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-Distilled-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers/LTX-2.3-Distilled-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-Distilled-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
File size: 505 Bytes
1cda4f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"_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
}
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