Image-to-Video
Diffusers
Safetensors
LTX2Pipeline
text-to-video
video-to-video
image-text-to-video
audio-to-video
text-to-audio
video-to-audio
audio-to-audio
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
ltx-2
ltx-2-3
ltx-video
ltxv
lightricks
Instructions to use diffusers/LTX-2.3-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers/LTX-2.3-Diffusers 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("diffusers/LTX-2.3-Diffusers", 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
| { | |
| "_class_name": "AutoencoderKLLTX2Audio", | |
| "_diffusers_version": "0.37.0.dev0", | |
| "attn_resolutions": null, | |
| "base_channels": 128, | |
| "causality_axis": "height", | |
| "ch_mult": [ | |
| 1, | |
| 2, | |
| 4 | |
| ], | |
| "double_z": true, | |
| "dropout": 0.0, | |
| "in_channels": 2, | |
| "is_causal": true, | |
| "latent_channels": 8, | |
| "mel_bins": 64, | |
| "mel_hop_length": 160, | |
| "mid_block_add_attention": false, | |
| "norm_type": "pixel", | |
| "num_res_blocks": 2, | |
| "output_channels": 2, | |
| "resolution": 256, | |
| "sample_rate": 16000 | |
| } | |