[tests] fix audioldm2 tests. (#13293)

fix audioldm2 tests.

.ai/AGENTS.md

@@ -24,54 +24,10 @@ Strive to write code as simple and explicit as possible.
 
 ### Models
 - All layer calls should be visible directly in `forward` — avoid helper functions that hide `nn.Module` calls.
-- Try to not introduce graph breaks as much as possible for better compatibility with `torch.compile`. For example, DO NOT arbitrarily insert operations from NumPy in the forward implementations.
-- Attention must follow the diffusers pattern: both the `Attention` class and its processor are defined in the model file. The processor's `__call__` handles the actual compute and must use `dispatch_attention_fn` rather than calling `F.scaled_dot_product_attention` directly. The attention class inherits `AttentionModuleMixin` and declares `_default_processor_cls` and `_available_processors`.
+- Avoid graph breaks for `torch.compile` compatibility — do not insert NumPy operations in forward implementations and any other patterns that can break `torch.compile` compatibility with `fullgraph=True`.
+- See the **model-integration** skill for the attention pattern, pipeline rules, test setup instructions, and other important details.
 
-```python
-# transformer_mymodel.py
+## Skills
 
-class MyModelAttnProcessor:
-    _attention_backend = None
-    _parallel_config = None
-
-    def __call__(self, attn, hidden_states, attention_mask=None, ...):
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(hidden_states)
-        value = attn.to_v(hidden_states)
-        # reshape, apply rope, etc.
-        hidden_states = dispatch_attention_fn(
-            query, key, value,
-            attn_mask=attention_mask,
-            backend=self._attention_backend,
-            parallel_config=self._parallel_config,
-        )
-        hidden_states = hidden_states.flatten(2, 3)
-        return attn.to_out[0](hidden_states)
-
-
-class MyModelAttention(nn.Module, AttentionModuleMixin):
-    _default_processor_cls = MyModelAttnProcessor
-    _available_processors = [MyModelAttnProcessor]
-
-    def __init__(self, query_dim, heads=8, dim_head=64, ...):
-        super().__init__()
-        self.to_q = nn.Linear(query_dim, heads * dim_head, bias=False)
-        self.to_k = nn.Linear(query_dim, heads * dim_head, bias=False)
-        self.to_v = nn.Linear(query_dim, heads * dim_head, bias=False)
-        self.to_out = nn.ModuleList([nn.Linear(heads * dim_head, query_dim), nn.Dropout(0.0)])
-        self.set_processor(MyModelAttnProcessor())
-
-    def forward(self, hidden_states, attention_mask=None, **kwargs):
-        return self.processor(self, hidden_states, attention_mask, **kwargs)
-```
-
-Consult the implementations in `src/diffusers/models/transformers/` if you need further references.
-
-### Pipeline
-- All pipelines must inherit from `DiffusionPipeline`. Consult implementations in `src/diffusers/pipelines` in case you need references.
-- DO NOT use an existing pipeline class (e.g., `FluxPipeline`) to override another pipeline (e.g., `FluxImg2ImgPipeline` which will be a part of the core codebase (`src`).
-
-
-### Tests
-- Slow tests gated with `@slow` and `RUN_SLOW=1`
-- All model-level tests must use the `BaseModelTesterConfig`, `ModelTesterMixin`, `MemoryTesterMixin`, `AttentionTesterMixin`, `LoraTesterMixin`, and `TrainingTesterMixin` classes initially to write the tests. Any additional tests should be added after discussions with the maintainers. Use `tests/models/transformers/test_models_transformer_flux.py` as a reference.
+Task-specific guides live in `.ai/skills/` and are loaded on demand by AI agents.
+Available skills: **model-integration** (adding/converting pipelines), **parity-testing** (debugging numerical parity).

.ai/skills/model-integration/SKILL.md

@@ -0,0 +1,167 @@
+---
+name: integrating-models
+description: >
+  Use when adding a new model or pipeline to diffusers, setting up file
+  structure for a new model, converting a pipeline to modular format, or
+  converting weights for a new version of an already-supported model.
+---
+
+## Goal
+
+Integrate a new model into diffusers end-to-end. The overall flow:
+
+1. **Gather info** — ask the user for the reference repo, setup guide, a runnable inference script, and other objectives such as standard vs modular.
+2. **Confirm the plan** — once you have everything, tell the user exactly what you'll do: e.g. "I'll integrate model X with pipeline Y into diffusers based on your script. I'll run parity tests (model-level and pipeline-level) using the `parity-testing` skill to verify numerical correctness against the reference."
+3. **Implement** — write the diffusers code (model, pipeline, scheduler if needed), convert weights, register in `__init__.py`.
+4. **Parity test** — use the `parity-testing` skill to verify component and e2e parity against the reference implementation.
+5. **Deliver a unit test** — provide a self-contained test script that runs the diffusers implementation, checks numerical output (np allclose), and saves an image/video for visual verification. This is what the user runs to confirm everything works.
+
+Work one workflow at a time — get it to full parity before moving on.
+
+## Setup — gather before starting
+
+Before writing any code, gather info in this order:
+
+1. **Reference repo** — ask for the github link. If they've already set it up locally, ask for the path. Otherwise, ask what setup steps are needed (install deps, download checkpoints, set env vars, etc.) and run through them before proceeding.
+2. **Inference script** — ask for a runnable end-to-end script for a basic workflow first (e.g. T2V). Then ask what other workflows they want to support (I2V, V2V, etc.) and agree on the full implementation order together.
+3. **Standard vs modular** — standard pipelines, modular, or both?
+
+Use `AskUserQuestion` with structured choices for step 3 when the options are known.
+
+## Standard Pipeline Integration
+
+### File structure for a new model
+
+```
+src/diffusers/
+  models/transformers/transformer_<model>.py     # The core model
+  schedulers/scheduling_<model>.py               # If model needs a custom scheduler
+  pipelines/<model>/
+    __init__.py
+    pipeline_<model>.py                          # Main pipeline
+    pipeline_<model>_<variant>.py                # Variant pipelines (e.g. pyramid, distilled)
+    pipeline_output.py                           # Output dataclass
+  loaders/lora_pipeline.py                       # LoRA mixin (add to existing file)
+
+tests/
+  models/transformers/test_models_transformer_<model>.py
+  pipelines/<model>/test_<model>.py
+  lora/test_lora_layers_<model>.py
+
+docs/source/en/api/
+  pipelines/<model>.md
+  models/<model>_transformer3d.md                # or appropriate name
+```
+
+### Integration checklist
+
+- [ ] Implement transformer model with `from_pretrained` support
+- [ ] Implement or reuse scheduler
+- [ ] Implement pipeline(s) with `__call__` method
+- [ ] Add LoRA support if applicable
+- [ ] Register all classes in `__init__.py` files (lazy imports)
+- [ ] Write unit tests (model, pipeline, LoRA)
+- [ ] Write docs
+- [ ] Run `make style` and `make quality`
+- [ ] Test parity with reference implementation (see `parity-testing` skill)
+
+### Attention pattern
+
+Attention must follow the diffusers pattern: both the `Attention` class and its processor are defined in the model file. The processor's `__call__` handles the actual compute and must use `dispatch_attention_fn` rather than calling `F.scaled_dot_product_attention` directly. The attention class inherits `AttentionModuleMixin` and declares `_default_processor_cls` and `_available_processors`.
+
+```python
+# transformer_mymodel.py
+
+class MyModelAttnProcessor:
+    _attention_backend = None
+    _parallel_config = None
+
+    def __call__(self, attn, hidden_states, attention_mask=None, ...):
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+        # reshape, apply rope, etc.
+        hidden_states = dispatch_attention_fn(
+            query, key, value,
+            attn_mask=attention_mask,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+        hidden_states = hidden_states.flatten(2, 3)
+        return attn.to_out[0](hidden_states)
+
+
+class MyModelAttention(nn.Module, AttentionModuleMixin):
+    _default_processor_cls = MyModelAttnProcessor
+    _available_processors = [MyModelAttnProcessor]
+
+    def __init__(self, query_dim, heads=8, dim_head=64, ...):
+        super().__init__()
+        self.to_q = nn.Linear(query_dim, heads * dim_head, bias=False)
+        self.to_k = nn.Linear(query_dim, heads * dim_head, bias=False)
+        self.to_v = nn.Linear(query_dim, heads * dim_head, bias=False)
+        self.to_out = nn.ModuleList([nn.Linear(heads * dim_head, query_dim), nn.Dropout(0.0)])
+        self.set_processor(MyModelAttnProcessor())
+
+    def forward(self, hidden_states, attention_mask=None, **kwargs):
+        return self.processor(self, hidden_states, attention_mask, **kwargs)
+```
+
+Consult the implementations in `src/diffusers/models/transformers/` if you need further references.
+
+### Implementation rules
+
+1. **Don't combine structural changes with behavioral changes.** Restructuring code to fit diffusers APIs (ModelMixin, ConfigMixin, etc.) is unavoidable. But don't also "improve" the algorithm, refactor computation order, or rename internal variables for aesthetics. Keep numerical logic as close to the reference as possible, even if it looks unclean. For standard → modular, this is stricter: copy loop logic verbatim and only restructure into blocks. Clean up in a separate commit after parity is confirmed.
+2. **Pipelines must inherit from `DiffusionPipeline`.** Consult implementations in `src/diffusers/pipelines` in case you need references.
+3. **Don't subclass an existing pipeline for a variant.** DO NOT use an existing pipeline class (e.g., `FluxPipeline`) to override another pipeline (e.g., `FluxImg2ImgPipeline`) which will be a part of the core codebase (`src`).
+
+### Test setup
+
+- Slow tests gated with `@slow` and `RUN_SLOW=1`
+- All model-level tests must use the `BaseModelTesterConfig`, `ModelTesterMixin`, `MemoryTesterMixin`, `AttentionTesterMixin`, `LoraTesterMixin`, and `TrainingTesterMixin` classes initially to write the tests. Any additional tests should be added after discussions with the maintainers. Use `tests/models/transformers/test_models_transformer_flux.py` as a reference.
+
+### Common diffusers conventions
+
+- Pipelines inherit from `DiffusionPipeline`
+- Models use `ModelMixin` with `register_to_config` for config serialization
+- Schedulers use `SchedulerMixin` with `ConfigMixin`
+- Use `@torch.no_grad()` on pipeline `__call__`
+- Support `output_type="latent"` for skipping VAE decode
+- Support `generator` parameter for reproducibility
+- Use `self.progress_bar(timesteps)` for progress tracking
+
+## Gotchas
+
+1. **Forgetting `__init__.py` lazy imports.** Every new class must be registered in the appropriate `__init__.py` with lazy imports. Missing this causes `ImportError` that only shows up when users try `from diffusers import YourNewClass`.
+
+2. **Using `einops` or other non-PyTorch deps.** Reference implementations often use `einops.rearrange`. Always rewrite with native PyTorch (`reshape`, `permute`, `unflatten`). Don't add the dependency. If a dependency is truly unavoidable, guard its import: `if is_my_dependency_available(): import my_dependency`.
+
+3. **Missing `make fix-copies` after `# Copied from`.** If you add `# Copied from` annotations, you must run `make fix-copies` to propagate them. CI will fail otherwise.
+
+4. **Wrong `_supports_cache_class` / `_no_split_modules`.** These class attributes control KV cache and device placement. Copy from a similar model and verify -- wrong values cause silent correctness bugs or OOM errors.
+
+5. **Missing `@torch.no_grad()` on pipeline `__call__`.** Forgetting this causes GPU OOM from gradient accumulation during inference.
+
+6. **Config serialization gaps.** Every `__init__` parameter in a `ModelMixin` subclass must be captured by `register_to_config`. If you add a new param but forget to register it, `from_pretrained` will silently use the default instead of the saved value.
+
+7. **Forgetting to update `_import_structure` and `_lazy_modules`.** The top-level `src/diffusers/__init__.py` has both -- missing either one causes partial import failures.
+
+8. **Hardcoded dtype in model forward.** Don't hardcode `torch.float32` or `torch.bfloat16` in the model's forward pass. Use the dtype of the input tensors or `self.dtype` so the model works with any precision.
+
+---
+
+## Modular Pipeline Conversion
+
+See [modular-conversion.md](modular-conversion.md) for the full guide on converting standard pipelines to modular format, including block types, build order, guider abstraction, and conversion checklist.
+
+---
+
+## Weight Conversion Tips
+
+<!-- TODO: Add concrete examples as we encounter them. Common patterns to watch for:
+  - Fused QKV weights that need splitting into separate Q, K, V
+  - Scale/shift ordering differences (reference stores [shift, scale], diffusers expects [scale, shift])
+  - Weight transpositions (linear stored as transposed conv, or vice versa)
+  - Interleaved head dimensions that need reshaping
+  - Bias terms absorbed into different layers
+  Add each with a before/after code snippet showing the conversion. -->

.ai/skills/model-integration/modular-conversion.md

@@ -0,0 +1,152 @@
+# Modular Pipeline Conversion Reference
+
+## When to use
+
+Modular pipelines break a monolithic `__call__` into composable blocks. Convert when:
+- The model supports multiple workflows (T2V, I2V, V2V, etc.)
+- Users need to swap guidance strategies (CFG, CFG-Zero*, PAG)
+- You want to share blocks across pipeline variants
+
+## File structure
+
+```
+src/diffusers/modular_pipelines/<model>/
+  __init__.py                          # Lazy imports
+  modular_pipeline.py                  # Pipeline class (tiny, mostly config)
+  encoders.py                          # Text encoder + image/video VAE encoder blocks
+  before_denoise.py                    # Pre-denoise setup blocks
+  denoise.py                           # The denoising loop blocks
+  decoders.py                          # VAE decode block
+  modular_blocks_<model>.py            # Block assembly (AutoBlocks)
+```
+
+## Block types decision tree
+
+```
+Is this a single operation?
+  YES -> ModularPipelineBlocks (leaf block)
+
+Does it run multiple blocks in sequence?
+  YES -> SequentialPipelineBlocks
+    Does it iterate (e.g. chunk loop)?
+      YES -> LoopSequentialPipelineBlocks
+
+Does it choose ONE block based on which input is present?
+  Is the selection 1:1 with trigger inputs?
+    YES -> AutoPipelineBlocks (simple trigger mapping)
+    NO  -> ConditionalPipelineBlocks (custom select_block method)
+```
+
+## Build order (easiest first)
+
+1. `decoders.py` -- Takes latents, runs VAE decode, returns images/videos
+2. `encoders.py` -- Takes prompt, returns prompt_embeds. Add image/video VAE encoder if needed
+3. `before_denoise.py` -- Timesteps, latent prep, noise setup. Each logical operation = one block
+4. `denoise.py` -- The hardest. Convert guidance to guider abstraction
+
+## Key pattern: Guider abstraction
+
+Original pipeline has guidance baked in:
+```python
+for i, t in enumerate(timesteps):
+    noise_pred = self.transformer(latents, prompt_embeds, ...)
+    if self.do_classifier_free_guidance:
+        noise_uncond = self.transformer(latents, negative_prompt_embeds, ...)
+        noise_pred = noise_uncond + scale * (noise_pred - noise_uncond)
+    latents = self.scheduler.step(noise_pred, t, latents).prev_sample
+```
+
+Modular pipeline separates concerns:
+```python
+guider_inputs = {
+    "encoder_hidden_states": (prompt_embeds, negative_prompt_embeds),
+}
+
+for i, t in enumerate(timesteps):
+    components.guider.set_state(step=i, num_inference_steps=num_steps, timestep=t)
+    guider_state = components.guider.prepare_inputs(guider_inputs)
+
+    for batch in guider_state:
+        components.guider.prepare_models(components.transformer)
+        cond_kwargs = {k: getattr(batch, k) for k in guider_inputs}
+        context_name = getattr(batch, components.guider._identifier_key)
+        with components.transformer.cache_context(context_name):
+            batch.noise_pred = components.transformer(
+                hidden_states=latents, timestep=timestep,
+                return_dict=False, **cond_kwargs, **shared_kwargs,
+            )[0]
+        components.guider.cleanup_models(components.transformer)
+
+    noise_pred = components.guider(guider_state)[0]
+    latents = components.scheduler.step(noise_pred, t, latents, generator=generator)[0]
+```
+
+## Key pattern: Chunk loops for video models
+
+Use `LoopSequentialPipelineBlocks` for outer loop:
+```python
+class ChunkDenoiseStep(LoopSequentialPipelineBlocks):
+    block_classes = [PrepareChunkStep, NoiseGenStep, DenoiseInnerStep, UpdateStep]
+```
+
+Note: blocks inside `LoopSequentialPipelineBlocks` receive `(components, block_state, k)` where `k` is the loop iteration index.
+
+## Key pattern: Workflow selection
+
+```python
+class AutoDenoise(ConditionalPipelineBlocks):
+    block_classes = [V2VDenoiseStep, I2VDenoiseStep, T2VDenoiseStep]
+    block_trigger_inputs = ["video_latents", "image_latents"]
+    default_block_name = "text2video"
+```
+
+## Standard InputParam/OutputParam templates
+
+```python
+# Inputs
+InputParam.template("prompt")              # str, required
+InputParam.template("negative_prompt")     # str, optional
+InputParam.template("image")               # PIL.Image, optional
+InputParam.template("generator")           # torch.Generator, optional
+InputParam.template("num_inference_steps") # int, default=50
+InputParam.template("latents")             # torch.Tensor, optional
+
+# Outputs
+OutputParam.template("prompt_embeds")
+OutputParam.template("negative_prompt_embeds")
+OutputParam.template("image_latents")
+OutputParam.template("latents")
+OutputParam.template("videos")
+OutputParam.template("images")
+```
+
+## ComponentSpec patterns
+
+```python
+# Heavy models - loaded from pretrained
+ComponentSpec("transformer", YourTransformerModel)
+ComponentSpec("vae", AutoencoderKL)
+
+# Lightweight objects - created inline from config
+ComponentSpec(
+    "guider",
+    ClassifierFreeGuidance,
+    config=FrozenDict({"guidance_scale": 7.5}),
+    default_creation_method="from_config"
+)
+```
+
+## Conversion checklist
+
+- [ ] Read original pipeline's `__call__` end-to-end, map stages
+- [ ] Write test scripts (reference + target) with identical seeds
+- [ ] Create file structure under `modular_pipelines/<model>/`
+- [ ] Write decoder block (simplest)
+- [ ] Write encoder blocks (text, image, video)
+- [ ] Write before_denoise blocks (timesteps, latent prep, noise)
+- [ ] Write denoise block with guider abstraction (hardest)
+- [ ] Create pipeline class with `default_blocks_name`
+- [ ] Assemble blocks in `modular_blocks_<model>.py`
+- [ ] Wire up `__init__.py` with lazy imports
+- [ ] Run `make style` and `make quality`
+- [ ] Test all workflows for parity with reference

