Dataset Viewer
The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.
LTX-Video 2.5 Preprocessed Dataset
Preprocessed training data for LTX-Video 2.5 (joint audio + video), stored as PyTorch tensors. Every file is a torch.save'd dict and can be loaded with:
import torch
d = torch.load("latents/10s/clip000001.pt", map_location="cpu", weights_only=True)
Structure
Clips are split by duration bucket (5s, 10s) and modality. File names are shared across folders: clipNNNNNN.pt refers to the same source clip in every folder that contains it.
| Folder | Clips (5s / 10s) | Size | Contents |
|---|---|---|---|
latents/ |
10,842 / 43,860 | ~332 GB | Video latents |
audio_latents/ |
10,842 / 43,860 | ~6.1 GB | Audio latents |
conditions/ |
10,847 / 43,860 | ~643 GB | Prompt conditioning (text embeds) |
Total: ~979 GB, 164,111 files.
File formats
latents/{5s,10s}/clipNNNNNN.pt — video latents
| Key | Type / shape | Notes |
|---|---|---|
latents |
bfloat16, (128, T, 22, 40) |
VAE latent; T = 16 (5s) or 32 (10s) |
num_frames |
int |
Latent temporal frames (T) |
height, width |
int |
Latent spatial dims: 22 x 40 |
fps |
float |
25.0 |
audio_latents/{5s,10s}/clipNNNNNN.pt — audio latents
| Key | Type / shape | Notes |
|---|---|---|
latents |
float32, (8, T, 16) |
Audio VAE latent; T = 122 (5s) or 250 (10s) |
num_time_steps |
int |
Latent time steps (T) |
frequency_bins |
int |
16 |
duration |
float |
Seconds (~4.84 for 5s clips, ~9.96 for 10s clips) |
conditions/{5s,10s}/clipNNNNNN.pt — prompt conditioning
| Key | Type / shape | Notes |
|---|---|---|
video_prompt_embeds |
bfloat16, (1024, 4096) |
Video text-encoder embeds |
audio_prompt_embeds |
bfloat16, (1024, 2048) |
Audio text-encoder embeds |
prompt_attention_mask |
int64, (1024,) |
Attention mask for the prompt tokens |
- Downloads last month
- 42