Instructions to use hf-internal-testing/tiny-LTXLatentUpsamplePipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-LTXLatentUpsamplePipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-LTXLatentUpsamplePipeline", 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
| { | |
| "_class_name": "LTXLatentUpsamplePipeline", | |
| "_diffusers_version": "0.39.0.dev0", | |
| "latent_upsampler": [ | |
| "diffusers", | |
| "LTXLatentUpsamplerModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLLTXVideo" | |
| ] | |
| } | |