.ai/skills/parity-testing/SKILL.md

@@ -0,0 +1,170 @@
+---
+name: testing-parity
+description: >
+  Use when debugging or verifying numerical parity between pipeline
+  implementations (e.g., research repo vs diffusers, standard vs modular).
+  Also relevant when outputs look wrong — washed out, pixelated, or have
+  visual artifacts — as these are usually parity bugs.
+---
+
+## Setup — gather before starting
+
+Before writing any test code, gather:
+
+1. **Which two implementations** are being compared (e.g. research repo → diffusers, standard → modular, or research → modular). Use `AskUserQuestion` with structured choices if not already clear.
+2. **Two equivalent runnable scripts** — one for each implementation, both expected to produce identical output given the same inputs. These scripts define what "parity" means concretely.
+
+When invoked from the `model-integration` skill, you already have context: the reference script comes from step 2 of setup, and the diffusers script is the one you just wrote. You just need to make sure both scripts are runnable and use the same inputs/seed/params.
+
+## Test strategy
+
+**Component parity (CPU/float32) -- always run, as you build.**
+Test each component before assembling the pipeline. This is the foundation -- if individual pieces are wrong, the pipeline can't be right. Each component in isolation, strict max_diff < 1e-3.
+
+Test freshly converted checkpoints and saved checkpoints.
+- **Fresh**: convert from checkpoint weights, compare against reference (catches conversion bugs)
+- **Saved**: load from saved model on disk, compare against reference (catches stale saves)
+
+Keep component test scripts around -- you will need to re-run them during pipeline debugging with different inputs or config values.
+
+Template -- one self-contained script per component, reference and diffusers side-by-side:
+```python
+@torch.inference_mode()
+def test_my_component(mode="fresh", model_path=None):
+    # 1. Deterministic input
+    gen = torch.Generator().manual_seed(42)
+    x = torch.randn(1, 3, 64, 64, generator=gen, dtype=torch.float32)
+
+    # 2. Reference: load from checkpoint, run, free
+    ref_model = ReferenceModel.from_config(config)
+    ref_model.load_state_dict(load_weights("prefix"), strict=True)
+    ref_model = ref_model.float().eval()
+    ref_out = ref_model(x).clone()
+    del ref_model
+
+    # 3. Diffusers: fresh (convert weights) or saved (from_pretrained)
+    if mode == "fresh":
+        diff_model = convert_my_component(load_weights("prefix"))
+    else:
+        diff_model = DiffusersModel.from_pretrained(model_path, torch_dtype=torch.float32)
+    diff_model = diff_model.float().eval()
+    diff_out = diff_model(x)
+    del diff_model
+
+    # 4. Compare in same script -- no saving to disk
+    max_diff = (ref_out - diff_out).abs().max().item()
+    assert max_diff < 1e-3, f"FAIL: max_diff={max_diff:.2e}"
+```
+Key points: (a) both reference and diffusers component in one script -- never split into separate scripts that save/load intermediates, (b) deterministic input via seeded generator, (c) load one model at a time to fit in CPU RAM, (d) `.clone()` the reference output before deleting the model.
+
+**E2E visual (GPU/bfloat16) -- once the pipeline is assembled.**
+Both pipelines generate independently with identical seeds/params. Save outputs and compare visually. If outputs look identical, you're done -- no need for deeper testing.
+
+**Pipeline stage tests -- only if E2E fails and you need to isolate the bug.**
+If the user already suspects where divergence is, start there. Otherwise, work through stages in order.
+
+First, **match noise generation**: the way initial noise/latents are constructed (seed handling, generator, randn call order) often differs between the two scripts. If the noise doesn't match, nothing downstream will match. Check how noise is initialized in the diffusers script — if it doesn't match the reference, temporarily change it to match. Note what you changed so it can be reverted after parity is confirmed.
+
+For small models, run on CPU/float32 for strict comparison. For large models (e.g. 22B params), CPU/float32 is impractical -- use GPU/bfloat16 with `enable_model_cpu_offload()` and relax tolerances (max_diff < 1e-1 for bfloat16 is typical for passing tests; cosine similarity > 0.9999 is a good secondary check).
+
+Test encode and decode stages first -- they're simpler and bugs there are easier to fix. Only debug the denoising loop if encode and decode both pass.
+
+The challenge: pipelines are monolithic `__call__` methods -- you can't just call "the encode part". See [checkpoint-mechanism.md](checkpoint-mechanism.md) for the checkpoint class that lets you stop, save, or inject tensors at named locations inside the pipeline.
+
+**Stage test order — encode, decode, then denoise:**
+
+- **`encode`** (test first): Stop both pipelines at `"preloop"`. Compare **every single variable** that will be consumed by the denoising loop -- not just latents and sigmas, but also prompt embeddings, attention masks, positional coordinates, connector outputs, and any conditioning inputs.
+- **`decode`** (test second, before denoise): Run the reference pipeline fully -- checkpoint the post-loop latents AND let it finish to get the decoded output. Then feed those same post-loop latents through the diffusers pipeline's decode path. Compare both numerically AND visually.
+- **`denoise`** (test last): Run both pipelines with realistic `num_steps` (e.g. 30) so the scheduler computes correct sigmas/timesteps, but stop after 2 loop iterations using `after_step_1`. Don't set `num_steps=2` -- that produces unrealistic sigma schedules.
+
+```python
+# Encode stage -- stop before the loop, compare ALL inputs:
+ref_ckpts = {"preloop": Checkpoint(save=True, stop=True)}
+run_reference_pipeline(ref_ckpts)
+ref_data = ref_ckpts["preloop"].data
+
+diff_ckpts = {"preloop": Checkpoint(save=True, stop=True)}
+run_diffusers_pipeline(diff_ckpts)
+diff_data = diff_ckpts["preloop"].data
+
+# Compare EVERY variable consumed by the denoise loop:
+compare_tensors("latents", ref_data["latents"], diff_data["latents"])
+compare_tensors("sigmas", ref_data["sigmas"], diff_data["sigmas"])
+compare_tensors("prompt_embeds", ref_data["prompt_embeds"], diff_data["prompt_embeds"])
+# ... every single tensor the transformer forward() will receive
+```
+
+**E2E-injected visual test**: Once you've identified a suspected root cause using stage tests, confirm it with an e2e-injected run -- inject the known-good tensor from reference and generate a full video. If the output looks identical to reference, you've confirmed the root cause.
+
+## Debugging technique: Injection for root-cause isolation
+
+When stage tests show divergence, **inject a known-good tensor from one pipeline into the other** to test whether the remaining code is correct.
+
+The principle: if you suspect input X is the root cause of divergence in stage S:
+1. Run the reference pipeline and capture X
+2. Run the diffusers pipeline but **replace** its X with the reference's X (via checkpoint load)
+3. Compare outputs of stage S
+
+If outputs now match: X was the root cause. If they still diverge: the bug is in the stage logic itself, not in X.
+
+| What you're testing | What you inject | Where you inject |
+|---|---|---|
+| Is the decode stage correct? | Post-loop latents from reference | Before decode |
+| Is the denoise loop correct? | Pre-loop latents from reference | Before the loop |
+| Is step N correct? | Post-step-(N-1) latents from reference | Before step N |
+
+**Per-step accumulation tracing**: When injection confirms the loop is correct but you want to understand *how* a small initial difference compounds, capture `after_step_{i}` for every step and plot the max_diff curve. A healthy curve stays bounded; an exponential blowup in later steps points to an amplification mechanism (see Pitfall #13 in [pitfalls.md](pitfalls.md)).
+
+## Debugging technique: Visual comparison via frame extraction
+
+For video pipelines, numerical metrics alone can be misleading. Extract and view individual frames:
+
+```python
+import numpy as np
+from PIL import Image
+
+def extract_frames(video_np, frame_indices):
+    """video_np: (frames, H, W, 3) float array in [0, 1]"""
+    for idx in frame_indices:
+        frame = (video_np[idx] * 255).clip(0, 255).astype(np.uint8)
+        img = Image.fromarray(frame)
+        img.save(f"frame_{idx}.png")
+
+# Compare specific frames from both pipelines
+extract_frames(ref_video, [0, 60, 120])
+extract_frames(diff_video, [0, 60, 120])
+```
+
+## Testing rules
+
+1. **Never use reference code in the diffusers test path.** Each side must use only its own code.
+2. **Never monkey-patch model internals in tests.** Do not replace `model.forward` or patch internal methods.
+3. **Debugging instrumentation must be non-destructive.** Checkpoint captures for debugging are fine, but must not alter control flow or outputs.
+4. **Prefer CPU/float32 for numerical comparison when practical.** Float32 avoids bfloat16 precision noise that obscures real bugs. But for large models (22B+), GPU/bfloat16 with `enable_model_cpu_offload()` is necessary -- use relaxed tolerances and cosine similarity as a secondary metric.
+5. **Test both fresh conversion AND saved model.** Fresh catches conversion logic bugs; saved catches stale/corrupted weights from previous runs.
+6. **Diff configs before debugging.** Before investigating any divergence, dump and compare all config values. A 30-second config diff prevents hours of debugging based on wrong assumptions.
+7. **Never modify cached/downloaded model configs directly.** Don't edit files in `~/.cache/huggingface/`. Instead, save to a local directory or open a PR on the upstream repo.
+8. **Compare ALL loop inputs in the encode test.** The preloop checkpoint must capture every single tensor the transformer forward() will receive.
+
+## Comparison utilities
+
+```python
+def compare_tensors(name: str, a: torch.Tensor, b: torch.Tensor, tol: float = 1e-3) -> bool:
+    if a.shape != b.shape:
+        print(f"  FAIL {name}: shape mismatch {a.shape} vs {b.shape}")
+        return False
+    diff = (a.float() - b.float()).abs()
+    max_diff = diff.max().item()
+    mean_diff = diff.mean().item()
+    cos = torch.nn.functional.cosine_similarity(
+        a.float().flatten().unsqueeze(0), b.float().flatten().unsqueeze(0)
+    ).item()
+    passed = max_diff < tol
+    print(f"  {'PASS' if passed else 'FAIL'} {name}: max={max_diff:.2e}, mean={mean_diff:.2e}, cos={cos:.5f}")
+    return passed
+```
+Cosine similarity is especially useful for GPU/bfloat16 tests where max_diff can be noisy -- `cos > 0.9999` is a strong signal even when max_diff exceeds tolerance.
+
+## Gotchas
+
+See [pitfalls.md](pitfalls.md) for the full list of gotchas to watch for during parity testing.

.ai/skills/parity-testing/checkpoint-mechanism.md

@@ -0,0 +1,103 @@
+# Checkpoint Mechanism for Stage Testing
+
+## Overview
+
+Pipelines are monolithic `__call__` methods -- you can't just call "the encode part". The checkpoint mechanism lets you stop, save, or inject tensors at named locations inside the pipeline.
+
+## The Checkpoint class
+
+Add a `_checkpoints` argument to both the diffusers pipeline and the reference implementation.
+
+```python
+@dataclass
+class Checkpoint:
+    save: bool = False   # capture variables into ckpt.data
+    stop: bool = False   # halt pipeline after this point
+    load: bool = False   # inject ckpt.data into local variables
+    data: dict = field(default_factory=dict)
+```
+
+## Pipeline instrumentation
+
+The pipeline accepts an optional `dict[str, Checkpoint]`. Place checkpoint calls at boundaries between pipeline stages -- after each encoder, before the denoising loop (capture all loop inputs), after each loop iteration, after the loop (capture final latents before decode).
+
+```python
+def __call__(self, prompt, ..., _checkpoints=None):
+    # --- text encoding ---
+    prompt_embeds = self.text_encoder(prompt)
+    _maybe_checkpoint(_checkpoints, "text_encoding", {
+        "prompt_embeds": prompt_embeds,
+    })
+
+    # --- prepare latents, sigmas, positions ---
+    latents = self.prepare_latents(...)
+    sigmas = self.scheduler.sigmas
+    # ...
+
+    _maybe_checkpoint(_checkpoints, "preloop", {
+        "latents": latents,
+        "sigmas": sigmas,
+        "prompt_embeds": prompt_embeds,
+        "prompt_attention_mask": prompt_attention_mask,
+        "video_coords": video_coords,
+        # capture EVERYTHING the loop needs -- every tensor the transformer
+        # forward() receives. Missing even one variable here means you can't
+        # tell if it's the source of divergence during denoise debugging.
+    })
+
+    # --- denoising loop ---
+    for i, t in enumerate(timesteps):
+        noise_pred = self.transformer(latents, t, prompt_embeds, ...)
+        latents = self.scheduler.step(noise_pred, t, latents)[0]
+
+        _maybe_checkpoint(_checkpoints, f"after_step_{i}", {
+            "latents": latents,
+        })
+
+    _maybe_checkpoint(_checkpoints, "post_loop", {
+        "latents": latents,
+    })
+
+    # --- decode ---
+    video = self.vae.decode(latents)
+    return video
+```
+
+## The helper function
+
+Each `_maybe_checkpoint` call does three things based on the Checkpoint's flags: `save` captures the local variables into `ckpt.data`, `load` injects pre-populated `ckpt.data` back into local variables, `stop` halts execution (raises an exception caught at the top level).
+
+```python
+def _maybe_checkpoint(checkpoints, name, data):
+    if not checkpoints:
+        return
+    ckpt = checkpoints.get(name)
+    if ckpt is None:
+        return
+    if ckpt.save:
+        ckpt.data.update(data)
+    if ckpt.stop:
+        raise PipelineStop  # caught at __call__ level, returns None
+```
+
+## Injection support
+
+Add `load` support at each checkpoint where you might want to inject:
+
+```python
+_maybe_checkpoint(_checkpoints, "preloop", {"latents": latents, ...})
+
+# Load support: replace local variables with injected data
+if _checkpoints:
+    ckpt = _checkpoints.get("preloop")
+    if ckpt is not None and ckpt.load:
+        latents = ckpt.data["latents"].to(device=device, dtype=latents.dtype)
+```
+
+## Key insight
+
+The checkpoint dict is passed into the pipeline and mutated in-place. After the pipeline returns (or stops early), you read back `ckpt.data` to get the captured tensors. Both pipelines save under their own key names, so the test maps between them (e.g. reference `"video_state.latent"` -> diffusers `"latents"`).
+
+## Memory management for large models
+
+For large models, free the source pipeline's GPU memory before loading the target pipeline. Clone injected tensors to CPU, delete everything else, then run the target with `enable_model_cpu_offload()`.

.ai/skills/parity-testing/pitfalls.md

@@ -0,0 +1,116 @@
+# Complete Pitfalls Reference
+
+## 1. Global CPU RNG
+`MultivariateNormal.sample()` uses the global CPU RNG, not `torch.Generator`. Must call `torch.manual_seed(seed)` before each pipeline run. A `generator=` kwarg won't help.
+
+## 2. Timestep dtype
+Many transformers expect `int64` timesteps. `get_timestep_embedding` casts to float, so `745.3` and `745` produce different embeddings. Match the reference's casting.
+
+## 3. Guidance parameter mapping
+Parameter names may differ: reference `zero_steps=1` (meaning `i <= 1`, 2 steps) vs target `zero_init_steps=2` (meaning `step < 2`, same thing). Check exact semantics.
+
+## 4. `patch_size` in noise generation
+If noise generation depends on `patch_size` (e.g. `sample_block_noise`), it must be passed through. Missing it changes noise spatial structure.
+
+## 5. Variable shadowing in nested loops
+Nested loops (stages -> chunks -> timesteps) can shadow variable names. If outer loop uses `latents` and inner loop also assigns to `latents`, scoping must match the reference.
+
+## 6. Float precision differences -- don't dismiss them
+Target may compute in float32 where reference used bfloat16. Small per-element diffs (1e-3 to 1e-2) *look* harmless but can compound catastrophically over iterative processes like denoising loops (see Pitfalls #11 and #13). Before dismissing a precision difference: (a) check whether it feeds into an iterative process, (b) if so, trace the accumulation curve over all iterations to see if it stays bounded or grows exponentially. Only truly non-iterative precision diffs (e.g. in a single-pass encoder) are safe to accept.
+
+## 7. Scheduler state reset between stages
+Some schedulers accumulate state (e.g. `model_outputs` in UniPC) that must be cleared between stages.
+
+## 8. Component access
+Standard: `self.transformer`. Modular: `components.transformer`. Missing this causes AttributeError.
+
+## 9. Guider state across stages
+In multi-stage denoising, the guider's internal state (e.g. `zero_init_steps`) may need save/restore between stages.
+
+## 10. Model storage location
+NEVER store converted models in `/tmp/` -- temporary directories get wiped on restart. Always save converted checkpoints under a persistent path in the project repo (e.g. `models/ltx23-diffusers/`).
+
+## 11. Noise dtype mismatch (causes washed-out output)
+
+Reference code often generates noise in float32 then casts to model dtype (bfloat16) before storing:
+
+```python
+noise = torch.randn(..., dtype=torch.float32, generator=gen)
+noise = noise.to(dtype=model_dtype)  # bfloat16 -- values get quantized
+```
+
+Diffusers pipelines may keep latents in float32 throughout the loop. The per-element difference is only ~1.5e-02, but this compounds over 30 denoising steps via 1/sigma amplification (Pitfall #13) and produces completely washed-out output.
+
+**Fix**: Match the reference -- generate noise in the model's working dtype:
+```python
+latent_dtype = self.transformer.dtype  # e.g. bfloat16
+latents = self.prepare_latents(..., dtype=latent_dtype, ...)
+```
+
+**Detection**: Encode stage test shows initial latent max_diff of exactly ~1.5e-02. This specific magnitude is the signature of float32->bfloat16 quantization error.
+
+## 12. RoPE position dtype
+
+RoPE cosine/sine values are sensitive to position coordinate dtype. If reference uses bfloat16 positions but diffusers uses float32, the RoPE output diverges significantly (max_diff up to 2.0). Different modalities may use different position dtypes (e.g. video bfloat16, audio float32) -- check the reference carefully.
+
+## 13. 1/sigma error amplification in Euler denoising
+
+In Euler/flow-matching, the velocity formula divides by sigma: `v = (latents - pred_x0) / sigma`. As sigma shrinks from ~1.0 (step 0) to ~0.001 (step 29), errors are amplified up to 1000x. A 1.5e-02 init difference grows linearly through mid-steps, then exponentially in final steps, reaching max_diff ~6.0. This is why dtype mismatches (Pitfalls #11, #12) that seem tiny at init produce visually broken output. Use per-step accumulation tracing to diagnose.
+
+## 14. Config value assumptions -- always diff, never assume
+
+When debugging parity, don't assume config values match code defaults. The published model checkpoint may override defaults with different values. A wrong assumption about a single config field can send you down hours of debugging in the wrong direction.
+
+**The pattern that goes wrong:**
+1. You see `param_x` has default `1` in the code
+2. The reference code also uses `param_x` with a default of `1`
+3. You assume both sides use `1` and apply a "fix" based on that
+4. But the actual checkpoint config has `param_x: 1000`, and so does the published diffusers config
+5. Your "fix" now *creates* divergence instead of fixing it
+
+**Prevention -- config diff first:**
+```python
+# Reference: read from checkpoint metadata (no model loading needed)
+from safetensors import safe_open
+import json
+ref_config = json.loads(safe_open(checkpoint_path, framework="pt").metadata()["config"])
+
+# Diffusers: read from model config
+from diffusers import MyModel
+diff_model = MyModel.from_pretrained(model_path, subfolder="transformer")
+diff_config = dict(diff_model.config)
+
+# Compare all values
+for key in sorted(set(list(ref_config.get("transformer", {}).keys()) + list(diff_config.keys()))):
+    ref_val = ref_config.get("transformer", {}).get(key, "MISSING")
+    diff_val = diff_config.get(key, "MISSING")
+    if ref_val != diff_val:
+        print(f"  DIFF {key}: ref={ref_val}, diff={diff_val}")
+```
+
+Run this **before** writing any hooks, analysis code, or fixes. It takes 30 seconds and catches wrong assumptions immediately.
+
+**When debugging divergence -- trace values, don't reason about them:**
+If two implementations diverge, hook the actual intermediate values at the point of divergence rather than reading code to figure out what the values "should" be. Code analysis builds on assumptions; value tracing reveals facts.
+
+## 15. Decoder config mismatch (causes pixelated artifacts)
+
+The upstream model config may have wrong values for decoder-specific parameters (e.g. `upsample_residual`, `upsample_type`). These control whether the decoder uses skip connections in upsampling -- getting them wrong produces severe pixelation or blocky artifacts.
+
+**Detection**: Feed identical post-loop latents through both decoders. If max pixel diff is large (PSNR < 40 dB) on CPU/float32, it's a real bug, not precision noise. Trace through decoder blocks (conv_in -> mid_block -> up_blocks) to find where divergence starts.
+
+**Fix**: Correct the config value. Don't edit cached files in `~/.cache/huggingface/` -- either save to a local model directory or open a PR on the upstream repo (see Testing Rule #7).
+
+## 16. Incomplete injection tests -- inject ALL variables or the test is invalid
+
+When doing injection tests (feeding reference tensors into the diffusers pipeline), you must inject **every** divergent input, including sigmas/timesteps. A common mistake: the preloop checkpoint saves sigmas but the injection code only loads latents and embeddings. The test then runs with different sigma schedules, making it impossible to isolate the real cause.
+
+**Prevention**: After writing injection code, verify by listing every variable the injected stage consumes and checking each one is either (a) injected from reference, or (b) confirmed identical between pipelines.
+
+## 17. bf16 connector/encoder divergence -- don't chase it
+
+When running on GPU/bfloat16, multi-layer encoders (e.g. 8-layer connector transformers) accumulate bf16 rounding noise that looks alarming (max_diff 0.3-2.7). Before investigating, re-run the component test on CPU/float32. If it passes (max_diff < 1e-4), the divergence is pure precision noise, not a code bug. Don't spend hours tracing through layers -- confirm on CPU/float32 and move on.
+
+## 18. Stale test fixtures
+
+When using saved tensors for cross-pipeline comparison, always ensure both sets of tensors were captured from the same run configuration (same seed, same config, same code version). Mixing fixtures from different runs (e.g. reference tensors from yesterday, diffusers tensors from today after a code change) creates phantom divergence that wastes debugging time. Regenerate both sides in a single test script execution.

