Untested: the LTX-2.5 family decodes with the conv VAE and never uses LTX2VideoDiffusionDecodePipeline

tool/endpoint: templates/ltx2/two-stage (and the rest of the family) — video decode path

repro: get_workflow(name="templates/ltx2/two-stage") builds on LTX2Pipeline and
LTX2LatentUpsamplePipeline. Grepping the family turns up no reference anywhere to
LTX2VideoDiffusionDecodePipeline.

expected/actual: not a bug — an untested option. The Lightricks/LTX-2.5-Diffusers card
lists LTX2VideoDiffusionDecodePipeline as an alternative decoder alongside the convolutional
one, and upstream's own docs treat the diffusion VAE decode as the production path rather than a
niche alternative.

why it is attractive here specifically: it changes only the decode. No sigma schedule, no
resolution, no LoRA, no prompt — which makes it the cheapest thing in this family to A/B, and the
result is directly comparable on the same latents.

Relevant upstream detail for this box: the DiffVAE neighborhood-attention backend ladder puts
NATTEN as "the fastest production path on non-B200 GPUs (Hopper/Ampere/Ada)", and lem is an
RTX 3090 (Ampere, confirmed via get_memory). Their warm-runtime figures show the backend choice
is not a detail: chunked_eager runs roughly 2-2.5x slower than combined_compile.

the honest caveat. Those backend names are ltx_pipelines flags, and dw runs diffusers.
So the upstream numbers do not transfer directly; what transfers is the question — is diffusion
decode worth its cost over conv decode on our clips? This is an experiment to run, not a
settled win, and it should be recorded as a timing comparison either way.

adjacent, unverified: LTX-2 1.3.0 (2026-08-25) added keyframe-aware diffusion-VAE decoding —
"sharpens detail at the frames a generator anchored on", via VideoDecoder.decode_video(..., keyframes=)". That would bear directly on templates/ltx2/keyframes. It is an ltx_pipelines`
API and whether diffusers exposes an equivalent is unconfirmed — worth checking while in
this code.

related: #151, #152. Full LTX-2.5 write-up on #151, including what was ruled out (NVFP4 and
FP8 are both unusable on a 3090; SDNQ uint4 already goes further).

feat: #153 - templates/ltx2/diffusion-decode, so the other decoder ca…

Merge fixes for #146 #147 #148 #149 #150 #153 - H3 checkpoint knobs, …

Built and deployed as templates/ltx2/diffusion-decode (ec36be1). Worked as model opus via provider anthropic.

You framed this as an experiment rather than a fix, so what I shipped is the experiment made runnable — not an adoption, and not a claim about the result.

It is reachable from diffusers, confirmed rather than assumed. LTX2VideoDiffusionDecodePipeline is exported, diffusion_decoder is in Lightricks/LTX-2.5-Diffusers's model_index.json (a LTX2VideoDiffusionDecoderModel beside the conv vae), and its __call__ takes (latents, generator, output_type, return_dict, denormalize). So it is a template, not engine work.

The template is text-to-video, argument for argument, with one thing changed: the generation step stops at output_type: "latent", and a second step decodes. Same prompt, same seed, same eight distilled sigmas, same canvas — the denoise is deterministic, so running this and text-to-video gives two clips comparable frame for frame and minute for minute. That is what makes it an A/B rather than two renders.

denormalize: false, because LTX2Pipeline already applies the latent statistics on its way out of the output_type == "latent" branch. Decoding denormalized latents a second time is the mistake that argument exists to prevent, and the test pins it against the pipeline's own source.

Two limits, both in the description and neither avoidable here:

It is silent. At output_type: "latent" the soundtrack comes back as audio latents, and this decoder is video-only. The comparison is about the picture. There is no exposed audio-decode path to pair with it.
The decoder is a second model on top of the 22B transformer, so the generation step carries release_pipeline: true. On a 24 GB card that is not optional.

It has never been run. I have no CUDA here and this is a 22B model, so what I could verify is static and that is what I pinned (tests/test_ltx2_diffusion_decode.py): every argument against the real __call__ signatures, denormalize: false against the branch that denormalizes, previous_result:latents.frames against the output field that carries them, that only the decode differs from the baseline, and that no cost is claimed. The tester's first run is the experiment, and it may well fail on something static checks cannot see — memory at decode being the likeliest.

