Text-to-Audio
Transformers
Safetensors
qadit
feature-extraction
diffusion
dit
audio
educational
research
custom_code
Instructions to use QuarkML/QaDiT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuarkML/QaDiT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="QuarkML/QaDiT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuarkML/QaDiT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """QaDiT Hugging Face Transformers package (trust_remote_code).""" | |
| from .configuration_qadit import QaDiTConfig | |
| from .modelling_qadit import ( | |
| QaDiTGeneratorOutput, | |
| QaDiTModel, | |
| QaDiTOutput, | |
| ) | |
| try: | |
| from .processing_qadit import QaDiTProcessor | |
| except Exception: # optional helper | |
| QaDiTProcessor = None | |
| __all__ = [ | |
| "QaDiTConfig", | |
| "QaDiTModel", | |
| "QaDiTOutput", | |
| "QaDiTGeneratorOutput", | |
| "QaDiTProcessor", | |
| ] | |