.github/workflows/release_tests_fast.yml

@@ -4,6 +4,7 @@
 name: (Release) Fast GPU Tests on main
 
 on:
+  workflow_dispatch:
   push:
     branches:
       - "v*.*.*-release"
@@ -33,6 +34,7 @@ jobs:
       - name: Install dependencies
         run: |
           uv pip install -e ".[quality]"
+          uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
       - name: Environment
         run: |
           python utils/print_env.py
@@ -74,6 +76,7 @@ jobs:
         run: |
           uv pip install -e ".[quality]"
           uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
       - name: Environment
         run: |
           python utils/print_env.py
@@ -125,6 +128,7 @@ jobs:
         uv pip install -e ".[quality]"
         uv pip install peft@git+https://github.com/huggingface/peft.git
         uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+        uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
 
     - name: Environment
       run: |
@@ -175,6 +179,7 @@ jobs:
           uv pip install -e ".[quality]"
           uv pip install peft@git+https://github.com/huggingface/peft.git
           uv pip uninstall accelerate && uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+          uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
 
       - name: Environment
         run: |
@@ -232,6 +237,7 @@ jobs:
     - name: Install dependencies
       run: |
         uv pip install -e ".[quality,training]"
+        uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
     - name: Environment
       run: |
         python utils/print_env.py
@@ -274,6 +280,7 @@ jobs:
     - name: Install dependencies
       run: |
         uv pip install -e ".[quality,training]"
+        uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
     - name: Environment
       run: |
         python utils/print_env.py
@@ -316,6 +323,7 @@ jobs:
     - name: Install dependencies
       run: |
         uv pip install -e ".[quality,training]"
+        uv pip uninstall transformers huggingface_hub && uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
 
     - name: Environment
       run: |

.gitignore

@@ -182,4 +182,6 @@ wandb
 
 # AI agent generated symlinks
 /AGENTS.md
-/CLAUDE.md
+/CLAUDE.md
+/.agents/skills
+/.claude/skills

Makefile

@@ -103,9 +103,16 @@ post-patch:
 
 codex:
 	ln -snf .ai/AGENTS.md AGENTS.md
+	mkdir -p .agents
+	rm -rf .agents/skills
+	ln -snf ../.ai/skills .agents/skills
 
 claude:
 	ln -snf .ai/AGENTS.md CLAUDE.md
+	mkdir -p .claude
+	rm -rf .claude/skills
+	ln -snf ../.ai/skills .claude/skills
 
 clean-ai:
 	rm -f AGENTS.md CLAUDE.md
+	rm -rf .agents/skills .claude/skills

docs/source/en/conceptual/contribution.md

@@ -572,9 +572,9 @@ For documentation strings, 🧨 Diffusers follows the [Google style](https://goo
 
 The repository keeps AI-agent configuration in `.ai/` and exposes local agent files via symlinks.
 
-- **Source of truth** — edit `.ai/AGENTS.md` (and any future `.ai/skills/`)
-- **Don't edit** generated root-level `AGENTS.md` or `CLAUDE.md` — they are symlinks
+- **Source of truth** — edit files under `.ai/` (`AGENTS.md` for coding guidelines, `skills/` for on-demand task knowledge)
+- **Don't edit** generated root-level `AGENTS.md`, `CLAUDE.md`, or `.agents/skills`/`.claude/skills` — they are symlinks
 - Setup commands:
-  - `make codex` — symlink for OpenAI Codex
-  - `make claude` — symlink for Claude Code
-  - `make clean-ai` — remove generated symlinks
+  - `make codex` — symlink guidelines + skills for OpenAI Codex
+  - `make claude` — symlink guidelines + skills for Claude Code
+  - `make clean-ai` — remove all generated symlinks

docs/source/en/optimization/attention_backends.md

@@ -143,6 +143,7 @@ Refer to the table below for a complete list of available attention backends and
 | `flash_varlen` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention |
 | `flash_varlen_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention from kernels |
 | `aiter` | [AI Tensor Engine for ROCm](https://github.com/ROCm/aiter) | FlashAttention for AMD ROCm |
+| `flash_4_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-4 |
 | `_flash_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 |
 | `_flash_varlen_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 |
 | `_flash_3_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 from kernels |

scripts/convert_ltx2_to_diffusers.py

@@ -7,7 +7,7 @@ import safetensors.torch
 import torch
 from accelerate import init_empty_weights
 from huggingface_hub import hf_hub_download
-from transformers import AutoTokenizer, Gemma3ForConditionalGeneration
+from transformers import AutoTokenizer, Gemma3ForConditionalGeneration, Gemma3Processor
 
 from diffusers import (
     AutoencoderKLLTX2Audio,
@@ -17,7 +17,7 @@ from diffusers import (
     LTX2Pipeline,
     LTX2VideoTransformer3DModel,
 )
-from diffusers.pipelines.ltx2 import LTX2LatentUpsamplerModel, LTX2TextConnectors, LTX2Vocoder
+from diffusers.pipelines.ltx2 import LTX2LatentUpsamplerModel, LTX2TextConnectors, LTX2Vocoder, LTX2VocoderWithBWE
 from diffusers.utils.import_utils import is_accelerate_available
 
 
@@ -44,6 +44,12 @@ LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT = {
     "k_norm": "norm_k",
 }
 
+LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT = {
+    **LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT,
+    "audio_prompt_adaln_single": "audio_prompt_adaln",
+    "prompt_adaln_single": "prompt_adaln",
+}
+
 LTX_2_0_VIDEO_VAE_RENAME_DICT = {
     # Encoder
     "down_blocks.0": "down_blocks.0",
@@ -72,6 +78,13 @@ LTX_2_0_VIDEO_VAE_RENAME_DICT = {
     "per_channel_statistics.std-of-means": "latents_std",
 }
 
+LTX_2_3_VIDEO_VAE_RENAME_DICT = {
+    **LTX_2_0_VIDEO_VAE_RENAME_DICT,
+    # Decoder extra blocks
+    "up_blocks.7": "up_blocks.3.upsamplers.0",
+    "up_blocks.8": "up_blocks.3",
+}
+
 LTX_2_0_AUDIO_VAE_RENAME_DICT = {
     "per_channel_statistics.mean-of-means": "latents_mean",
     "per_channel_statistics.std-of-means": "latents_std",
@@ -84,10 +97,34 @@ LTX_2_0_VOCODER_RENAME_DICT = {
     "conv_post": "conv_out",
 }
 
-LTX_2_0_TEXT_ENCODER_RENAME_DICT = {
+LTX_2_3_VOCODER_RENAME_DICT = {
+    # Handle upsamplers ("ups" --> "upsamplers") due to name clash
+    "resblocks": "resnets",
+    "conv_pre": "conv_in",
+    "conv_post": "conv_out",
+    "act_post": "act_out",
+    "downsample.lowpass": "downsample",
+}
+
+LTX_2_0_CONNECTORS_KEYS_RENAME_DICT = {
+    "connectors.": "",
     "video_embeddings_connector": "video_connector",
     "audio_embeddings_connector": "audio_connector",
     "transformer_1d_blocks": "transformer_blocks",
+    "text_embedding_projection.aggregate_embed": "text_proj_in",
+    # Attention QK Norms
+    "q_norm": "norm_q",
+    "k_norm": "norm_k",
+}
+
+LTX_2_3_CONNECTORS_KEYS_RENAME_DICT = {
+    "connectors.": "",
+    "video_embeddings_connector": "video_connector",
+    "audio_embeddings_connector": "audio_connector",
+    "transformer_1d_blocks": "transformer_blocks",
+    # LTX-2.3 uses per-modality embedding projections
+    "text_embedding_projection.audio_aggregate_embed": "audio_text_proj_in",
+    "text_embedding_projection.video_aggregate_embed": "video_text_proj_in",
     # Attention QK Norms
     "q_norm": "norm_q",
     "k_norm": "norm_k",
@@ -129,23 +166,24 @@ def convert_ltx2_audio_vae_per_channel_statistics(key: str, state_dict: dict[str
     return
 
 
+def convert_ltx2_3_vocoder_upsamplers(key: str, state_dict: dict[str, Any]) -> None:
+    # Skip if not a weight, bias
+    if ".weight" not in key and ".bias" not in key:
+        return
+
+    if ".ups." in key:
+        new_key = key.replace(".ups.", ".upsamplers.")
+        param = state_dict.pop(key)
+        state_dict[new_key] = param
+    return
+
+
 LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP = {
     "video_embeddings_connector": remove_keys_inplace,
     "audio_embeddings_connector": remove_keys_inplace,
     "adaln_single": convert_ltx2_transformer_adaln_single,
 }
 
-LTX_2_0_CONNECTORS_KEYS_RENAME_DICT = {
-    "connectors.": "",
-    "video_embeddings_connector": "video_connector",
-    "audio_embeddings_connector": "audio_connector",
-    "transformer_1d_blocks": "transformer_blocks",
-    "text_embedding_projection.aggregate_embed": "text_proj_in",
-    # Attention QK Norms
-    "q_norm": "norm_q",
-    "k_norm": "norm_k",
-}
-
 LTX_2_0_VAE_SPECIAL_KEYS_REMAP = {
     "per_channel_statistics.channel": remove_keys_inplace,
     "per_channel_statistics.mean-of-stds": remove_keys_inplace,
@@ -155,13 +193,19 @@ LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP = {}
 
 LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP = {}
 
+LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP = {
+    ".ups.": convert_ltx2_3_vocoder_upsamplers,
+}
+
+LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP = {}
+
 
 def split_transformer_and_connector_state_dict(state_dict: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
     connector_prefixes = (
         "video_embeddings_connector",
         "audio_embeddings_connector",
         "transformer_1d_blocks",
-        "text_embedding_projection.aggregate_embed",
+        "text_embedding_projection",
         "connectors.",
         "video_connector",
         "audio_connector",
@@ -225,7 +269,7 @@ def get_ltx2_transformer_config(version: str) -> tuple[dict[str, Any], dict[str,
         special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP
     elif version == "2.0":
         config = {
-            "model_id": "diffusers-internal-dev/new-ltx-model",
+            "model_id": "Lightricks/LTX-2",
             "diffusers_config": {
                 "in_channels": 128,
                 "out_channels": 128,
@@ -238,6 +282,8 @@ def get_ltx2_transformer_config(version: str) -> tuple[dict[str, Any], dict[str,
                 "pos_embed_max_pos": 20,
                 "base_height": 2048,
                 "base_width": 2048,
+                "gated_attn": False,
+                "cross_attn_mod": False,
                 "audio_in_channels": 128,
                 "audio_out_channels": 128,
                 "audio_patch_size": 1,
@@ -249,6 +295,8 @@ def get_ltx2_transformer_config(version: str) -> tuple[dict[str, Any], dict[str,
                 "audio_pos_embed_max_pos": 20,
                 "audio_sampling_rate": 16000,
                 "audio_hop_length": 160,
+                "audio_gated_attn": False,
+                "audio_cross_attn_mod": False,
                 "num_layers": 48,
                 "activation_fn": "gelu-approximate",
                 "qk_norm": "rms_norm_across_heads",
@@ -263,10 +311,62 @@ def get_ltx2_transformer_config(version: str) -> tuple[dict[str, Any], dict[str,
                 "timestep_scale_multiplier": 1000,
                 "cross_attn_timestep_scale_multiplier": 1000,
                 "rope_type": "split",
+                "use_prompt_embeddings": True,
+                "perturbed_attn": False,
             },
         }
         rename_dict = LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT
         special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP
+    elif version == "2.3":
+        config = {
+            "model_id": "Lightricks/LTX-2.3",
+            "diffusers_config": {
+                "in_channels": 128,
+                "out_channels": 128,
+                "patch_size": 1,
+                "patch_size_t": 1,
+                "num_attention_heads": 32,
+                "attention_head_dim": 128,
+                "cross_attention_dim": 4096,
+                "vae_scale_factors": (8, 32, 32),
+                "pos_embed_max_pos": 20,
+                "base_height": 2048,
+                "base_width": 2048,
+                "gated_attn": True,
+                "cross_attn_mod": True,
+                "audio_in_channels": 128,
+                "audio_out_channels": 128,
+                "audio_patch_size": 1,
+                "audio_patch_size_t": 1,
+                "audio_num_attention_heads": 32,
+                "audio_attention_head_dim": 64,
+                "audio_cross_attention_dim": 2048,
+                "audio_scale_factor": 4,
+                "audio_pos_embed_max_pos": 20,
+                "audio_sampling_rate": 16000,
+                "audio_hop_length": 160,
+                "audio_gated_attn": True,
+                "audio_cross_attn_mod": True,
+                "num_layers": 48,
+                "activation_fn": "gelu-approximate",
+                "qk_norm": "rms_norm_across_heads",
+                "norm_elementwise_affine": False,
+                "norm_eps": 1e-6,
+                "caption_channels": 3840,
+                "attention_bias": True,
+                "attention_out_bias": True,
+                "rope_theta": 10000.0,
+                "rope_double_precision": True,
+                "causal_offset": 1,
+                "timestep_scale_multiplier": 1000,
+                "cross_attn_timestep_scale_multiplier": 1000,
+                "rope_type": "split",
+                "use_prompt_embeddings": False,
+                "perturbed_attn": True,
+            },
+        }
+        rename_dict = LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT
+        special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP
     return config, rename_dict, special_keys_remap
 
 
@@ -293,7 +393,7 @@ def get_ltx2_connectors_config(version: str) -> tuple[dict[str, Any], dict[str,
         }
     elif version == "2.0":
         config = {
-            "model_id": "diffusers-internal-dev/new-ltx-model",
+            "model_id": "Lightricks/LTX-2",
             "diffusers_config": {
                 "caption_channels": 3840,
                 "text_proj_in_factor": 49,
@@ -301,20 +401,52 @@ def get_ltx2_connectors_config(version: str) -> tuple[dict[str, Any], dict[str,
                 "video_connector_attention_head_dim": 128,
                 "video_connector_num_layers": 2,
                 "video_connector_num_learnable_registers": 128,
+                "video_gated_attn": False,
                 "audio_connector_num_attention_heads": 30,
                 "audio_connector_attention_head_dim": 128,
                 "audio_connector_num_layers": 2,
                 "audio_connector_num_learnable_registers": 128,
+                "audio_gated_attn": False,
                 "connector_rope_base_seq_len": 4096,
                 "rope_theta": 10000.0,
                 "rope_double_precision": True,
                 "causal_temporal_positioning": False,
                 "rope_type": "split",
+                "per_modality_projections": False,
+                "proj_bias": False,
             },
         }
-
-    rename_dict = LTX_2_0_CONNECTORS_KEYS_RENAME_DICT
-    special_keys_remap = {}
+        rename_dict = LTX_2_0_CONNECTORS_KEYS_RENAME_DICT
+        special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP
+    elif version == "2.3":
+        config = {
+            "model_id": "Lightricks/LTX-2.3",
+            "diffusers_config": {
+                "caption_channels": 3840,
+                "text_proj_in_factor": 49,
+                "video_connector_num_attention_heads": 32,
+                "video_connector_attention_head_dim": 128,
+                "video_connector_num_layers": 8,
+                "video_connector_num_learnable_registers": 128,
+                "video_gated_attn": True,
+                "audio_connector_num_attention_heads": 32,
+                "audio_connector_attention_head_dim": 64,
+                "audio_connector_num_layers": 8,
+                "audio_connector_num_learnable_registers": 128,
+                "audio_gated_attn": True,
+                "connector_rope_base_seq_len": 4096,
+                "rope_theta": 10000.0,
+                "rope_double_precision": True,
+                "causal_temporal_positioning": False,
+                "rope_type": "split",
+                "per_modality_projections": True,
+                "video_hidden_dim": 4096,
+                "audio_hidden_dim": 2048,
+                "proj_bias": True,
+            },
+        }
+        rename_dict = LTX_2_3_CONNECTORS_KEYS_RENAME_DICT
+        special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP
 
     return config, rename_dict, special_keys_remap
 
@@ -416,7 +548,7 @@ def get_ltx2_video_vae_config(
         special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP
     elif version == "2.0":
         config = {
-            "model_id": "diffusers-internal-dev/dummy-ltx2",
+            "model_id": "Lightricks/LTX-2",
             "diffusers_config": {
                 "in_channels": 3,
                 "out_channels": 3,
@@ -435,6 +567,7 @@ def get_ltx2_video_vae_config(
                 "decoder_spatio_temporal_scaling": (True, True, True),
                 "decoder_inject_noise": (False, False, False, False),
                 "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
+                "upsample_type": ("spatiotemporal", "spatiotemporal", "spatiotemporal"),
                 "upsample_residual": (True, True, True),
                 "upsample_factor": (2, 2, 2),
                 "timestep_conditioning": timestep_conditioning,
@@ -451,6 +584,44 @@ def get_ltx2_video_vae_config(
         }
         rename_dict = LTX_2_0_VIDEO_VAE_RENAME_DICT
         special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP
+    elif version == "2.3":
+        config = {
+            "model_id": "Lightricks/LTX-2.3",
+            "diffusers_config": {
+                "in_channels": 3,
+                "out_channels": 3,
+                "latent_channels": 128,
+                "block_out_channels": (256, 512, 1024, 1024),
+                "down_block_types": (
+                    "LTX2VideoDownBlock3D",
+                    "LTX2VideoDownBlock3D",
+                    "LTX2VideoDownBlock3D",
+                    "LTX2VideoDownBlock3D",
+                ),
+                "decoder_block_out_channels": (256, 512, 512, 1024),
+                "layers_per_block": (4, 6, 4, 2, 2),
+                "decoder_layers_per_block": (4, 6, 4, 2, 2),
+                "spatio_temporal_scaling": (True, True, True, True),
+                "decoder_spatio_temporal_scaling": (True, True, True, True),
+                "decoder_inject_noise": (False, False, False, False, False),
+                "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
+                "upsample_type": ("spatiotemporal", "spatiotemporal", "temporal", "spatial"),
+                "upsample_residual": (False, False, False, False),
+                "upsample_factor": (2, 2, 1, 2),
+                "timestep_conditioning": timestep_conditioning,
+                "patch_size": 4,
+                "patch_size_t": 1,
+                "resnet_norm_eps": 1e-6,
+                "encoder_causal": True,
+                "decoder_causal": False,
+                "encoder_spatial_padding_mode": "zeros",
+                "decoder_spatial_padding_mode": "zeros",
+                "spatial_compression_ratio": 32,
+                "temporal_compression_ratio": 8,
+            },
+        }
+        rename_dict = LTX_2_3_VIDEO_VAE_RENAME_DICT
+        special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP
     return config, rename_dict, special_keys_remap
 
 
@@ -485,7 +656,7 @@ def convert_ltx2_video_vae(
 def get_ltx2_audio_vae_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
     if version == "2.0":
         config = {
-            "model_id": "diffusers-internal-dev/new-ltx-model",
+            "model_id": "Lightricks/LTX-2",
             "diffusers_config": {
                 "base_channels": 128,
                 "output_channels": 2,
@@ -508,6 +679,31 @@ def get_ltx2_audio_vae_config(version: str) -> tuple[dict[str, Any], dict[str, A
         }
         rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT
         special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP
+    elif version == "2.3":
+        config = {
+            "model_id": "Lightricks/LTX-2.3",
+            "diffusers_config": {
+                "base_channels": 128,
+                "output_channels": 2,
+                "ch_mult": (1, 2, 4),
+                "num_res_blocks": 2,
+                "attn_resolutions": None,
+                "in_channels": 2,
+                "resolution": 256,
+                "latent_channels": 8,
+                "norm_type": "pixel",
+                "causality_axis": "height",
+                "dropout": 0.0,
+                "mid_block_add_attention": False,
+                "sample_rate": 16000,
+                "mel_hop_length": 160,
+                "is_causal": True,
+                "mel_bins": 64,
+                "double_z": True,
+            },  # Same config as LTX-2.0
+        }
+        rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT
+        special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP
     return config, rename_dict, special_keys_remap
 