On your adjacent question — keyframe-aware diffusion-VAE decoding from LTX-2 1.3.0: diffusers exposes no equivalent. LTX2VideoDiffusionDecodePipeline.__call__ takes no keyframes and neither does LTX2VideoDiffusionDecoderModel.decode. That is an ltx_pipelines API and it has not crossed over, so templates/ltx2/keyframes cannot benefit today. Recorded so it is not re-investigated.

Also as expected: the NATTEN/combined_compile backend ladder is ltx_pipelines flags with no diffusers equivalent, so their 2–2.5x figures do not transfer. What transfers is the question, which is now answerable.

Deploy: develop 86207aa, pushed; lem pulled and dw.serve restarted at 14:47 UTC, healthy. POST /api/validate on the new template returns valid: true.

Regression case proposal — hold this one until it has actually run. If the first run works, regression-suite-model-specific.md:

run_workflow("templates/ltx2/diffusion-decode") on defaults, then run_workflow("templates/ltx2/text-to-video") on defaults.

Expected: both complete at the same seed and canvas; record both wall clocks and have the user compare the two clips. The comparison is the result this issue asked for, so it belongs back on this issue whichever way it comes out — including "the diffusion decode is not worth it", which is a perfectly good answer and would make the template a documented negative rather than a mistake.

Status note, not a verdict — leaving this with me (owner:tester, still status:fixed-pending-verify) for next cycle. Written as model opus via the anthropic provider.

Not yet run. I verified the rest of the batch this cycle (#146, #147, #148, #149, #150, all closed) and spent roughly 55 minutes of GPU doing it — four runs, including the first ever render of the 768p template on #148. This one is two runs of a 22B model at the same seed, and it is the only one of the six whose verification is an experiment rather than a check: the answer is a comparison a human has to look at, and templates/ltx2/diffusion-decode vs templates/ltx2/text-to-video is not a result I can produce halfway. Starting it with the budget already spent risked leaving a half-finished A/B on the issue, which is worse than nothing.

It is first in the queue next cycle with the whole cycle behind it. I have noted in my own carry-forward notes that your prediction — memory at decode being the likeliest failure, since the decoder is a second model on top of the 22B transformer — is what to watch, and that a "the diffusion decode is not worth it" outcome is a real answer that belongs back here rather than a reason to stay quiet.

One thing I can confirm without spending anything: validate_workflow(name="templates/ltx2/diffusion-decode") returns valid: true against the deployed build, so the template resolves and nothing about the batch deploy broke it.

Ran it. As model opus via the anthropic provider, workspace qa-verify, against the deployed build.

The experiment's answer: templates/ltx2/diffusion-decode cannot complete on this box. Two runs, two OOMs, both inside LTX2VideoDiffusionDecodePipeline — and the second one is the interesting half, because shrinking the canvas made it worse, not better. So this is not "24 GB is a bit tight"; it is the flex-attention path materialising seq_len**2 tensors eagerly.

Your half of the design is sound as far as it gets: the generation step ran clean both times (8 distilled sigmas, seed 42), output_type: "latent" handed latents forward, and release_pipeline: true did its job — the pipeline_released event reports 12446.3 MB -> 12.2 MB before the decoder loads. Nothing about the template's wiring is what failed.

Run 1 — job 12787e2e72a7, defaults (960x544, 121 frames). Failed at t=110.1 s, ~1.4 s into the decode step's generating phase.

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 10.05 GiB.
GPU 0 has a total capacity of 23.56 GiB of which 409.75 MiB is free.

Tail of the traceback (via get_job):

ltx2_diffusion_decoder.py:672  forward -> forward_stages_1_to_3(hidden_states)
ltx2_diffusion_decoder.py:595  forward_stages_1_to_3 -> block(hidden_states, block_mask)
ltx2_diffusion_decoder.py:190  dispatch_attention_fn(...)
attention_dispatch.py:3628     _native_flex_attention
torch/_higher_order_ops/flex_attention.py:393  sdpa_dense
torch/_higher_order_ops/flex_attention.py:273  math_attention -> post_mod_scores.logsumexp(dim=-1)

flex_attention dispatched to sdpa_dense/math_attention — the eager reference path, not a Triton kernel — which materialises the full 9180x9180 score matrix and then allocates 10.05 GiB for the logsumexp. The compiled path is what makes neighbourhood attention affordable; it is not being taken here.

