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-video
ltxv
lightricks
Instructions to use Lightricks/LTX-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-2 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("Lightricks/LTX-2", torch_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") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 544 Bytes
7bdc1de | 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | {
"_class_name": "LTX2Vocoder",
"_diffusers_version": "0.37.0.dev0",
"hidden_channels": 1024,
"in_channels": 128,
"leaky_relu_negative_slope": 0.1,
"out_channels": 2,
"output_sampling_rate": 24000,
"resnet_dilations": [
[
1,
3,
5
],
[
1,
3,
5
],
[
1,
3,
5
]
],
"resnet_kernel_sizes": [
3,
7,
11
],
"upsample_factors": [
6,
5,
2,
2,
2
],
"upsample_kernel_sizes": [
16,
15,
8,
4,
4
]
}
|