 
@@ -540,7 +736,7 @@ def convert_ltx2_audio_vae(original_state_dict: dict[str, Any], version: str) ->
 def get_ltx2_vocoder_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
     if version == "2.0":
         config = {
-            "model_id": "diffusers-internal-dev/new-ltx-model",
+            "model_id": "Lightricks/LTX-2",
             "diffusers_config": {
                 "in_channels": 128,
                 "hidden_channels": 1024,
@@ -549,21 +745,71 @@ def get_ltx2_vocoder_config(version: str) -> tuple[dict[str, Any], dict[str, Any
                 "upsample_factors": [6, 5, 2, 2, 2],
                 "resnet_kernel_sizes": [3, 7, 11],
                 "resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+                "act_fn": "leaky_relu",
                 "leaky_relu_negative_slope": 0.1,
+                "antialias": False,
+                "final_act_fn": "tanh",
+                "final_bias": True,
                 "output_sampling_rate": 24000,
             },
         }
         rename_dict = LTX_2_0_VOCODER_RENAME_DICT
         special_keys_remap = LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP
+    elif version == "2.3":
+        config = {
+            "model_id": "Lightricks/LTX-2.3",
+            "diffusers_config": {
+                "in_channels": 128,
+                "hidden_channels": 1536,
+                "out_channels": 2,
+                "upsample_kernel_sizes": [11, 4, 4, 4, 4, 4],
+                "upsample_factors": [5, 2, 2, 2, 2, 2],
+                "resnet_kernel_sizes": [3, 7, 11],
+                "resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+                "act_fn": "snakebeta",
+                "leaky_relu_negative_slope": 0.1,
+                "antialias": True,
+                "antialias_ratio": 2,
+                "antialias_kernel_size": 12,
+                "final_act_fn": None,
+                "final_bias": False,
+                "bwe_in_channels": 128,
+                "bwe_hidden_channels": 512,
+                "bwe_out_channels": 2,
+                "bwe_upsample_kernel_sizes": [12, 11, 4, 4, 4],
+                "bwe_upsample_factors": [6, 5, 2, 2, 2],
+                "bwe_resnet_kernel_sizes": [3, 7, 11],
+                "bwe_resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+                "bwe_act_fn": "snakebeta",
+                "bwe_leaky_relu_negative_slope": 0.1,
+                "bwe_antialias": True,
+                "bwe_antialias_ratio": 2,
+                "bwe_antialias_kernel_size": 12,
+                "bwe_final_act_fn": None,
+                "bwe_final_bias": False,
+                "filter_length": 512,
+                "hop_length": 80,
+                "window_length": 512,
+                "num_mel_channels": 64,
+                "input_sampling_rate": 16000,
+                "output_sampling_rate": 48000,
+            },
+        }
+        rename_dict = LTX_2_3_VOCODER_RENAME_DICT
+        special_keys_remap = LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP
     return config, rename_dict, special_keys_remap
 
 
 def convert_ltx2_vocoder(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]:
     config, rename_dict, special_keys_remap = get_ltx2_vocoder_config(version)
     diffusers_config = config["diffusers_config"]
+    if version == "2.3":
+        vocoder_cls = LTX2VocoderWithBWE
+    else:
+        vocoder_cls = LTX2Vocoder
 
     with init_empty_weights():
-        vocoder = LTX2Vocoder.from_config(diffusers_config)
+        vocoder = vocoder_cls.from_config(diffusers_config)
 
     # Handle official code --> diffusers key remapping via the remap dict
     for key in list(original_state_dict.keys()):
@@ -594,6 +840,18 @@ def get_ltx2_spatial_latent_upsampler_config(version: str):
             "spatial_upsample": True,
             "temporal_upsample": False,
             "rational_spatial_scale": 2.0,
+            "use_rational_resampler": True,
+        }
+    elif version == "2.3":
+        config = {
+            "in_channels": 128,
+            "mid_channels": 1024,
+            "num_blocks_per_stage": 4,
+            "dims": 3,
+            "spatial_upsample": True,
+            "temporal_upsample": False,
+            "rational_spatial_scale": 2.0,
+            "use_rational_resampler": False,
         }
     else:
         raise ValueError(f"Unsupported version: {version}")
@@ -651,13 +909,17 @@ def get_model_state_dict_from_combined_ckpt(combined_ckpt: dict[str, Any], prefi
     model_state_dict = {}
     for param_name, param in combined_ckpt.items():
         if param_name.startswith(prefix):
-            model_state_dict[param_name.replace(prefix, "")] = param
+            model_state_dict[param_name.removeprefix(prefix)] = param
 
     if prefix == "model.diffusion_model.":
         # Some checkpoints store the text connector projection outside the diffusion model prefix.
-        connector_key = "text_embedding_projection.aggregate_embed.weight"
-        if connector_key in combined_ckpt and connector_key not in model_state_dict:
-            model_state_dict[connector_key] = combined_ckpt[connector_key]
+        connector_prefixes = ["text_embedding_projection"]
+        for param_name, param in combined_ckpt.items():
+            for prefix in connector_prefixes:
+                if param_name.startswith(prefix):
+                    # Check to make sure we're not overwriting an existing key
+                    if param_name not in model_state_dict:
+                        model_state_dict[param_name] = combined_ckpt[param_name]
 
     return model_state_dict
 
@@ -686,7 +948,7 @@ def get_args():
         "--version",
         type=str,
         default="2.0",
-        choices=["test", "2.0"],
+        choices=["test", "2.0", "2.3"],
         help="Version of the LTX 2.0 model",
     )
 
@@ -748,6 +1010,11 @@ def get_args():
         action="store_true",
         help="Whether to save a latent upsampling pipeline",
     )
+    parser.add_argument(
+        "--add_processor",
+        action="store_true",
+        help="Whether to add a Gemma3Processor to the pipeline for prompt enhancement.",
+    )
 
     parser.add_argument("--vae_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"])
     parser.add_argument("--audio_vae_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"])
@@ -756,6 +1023,12 @@ def get_args():
     parser.add_argument("--text_encoder_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"])
 
     parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
+    parser.add_argument(
+        "--upsample_output_path",
+        type=str,
+        default=None,
+        help="Path where converted upsampling pipeline should be saved",
+    )
 
     return parser.parse_args()
 
@@ -787,7 +1060,7 @@ def main(args):
             args.audio_vae,
             args.dit,
             args.vocoder,
-            args.text_encoder,
+            args.connectors,
             args.full_pipeline,
             args.upsample_pipeline,
         ]
@@ -852,7 +1125,12 @@ def main(args):
         if not args.full_pipeline:
             tokenizer.save_pretrained(os.path.join(args.output_path, "tokenizer"))
 
-    if args.latent_upsampler or args.full_pipeline or args.upsample_pipeline:
+        if args.add_processor:
+            processor = Gemma3Processor.from_pretrained(args.text_encoder_model_id)
+            if not args.full_pipeline:
+                processor.save_pretrained(os.path.join(args.output_path, "processor"))
+
+    if args.latent_upsampler or args.upsample_pipeline:
         original_latent_upsampler_ckpt = load_hub_or_local_checkpoint(
             repo_id=args.original_state_dict_repo_id, filename=args.latent_upsampler_filename
         )
@@ -866,14 +1144,26 @@ def main(args):
             latent_upsampler.save_pretrained(os.path.join(args.output_path, "latent_upsampler"))
 
     if args.full_pipeline:
-        scheduler = FlowMatchEulerDiscreteScheduler(
-            use_dynamic_shifting=True,
-            base_shift=0.95,
-            max_shift=2.05,
-            base_image_seq_len=1024,
-            max_image_seq_len=4096,
-            shift_terminal=0.1,
-        )
+        is_distilled_ckpt = "distilled" in args.combined_filename
+        if is_distilled_ckpt:
+            # Disable dynamic shifting and terminal shift so that distilled sigmas are used as-is
+            scheduler = FlowMatchEulerDiscreteScheduler(
+                use_dynamic_shifting=False,
+                base_shift=0.95,
+                max_shift=2.05,
+                base_image_seq_len=1024,
+                max_image_seq_len=4096,
+                shift_terminal=None,
+            )
+        else:
+            scheduler = FlowMatchEulerDiscreteScheduler(
+                use_dynamic_shifting=True,
+                base_shift=0.95,
+                max_shift=2.05,
+                base_image_seq_len=1024,
+                max_image_seq_len=4096,
+                shift_terminal=0.1,
+            )
 
         pipe = LTX2Pipeline(
             scheduler=scheduler,
@@ -891,10 +1181,12 @@ def main(args):
     if args.upsample_pipeline:
         pipe = LTX2LatentUpsamplePipeline(vae=vae, latent_upsampler=latent_upsampler)
 
-        # Put latent upsampling pipeline in its own subdirectory so it doesn't mess with the full pipeline
-        pipe.save_pretrained(
-            os.path.join(args.output_path, "upsample_pipeline"), safe_serialization=True, max_shard_size="5GB"
-        )
+        # As two diffusers pipelines cannot be in the same directory, save the upsampling pipeline to its own directory
+        if args.upsample_output_path:
+            upsample_output_path = args.upsample_output_path
+        else:
+            upsample_output_path = args.output_path
+        pipe.save_pretrained(upsample_output_path, safe_serialization=True, max_shard_size="5GB")
 
 
 if __name__ == "__main__":

scripts/convert_sana_video_to_diffusers.py

@@ -12,6 +12,7 @@ from termcolor import colored
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from diffusers import (
+    AutoencoderKLLTX2Video,
     AutoencoderKLWan,
     DPMSolverMultistepScheduler,
     FlowMatchEulerDiscreteScheduler,
@@ -24,7 +25,10 @@ from diffusers.utils.import_utils import is_accelerate_available
 
 CTX = init_empty_weights if is_accelerate_available else nullcontext
 
-ckpt_ids = ["Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth"]
+ckpt_ids = [
+    "Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth",
+    "Efficient-Large-Model/SANA-Video_2B_720p/checkpoints/SANA_Video_2B_720p_LTXVAE.pth",
+]
 # https://github.com/NVlabs/Sana/blob/main/inference_video_scripts/inference_sana_video.py
 
 
@@ -92,12 +96,22 @@ def main(args):
     if args.video_size == 480:
         sample_size = 30  # Wan-VAE: 8xp2 downsample factor
         patch_size = (1, 2, 2)
+        in_channels = 16
+        out_channels = 16
     elif args.video_size == 720:
-        sample_size = 22  # Wan-VAE: 32xp1 downsample factor
+        sample_size = 22  # DC-AE-V: 32xp1 downsample factor
         patch_size = (1, 1, 1)
+        in_channels = 32
+        out_channels = 32
     else:
         raise ValueError(f"Video size {args.video_size} is not supported.")
 
+    if args.vae_type == "ltx2":
+        sample_size = 22
+        patch_size = (1, 1, 1)
+        in_channels = 128
+        out_channels = 128
+
     for depth in range(layer_num):
         # Transformer blocks.
         converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop(
@@ -182,8 +196,8 @@ def main(args):
     # Transformer
     with CTX():
         transformer_kwargs = {
-            "in_channels": 16,
-            "out_channels": 16,
+            "in_channels": in_channels,
+            "out_channels": out_channels,
             "num_attention_heads": 20,
             "attention_head_dim": 112,
             "num_layers": 20,
@@ -235,9 +249,12 @@ def main(args):
     else:
         print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"]))
         # VAE
-        vae = AutoencoderKLWan.from_pretrained(
-            "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32
-        )
+        if args.vae_type == "ltx2":
+            vae_path = args.vae_path or "Lightricks/LTX-2"
+            vae = AutoencoderKLLTX2Video.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32)
+        else:
+            vae_path = args.vae_path or "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+            vae = AutoencoderKLWan.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32)
 
         # Text Encoder
         text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it"
@@ -314,7 +331,23 @@ if __name__ == "__main__":
         choices=["flow-dpm_solver", "flow-euler", "uni-pc"],
         help="Scheduler type to use.",
     )
-    parser.add_argument("--task", default="t2v", type=str, required=True, help="Task to convert, t2v or i2v.")
+    parser.add_argument(
+        "--vae_type",
+        default="wan",
+        type=str,
+        choices=["wan", "ltx2"],
+        help="VAE type to use for saving full pipeline (ltx2 uses patchify 1x1x1).",
+    )
+    parser.add_argument(
+        "--vae_path",
+        default=None,
+        type=str,
+        required=False,
+        help="Optional VAE path or repo id. If not set, a default is used per VAE type.",
+    )
+    parser.add_argument(
+        "--task", default="t2v", type=str, required=True, choices=["t2v", "i2v"], help="Task to convert, t2v or i2v."
+    )
     parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
     parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.")
     parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.")

src/diffusers/loaders/lora_conversion_utils.py

@@ -2156,6 +2156,9 @@ def _convert_non_diffusers_ltx2_lora_to_diffusers(state_dict, non_diffusers_pref
             "scale_shift_table_a2v_ca_audio": "audio_a2v_cross_attn_scale_shift_table",
             "q_norm": "norm_q",
             "k_norm": "norm_k",
+            # LTX-2.3
+            "audio_prompt_adaln_single": "audio_prompt_adaln",
+            "prompt_adaln_single": "prompt_adaln",
         }
     else:
         rename_dict = {"aggregate_embed": "text_proj_in"}

src/diffusers/models/attention_dispatch.py

@@ -229,6 +229,7 @@ class AttentionBackendName(str, Enum):
     FLASH_HUB = "flash_hub"
     FLASH_VARLEN = "flash_varlen"
     FLASH_VARLEN_HUB = "flash_varlen_hub"
+    FLASH_4_HUB = "flash_4_hub"
     _FLASH_3 = "_flash_3"
     _FLASH_VARLEN_3 = "_flash_varlen_3"
     _FLASH_3_HUB = "_flash_3_hub"
@@ -358,6 +359,11 @@ _HUB_KERNELS_REGISTRY: dict["AttentionBackendName", _HubKernelConfig] = {
         function_attr="sageattn",
         version=1,
     ),
+    AttentionBackendName.FLASH_4_HUB: _HubKernelConfig(
+        repo_id="kernels-staging/flash-attn4",
+        function_attr="flash_attn_func",
+        version=0,
+    ),
 }
 
 
@@ -521,6 +527,7 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
         AttentionBackendName._FLASH_3_HUB,
         AttentionBackendName._FLASH_3_VARLEN_HUB,
         AttentionBackendName.SAGE_HUB,
+        AttentionBackendName.FLASH_4_HUB,
     ]:
         if not is_kernels_available():
             raise RuntimeError(
@@ -531,6 +538,11 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
                 f"Backend '{backend.value}' needs to be used with a `kernels` version of at least 0.12. Please update with `pip install -U kernels`."
             )
 
+        if backend == AttentionBackendName.FLASH_4_HUB and not is_kernels_available(">=", "0.12.3"):
+            raise RuntimeError(
+                f"Backend '{backend.value}' needs to be used with a `kernels` version of at least 0.12.3. Please update with `pip install -U kernels`."
+            )
+
     elif backend == AttentionBackendName.AITER:
         if not _CAN_USE_AITER_ATTN:
             raise RuntimeError(
@@ -2676,6 +2688,37 @@ def _flash_attention_3_varlen_hub(
     return (out, lse) if return_lse else out
 
 
+@_AttentionBackendRegistry.register(
+    AttentionBackendName.FLASH_4_HUB,
+    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
+    supports_context_parallel=False,
+)
+def _flash_attention_4_hub(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attn_mask: torch.Tensor | None = None,
+    scale: float | None = None,
+    is_causal: bool = False,
+    return_lse: bool = False,
+    _parallel_config: "ParallelConfig" | None = None,
+) -> torch.Tensor:
+    if attn_mask is not None:
+        raise ValueError("`attn_mask` is not supported for flash-attn 4.")
+
+    func = _HUB_KERNELS_REGISTRY[AttentionBackendName.FLASH_4_HUB].kernel_fn
+    out = func(
+        q=query,
+        k=key,
+        v=value,
+        softmax_scale=scale,
+        causal=is_causal,
+    )
+    if isinstance(out, tuple):
+        return (out[0], out[1]) if return_lse else out[0]
+    return out
+
+
 @_AttentionBackendRegistry.register(
     AttentionBackendName._FLASH_VARLEN_3,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],

src/diffusers/models/autoencoders/autoencoder_kl_ltx2.py

@@ -237,7 +237,7 @@ class LTX2VideoResnetBlock3d(nn.Module):
 
 
 # Like LTX 1.0 LTXVideoDownsampler3d, but uses new causal Conv3d
-class LTXVideoDownsampler3d(nn.Module):
+class LTX2VideoDownsampler3d(nn.Module):
     def __init__(
         self,
         in_channels: int,
@@ -285,10 +285,11 @@ class LTXVideoDownsampler3d(nn.Module):
 
 
 # Like LTX 1.0 LTXVideoUpsampler3d, but uses new causal Conv3d
-class LTXVideoUpsampler3d(nn.Module):
+class LTX2VideoUpsampler3d(nn.Module):
     def __init__(
         self,
         in_channels: int,
+        out_channels: int | None = None,
         stride: int | tuple[int, int, int] = 1,
         residual: bool = False,
         upscale_factor: int = 1,
@@ -300,7 +301,8 @@ class LTXVideoUpsampler3d(nn.Module):
         self.residual = residual
         self.upscale_factor = upscale_factor
 
-        out_channels = (in_channels * stride[0] * stride[1] * stride[2]) // upscale_factor
+        out_channels = out_channels or in_channels
+        out_channels = (out_channels * stride[0] * stride[1] * stride[2]) // upscale_factor
 
         self.conv = LTX2VideoCausalConv3d(
             in_channels=in_channels,
@@ -408,7 +410,7 @@ class LTX2VideoDownBlock3D(nn.Module):
                 )
             elif downsample_type == "spatial":
                 self.downsamplers.append(
-                    LTXVideoDownsampler3d(
+                    LTX2VideoDownsampler3d(
                         in_channels=in_channels,
                         out_channels=out_channels,
                         stride=(1, 2, 2),
@@ -417,7 +419,7 @@ class LTX2VideoDownBlock3D(nn.Module):
                 )
             elif downsample_type == "temporal":
                 self.downsamplers.append(
-                    LTXVideoDownsampler3d(
+                    LTX2VideoDownsampler3d(
                         in_channels=in_channels,
                         out_channels=out_channels,
                         stride=(2, 1, 1),
@@ -426,7 +428,7 @@ class LTX2VideoDownBlock3D(nn.Module):
                 )
             elif downsample_type == "spatiotemporal":
                 self.downsamplers.append(
-                    LTXVideoDownsampler3d(
+                    LTX2VideoDownsampler3d(
                         in_channels=in_channels,
                         out_channels=out_channels,
                         stride=(2, 2, 2),
@@ -580,6 +582,7 @@ class LTX2VideoUpBlock3d(nn.Module):
         resnet_eps: float = 1e-6,
         resnet_act_fn: str = "swish",
         spatio_temporal_scale: bool = True,
+        upsample_type: str = "spatiotemporal",
         inject_noise: bool = False,
         timestep_conditioning: bool = False,
         upsample_residual: bool = False,
@@ -609,16 +612,23 @@ class LTX2VideoUpBlock3d(nn.Module):
 
         self.upsamplers = None
         if spatio_temporal_scale:
-            self.upsamplers = nn.ModuleList(
-                [
-                    LTXVideoUpsampler3d(
-                        out_channels * upscale_factor,
-                        stride=(2, 2, 2),
-                        residual=upsample_residual,
-                        upscale_factor=upscale_factor,
-                        spatial_padding_mode=spatial_padding_mode,
-                    )
-                ]
+            self.upsamplers = nn.ModuleList()
+
+            if upsample_type == "spatial":
+                upsample_stride = (1, 2, 2)
+            elif upsample_type == "temporal":
+                upsample_stride = (2, 1, 1)
+            elif upsample_type == "spatiotemporal":
+                upsample_stride = (2, 2, 2)
+
+            self.upsamplers.append(
+                LTX2VideoUpsampler3d(
+                    in_channels=out_channels * upscale_factor,
+                    stride=upsample_stride,
+                    residual=upsample_residual,
+                    upscale_factor=upscale_factor,
+                    spatial_padding_mode=spatial_padding_mode,
+                )
             )
 