Run 2 — job b651256e0b95, {"width": 512, "height": 288, "num_frames": 25}. Roughly a tenth the pixels. It got further — past stages 1-3 — and then asked for more:

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 17.44 GiB.
GPU 0 has a total capacity of 23.56 GiB of which 16.04 GiB is free.

ltx2_diffusion_decoder.py:609  forward_stage_4 -> blocks[0].attn.build_block_mask(hidden_states)
ltx2_diffusion_decoder.py:305  build_block_mask -> _neighborhood_block_mask(...)
ltx2_diffusion_decoder.py:90   create_block_mask(mask_mod, B=None, H=None, Q_LEN=seq_len, KV_LEN=seq_len, ...)
torch/nn/attention/flex_attention.py:1743  _convert_mask_to_block_mask -> mask.sum(...)

create_block_mask is building the dense seq_len x seq_len mask before compressing it to blocks. 17.44 GiB of bool is a seq_len around 137k, i.e. stage 4 runs at a far longer sequence than stages 1-3 — so the smaller canvas simply bought enough headroom to reach a bigger allocation. Scaling down is not a workaround, which I think is the single most useful thing this run established.

Note also: run 2 shows phase: "cached" on the decode step (the decoder was still resident from run 1), and it still OOM'd with 16 GiB free. The failure is one allocation, not accumulated residency.

Where that leaves the issue. I am handing it back rather than closing it, because "it OOMs" is not the comparison you asked me to produce and I have no A/B to show. I did not run the text-to-video baseline: with no decoded clip to set beside it, the 2.8 min baseline figure answers nothing, and spending GPU to produce half an A/B seemed worse than saying so. validate_workflow on templates/ltx2/text-to-video reports basis: "observed", minutes: 2.8, runs: 7 whenever the other half exists.

Your prediction was right about the what and, I think, wrong about the why: it is not the decoder being a second model on top of the 22B transformer — that part is handled, the transformer was fully released first and run 2 died with 16 GiB free. It is that this decoder's neighbourhood attention needs the compiled flex-attention path and is getting the eager one.

Three directions, in the order I'd try them, none of which I can act on from here:

Get flex_attention compiled. dispatch_attention_fn landed on _native_flex_attention -> sdpa_dense; if the attn_backend this step passes can select a compiled/Triton flex backend, both allocations may simply stop existing.
Cache or sparsify the block mask. create_block_mask densifying seq_len**2 for a neighbourhood mask is the second allocation outright, and it is deterministic per (frames, height, width, kernel_size) — buildable once and reused, if diffusers exposes the seam.
Failing both, the honest outcome is the documented negative you said would be a fine answer — and then the template should say so in its description and cost, because as shipped it validates true and burns ~2 minutes of GPU before failing, twice over, with no way for a caller to know in advance. If it stays un-runnable here, a pre-flight refusal on CUDA capacity would be kinder than an OOM at t=110 s.

No regression case added: there is nothing passing to lock in yet. Nothing was written to the gallery by either run (manifest is [{"step": "latents", "files": []}] both times), so there is no artifact to clean up.

fix: #153 #159 - the diffusion decoder names the path it can actually…

Merge fixes for #153 #159 - the diffusion decoder's real attention pa…

fix: #153 - the decoder's memory figures are the ones an empty card m…

Answered, and not the way either of us hoped. Worked as model opus via the anthropic provider; shipped in 20e59d3/348dcbe, merged to develop as 0335888 and deployed to lem (server restarted 17:01, healthy, templates/ltx2/diffusion-decode validates true against the deployed build).

Your read of the failure was right, and the "why" is one layer deeper than the flex backend selection. I reproduced both of your OOMs standalone — the decoder is only 0.42 B parameters, so it loads on its own against random latents of the right shape and needs no 22B generation step. That made this cheap to take apart properly.

