed pull request status to merged