         resnets = []
@@ -716,7 +726,7 @@ class LTX2VideoEncoder3d(nn.Module):
             "LTX2VideoDownBlock3D",
             "LTX2VideoDownBlock3D",
         ),
-        spatio_temporal_scaling: tuple[bool, ...] = (True, True, True, True),
+        spatio_temporal_scaling: bool | tuple[bool, ...] = (True, True, True, True),
         layers_per_block: tuple[int, ...] = (4, 6, 6, 2, 2),
         downsample_type: tuple[str, ...] = ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
         patch_size: int = 4,
@@ -726,6 +736,9 @@ class LTX2VideoEncoder3d(nn.Module):
         spatial_padding_mode: str = "zeros",
     ):
         super().__init__()
+        num_encoder_blocks = len(layers_per_block)
+        if isinstance(spatio_temporal_scaling, bool):
+            spatio_temporal_scaling = (spatio_temporal_scaling,) * (num_encoder_blocks - 1)
 
         self.patch_size = patch_size
         self.patch_size_t = patch_size_t
@@ -860,19 +873,27 @@ class LTX2VideoDecoder3d(nn.Module):
         in_channels: int = 128,
         out_channels: int = 3,
         block_out_channels: tuple[int, ...] = (256, 512, 1024),
-        spatio_temporal_scaling: tuple[bool, ...] = (True, True, True),
+        spatio_temporal_scaling: bool | tuple[bool, ...] = (True, True, True),
         layers_per_block: tuple[int, ...] = (5, 5, 5, 5),
+        upsample_type: tuple[str, ...] = ("spatiotemporal", "spatiotemporal", "spatiotemporal"),
         patch_size: int = 4,
         patch_size_t: int = 1,
         resnet_norm_eps: float = 1e-6,
         is_causal: bool = False,
-        inject_noise: tuple[bool, ...] = (False, False, False),
+        inject_noise: bool | tuple[bool, ...] = (False, False, False),
         timestep_conditioning: bool = False,
-        upsample_residual: tuple[bool, ...] = (True, True, True),
+        upsample_residual: bool | tuple[bool, ...] = (True, True, True),
         upsample_factor: tuple[bool, ...] = (2, 2, 2),
         spatial_padding_mode: str = "reflect",
     ) -> None:
         super().__init__()
+        num_decoder_blocks = len(layers_per_block)
+        if isinstance(spatio_temporal_scaling, bool):
+            spatio_temporal_scaling = (spatio_temporal_scaling,) * (num_decoder_blocks - 1)
+        if isinstance(inject_noise, bool):
+            inject_noise = (inject_noise,) * num_decoder_blocks
+        if isinstance(upsample_residual, bool):
+            upsample_residual = (upsample_residual,) * (num_decoder_blocks - 1)
 
         self.patch_size = patch_size
         self.patch_size_t = patch_size_t
@@ -917,6 +938,7 @@ class LTX2VideoDecoder3d(nn.Module):
                 num_layers=layers_per_block[i + 1],
                 resnet_eps=resnet_norm_eps,
                 spatio_temporal_scale=spatio_temporal_scaling[i],
+                upsample_type=upsample_type[i],
                 inject_noise=inject_noise[i + 1],
                 timestep_conditioning=timestep_conditioning,
                 upsample_residual=upsample_residual[i],
@@ -1058,11 +1080,12 @@ class AutoencoderKLLTX2Video(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig
         decoder_block_out_channels: tuple[int, ...] = (256, 512, 1024),
         layers_per_block: tuple[int, ...] = (4, 6, 6, 2, 2),
         decoder_layers_per_block: tuple[int, ...] = (5, 5, 5, 5),
-        spatio_temporal_scaling: tuple[bool, ...] = (True, True, True, True),
-        decoder_spatio_temporal_scaling: tuple[bool, ...] = (True, True, True),
-        decoder_inject_noise: tuple[bool, ...] = (False, False, False, False),
+        spatio_temporal_scaling: bool | tuple[bool, ...] = (True, True, True, True),
+        decoder_spatio_temporal_scaling: bool | tuple[bool, ...] = (True, True, True),
+        decoder_inject_noise: bool | tuple[bool, ...] = (False, False, False, False),
         downsample_type: tuple[str, ...] = ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
-        upsample_residual: tuple[bool, ...] = (True, True, True),
+        upsample_type: tuple[str, ...] = ("spatiotemporal", "spatiotemporal", "spatiotemporal"),
+        upsample_residual: bool | tuple[bool, ...] = (True, True, True),
         upsample_factor: tuple[int, ...] = (2, 2, 2),
         timestep_conditioning: bool = False,
         patch_size: int = 4,
@@ -1077,6 +1100,16 @@ class AutoencoderKLLTX2Video(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig
         temporal_compression_ratio: int = None,
     ) -> None:
         super().__init__()
+        num_encoder_blocks = len(layers_per_block)
+        num_decoder_blocks = len(decoder_layers_per_block)
+        if isinstance(spatio_temporal_scaling, bool):
+            spatio_temporal_scaling = (spatio_temporal_scaling,) * (num_encoder_blocks - 1)
+        if isinstance(decoder_spatio_temporal_scaling, bool):
+            decoder_spatio_temporal_scaling = (decoder_spatio_temporal_scaling,) * (num_decoder_blocks - 1)
+        if isinstance(decoder_inject_noise, bool):
+            decoder_inject_noise = (decoder_inject_noise,) * num_decoder_blocks
+        if isinstance(upsample_residual, bool):
+            upsample_residual = (upsample_residual,) * (num_decoder_blocks - 1)
 
         self.encoder = LTX2VideoEncoder3d(
             in_channels=in_channels,
@@ -1098,6 +1131,7 @@ class AutoencoderKLLTX2Video(ModelMixin, AutoencoderMixin, ConfigMixin, FromOrig
             block_out_channels=decoder_block_out_channels,
             spatio_temporal_scaling=decoder_spatio_temporal_scaling,
             layers_per_block=decoder_layers_per_block,
+            upsample_type=upsample_type,
             patch_size=patch_size,
             patch_size_t=patch_size_t,
             resnet_norm_eps=resnet_norm_eps,

src/diffusers/models/transformers/transformer_helios.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import math
-from functools import lru_cache
 from typing import Any
 
 import torch
@@ -343,7 +342,6 @@ class HeliosRotaryPosEmbed(nn.Module):
         return freqs.cos(), freqs.sin()
 
     @torch.no_grad()
-    @lru_cache(maxsize=32)
     def _get_spatial_meshgrid(self, height, width, device_str):
         device = torch.device(device_str)
         grid_y_coords = torch.arange(height, device=device, dtype=torch.float32)

src/diffusers/models/transformers/transformer_ltx2.py

@@ -178,6 +178,10 @@ class LTX2AudioVideoAttnProcessor:
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
 
+        if attn.to_gate_logits is not None:
+            # Calculate gate logits on original hidden_states
+            gate_logits = attn.to_gate_logits(hidden_states)
+
         query = attn.to_q(hidden_states)
         key = attn.to_k(encoder_hidden_states)
         value = attn.to_v(encoder_hidden_states)
@@ -212,6 +216,112 @@ class LTX2AudioVideoAttnProcessor:
         hidden_states = hidden_states.flatten(2, 3)
         hidden_states = hidden_states.to(query.dtype)
 
+        if attn.to_gate_logits is not None:
+            hidden_states = hidden_states.unflatten(2, (attn.heads, -1))  # [B, T, H, D]
+            # The factor of 2.0 is so that if the gates logits are zero-initialized the initial gates are all 1
+            gates = 2.0 * torch.sigmoid(gate_logits)  # [B, T, H]
+            hidden_states = hidden_states * gates.unsqueeze(-1)
+            hidden_states = hidden_states.flatten(2, 3)
+
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+        return hidden_states
+
+
+class LTX2PerturbedAttnProcessor:
+    r"""
+    Processor which implements attention with perturbation masking and per-head gating for LTX-2.X models.
+    """
+
+    _attention_backend = None
+    _parallel_config = None
+
+    def __init__(self):
+        if is_torch_version("<", "2.0"):
+            raise ValueError(
+                "LTX attention processors require a minimum PyTorch version of 2.0. Please upgrade your PyTorch installation."
+            )
+
+    def __call__(
+        self,
+        attn: "LTX2Attention",
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        query_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+        key_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+        perturbation_mask: torch.Tensor | None = None,
+        all_perturbed: bool | None = None,
+    ) -> torch.Tensor:
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        if attn.to_gate_logits is not None:
+            # Calculate gate logits on original hidden_states
+            gate_logits = attn.to_gate_logits(hidden_states)
+
+        value = attn.to_v(encoder_hidden_states)
+        if all_perturbed is None:
+            all_perturbed = torch.all(perturbation_mask == 0) if perturbation_mask is not None else False
+
+        if all_perturbed:
+            # Skip attention, use the value projection value
+            hidden_states = value
+        else:
+            query = attn.to_q(hidden_states)
+            key = attn.to_k(encoder_hidden_states)
+
+            query = attn.norm_q(query)
+            key = attn.norm_k(key)
+
+            if query_rotary_emb is not None:
+                if attn.rope_type == "interleaved":
+                    query = apply_interleaved_rotary_emb(query, query_rotary_emb)
+                    key = apply_interleaved_rotary_emb(
+                        key, key_rotary_emb if key_rotary_emb is not None else query_rotary_emb
+                    )
+                elif attn.rope_type == "split":
+                    query = apply_split_rotary_emb(query, query_rotary_emb)
+                    key = apply_split_rotary_emb(
+                        key, key_rotary_emb if key_rotary_emb is not None else query_rotary_emb
+                    )
+
+            query = query.unflatten(2, (attn.heads, -1))
+            key = key.unflatten(2, (attn.heads, -1))
+            value = value.unflatten(2, (attn.heads, -1))
+
+            hidden_states = dispatch_attention_fn(
+                query,
+                key,
+                value,
+                attn_mask=attention_mask,
+                dropout_p=0.0,
+                is_causal=False,
+                backend=self._attention_backend,
+                parallel_config=self._parallel_config,
+            )
+            hidden_states = hidden_states.flatten(2, 3)
+            hidden_states = hidden_states.to(query.dtype)
+
+            if perturbation_mask is not None:
+                value = value.flatten(2, 3)
+                hidden_states = torch.lerp(value, hidden_states, perturbation_mask)
+
+        if attn.to_gate_logits is not None:
+            hidden_states = hidden_states.unflatten(2, (attn.heads, -1))  # [B, T, H, D]
+            # The factor of 2.0 is so that if the gates logits are zero-initialized the initial gates are all 1
+            gates = 2.0 * torch.sigmoid(gate_logits)  # [B, T, H]
+            hidden_states = hidden_states * gates.unsqueeze(-1)
+            hidden_states = hidden_states.flatten(2, 3)
+
         hidden_states = attn.to_out[0](hidden_states)
         hidden_states = attn.to_out[1](hidden_states)
         return hidden_states
@@ -224,7 +334,7 @@ class LTX2Attention(torch.nn.Module, AttentionModuleMixin):
     """
 
     _default_processor_cls = LTX2AudioVideoAttnProcessor
-    _available_processors = [LTX2AudioVideoAttnProcessor]
+    _available_processors = [LTX2AudioVideoAttnProcessor, LTX2PerturbedAttnProcessor]
 
     def __init__(
         self,
@@ -240,6 +350,7 @@ class LTX2Attention(torch.nn.Module, AttentionModuleMixin):
         norm_eps: float = 1e-6,
         norm_elementwise_affine: bool = True,
         rope_type: str = "interleaved",
+        apply_gated_attention: bool = False,
         processor=None,
     ):
         super().__init__()
@@ -266,6 +377,12 @@ class LTX2Attention(torch.nn.Module, AttentionModuleMixin):
         self.to_out.append(torch.nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
         self.to_out.append(torch.nn.Dropout(dropout))
 
+        if apply_gated_attention:
+            # Per head gate values
+            self.to_gate_logits = torch.nn.Linear(query_dim, heads, bias=True)
+        else:
+            self.to_gate_logits = None
+
         if processor is None:
             processor = self._default_processor_cls()
         self.set_processor(processor)
@@ -321,6 +438,10 @@ class LTX2VideoTransformerBlock(nn.Module):
         audio_num_attention_heads: int,
         audio_attention_head_dim,
         audio_cross_attention_dim: int,
+        video_gated_attn: bool = False,
+        video_cross_attn_adaln: bool = False,
+        audio_gated_attn: bool = False,
+        audio_cross_attn_adaln: bool = False,
         qk_norm: str = "rms_norm_across_heads",
         activation_fn: str = "gelu-approximate",
         attention_bias: bool = True,
@@ -328,9 +449,16 @@ class LTX2VideoTransformerBlock(nn.Module):
         eps: float = 1e-6,
         elementwise_affine: bool = False,
         rope_type: str = "interleaved",
+        perturbed_attn: bool = False,
     ):
         super().__init__()
 
+        self.perturbed_attn = perturbed_attn
+        if perturbed_attn:
+            attn_processor_cls = LTX2PerturbedAttnProcessor
+        else:
+            attn_processor_cls = LTX2AudioVideoAttnProcessor
+
         # 1. Self-Attention (video and audio)
         self.norm1 = RMSNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
         self.attn1 = LTX2Attention(
@@ -343,6 +471,8 @@ class LTX2VideoTransformerBlock(nn.Module):
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
             rope_type=rope_type,
+            apply_gated_attention=video_gated_attn,
+            processor=attn_processor_cls(),
         )
 
         self.audio_norm1 = RMSNorm(audio_dim, eps=eps, elementwise_affine=elementwise_affine)
@@ -356,6 +486,8 @@ class LTX2VideoTransformerBlock(nn.Module):
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
             rope_type=rope_type,
+            apply_gated_attention=audio_gated_attn,
+            processor=attn_processor_cls(),
         )
 
         # 2. Prompt Cross-Attention
@@ -370,6 +502,8 @@ class LTX2VideoTransformerBlock(nn.Module):
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
             rope_type=rope_type,
+            apply_gated_attention=video_gated_attn,
+            processor=attn_processor_cls(),
         )
 
         self.audio_norm2 = RMSNorm(audio_dim, eps=eps, elementwise_affine=elementwise_affine)
@@ -383,6 +517,8 @@ class LTX2VideoTransformerBlock(nn.Module):
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
             rope_type=rope_type,
+            apply_gated_attention=audio_gated_attn,
+            processor=attn_processor_cls(),
         )
 
         # 3. Audio-to-Video (a2v) and Video-to-Audio (v2a) Cross-Attention
@@ -398,6 +534,8 @@ class LTX2VideoTransformerBlock(nn.Module):
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
             rope_type=rope_type,
+            apply_gated_attention=video_gated_attn,
+            processor=attn_processor_cls(),
         )
 
         # Video-to-Audio (v2a) Attention --> Q: Audio; K,V: Video
@@ -412,6 +550,8 @@ class LTX2VideoTransformerBlock(nn.Module):
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
             rope_type=rope_type,
+            apply_gated_attention=audio_gated_attn,
+            processor=attn_processor_cls(),
         )
 
         # 4. Feedforward layers
@@ -422,14 +562,36 @@ class LTX2VideoTransformerBlock(nn.Module):
         self.audio_ff = FeedForward(audio_dim, activation_fn=activation_fn)
 
         # 5. Per-Layer Modulation Parameters
-        # Self-Attention / Feedforward AdaLayerNorm-Zero mod params
-        self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
-        self.audio_scale_shift_table = nn.Parameter(torch.randn(6, audio_dim) / audio_dim**0.5)
+        # Self-Attention (attn1) / Feedforward AdaLayerNorm-Zero mod params
+        # 6 base mod params for text cross-attn K,V; if cross_attn_adaln, also has mod params for Q
+        self.video_cross_attn_adaln = video_cross_attn_adaln
+        self.audio_cross_attn_adaln = audio_cross_attn_adaln
+        video_mod_param_num = 9 if self.video_cross_attn_adaln else 6
+        audio_mod_param_num = 9 if self.audio_cross_attn_adaln else 6
+        self.scale_shift_table = nn.Parameter(torch.randn(video_mod_param_num, dim) / dim**0.5)
+        self.audio_scale_shift_table = nn.Parameter(torch.randn(audio_mod_param_num, audio_dim) / audio_dim**0.5)
+
+        # Prompt cross-attn (attn2) additional modulation params
+        self.cross_attn_adaln = video_cross_attn_adaln or audio_cross_attn_adaln
+        if self.cross_attn_adaln:
+            self.prompt_scale_shift_table = nn.Parameter(torch.randn(2, dim))
+            self.audio_prompt_scale_shift_table = nn.Parameter(torch.randn(2, audio_dim))
 
         # Per-layer a2v, v2a Cross-Attention mod params
         self.video_a2v_cross_attn_scale_shift_table = nn.Parameter(torch.randn(5, dim))
         self.audio_a2v_cross_attn_scale_shift_table = nn.Parameter(torch.randn(5, audio_dim))
 
+    @staticmethod
+    def get_mod_params(
+        scale_shift_table: torch.Tensor, temb: torch.Tensor, batch_size: int
+    ) -> tuple[torch.Tensor, ...]:
+        num_ada_params = scale_shift_table.shape[0]
+        ada_values = scale_shift_table[None, None].to(temb.device) + temb.reshape(
+            batch_size, temb.shape[1], num_ada_params, -1
+        )
+        ada_params = ada_values.unbind(dim=2)
+        return ada_params
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -442,143 +604,181 @@ class LTX2VideoTransformerBlock(nn.Module):
         temb_ca_audio_scale_shift: torch.Tensor,
         temb_ca_gate: torch.Tensor,
         temb_ca_audio_gate: torch.Tensor,
+        temb_prompt: torch.Tensor | None = None,
+        temb_prompt_audio: torch.Tensor | None = None,
         video_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
         audio_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
         ca_video_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
         ca_audio_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
         encoder_attention_mask: torch.Tensor | None = None,
         audio_encoder_attention_mask: torch.Tensor | None = None,
+        self_attention_mask: torch.Tensor | None = None,
+        audio_self_attention_mask: torch.Tensor | None = None,
         a2v_cross_attention_mask: torch.Tensor | None = None,
         v2a_cross_attention_mask: torch.Tensor | None = None,
+        use_a2v_cross_attention: bool = True,
+        use_v2a_cross_attention: bool = True,
+        perturbation_mask: torch.Tensor | None = None,
+        all_perturbed: bool | None = None,
     ) -> torch.Tensor:
         batch_size = hidden_states.size(0)
 
         # 1. Video and Audio Self-Attention
-        norm_hidden_states = self.norm1(hidden_states)
+        # 1.1. Video Self-Attention
+        video_ada_params = self.get_mod_params(self.scale_shift_table, temb, batch_size)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = video_ada_params[:6]
+        if self.video_cross_attn_adaln:
+            shift_text_q, scale_text_q, gate_text_q = video_ada_params[6:9]
 
-        num_ada_params = self.scale_shift_table.shape[0]
-        ada_values = self.scale_shift_table[None, None].to(temb.device) + temb.reshape(
-            batch_size, temb.size(1), num_ada_params, -1
-        )
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ada_values.unbind(dim=2)
+        norm_hidden_states = self.norm1(hidden_states)
         norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
 
-        attn_hidden_states = self.attn1(
-            hidden_states=norm_hidden_states,
-            encoder_hidden_states=None,
-            query_rotary_emb=video_rotary_emb,
-        )
+        video_self_attn_args = {
+            "hidden_states": norm_hidden_states,
+            "encoder_hidden_states": None,
+            "query_rotary_emb": video_rotary_emb,
+            "attention_mask": self_attention_mask,
+        }
+        if self.perturbed_attn:
+            video_self_attn_args["perturbation_mask"] = perturbation_mask
+            video_self_attn_args["all_perturbed"] = all_perturbed
+
+        attn_hidden_states = self.attn1(**video_self_attn_args)
         hidden_states = hidden_states + attn_hidden_states * gate_msa
 
-        norm_audio_hidden_states = self.audio_norm1(audio_hidden_states)
-
-        num_audio_ada_params = self.audio_scale_shift_table.shape[0]
-        audio_ada_values = self.audio_scale_shift_table[None, None].to(temb_audio.device) + temb_audio.reshape(
-            batch_size, temb_audio.size(1), num_audio_ada_params, -1
-        )
+        # 1.2. Audio Self-Attention
+        audio_ada_params = self.get_mod_params(self.audio_scale_shift_table, temb_audio, batch_size)
         audio_shift_msa, audio_scale_msa, audio_gate_msa, audio_shift_mlp, audio_scale_mlp, audio_gate_mlp = (
-            audio_ada_values.unbind(dim=2)
+            audio_ada_params[:6]
         )
+        if self.audio_cross_attn_adaln:
+            audio_shift_text_q, audio_scale_text_q, audio_gate_text_q = audio_ada_params[6:9]
+
+        norm_audio_hidden_states = self.audio_norm1(audio_hidden_states)
         norm_audio_hidden_states = norm_audio_hidden_states * (1 + audio_scale_msa) + audio_shift_msa
 