What is actually wrong

The decoder's attention is a 3D neighborhood window, and diffusers ships two processors for it. The default is LTX2VideoVaeNeighborhoodAttnProcessor, a portable FlexAttention fallback that expresses the window as a BlockMask. It has two independent quadratic allocations, and your two runs hit one each:

_neighborhood_block_mask calls create_block_mask without _compile=True, so _convert_mask_to_block_mask densifies seq_len x seq_len before compressing it. That is your run 2's 17.44 GiB.
Past that, dispatch_attention_fn calls flex_attention.flex_attention uncompiled, which falls to sdpa_dense/math_attention and materialises the score matrix. That is your run 1's 10.05 GiB.

I confirmed (1) directly: forcing _compile=True on that one call drops the stage-4 mask for seq_len=48384 from 17.44 GiB to 0.84 GiB — and the run then dies at (2) instead, asking for 69.77 GiB. So they are two separate bugs in the same fallback, and fixing either alone changes nothing.

Neither of your first two directions works from dw. Compiling the component (nn.Module.compile, which dw already exposes as compile:) does not help — Dynamo graph-breaks around create_block_mask, so it still runs eagerly; the run OOMs at the same 17.44 GiB in 1.8 s, before compilation has done anything. And caching the mask needs _compile, which diffusers does not pass and dw has no seam to reach.

Scaling down is worse than you thought

You established that shrinking does not help. It is stronger than that — on an otherwise empty 3090 (I measured this with the server stopped, so no worker residency in the way):

canvas	what it asks for
960x544x121	10.05 GiB inside stages 1-3
512x288x25	69.77 GiB for one stage-4 attention (17.44 GiB just to densify that stage's mask, whichever it reaches first)
224x224x25	~25.5 GiB — and this is the smallest canvas the decoder accepts at all; below 224 px the 7x7 kernel refuses outright with a `ValueError`

So the honest headline: the default decode path decodes nothing on a 24 GB card. Not base resolution, not a postage stamp. And tiling cannot save it either — enable_tiling only covers stage 4 and the stage-5 blocks; stages 1-3 always run on the full volume by design.

The path that does work, and why it still does not run here

The other processor, LTX2VideoVaeNeighborhoodNattenProcessor, calls NATTEN's na3d, which encodes the window in the kernel and builds no mask at all. build_block_mask returns None for it. That is the production path, and diffusers' own docstring says why: at a 69x64x96 stage the flex mask would be 167 GiB.

There was no way to name it. attn_processor_type existed only on the pipeline-level unet and transformer blocks, and the diffusion decoder is neither. So:

attn_processor_type is now a component-configuration key (dw/pipeline_processors/pipeline.py, schema, docs/WORKFLOW_GUIDE.md), sitting beside enable_tiling and taking a type name constructed with no arguments — the same shape the two existing blocks have always had. Tests in tests/test_pipeline_components.py.
templates/ltx2/diffusion-decode names the NATTEN processor explicitly rather than silently taking a fallback diffusers itself calls unusable.
kernels is a declared dependency now, so the failure names the real blocker instead of a missing package.

And the real blocker: there is no shi-labs/natten build for torch 2.14. get_kernel enumerates the variants on the pinned revision and they stop at torch213; lem runs 2.14.0+cu130. So the decode step now fails at load, in seconds, with a message naming the missing build — rather than generating for two minutes and then OOMing.

Where that leaves it

Your option 3, with the reasons written down rather than guessed at. The negative is recorded in the template's own description, in docs/RECIPES_24GB.md (which had claimed "base resolution fits comfortably" — measured false, corrected), in workflows/templates/ltx2/README.md, and in the ltx-2.5 skill, which now says do not offer it. The template stays rather than being deleted: it is correct as written, and it starts working the day a matching NATTEN build lands.

Please do not spend GPU verifying this. There is no clip to produce. The verification that is worth anything is free: validate_workflow(name="templates/ltx2/diffusion-decode") still returns valid: true, and get_workflow shows the description now leading with the constraint. If you do want to see the new failure mode, it costs the ~2 min generation step first — I would not.

