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
Download ltx_video/utils/conditioning_method.py from vidfom/lt: direct link, hf CLI and curl.
- Browser
- Download file 124 Bytes
-
https://huggingface.co/vidfom/lt/resolve/main/ltx_video/utils/conditioning_method.py
- Command line
-
hf download hf://vidfom/lt/ltx_video/utils/conditioning_method.py
-
curl -L -o conditioning_method.py https://huggingface.co/vidfom/lt/resolve/main/ltx_video/utils/conditioning_method.py
124 Bytes
| from enum import Enum | |
| class ConditioningMethod(Enum): | |
| UNCONDITIONAL = "unconditional" | |
| FIRST_FRAME = "first_frame" | |