Text-to-Audio
Diffusers
English
music
art
song-generation
lyrics-to-song
flow-matching
direct-preference-optimization
Instructions to use declare-lab/JAM-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use declare-lab/JAM-0.5 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("declare-lab/JAM-0.5", 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
Commit History
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Improve model card: Add comprehensive content, fix links, and update metadata
Verified signatureThis PR significantly improves the model card for JAM by: * **Updating Metadata**: Adding the `license: other` tag to accurately reflect the non-commercial use restrictions detailed in the license text, and enriching `tags` with more specific keywords like `song-generation`, `lyrics-to-song`, `flow-matching`, and `direct-preference-optimization` for better discoverability. * **Enhancing Content**: Replacing the existing content with the comprehensive and well-structured information from the project's GitHub README. This includes: * Adding the "News" section. * Adding a dedicated "Paper Abstract" section for quick reference. * Adding the "The Pipeline" visual explanation. * **Fixing Links**: * Correcting the incomplete arXiv badge link to the full arXiv URL. * Adding a new badge that links directly to the Hugging Face paper page (`https://huggingface.co/papers/2507.20880`). * Updating relative image paths (e.g., `jam-teaser.png`, `jam.png`) to …5048b1d
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