Instructions to use Video-Reason/VBVR-Pro-LTX2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Video-Reason/VBVR-Pro-LTX2.3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Video-Reason/VBVR-Pro-LTX2.3", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Download text_encoder/generation_config.json from Video-Reason/VBVR-Pro-LTX2.3: direct link, hf CLI and curl.
- Browser
- Download file 168 Bytes
-
https://huggingface.co/Video-Reason/VBVR-Pro-LTX2.3/resolve/main/text_encoder/generation_config.json
- Command line
-
hf download hf://Video-Reason/VBVR-Pro-LTX2.3/text_encoder/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/Video-Reason/VBVR-Pro-LTX2.3/resolve/main/text_encoder/generation_config.json
168 Bytes
| { | |
| "cache_implementation": "hybrid", | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 1, | |
| 106 | |
| ], | |
| "top_k": 64, | |
| "top_p": 0.95, | |
| "transformers_version": "4.57.3" | |
| } | |