-        attn_audio_hidden_states = self.audio_attn1(
-            hidden_states=norm_audio_hidden_states,
-            encoder_hidden_states=None,
-            query_rotary_emb=audio_rotary_emb,
-        )
+        audio_self_attn_args = {
+            "hidden_states": norm_audio_hidden_states,
+            "encoder_hidden_states": None,
+            "query_rotary_emb": audio_rotary_emb,
+            "attention_mask": audio_self_attention_mask,
+        }
+        if self.perturbed_attn:
+            audio_self_attn_args["perturbation_mask"] = perturbation_mask
+            audio_self_attn_args["all_perturbed"] = all_perturbed
+
+        attn_audio_hidden_states = self.audio_attn1(**audio_self_attn_args)
         audio_hidden_states = audio_hidden_states + attn_audio_hidden_states * audio_gate_msa
 
-        # 2. Video and Audio Cross-Attention with the text embeddings
+        # 2. Video and Audio Cross-Attention with the text embeddings (Q: Video or Audio; K,V: Text)
+        if self.cross_attn_adaln:
+            video_prompt_ada_params = self.get_mod_params(self.prompt_scale_shift_table, temb_prompt, batch_size)
+            shift_text_kv, scale_text_kv = video_prompt_ada_params
+
+            audio_prompt_ada_params = self.get_mod_params(
+                self.audio_prompt_scale_shift_table, temb_prompt_audio, batch_size
+            )
+            audio_shift_text_kv, audio_scale_text_kv = audio_prompt_ada_params
+
+        # 2.1. Video-Text Cross-Attention (Q: Video; K,V: Text)
         norm_hidden_states = self.norm2(hidden_states)
+        if self.video_cross_attn_adaln:
+            norm_hidden_states = norm_hidden_states * (1 + scale_text_q) + shift_text_q
+        if self.cross_attn_adaln:
+            encoder_hidden_states = encoder_hidden_states * (1 + scale_text_kv) + shift_text_kv
+
         attn_hidden_states = self.attn2(
             norm_hidden_states,
             encoder_hidden_states=encoder_hidden_states,
             query_rotary_emb=None,
             attention_mask=encoder_attention_mask,
         )
+        if self.video_cross_attn_adaln:
+            attn_hidden_states = attn_hidden_states * gate_text_q
         hidden_states = hidden_states + attn_hidden_states
 
+        # 2.2. Audio-Text Cross-Attention
         norm_audio_hidden_states = self.audio_norm2(audio_hidden_states)
+        if self.audio_cross_attn_adaln:
+            norm_audio_hidden_states = norm_audio_hidden_states * (1 + audio_scale_text_q) + audio_shift_text_q
+        if self.cross_attn_adaln:
+            audio_encoder_hidden_states = audio_encoder_hidden_states * (1 + audio_scale_text_kv) + audio_shift_text_kv
+
         attn_audio_hidden_states = self.audio_attn2(
             norm_audio_hidden_states,
             encoder_hidden_states=audio_encoder_hidden_states,
             query_rotary_emb=None,
             attention_mask=audio_encoder_attention_mask,
         )
+        if self.audio_cross_attn_adaln:
+            attn_audio_hidden_states = attn_audio_hidden_states * audio_gate_text_q
         audio_hidden_states = audio_hidden_states + attn_audio_hidden_states
 
         # 3. Audio-to-Video (a2v) and Video-to-Audio (v2a) Cross-Attention
-        norm_hidden_states = self.audio_to_video_norm(hidden_states)
-        norm_audio_hidden_states = self.video_to_audio_norm(audio_hidden_states)
+        if use_a2v_cross_attention or use_v2a_cross_attention:
+            norm_hidden_states = self.audio_to_video_norm(hidden_states)
+            norm_audio_hidden_states = self.video_to_audio_norm(audio_hidden_states)
 
-        # Combine global and per-layer cross attention modulation parameters
-        # Video
-        video_per_layer_ca_scale_shift = self.video_a2v_cross_attn_scale_shift_table[:4, :]
-        video_per_layer_ca_gate = self.video_a2v_cross_attn_scale_shift_table[4:, :]
+            # 3.1. Combine global and per-layer cross attention modulation parameters
+            # Video
+            video_per_layer_ca_scale_shift = self.video_a2v_cross_attn_scale_shift_table[:4, :]
+            video_per_layer_ca_gate = self.video_a2v_cross_attn_scale_shift_table[4:, :]
 
-        video_ca_scale_shift_table = (
-            video_per_layer_ca_scale_shift[:, :, ...].to(temb_ca_scale_shift.dtype)
-            + temb_ca_scale_shift.reshape(batch_size, temb_ca_scale_shift.shape[1], 4, -1)
-        ).unbind(dim=2)
-        video_ca_gate = (
-            video_per_layer_ca_gate[:, :, ...].to(temb_ca_gate.dtype)
-            + temb_ca_gate.reshape(batch_size, temb_ca_gate.shape[1], 1, -1)
-        ).unbind(dim=2)
+            video_ca_ada_params = self.get_mod_params(video_per_layer_ca_scale_shift, temb_ca_scale_shift, batch_size)
+            video_ca_gate_param = self.get_mod_params(video_per_layer_ca_gate, temb_ca_gate, batch_size)
 
-        video_a2v_ca_scale, video_a2v_ca_shift, video_v2a_ca_scale, video_v2a_ca_shift = video_ca_scale_shift_table
-        a2v_gate = video_ca_gate[0].squeeze(2)
+            video_a2v_ca_scale, video_a2v_ca_shift, video_v2a_ca_scale, video_v2a_ca_shift = video_ca_ada_params
+            a2v_gate = video_ca_gate_param[0].squeeze(2)
 
-        # Audio
-        audio_per_layer_ca_scale_shift = self.audio_a2v_cross_attn_scale_shift_table[:4, :]
-        audio_per_layer_ca_gate = self.audio_a2v_cross_attn_scale_shift_table[4:, :]
+            # Audio
+            audio_per_layer_ca_scale_shift = self.audio_a2v_cross_attn_scale_shift_table[:4, :]
+            audio_per_layer_ca_gate = self.audio_a2v_cross_attn_scale_shift_table[4:, :]
 
-        audio_ca_scale_shift_table = (
-            audio_per_layer_ca_scale_shift[:, :, ...].to(temb_ca_audio_scale_shift.dtype)
-            + temb_ca_audio_scale_shift.reshape(batch_size, temb_ca_audio_scale_shift.shape[1], 4, -1)
-        ).unbind(dim=2)
-        audio_ca_gate = (
-            audio_per_layer_ca_gate[:, :, ...].to(temb_ca_audio_gate.dtype)
-            + temb_ca_audio_gate.reshape(batch_size, temb_ca_audio_gate.shape[1], 1, -1)
-        ).unbind(dim=2)
+            audio_ca_ada_params = self.get_mod_params(
+                audio_per_layer_ca_scale_shift, temb_ca_audio_scale_shift, batch_size
+            )
+            audio_ca_gate_param = self.get_mod_params(audio_per_layer_ca_gate, temb_ca_audio_gate, batch_size)
 
-        audio_a2v_ca_scale, audio_a2v_ca_shift, audio_v2a_ca_scale, audio_v2a_ca_shift = audio_ca_scale_shift_table
-        v2a_gate = audio_ca_gate[0].squeeze(2)
+            audio_a2v_ca_scale, audio_a2v_ca_shift, audio_v2a_ca_scale, audio_v2a_ca_shift = audio_ca_ada_params
+            v2a_gate = audio_ca_gate_param[0].squeeze(2)
 
-        # Audio-to-Video Cross Attention: Q: Video; K,V: Audio
-        mod_norm_hidden_states = norm_hidden_states * (1 + video_a2v_ca_scale.squeeze(2)) + video_a2v_ca_shift.squeeze(
-            2
-        )
-        mod_norm_audio_hidden_states = norm_audio_hidden_states * (
-            1 + audio_a2v_ca_scale.squeeze(2)
-        ) + audio_a2v_ca_shift.squeeze(2)
+            # 3.2. Audio-to-Video Cross Attention: Q: Video; K,V: Audio
+            if use_a2v_cross_attention:
+                mod_norm_hidden_states = norm_hidden_states * (
+                    1 + video_a2v_ca_scale.squeeze(2)
+                ) + video_a2v_ca_shift.squeeze(2)
+                mod_norm_audio_hidden_states = norm_audio_hidden_states * (
+                    1 + audio_a2v_ca_scale.squeeze(2)
+                ) + audio_a2v_ca_shift.squeeze(2)
 
-        a2v_attn_hidden_states = self.audio_to_video_attn(
-            mod_norm_hidden_states,
-            encoder_hidden_states=mod_norm_audio_hidden_states,
-            query_rotary_emb=ca_video_rotary_emb,
-            key_rotary_emb=ca_audio_rotary_emb,
-            attention_mask=a2v_cross_attention_mask,
-        )
+                a2v_attn_hidden_states = self.audio_to_video_attn(
+                    mod_norm_hidden_states,
+                    encoder_hidden_states=mod_norm_audio_hidden_states,
+                    query_rotary_emb=ca_video_rotary_emb,
+                    key_rotary_emb=ca_audio_rotary_emb,
+                    attention_mask=a2v_cross_attention_mask,
+                )
 
-        hidden_states = hidden_states + a2v_gate * a2v_attn_hidden_states
+                hidden_states = hidden_states + a2v_gate * a2v_attn_hidden_states
 
-        # Video-to-Audio Cross Attention: Q: Audio; K,V: Video
-        mod_norm_hidden_states = norm_hidden_states * (1 + video_v2a_ca_scale.squeeze(2)) + video_v2a_ca_shift.squeeze(
-            2
-        )
-        mod_norm_audio_hidden_states = norm_audio_hidden_states * (
-            1 + audio_v2a_ca_scale.squeeze(2)
-        ) + audio_v2a_ca_shift.squeeze(2)
+            # 3.3. Video-to-Audio Cross Attention: Q: Audio; K,V: Video
+            if use_v2a_cross_attention:
+                mod_norm_hidden_states = norm_hidden_states * (
+                    1 + video_v2a_ca_scale.squeeze(2)
+                ) + video_v2a_ca_shift.squeeze(2)
+                mod_norm_audio_hidden_states = norm_audio_hidden_states * (
+                    1 + audio_v2a_ca_scale.squeeze(2)
+                ) + audio_v2a_ca_shift.squeeze(2)
 
-        v2a_attn_hidden_states = self.video_to_audio_attn(
-            mod_norm_audio_hidden_states,
-            encoder_hidden_states=mod_norm_hidden_states,
-            query_rotary_emb=ca_audio_rotary_emb,
-            key_rotary_emb=ca_video_rotary_emb,
-            attention_mask=v2a_cross_attention_mask,
-        )
+                v2a_attn_hidden_states = self.video_to_audio_attn(
+                    mod_norm_audio_hidden_states,
+                    encoder_hidden_states=mod_norm_hidden_states,
+                    query_rotary_emb=ca_audio_rotary_emb,
+                    key_rotary_emb=ca_video_rotary_emb,
+                    attention_mask=v2a_cross_attention_mask,
+                )
 
-        audio_hidden_states = audio_hidden_states + v2a_gate * v2a_attn_hidden_states
+                audio_hidden_states = audio_hidden_states + v2a_gate * v2a_attn_hidden_states
 
         # 4. Feedforward
         norm_hidden_states = self.norm3(hidden_states) * (1 + scale_mlp) + shift_mlp
@@ -918,6 +1118,8 @@ class LTX2VideoTransformer3DModel(
         pos_embed_max_pos: int = 20,
         base_height: int = 2048,
         base_width: int = 2048,
+        gated_attn: bool = False,
+        cross_attn_mod: bool = False,
         audio_in_channels: int = 128,  # Audio Arguments
         audio_out_channels: int | None = 128,
         audio_patch_size: int = 1,
@@ -929,6 +1131,8 @@ class LTX2VideoTransformer3DModel(
         audio_pos_embed_max_pos: int = 20,
         audio_sampling_rate: int = 16000,
         audio_hop_length: int = 160,
+        audio_gated_attn: bool = False,
+        audio_cross_attn_mod: bool = False,
         num_layers: int = 48,  # Shared arguments
         activation_fn: str = "gelu-approximate",
         qk_norm: str = "rms_norm_across_heads",
@@ -943,6 +1147,8 @@ class LTX2VideoTransformer3DModel(
         timestep_scale_multiplier: int = 1000,
         cross_attn_timestep_scale_multiplier: int = 1000,
         rope_type: str = "interleaved",
+        use_prompt_embeddings=True,
+        perturbed_attn: bool = False,
     ) -> None:
         super().__init__()
 
@@ -956,17 +1162,25 @@ class LTX2VideoTransformer3DModel(
         self.audio_proj_in = nn.Linear(audio_in_channels, audio_inner_dim)
 
         # 2. Prompt embeddings
-        self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
-        self.audio_caption_projection = PixArtAlphaTextProjection(
-            in_features=caption_channels, hidden_size=audio_inner_dim
-        )
+        if use_prompt_embeddings:
+            # LTX-2.0; LTX-2.3 uses per-modality feature projections in the connector instead
+            self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
+            self.audio_caption_projection = PixArtAlphaTextProjection(
+                in_features=caption_channels, hidden_size=audio_inner_dim
+            )
 
         # 3. Timestep Modulation Params and Embedding
+        self.prompt_modulation = cross_attn_mod or audio_cross_attn_mod  # used by LTX-2.3
+
         # 3.1. Global Timestep Modulation Parameters (except for cross-attention) and timestep + size embedding
         # time_embed and audio_time_embed calculate both the timestep embedding and (global) modulation parameters
-        self.time_embed = LTX2AdaLayerNormSingle(inner_dim, num_mod_params=6, use_additional_conditions=False)
+        video_time_emb_mod_params = 9 if cross_attn_mod else 6
+        audio_time_emb_mod_params = 9 if audio_cross_attn_mod else 6
+        self.time_embed = LTX2AdaLayerNormSingle(
+            inner_dim, num_mod_params=video_time_emb_mod_params, use_additional_conditions=False
+        )
         self.audio_time_embed = LTX2AdaLayerNormSingle(
-            audio_inner_dim, num_mod_params=6, use_additional_conditions=False
+            audio_inner_dim, num_mod_params=audio_time_emb_mod_params, use_additional_conditions=False
         )
 
         # 3.2. Global Cross Attention Modulation Parameters
@@ -995,6 +1209,13 @@ class LTX2VideoTransformer3DModel(
         self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
         self.audio_scale_shift_table = nn.Parameter(torch.randn(2, audio_inner_dim) / audio_inner_dim**0.5)
 
+        # 3.4. Prompt Scale/Shift Modulation parameters (LTX-2.3)
+        if self.prompt_modulation:
+            self.prompt_adaln = LTX2AdaLayerNormSingle(inner_dim, num_mod_params=2, use_additional_conditions=False)
+            self.audio_prompt_adaln = LTX2AdaLayerNormSingle(
+                audio_inner_dim, num_mod_params=2, use_additional_conditions=False
+            )
+
         # 4. Rotary Positional Embeddings (RoPE)
         # Self-Attention
         self.rope = LTX2AudioVideoRotaryPosEmbed(
@@ -1071,6 +1292,10 @@ class LTX2VideoTransformer3DModel(
                     audio_num_attention_heads=audio_num_attention_heads,
                     audio_attention_head_dim=audio_attention_head_dim,
                     audio_cross_attention_dim=audio_cross_attention_dim,
+                    video_gated_attn=gated_attn,
+                    video_cross_attn_adaln=cross_attn_mod,
+                    audio_gated_attn=audio_gated_attn,
+                    audio_cross_attn_adaln=audio_cross_attn_mod,
                     qk_norm=qk_norm,
                     activation_fn=activation_fn,
                     attention_bias=attention_bias,
@@ -1078,6 +1303,7 @@ class LTX2VideoTransformer3DModel(
                     eps=norm_eps,
                     elementwise_affine=norm_elementwise_affine,
                     rope_type=rope_type,
+                    perturbed_attn=perturbed_attn,
                 )
                 for _ in range(num_layers)
             ]
@@ -1101,6 +1327,8 @@ class LTX2VideoTransformer3DModel(
         audio_encoder_hidden_states: torch.Tensor,
         timestep: torch.LongTensor,
         audio_timestep: torch.LongTensor | None = None,
+        sigma: torch.Tensor | None = None,
+        audio_sigma: torch.Tensor | None = None,
         encoder_attention_mask: torch.Tensor | None = None,
         audio_encoder_attention_mask: torch.Tensor | None = None,
         num_frames: int | None = None,
@@ -1110,6 +1338,10 @@ class LTX2VideoTransformer3DModel(
         audio_num_frames: int | None = None,
         video_coords: torch.Tensor | None = None,
         audio_coords: torch.Tensor | None = None,
+        isolate_modalities: bool = False,
+        spatio_temporal_guidance_blocks: list[int] | None = None,
+        perturbation_mask: torch.Tensor | None = None,
+        use_cross_timestep: bool = False,
         attention_kwargs: dict[str, Any] | None = None,
         return_dict: bool = True,
     ) -> torch.Tensor:
@@ -1131,6 +1363,13 @@ class LTX2VideoTransformer3DModel(
             audio_timestep (`torch.Tensor`, *optional*):
                 Input timestep of shape `(batch_size,)` or `(batch_size, num_audio_tokens)` for audio modulation
                 params. This is only used by certain pipelines such as the I2V pipeline.
+            sigma (`torch.Tensor`, *optional*):
+                Input scaled timestep of shape (batch_size,). Used for video prompt cross attention modulation in
+                models such as LTX-2.3.
+            audio_sigma (`torch.Tensor`, *optional*):
+                Input scaled timestep of shape (batch_size,). Used for audio prompt cross attention modulation in
+                models such as LTX-2.3. If `sigma` is supplied but `audio_sigma` is not, `audio_sigma` will be set to
+                the provided `sigma` value.
             encoder_attention_mask (`torch.Tensor`, *optional*):
                 Optional multiplicative text attention mask of shape `(batch_size, text_seq_len)`.
             audio_encoder_attention_mask (`torch.Tensor`, *optional*):
@@ -1152,6 +1391,21 @@ class LTX2VideoTransformer3DModel(
             audio_coords (`torch.Tensor`, *optional*):
                 The audio coordinates to be used when calculating the rotary positional embeddings (RoPE) of shape
                 `(batch_size, 1, num_audio_tokens, 2)`. If not supplied, this will be calculated inside `forward`.
+            isolate_modalities (`bool`, *optional*, defaults to `False`):
+                Whether to isolate each modality by turning off cross-modality (audio-to-video and video-to-audio)
+                cross attention (for all blocks). Use for modality guidance in LTX-2.3.
+            spatio_temporal_guidance_blocks (`list[int]`, *optional*, defaults to `None`):
+                The transformer block indices at which to apply spatio-temporal guidance (STG), which shortcuts the
+                self-attention operations by simply using the values rather than the full scaled dot-product attention
+                (SDPA) operation. If `None` or empty, STG will not be applied to any block.
+            perturbation_mask (`torch.Tensor`, *optional*):
+                Perturbation mask for STG of shape `(batch_size,)` or `(batch_size, 1, 1)`. Should be 0 at batch
+                elements where STG should be applied and 1 elsewhere. If STG is being used but `peturbation_mask` is
+                not supplied, will default to applying STG (perturbing) all batch elements.
+            use_cross_timestep (`bool` *optional*, defaults to `False`):
+                Whether to use the cross modality (audio is the cross modality of video, and vice versa) sigma when
+                calculating the cross attention modulation parameters. `True` is the newer (e.g. LTX-2.3) behavior;
+                `False` is the legacy LTX-2.0 behavior.
             attention_kwargs (`dict[str, Any]`, *optional*):
                 Optional dict of keyword args to be passed to the attention processor.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -1165,6 +1419,7 @@ class LTX2VideoTransformer3DModel(
         """
         # Determine timestep for audio.
         audio_timestep = audio_timestep if audio_timestep is not None else timestep
+        audio_sigma = audio_sigma if audio_sigma is not None else sigma
 
