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
File size: 324 Bytes
964f845 | 1 2 3 4 5 6 7 8 9 10 11 12 | # This makes llava/utils a proper Python package
from .validation_utils import ValidationRunner, create_comparison_grid, save_validation_grid
from .progress_utils import TrainingProgressTracker
__all__ = [
"ValidationRunner",
"create_comparison_grid",
"save_validation_grid",
"TrainingProgressTracker",
]
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