Instructions to use optimum-intel-internal-testing/tiny-random-ltx-video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use optimum-intel-internal-testing/tiny-random-ltx-video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("optimum-intel-internal-testing/tiny-random-ltx-video", 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 model_index.json from optimum-intel-internal-testing/tiny-random-ltx-video: direct link, hf CLI and curl.
- Browser
- Download file 411 Bytes
-
https://huggingface.co/optimum-intel-internal-testing/tiny-random-ltx-video/resolve/main/model_index.json
- Command line
-
hf download hf://optimum-intel-internal-testing/tiny-random-ltx-video/model_index.json
-
curl -L -o model_index.json https://huggingface.co/optimum-intel-internal-testing/tiny-random-ltx-video/resolve/main/model_index.json
411 Bytes
| { | |
| "_class_name": "LTXPipeline", | |
| "_diffusers_version": "0.33.1", | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "T5EncoderModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "T5TokenizerFast" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "LTXVideoTransformer3DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLLTXVideo" | |
| ] | |
| } | |