         # convert encoder_attention_mask to a bias the same way we do for attention_mask
         if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
@@ -1223,14 +1478,28 @@ class LTX2VideoTransformer3DModel(
         temb_audio = temb_audio.view(batch_size, -1, temb_audio.size(-1))
         audio_embedded_timestep = audio_embedded_timestep.view(batch_size, -1, audio_embedded_timestep.size(-1))
 
+        if self.prompt_modulation:
+            # LTX-2.3
+            temb_prompt, _ = self.prompt_adaln(
+                sigma.flatten(), batch_size=batch_size, hidden_dtype=hidden_states.dtype
+            )
+            temb_prompt_audio, _ = self.audio_prompt_adaln(
+                audio_sigma.flatten(), batch_size=batch_size, hidden_dtype=audio_hidden_states.dtype
+            )
+            temb_prompt = temb_prompt.view(batch_size, -1, temb_prompt.size(-1))
+            temb_prompt_audio = temb_prompt_audio.view(batch_size, -1, temb_prompt_audio.size(-1))
+        else:
+            temb_prompt = temb_prompt_audio = None
+
         # 3.2. Prepare global modality cross attention modulation parameters
+        video_ca_timestep = audio_sigma.flatten() if use_cross_timestep else timestep.flatten()
         video_cross_attn_scale_shift, _ = self.av_cross_attn_video_scale_shift(
-            timestep.flatten(),
+            video_ca_timestep,
             batch_size=batch_size,
             hidden_dtype=hidden_states.dtype,
         )
         video_cross_attn_a2v_gate, _ = self.av_cross_attn_video_a2v_gate(
-            timestep.flatten() * timestep_cross_attn_gate_scale_factor,
+            video_ca_timestep * timestep_cross_attn_gate_scale_factor,
             batch_size=batch_size,
             hidden_dtype=hidden_states.dtype,
         )
@@ -1239,13 +1508,14 @@ class LTX2VideoTransformer3DModel(
         )
         video_cross_attn_a2v_gate = video_cross_attn_a2v_gate.view(batch_size, -1, video_cross_attn_a2v_gate.shape[-1])
 
+        audio_ca_timestep = sigma.flatten() if use_cross_timestep else audio_timestep.flatten()
         audio_cross_attn_scale_shift, _ = self.av_cross_attn_audio_scale_shift(
-            audio_timestep.flatten(),
+            audio_ca_timestep,
             batch_size=batch_size,
             hidden_dtype=audio_hidden_states.dtype,
         )
         audio_cross_attn_v2a_gate, _ = self.av_cross_attn_audio_v2a_gate(
-            audio_timestep.flatten() * timestep_cross_attn_gate_scale_factor,
+            audio_ca_timestep * timestep_cross_attn_gate_scale_factor,
             batch_size=batch_size,
             hidden_dtype=audio_hidden_states.dtype,
         )
@@ -1254,15 +1524,30 @@ class LTX2VideoTransformer3DModel(
         )
         audio_cross_attn_v2a_gate = audio_cross_attn_v2a_gate.view(batch_size, -1, audio_cross_attn_v2a_gate.shape[-1])
 
-        # 4. Prepare prompt embeddings
-        encoder_hidden_states = self.caption_projection(encoder_hidden_states)
-        encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.size(-1))
+        # 4. Prepare prompt embeddings (LTX-2.0)
+        if self.config.use_prompt_embeddings:
+            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
+            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.size(-1))
 
-        audio_encoder_hidden_states = self.audio_caption_projection(audio_encoder_hidden_states)
-        audio_encoder_hidden_states = audio_encoder_hidden_states.view(batch_size, -1, audio_hidden_states.size(-1))
+            audio_encoder_hidden_states = self.audio_caption_projection(audio_encoder_hidden_states)
+            audio_encoder_hidden_states = audio_encoder_hidden_states.view(
+                batch_size, -1, audio_hidden_states.size(-1)
+            )
 
         # 5. Run transformer blocks
-        for block in self.transformer_blocks:
+        spatio_temporal_guidance_blocks = spatio_temporal_guidance_blocks or []
+        if len(spatio_temporal_guidance_blocks) > 0 and perturbation_mask is None:
+            # If STG is being used and perturbation_mask is not supplied, default to perturbing all batch elements.
+            perturbation_mask = torch.zeros((batch_size,))
+        if perturbation_mask is not None and perturbation_mask.ndim == 1:
+            perturbation_mask = perturbation_mask[:, None, None]  # unsqueeze to 3D to broadcast with hidden_states
+        all_perturbed = torch.all(perturbation_mask == 0) if perturbation_mask is not None else False
+        stg_blocks = set(spatio_temporal_guidance_blocks)
+
+        for block_idx, block in enumerate(self.transformer_blocks):
+            block_perturbation_mask = perturbation_mask if block_idx in stg_blocks else None
+            block_all_perturbed = all_perturbed if block_idx in stg_blocks else False
+
             if torch.is_grad_enabled() and self.gradient_checkpointing:
                 hidden_states, audio_hidden_states = self._gradient_checkpointing_func(
                     block,
@@ -1276,12 +1561,22 @@ class LTX2VideoTransformer3DModel(
                     audio_cross_attn_scale_shift,
                     video_cross_attn_a2v_gate,
                     audio_cross_attn_v2a_gate,
+                    temb_prompt,
+                    temb_prompt_audio,
                     video_rotary_emb,
                     audio_rotary_emb,
                     video_cross_attn_rotary_emb,
                     audio_cross_attn_rotary_emb,
                     encoder_attention_mask,
                     audio_encoder_attention_mask,
+                    None,  # self_attention_mask
+                    None,  # audio_self_attention_mask
+                    None,  # a2v_cross_attention_mask
+                    None,  # v2a_cross_attention_mask
+                    not isolate_modalities,  # use_a2v_cross_attention
+                    not isolate_modalities,  # use_v2a_cross_attention
+                    block_perturbation_mask,
+                    block_all_perturbed,
                 )
             else:
                 hidden_states, audio_hidden_states = block(
@@ -1295,12 +1590,22 @@ class LTX2VideoTransformer3DModel(
                     temb_ca_audio_scale_shift=audio_cross_attn_scale_shift,
                     temb_ca_gate=video_cross_attn_a2v_gate,
                     temb_ca_audio_gate=audio_cross_attn_v2a_gate,
+                    temb_prompt=temb_prompt,
+                    temb_prompt_audio=temb_prompt_audio,
                     video_rotary_emb=video_rotary_emb,
                     audio_rotary_emb=audio_rotary_emb,
                     ca_video_rotary_emb=video_cross_attn_rotary_emb,
                     ca_audio_rotary_emb=audio_cross_attn_rotary_emb,
                     encoder_attention_mask=encoder_attention_mask,
                     audio_encoder_attention_mask=audio_encoder_attention_mask,
+                    self_attention_mask=None,
+                    audio_self_attention_mask=None,
+                    a2v_cross_attention_mask=None,
+                    v2a_cross_attention_mask=None,
+                    use_a2v_cross_attention=not isolate_modalities,
+                    use_v2a_cross_attention=not isolate_modalities,
+                    perturbation_mask=block_perturbation_mask,
+                    all_perturbed=block_all_perturbed,
                 )
 
         # 6. Output layers (including unpatchification)

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -324,17 +324,18 @@ class AudioLDM2Pipeline(DiffusionPipeline):
             `inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
                 The sequence of generated hidden-states.
         """
-        cache_position_kwargs = {}
-        if is_transformers_version("<", "4.52.1"):
-            cache_position_kwargs["input_ids"] = inputs_embeds
-        else:
-            cache_position_kwargs["seq_length"] = inputs_embeds.shape[0]
-            cache_position_kwargs["device"] = (
-                self.language_model.device if getattr(self, "language_model", None) is not None else self.device
-            )
-        cache_position_kwargs["model_kwargs"] = model_kwargs
         max_new_tokens = max_new_tokens if max_new_tokens is not None else self.language_model.config.max_new_tokens
-        model_kwargs = self.language_model._get_initial_cache_position(**cache_position_kwargs)
+        if hasattr(self.language_model, "_get_initial_cache_position"):
+            cache_position_kwargs = {}
+            if is_transformers_version("<", "4.52.1"):
+                cache_position_kwargs["input_ids"] = inputs_embeds
+            else:
+                cache_position_kwargs["seq_length"] = inputs_embeds.shape[0]
+                cache_position_kwargs["device"] = (
+                    self.language_model.device if getattr(self, "language_model", None) is not None else self.device
+                )
+            cache_position_kwargs["model_kwargs"] = model_kwargs
+            model_kwargs = self.language_model._get_initial_cache_position(**cache_position_kwargs)
 
         for _ in range(max_new_tokens):
             # prepare model inputs

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -720,6 +720,7 @@ class LDMBertModel(LDMBertPreTrainedModel):
         super().__init__(config)
         self.model = LDMBertEncoder(config)
         self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)
+        self.post_init()
 
     def forward(
         self,

src/diffusers/pipelines/ltx2/__init__.py

@@ -28,7 +28,7 @@ else:
     _import_structure["pipeline_ltx2_condition"] = ["LTX2ConditionPipeline"]
     _import_structure["pipeline_ltx2_image2video"] = ["LTX2ImageToVideoPipeline"]
     _import_structure["pipeline_ltx2_latent_upsample"] = ["LTX2LatentUpsamplePipeline"]
-    _import_structure["vocoder"] = ["LTX2Vocoder"]
+    _import_structure["vocoder"] = ["LTX2Vocoder", "LTX2VocoderWithBWE"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -44,7 +44,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .pipeline_ltx2_condition import LTX2ConditionPipeline
         from .pipeline_ltx2_image2video import LTX2ImageToVideoPipeline
         from .pipeline_ltx2_latent_upsample import LTX2LatentUpsamplePipeline
-        from .vocoder import LTX2Vocoder
+        from .vocoder import LTX2Vocoder, LTX2VocoderWithBWE
 
 else:
     import sys

src/diffusers/pipelines/ltx2/connectors.py

@@ -1,3 +1,5 @@
+import math
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -9,6 +11,79 @@ from ...models.modeling_utils import ModelMixin
 from ...models.transformers.transformer_ltx2 import LTX2Attention, LTX2AudioVideoAttnProcessor
 
 
+def per_layer_masked_mean_norm(
+    text_hidden_states: torch.Tensor,
+    sequence_lengths: torch.Tensor,
+    device: str | torch.device,
+    padding_side: str = "left",
+    scale_factor: int = 8,
+    eps: float = 1e-6,
+):
+    """
+    Performs per-batch per-layer normalization using a masked mean and range on per-layer text encoder hidden_states.
+    Respects the padding of the hidden states.
+
+    Args:
+        text_hidden_states (`torch.Tensor` of shape `(batch_size, seq_len, hidden_dim, num_layers)`):
+            Per-layer hidden_states from a text encoder (e.g. `Gemma3ForConditionalGeneration`).
+        sequence_lengths (`torch.Tensor of shape `(batch_size,)`):
+            The number of valid (non-padded) tokens for each batch instance.
+        device: (`str` or `torch.device`, *optional*):
+            torch device to place the resulting embeddings on
+        padding_side: (`str`, *optional*, defaults to `"left"`):
+            Whether the text tokenizer performs padding on the `"left"` or `"right"`.
+        scale_factor (`int`, *optional*, defaults to `8`):
+            Scaling factor to multiply the normalized hidden states by.
+        eps (`float`, *optional*, defaults to `1e-6`):
+            A small positive value for numerical stability when performing normalization.
+
+    Returns:
+        `torch.Tensor` of shape `(batch_size, seq_len, hidden_dim * num_layers)`:
+            Normed and flattened text encoder hidden states.
+    """
+    batch_size, seq_len, hidden_dim, num_layers = text_hidden_states.shape
+    original_dtype = text_hidden_states.dtype
+
+    # Create padding mask
+    token_indices = torch.arange(seq_len, device=device).unsqueeze(0)
+    if padding_side == "right":
+        # For right padding, valid tokens are from 0 to sequence_length-1
+        mask = token_indices < sequence_lengths[:, None]  # [batch_size, seq_len]
+    elif padding_side == "left":
+        # For left padding, valid tokens are from (T - sequence_length) to T-1
+        start_indices = seq_len - sequence_lengths[:, None]  # [batch_size, 1]
+        mask = token_indices >= start_indices  # [B, T]
+    else:
+        raise ValueError(f"padding_side must be 'left' or 'right', got {padding_side}")
+    mask = mask[:, :, None, None]  # [batch_size, seq_len] --> [batch_size, seq_len, 1, 1]
+
+    # Compute masked mean over non-padding positions of shape (batch_size, 1, 1, seq_len)
+    masked_text_hidden_states = text_hidden_states.masked_fill(~mask, 0.0)
+    num_valid_positions = (sequence_lengths * hidden_dim).view(batch_size, 1, 1, 1)
+    masked_mean = masked_text_hidden_states.sum(dim=(1, 2), keepdim=True) / (num_valid_positions + eps)
+
+    # Compute min/max over non-padding positions of shape (batch_size, 1, 1 seq_len)
+    x_min = text_hidden_states.masked_fill(~mask, float("inf")).amin(dim=(1, 2), keepdim=True)
+    x_max = text_hidden_states.masked_fill(~mask, float("-inf")).amax(dim=(1, 2), keepdim=True)
+
+    # Normalization
+    normalized_hidden_states = (text_hidden_states - masked_mean) / (x_max - x_min + eps)
+    normalized_hidden_states = normalized_hidden_states * scale_factor
+
+    # Pack the hidden states to a 3D tensor (batch_size, seq_len, hidden_dim * num_layers)
+    normalized_hidden_states = normalized_hidden_states.flatten(2)
+    mask_flat = mask.squeeze(-1).expand(-1, -1, hidden_dim * num_layers)
+    normalized_hidden_states = normalized_hidden_states.masked_fill(~mask_flat, 0.0)
+    normalized_hidden_states = normalized_hidden_states.to(dtype=original_dtype)
+    return normalized_hidden_states
+
+
+def per_token_rms_norm(text_encoder_hidden_states: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
+    variance = torch.mean(text_encoder_hidden_states**2, dim=2, keepdim=True)
+    norm_text_encoder_hidden_states = text_encoder_hidden_states * torch.rsqrt(variance + eps)
+    return norm_text_encoder_hidden_states
+
+
 class LTX2RotaryPosEmbed1d(nn.Module):
     """
     1D rotary positional embeddings (RoPE) for the LTX 2.0 text encoder connectors.
@@ -106,6 +181,7 @@ class LTX2TransformerBlock1d(nn.Module):
         activation_fn: str = "gelu-approximate",
         eps: float = 1e-6,
         rope_type: str = "interleaved",
+        apply_gated_attention: bool = False,
     ):
         super().__init__()
 
@@ -115,8 +191,9 @@ class LTX2TransformerBlock1d(nn.Module):
             heads=num_attention_heads,
             kv_heads=num_attention_heads,
             dim_head=attention_head_dim,
-            processor=LTX2AudioVideoAttnProcessor(),
             rope_type=rope_type,
+            apply_gated_attention=apply_gated_attention,
+            processor=LTX2AudioVideoAttnProcessor(),
         )
 
         self.norm2 = torch.nn.RMSNorm(dim, eps=eps, elementwise_affine=False)
@@ -160,6 +237,7 @@ class LTX2ConnectorTransformer1d(nn.Module):
         eps: float = 1e-6,
         causal_temporal_positioning: bool = False,
         rope_type: str = "interleaved",
+        gated_attention: bool = False,
     ):
         super().__init__()
         self.num_attention_heads = num_attention_heads
@@ -188,6 +266,7 @@ class LTX2ConnectorTransformer1d(nn.Module):
                     num_attention_heads=num_attention_heads,
                     attention_head_dim=attention_head_dim,
                     rope_type=rope_type,
+                    apply_gated_attention=gated_attention,
                 )
                 for _ in range(num_layers)
             ]
@@ -260,24 +339,36 @@ class LTX2TextConnectors(ModelMixin, PeftAdapterMixin, ConfigMixin):
     @register_to_config
     def __init__(
         self,
-        caption_channels: int,
-        text_proj_in_factor: int,
-        video_connector_num_attention_heads: int,
-        video_connector_attention_head_dim: int,
-        video_connector_num_layers: int,
-        video_connector_num_learnable_registers: int | None,
-        audio_connector_num_attention_heads: int,
-        audio_connector_attention_head_dim: int,
-        audio_connector_num_layers: int,
-        audio_connector_num_learnable_registers: int | None,
-        connector_rope_base_seq_len: int,
-        rope_theta: float,
-        rope_double_precision: bool,
-        causal_temporal_positioning: bool,
+        caption_channels: int = 3840,  # default Gemma-3-12B text encoder hidden_size
+        text_proj_in_factor: int = 49,  # num_layers + 1 for embedding layer = 48 + 1 for Gemma-3-12B
+        video_connector_num_attention_heads: int = 30,
+        video_connector_attention_head_dim: int = 128,
+        video_connector_num_layers: int = 2,
+        video_connector_num_learnable_registers: int | None = 128,
+        video_gated_attn: bool = False,
+        audio_connector_num_attention_heads: int = 30,
+        audio_connector_attention_head_dim: int = 128,
+        audio_connector_num_layers: int = 2,
+        audio_connector_num_learnable_registers: int | None = 128,
+        audio_gated_attn: bool = False,
+        connector_rope_base_seq_len: int = 4096,
+        rope_theta: float = 10000.0,
+        rope_double_precision: bool = True,
+        causal_temporal_positioning: bool = False,
         rope_type: str = "interleaved",
+        per_modality_projections: bool = False,
+        video_hidden_dim: int = 4096,
+        audio_hidden_dim: int = 2048,
+        proj_bias: bool = False,
     ):
         super().__init__()
-        self.text_proj_in = nn.Linear(caption_channels * text_proj_in_factor, caption_channels, bias=False)
+        text_encoder_dim = caption_channels * text_proj_in_factor
+        if per_modality_projections:
+            self.video_text_proj_in = nn.Linear(text_encoder_dim, video_hidden_dim, bias=proj_bias)
+            self.audio_text_proj_in = nn.Linear(text_encoder_dim, audio_hidden_dim, bias=proj_bias)
+        else:
+            self.text_proj_in = nn.Linear(text_encoder_dim, caption_channels, bias=proj_bias)
+
         self.video_connector = LTX2ConnectorTransformer1d(
             num_attention_heads=video_connector_num_attention_heads,
             attention_head_dim=video_connector_attention_head_dim,
@@ -288,6 +379,7 @@ class LTX2TextConnectors(ModelMixin, PeftAdapterMixin, ConfigMixin):
             rope_double_precision=rope_double_precision,
             causal_temporal_positioning=causal_temporal_positioning,
             rope_type=rope_type,
+            gated_attention=video_gated_attn,
         )
         self.audio_connector = LTX2ConnectorTransformer1d(
             num_attention_heads=audio_connector_num_attention_heads,
@@ -299,26 +391,86 @@ class LTX2TextConnectors(ModelMixin, PeftAdapterMixin, ConfigMixin):
             rope_double_precision=rope_double_precision,
             causal_temporal_positioning=causal_temporal_positioning,
             rope_type=rope_type,
+            gated_attention=audio_gated_attn,
         )
 
     def forward(
-        self, text_encoder_hidden_states: torch.Tensor, attention_mask: torch.Tensor, additive_mask: bool = False
-    ):
-        # Convert to additive attention mask, if necessary
-        if not additive_mask:
-            text_dtype = text_encoder_hidden_states.dtype
-            attention_mask = (attention_mask - 1).reshape(attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
-            attention_mask = attention_mask.to(text_dtype) * torch.finfo(text_dtype).max
+        self,
+        text_encoder_hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        padding_side: str = "left",
+        scale_factor: int = 8,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Given per-layer text encoder hidden_states, extracts features and runs per-modality connectors to get text
+        embeddings for the LTX-2.X DiT models.
 
