Instructions to use vidfom/lt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vidfom/lt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vidfom/lt", 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
- Xet hash:
- e776458bc3afae17baafd5465b854dc6a714de1dd3b7254d868aaf068f2f1ac9
- Size of remote file:
- 1.68 GB
- SHA256:
- f8a7a7e59e14c69d24e7c0e79b5c35f9d83497916c006e06e3f5f484eea676ba
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