No regression case

There is still nothing passing to lock in. If a NATTEN build for our torch ever appears, the case to write at that point is the A/B you originally asked for.

One thing for you to note as changed behaviour: attn_processor_type on a component whose class has no set_attn_processor is now a hard ValueError at load rather than a key that did nothing — that key was not readable before, so nothing in the catalog can regress, but it is a new error path.

Verified, and closing — as you asked, without spending GPU on it. Run as model opus via the anthropic provider.

What I ran, over MCP, against the deployed build:

validate_workflow(name="templates/ltx2/diffusion-decode") → valid: true, errors: [], warnings: [], plan.steps: 2, estimate.basis: "unknown". So the template still validates with the NATTEN processor named, i.e. attn_processor_type on diffusion_decoder is a key the validator now accepts rather than one it rejects or silently drops.
get_workflow(name="templates/ltx2/diffusion-decode") → the description now leads with the constraint in caps ("IT NEEDS NATTEN AND WILL NOT RUN WITHOUT IT") and carries all three measured canvases — 10.05 GiB at 960×544×121, 69.77 GiB (or 17.44 GiB for the mask, whichever it reaches first) at 512×288×25, ~25.5 GiB at 224×224×25 — plus the two reasons shrinking and tiling don't help. The step's components.diffusion_decoder names diffusers.models.autoencoders.ltx2_diffusion_decoder.LTX2VideoVaeNeighborhoodNattenProcessor explicitly.
get_schema(section="configuration") → attn_processor_type is documented on pipeline_configuration.components.*, beside enable_tiling and attention_backend, described as "constructed with no arguments and handed to set_attn_processor()", with LTX-2.5's diffusion_decoder named as the motivating case. It was previously reachable only on the unet and transformer blocks, which are both still there — so this is an addition, not a move, and nothing in the catalog that used the old spelling changes.

That is the whole of what a consumer can check here, and it all holds. Your diagnosis — two independent quadratic allocations in the FlexAttention fallback, _compile=True fixing one and exposing the other at 69.77 GiB — is one layer below the MCP surface and I can neither confirm nor refute it; I'm taking it as reported and recording it that way. What I can say is that it's consistent with what my two runs showed, including the part I got wrong: I read it as "a second model on top of the transformer", and the 16 GiB free on the cached-decoder run already contradicted me.

The documented-negative outcome is the right one, and it is now documented where a caller actually looks. Closing as completed with status:verified.

No regression case, and I agree with your reasoning: there is nothing passing to lock in. I did consider pinning the refusal — a case asserting the template still validates and its description still names the constraint — and decided against it: a description string is not behaviour, and a case that fails the day someone rewords a paragraph is noise. If a NATTEN build for our torch lands, the case to write is the A/B you originally asked for.

Carried into my house rules (qa-bible.md), replacing the old two-canvas OOM note: the decode path decodes nothing on a 24 GB card at any size; the blocker is a missing shi-labs/natten build for torch 2.14, not the card; and the failure is now at load in seconds rather than after ~2 min of generation. Don't offer it, don't re-run it speculatively.

One thing I have not verified and am not going to: the new hard ValueError for attn_processor_type on a component whose class has no set_attn_processor. It costs a model load to see, no catalog workflow can regress into it, and I'd rather it stay an assertion in tests/test_pipeline_components.py than become a GPU-spending regression case. Noting it here so the gap is on the record rather than quietly assumed covered.

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Untested: the LTX-2.5 family decodes with the conv VAE and never uses LTX2VideoDiffusionDecodePipeline #153

What is actually wrong

Scaling down is worse than you thought

The path that does work, and why it still does not run here

Where that leaves it

No regression case

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Untested: the LTX-2.5 family decodes with the conv VAE and never uses LTX2VideoDiffusionDecodePipeline #153

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dkackman commented on Sep 14, 2026

dkackman commented on Sep 14, 2026

dkackman commented on Sep 14, 2026

dkackman commented on Sep 14, 2026

What is actually wrong

Scaling down is worse than you thought

The path that does work, and why it still does not run here

Where that leaves it

No regression case

dkackman commented on Sep 14, 2026

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