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# LTX2VideoDiffusionDecoderModel

The diffusion video decoder introduced in LTX-2.5 by Lightricks. Neighborhood-attention stages
upsample the latent into a context volume, and a final stage denoises pixels conditioned on that context.

It is a decoder, not an autoencoder: encoding stays with [`AutoencoderKLLTX2Video`], whose latent space this
consumes unchanged, so latents are interchangeable between the convolutional decoder and this one. Because it is
itself a diffusion model it is driven by [`LTX2VideoDiffusionDecodePipeline`] rather than being passed as a
pipeline's `vae`: run any LTX-2 pipeline with `output_type="latent"`, then decode.

```python
import torch
from diffusers import LTX2Pipeline, LTX2VideoDiffusionDecodePipeline, LTX2VideoDiffusionDecoderModel

pipe = LTX2Pipeline.from_pretrained("Lightricks/LTX-2.5-Diffusers", dtype=torch.bfloat16).to("cuda")
latents = pipe(prompt="a potter shaping a clay vase", output_type="latent").frames

decoder = LTX2VideoDiffusionDecoderModel.from_pretrained(
    "Lightricks/LTX-2.5-Diffusers", subfolder="diffusion_decoder", dtype=torch.bfloat16
).to("cuda")
decode_pipe = LTX2VideoDiffusionDecodePipeline(diffusion_decoder=decoder, scheduler=pipe.scheduler)

# `denormalize=False`: `output_type="latent"` already applied the latent statistics, so applying them
# again here would scale every channel by its std a second time.
# The decoder also draws the noise it denoises, so decoding is only reproducible with a generator.
video = decode_pipe(
    latents, generator=torch.Generator("cuda").manual_seed(0), denormalize=False
).frames[0]
```

`vae` is an optional component on the decode pipeline: it is only consulted for the latent statistics when
`denormalize=True`, and the decoder carries its own, so a decode-only workflow does not have to load a second
autoencoder.

## Attention backends

The neighborhood-attention window is expressed as a `BlockMask`, so the decoder runs on the `flex` attention
backend by default and needs no extra dependency. PyTorch does not compile `flex_attention` unless you ask it to,
and uncompiled it materializes the full score matrix — which is impractical at full-resolution sequence lengths.
For those, either compile the decoder or switch to [NATTEN](https://github.com/SHI-Labs/NATTEN)'s kernels, which
are also what the original implementation uses. The processor fetches NATTEN from the Hub
([`shi-labs/natten`](https://huggingface.co/shi-labs/natten)) through the
[`kernels`](https://github.com/huggingface/kernels) package, so it needs `pip install kernels` rather than a local
NATTEN build:

```python
from diffusers.models.autoencoders.ltx2_diffusion_decoder import LTX2VideoVaeNeighborhoodNattenProcessor

decoder.set_attn_processor(LTX2VideoVaeNeighborhoodNattenProcessor())
```

Fetching the kernel downloads code from the Hub, so the processor raises when remote code is disabled globally with
`DIFFUSERS_DISABLE_REMOTE_CODE=true`.

Every attention module in the decoder is the same neighborhood attention (per-stage differences like the kernel
size live on the module, not the processor), so `set_attn_processor` swaps them all with one shared instance.

Switching the *backend* (`decoder.set_attention_backend(...)`) to anything but `flex` raises: no other backend
accepts the `BlockMask`. Use the NATTEN processor above instead.

## Tiling

`decoder.enable_tiling()` decodes in overlapping tiles that are blended back together, bounding peak memory by the
tile size instead of the video size. The cheap early upsampling stages still see the full latent — only the last
upsampling stage and the diffusion stage, which dominate decode memory, run per tile — so tiling changes the output
only near tile borders. Because the diffusion stage denoises each tile separately, a tiled decode does not
reproduce the untiled result exactly; the default tile and overlap sizes match the reference implementation's.
Neighborhood attention rejects any grid smaller than its kernel, so a trailing remnant tile is merged into its
neighbor rather than decoded on its own.

## LTX2VideoDiffusionDecoderModel

[[autodoc]] LTX2VideoDiffusionDecoderModel
    - decode
    - enable_tiling
    - disable_tiling
    - all