Instructions to use chenzeyang1/T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chenzeyang1/T with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("chenzeyang1/T", torch_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
| """ | |
| Data loading utilities for LLaVA training. | |
| """ | |
| from .vlm_guided_ip2p_dataset import VLMGuidedIP2PDataset, create_dataloaders | |
| __all__ = [ | |
| 'VLMGuidedIP2PDataset', | |
| 'create_dataloaders', | |
| ] | |