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
File size: 1,478 Bytes
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"_class_name": "LTX2VocoderWithBWE",
"_diffusers_version": "0.37.0.dev0",
"act_fn": "snakebeta",
"antialias": true,
"antialias_kernel_size": 12,
"antialias_ratio": 2,
"bwe_act_fn": "snakebeta",
"bwe_antialias": true,
"bwe_antialias_kernel_size": 12,
"bwe_antialias_ratio": 2,
"bwe_final_act_fn": null,
"bwe_final_bias": false,
"bwe_hidden_channels": 512,
"bwe_in_channels": 128,
"bwe_leaky_relu_negative_slope": 0.1,
"bwe_out_channels": 2,
"bwe_resnet_dilations": [
[
1,
3,
5
],
[
1,
3,
5
],
[
1,
3,
5
]
],
"bwe_resnet_kernel_sizes": [
3,
7,
11
],
"bwe_upsample_factors": [
6,
5,
2,
2,
2
],
"bwe_upsample_kernel_sizes": [
12,
11,
4,
4,
4
],
"filter_length": 512,
"final_act_fn": null,
"final_bias": false,
"hidden_channels": 1536,
"hop_length": 80,
"in_channels": 128,
"input_sampling_rate": 16000,
"leaky_relu_negative_slope": 0.1,
"num_mel_channels": 64,
"out_channels": 2,
"output_sampling_rate": 48000,
"resnet_dilations": [
[
1,
3,
5
],
[
1,
3,
5
],
[
1,
3,
5
]
],
"resnet_kernel_sizes": [
3,
7,
11
],
"upsample_factors": [
5,
2,
2,
2,
2,
2
],
"upsample_kernel_sizes": [
11,
4,
4,
4,
4,
4
],
"window_length": 512
}
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