---
tags:
- ahead-of-time
- pytorch
- ltx-2.3
- zerogpu
library_name: diffusers
base_model: diffusers/LTX-2.3-Diffusers
---

# LTX-2.3 Transformer — AOTI build (in-context, **STG-capable**)

Ahead-of-time **precompiled** `transformer_blocks` of `LTX2VideoTransformer3DModel` for
**ZeroGPU (sm120 / cu130)**. Graph only (no weights) → works with base **and** distilled
LTX-2.3 and any **fused** LoRA. Dynamic over video/audio token counts.

This build compiles the **perturbation (STG) path as always-on tensor math**
(`torch.lerp(value, hidden_states, perturbation_mask)`), so ONE graph serves both
spatio-temporal-guidance (STG) and non-STG: `lerp(·, ·, ones)` is a no-op, and the real
mask blends at the STG block. Use this for the **base-model demos that keep STG**
(`stg_scale>0`, default `spatio_temporal_guidance_blocks=[28]`) — e.g. beard-removal,
day-to-night, reference-sheet. (Non-STG demos can use the plain Group A repo.)

## Use it (ZeroGPU) — load at the **root module level** + a small STG wrapper
```python
import spaces, torch
from diffusers import LTX2InContextPipeline

pipe = LTX2InContextPipeline.from_pretrained(
    "diffusers/LTX-2.3-Diffusers", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights(my_lora_state_dict, adapter_name="x")
pipe.fuse_lora(lora_scale=1.0); pipe.unload_lora_weights()
spaces.aoti_load(module=pipe.transformer, repo_id="ltx-community/LTX-2.3-Transformer-GroupC-STG-sm120-cu130-rb3")

# the compiled graph always runs the perturbation lerp, so feed a no-op ones mask when the
# transformer passes None (non-STG blocks / main pass); the STG pass still passes the real
# mask to block 28. Also force all_perturbed=False (the python skip-attention shortcut is gone).
for _blk in pipe.transformer.transformer_blocks:
    _c = _blk.forward
    def _fwd(*a, _c=_c, **kw):
        if kw.get("perturbation_mask", None) is None:
            _h = kw["hidden_states"]
            kw["perturbation_mask"] = torch.ones((_h.shape[0],1,1), device=_h.device, dtype=_h.dtype)
        kw["all_perturbed"] = False
        return _c(*a, **kw)
    _blk.forward = _fwd

@spaces.GPU
def generate(*args, **kwargs):
    return pipe(*args, **kwargs)
```

Public repo (graph only) → no token. Built with the bundled `job.py` (env `LTX_STG=1`).