-        text_encoder_hidden_states = self.text_proj_in(text_encoder_hidden_states)
+        Args:
+            text_encoder_hidden_states (`torch.Tensor`)):
+                Per-layer text encoder hidden_states. Can either be 4D with shape `(batch_size, seq_len,
+                caption_channels, text_proj_in_factor) or 3D with the last two dimensions flattened.
+            attention_mask (`torch.Tensor` of shape `(batch_size, seq_len)`):
+                Multiplicative binary attention mask where 1s indicate unmasked positions and 0s indicate masked
+                positions.
+            padding_side (`str`, *optional*, defaults to `"left"`):
+                The padding side used by the text encoder's text encoder (either `"left"` or `"right"`). Defaults to
+                `"left"` as this is what the default Gemma3-12B text encoder uses. Only used if
+                `per_modality_projections` is `False` (LTX-2.0 models).
+            scale_factor (`int`, *optional*, defaults to `8`):
+                Scale factor for masked mean/range normalization. Only used if `per_modality_projections` is `False`
+                (LTX-2.0 models).
+        """
+        if text_encoder_hidden_states.ndim == 3:
+            # Ensure shape is [batch_size, seq_len, caption_channels, text_proj_in_factor]
+            text_encoder_hidden_states = text_encoder_hidden_states.unflatten(2, (self.config.caption_channels, -1))
 
-        video_text_embedding, new_attn_mask = self.video_connector(text_encoder_hidden_states, attention_mask)
+        if self.config.per_modality_projections:
+            # LTX-2.3
+            norm_text_encoder_hidden_states = per_token_rms_norm(text_encoder_hidden_states)
 
-        attn_mask = (new_attn_mask < 1e-6).to(torch.int64)
-        attn_mask = attn_mask.reshape(video_text_embedding.shape[0], video_text_embedding.shape[1], 1)
-        video_text_embedding = video_text_embedding * attn_mask
-        new_attn_mask = attn_mask.squeeze(-1)
+            norm_text_encoder_hidden_states = norm_text_encoder_hidden_states.flatten(2, 3)
+            bool_mask = attention_mask.bool().unsqueeze(-1)
+            norm_text_encoder_hidden_states = torch.where(
+                bool_mask, norm_text_encoder_hidden_states, torch.zeros_like(norm_text_encoder_hidden_states)
+            )
 
-        audio_text_embedding, _ = self.audio_connector(text_encoder_hidden_states, attention_mask)
+            # Rescale norms with respect to video and audio dims for feature extractors
+            video_scale_factor = math.sqrt(self.config.video_hidden_dim / self.config.caption_channels)
+            video_norm_text_emb = norm_text_encoder_hidden_states * video_scale_factor
+            audio_scale_factor = math.sqrt(self.config.audio_hidden_dim / self.config.caption_channels)
+            audio_norm_text_emb = norm_text_encoder_hidden_states * audio_scale_factor
 
-        return video_text_embedding, audio_text_embedding, new_attn_mask
+            # Per-Modality Feature extractors
+            video_text_emb_proj = self.video_text_proj_in(video_norm_text_emb)
+            audio_text_emb_proj = self.audio_text_proj_in(audio_norm_text_emb)
+        else:
+            # LTX-2.0
+            sequence_lengths = attention_mask.sum(dim=-1)
+            norm_text_encoder_hidden_states = per_layer_masked_mean_norm(
+                text_hidden_states=text_encoder_hidden_states,
+                sequence_lengths=sequence_lengths,
+                device=text_encoder_hidden_states.device,
+                padding_side=padding_side,
+                scale_factor=scale_factor,
+            )
+
+            text_emb_proj = self.text_proj_in(norm_text_encoder_hidden_states)
+            video_text_emb_proj = text_emb_proj
+            audio_text_emb_proj = text_emb_proj
+
+        # Convert to additive attention mask for connectors
+        text_dtype = video_text_emb_proj.dtype
+        attention_mask = (attention_mask.to(torch.int64) - 1).to(text_dtype)
+        attention_mask = attention_mask.reshape(attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
+        add_attn_mask = attention_mask * torch.finfo(text_dtype).max
+
+        video_text_embedding, video_attn_mask = self.video_connector(video_text_emb_proj, add_attn_mask)
+
+        # Convert video attn mask to binary (multiplicative) mask and mask video text embedding
+        binary_attn_mask = (video_attn_mask < 1e-6).to(torch.int64)
+        binary_attn_mask = binary_attn_mask.reshape(video_text_embedding.shape[0], video_text_embedding.shape[1], 1)
+        video_text_embedding = video_text_embedding * binary_attn_mask
+
+        audio_text_embedding, _ = self.audio_connector(audio_text_emb_proj, add_attn_mask)
+
+        return video_text_embedding, audio_text_embedding, binary_attn_mask.squeeze(-1)

src/diffusers/pipelines/ltx2/latent_upsampler.py

@@ -195,7 +195,8 @@ class LTX2LatentUpsamplerModel(ModelMixin, ConfigMixin):
         dims: int = 3,
         spatial_upsample: bool = True,
         temporal_upsample: bool = False,
-        rational_spatial_scale: float | None = 2.0,
+        rational_spatial_scale: float = 2.0,
+        use_rational_resampler: bool = True,
     ):
         super().__init__()
 
@@ -220,7 +221,7 @@ class LTX2LatentUpsamplerModel(ModelMixin, ConfigMixin):
                 PixelShuffleND(3),
             )
         elif spatial_upsample:
-            if rational_spatial_scale is not None:
+            if use_rational_resampler:
                 self.upsampler = SpatialRationalResampler(mid_channels=mid_channels, scale=rational_spatial_scale)
             else:
                 self.upsampler = torch.nn.Sequential(

src/diffusers/pipelines/ltx2/pipeline_ltx2.py

@@ -18,7 +18,7 @@ from typing import Any, Callable
 
 import numpy as np
 import torch
-from transformers import Gemma3ForConditionalGeneration, GemmaTokenizer, GemmaTokenizerFast
+from transformers import Gemma3ForConditionalGeneration, Gemma3Processor, GemmaTokenizer, GemmaTokenizerFast
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...loaders import FromSingleFileMixin, LTX2LoraLoaderMixin
@@ -31,7 +31,7 @@ from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
 from .connectors import LTX2TextConnectors
 from .pipeline_output import LTX2PipelineOutput
-from .vocoder import LTX2Vocoder
+from .vocoder import LTX2Vocoder, LTX2VocoderWithBWE
 
 
 if is_torch_xla_available():
@@ -209,7 +209,7 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
     """
 
     model_cpu_offload_seq = "text_encoder->connectors->transformer->vae->audio_vae->vocoder"
-    _optional_components = []
+    _optional_components = ["processor"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
 
     def __init__(
@@ -221,7 +221,8 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         tokenizer: GemmaTokenizer | GemmaTokenizerFast,
         connectors: LTX2TextConnectors,
         transformer: LTX2VideoTransformer3DModel,
-        vocoder: LTX2Vocoder,
+        vocoder: LTX2Vocoder | LTX2VocoderWithBWE,
+        processor: Gemma3Processor | None = None,
     ):
         super().__init__()
 
@@ -234,6 +235,7 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
             transformer=transformer,
             vocoder=vocoder,
             scheduler=scheduler,
+            processor=processor,
         )
 
         self.vae_spatial_compression_ratio = (
@@ -268,73 +270,6 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
             self.tokenizer.model_max_length if getattr(self, "tokenizer", None) is not None else 1024
         )
 
-    @staticmethod
-    def _pack_text_embeds(
-        text_hidden_states: torch.Tensor,
-        sequence_lengths: torch.Tensor,
-        device: str | torch.device,
-        padding_side: str = "left",
-        scale_factor: int = 8,
-        eps: float = 1e-6,
-    ) -> torch.Tensor:
-        """
-        Packs and normalizes text encoder hidden states, respecting padding. Normalization is performed per-batch and
-        per-layer in a masked fashion (only over non-padded positions).
-
-        Args:
-            text_hidden_states (`torch.Tensor` of shape `(batch_size, seq_len, hidden_dim, num_layers)`):
-                Per-layer hidden_states from a text encoder (e.g. `Gemma3ForConditionalGeneration`).
-            sequence_lengths (`torch.Tensor of shape `(batch_size,)`):
-                The number of valid (non-padded) tokens for each batch instance.
-            device: (`str` or `torch.device`, *optional*):
-                torch device to place the resulting embeddings on
-            padding_side: (`str`, *optional*, defaults to `"left"`):
-                Whether the text tokenizer performs padding on the `"left"` or `"right"`.
-            scale_factor (`int`, *optional*, defaults to `8`):
-                Scaling factor to multiply the normalized hidden states by.
-            eps (`float`, *optional*, defaults to `1e-6`):
-                A small positive value for numerical stability when performing normalization.
-
-        Returns:
-            `torch.Tensor` of shape `(batch_size, seq_len, hidden_dim * num_layers)`:
-                Normed and flattened text encoder hidden states.
-        """
-        batch_size, seq_len, hidden_dim, num_layers = text_hidden_states.shape
-        original_dtype = text_hidden_states.dtype
-
-        # Create padding mask
-        token_indices = torch.arange(seq_len, device=device).unsqueeze(0)
-        if padding_side == "right":
-            # For right padding, valid tokens are from 0 to sequence_length-1
-            mask = token_indices < sequence_lengths[:, None]  # [batch_size, seq_len]
-        elif padding_side == "left":
-            # For left padding, valid tokens are from (T - sequence_length) to T-1
-            start_indices = seq_len - sequence_lengths[:, None]  # [batch_size, 1]
-            mask = token_indices >= start_indices  # [B, T]
-        else:
-            raise ValueError(f"padding_side must be 'left' or 'right', got {padding_side}")
-        mask = mask[:, :, None, None]  # [batch_size, seq_len] --> [batch_size, seq_len, 1, 1]
-
-        # Compute masked mean over non-padding positions of shape (batch_size, 1, 1, seq_len)
-        masked_text_hidden_states = text_hidden_states.masked_fill(~mask, 0.0)
-        num_valid_positions = (sequence_lengths * hidden_dim).view(batch_size, 1, 1, 1)
-        masked_mean = masked_text_hidden_states.sum(dim=(1, 2), keepdim=True) / (num_valid_positions + eps)
-
-        # Compute min/max over non-padding positions of shape (batch_size, 1, 1 seq_len)
-        x_min = text_hidden_states.masked_fill(~mask, float("inf")).amin(dim=(1, 2), keepdim=True)
-        x_max = text_hidden_states.masked_fill(~mask, float("-inf")).amax(dim=(1, 2), keepdim=True)
-
-        # Normalization
-        normalized_hidden_states = (text_hidden_states - masked_mean) / (x_max - x_min + eps)
-        normalized_hidden_states = normalized_hidden_states * scale_factor
-
-        # Pack the hidden states to a 3D tensor (batch_size, seq_len, hidden_dim * num_layers)
-        normalized_hidden_states = normalized_hidden_states.flatten(2)
-        mask_flat = mask.squeeze(-1).expand(-1, -1, hidden_dim * num_layers)
-        normalized_hidden_states = normalized_hidden_states.masked_fill(~mask_flat, 0.0)
-        normalized_hidden_states = normalized_hidden_states.to(dtype=original_dtype)
-        return normalized_hidden_states
-
     def _get_gemma_prompt_embeds(
         self,
         prompt: str | list[str],
@@ -387,16 +322,7 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         )
         text_encoder_hidden_states = text_encoder_outputs.hidden_states
         text_encoder_hidden_states = torch.stack(text_encoder_hidden_states, dim=-1)
-        sequence_lengths = prompt_attention_mask.sum(dim=-1)
-
-        prompt_embeds = self._pack_text_embeds(
-            text_encoder_hidden_states,
-            sequence_lengths,
-            device=device,
-            padding_side=self.tokenizer.padding_side,
-            scale_factor=scale_factor,
-        )
-        prompt_embeds = prompt_embeds.to(dtype=dtype)
+        prompt_embeds = text_encoder_hidden_states.flatten(2, 3).to(dtype=dtype)  # Pack to 3D
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         _, seq_len, _ = prompt_embeds.shape
@@ -494,6 +420,50 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
 
         return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
 
+    @torch.no_grad()
+    def enhance_prompt(
+        self,
+        prompt: str,
+        system_prompt: str,
+        max_new_tokens: int = 512,
+        seed: int = 10,
+        generator: torch.Generator | None = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        device: str | torch.device | None = None,
+    ):
+        """
+        Enhances the supplied `prompt` by generating a new prompt using the current text encoder (default is a
+        `transformers.Gemma3ForConditionalGeneration` model) from it and a system prompt.
+        """
+        device = device or self._execution_device
+        if generation_kwargs is None:
+            # Set to default generation kwargs
+            generation_kwargs = {"do_sample": True, "temperature": 0.7}
+
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": f"user prompt: {prompt}"},
+        ]
+        template = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        model_inputs = self.processor(text=template, images=None, return_tensors="pt").to(device)
+        self.text_encoder.to(device)
+
+        # `transformers.GenerationMixin.generate` does not support using a `torch.Generator` to control randomness,
+        # so manually apply a seed for reproducible generation.
+        if generator is not None:
+            # Overwrite seed to generator's initial seed
+            seed = generator.initial_seed()
+        torch.manual_seed(seed)
+        generated_sequences = self.text_encoder.generate(
+            **model_inputs,
+            max_new_tokens=max_new_tokens,
+            **generation_kwargs,
+        )  # tensor of shape [batch_size, seq_len]
+
+        generated_ids = [seq[len(model_inputs.input_ids[i]) :] for i, seq in enumerate(generated_sequences)]
+        enhanced_prompt = self.processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        return enhanced_prompt
+
     def check_inputs(
         self,
         prompt,
@@ -504,6 +474,9 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         negative_prompt_embeds=None,
         prompt_attention_mask=None,
         negative_prompt_attention_mask=None,
+        spatio_temporal_guidance_blocks=None,
+        stg_scale=None,
+        audio_stg_scale=None,
     ):
         if height % 32 != 0 or width % 32 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
@@ -547,6 +520,12 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                     f" {negative_prompt_attention_mask.shape}."
                 )
 
+        if ((stg_scale > 0.0) or (audio_stg_scale > 0.0)) and not spatio_temporal_guidance_blocks:
+            raise ValueError(
+                "Spatio-Temporal Guidance (STG) is specified but no STG blocks are supplied. Please supply a list of"
+                "block indices at which to apply STG in `spatio_temporal_guidance_blocks`"
+            )
+
     @staticmethod
     def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
         # Unpacked latents of shape are [B, C, F, H, W] are patched into tokens of shape [B, C, F // p_t, p_t, H // p, p, W // p, p].
@@ -734,7 +713,6 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
             latents = self._create_noised_state(latents, noise_scale, generator)
             return latents.to(device=device, dtype=dtype)
 
-        # TODO: confirm whether this logic is correct
         latent_mel_bins = num_mel_bins // self.audio_vae_mel_compression_ratio
 
         shape = (batch_size, num_channels_latents, audio_latent_length, latent_mel_bins)
@@ -749,6 +727,24 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         latents = self._pack_audio_latents(latents)
         return latents
 
+    def convert_velocity_to_x0(
+        self, sample: torch.Tensor, denoised_output: torch.Tensor, step_idx: int, scheduler: Any | None = None
+    ) -> torch.Tensor:
+        if scheduler is None:
+            scheduler = self.scheduler
+
+        sample_x0 = sample - denoised_output * scheduler.sigmas[step_idx]
+        return sample_x0
+
+    def convert_x0_to_velocity(
+        self, sample: torch.Tensor, denoised_output: torch.Tensor, step_idx: int, scheduler: Any | None = None
+    ) -> torch.Tensor:
+        if scheduler is None:
+            scheduler = self.scheduler
+
+        sample_v = (sample - denoised_output) / scheduler.sigmas[step_idx]
+        return sample_v
+
     @property
     def guidance_scale(self):
         return self._guidance_scale
@@ -757,9 +753,41 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
     def guidance_rescale(self):
         return self._guidance_rescale
 
+    @property
+    def stg_scale(self):
+        return self._stg_scale
+
+    @property
+    def modality_scale(self):
+        return self._modality_scale
+
+    @property
+    def audio_guidance_scale(self):
+        return self._audio_guidance_scale
+
+    @property
+    def audio_guidance_rescale(self):
+        return self._audio_guidance_rescale
+
+    @property
+    def audio_stg_scale(self):
+        return self._audio_stg_scale
+
+    @property
+    def audio_modality_scale(self):
+        return self._audio_modality_scale
+
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1.0
+        return (self._guidance_scale > 1.0) or (self._audio_guidance_scale > 1.0)
+
+    @property
+    def do_spatio_temporal_guidance(self):
+        return (self._stg_scale > 0.0) or (self._audio_stg_scale > 0.0)
+
+    @property
+    def do_modality_isolation_guidance(self):
+        return (self._modality_scale > 1.0) or (self._audio_modality_scale > 1.0)
 
     @property
     def num_timesteps(self):
@@ -791,7 +819,14 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         sigmas: list[float] | None = None,
         timesteps: list[int] = None,
         guidance_scale: float = 4.0,
+        stg_scale: float = 0.0,
+        modality_scale: float = 1.0,
         guidance_rescale: float = 0.0,
+        audio_guidance_scale: float | None = None,
+        audio_stg_scale: float | None = None,
+        audio_modality_scale: float | None = None,
+        audio_guidance_rescale: float | None = None,
+        spatio_temporal_guidance_blocks: list[int] | None = None,
         noise_scale: float = 0.0,
         num_videos_per_prompt: int = 1,
         generator: torch.Generator | list[torch.Generator] | None = None,
@@ -803,6 +838,11 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         negative_prompt_attention_mask: torch.Tensor | None = None,
         decode_timestep: float | list[float] = 0.0,
         decode_noise_scale: float | list[float] | None = None,
+        use_cross_timestep: bool = False,
+        system_prompt: str | None = None,
+        prompt_max_new_tokens: int = 512,
+        prompt_enhancement_kwargs: dict[str, Any] | None = None,
+        prompt_enhancement_seed: int = 10,
         output_type: str = "pil",
         return_dict: bool = True,
         attention_kwargs: dict[str, Any] | None = None,
@@ -841,13 +881,47 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                 Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                 of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
                 `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
-                the text `prompt`, usually at the expense of lower image quality.
+                the text `prompt`, usually at the expense of lower image quality. Used for the video modality (there is
+                a separate value `audio_guidance_scale` for the audio modality).
+            stg_scale (`float`, *optional*, defaults to `0.0`):
+                Video guidance scale for Spatio-Temporal Guidance (STG), proposed in [Spatiotemporal Skip Guidance for
+                Enhanced Video Diffusion Sampling](https://arxiv.org/abs/2411.18664). STG uses a CFG-like estimate
+                where we move the sample away from a weak sample from a perturbed version of the denoising model.
+                Enabling STG will result in an additional denoising model forward pass; the default value of `0.0`
+                means that STG is disabled.
+            modality_scale (`float`, *optional*, defaults to `1.0`):
+                Video guidance scale for LTX-2.X modality isolation guidance, where we move the sample away from a
+                weaker sample generated by the denoising model withy cross-modality (audio-to-video and video-to-audio)
+                cross attention disabled using a CFG-like estimate. Enabling modality guidance will result in an
+                additional denoising model forward pass; the default value of `1.0` means that modality guidance is
+                disabled.
             guidance_rescale (`float`, *optional*, defaults to 0.0):
                 Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                 Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
                 [Common Diffusion Noise Schedules and Sample Steps are
                 Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
-                using zero terminal SNR.
+                using zero terminal SNR. Used for the video modality.
+            audio_guidance_scale (`float`, *optional* defaults to `None`):
+                Audio guidance scale for CFG with respect to the negative prompt. The CFG update rule is the same for
+                video and audio, but they can use different values for the guidance scale. The LTX-2.X authors suggest
+                that the `audio_guidance_scale` should be higher relative to the video `guidance_scale` (e.g. for
+                LTX-2.3 they suggest 3.0 for video and 7.0 for audio). If `None`, defaults to the video value
+                `guidance_scale`.
+            audio_stg_scale (`float`, *optional*, defaults to `None`):
+                Audio guidance scale for STG. As with CFG, the STG update rule is otherwise the same for video and
+                audio. For LTX-2.3, a value of 1.0 is suggested for both video and audio. If `None`, defaults to the
+                video value `stg_scale`.
+            audio_modality_scale (`float`, *optional*, defaults to `None`):
+                Audio guidance scale for LTX-2.X modality isolation guidance. As with CFG, the modality guidance rule
+                is otherwise the same for video and audio. For LTX-2.3, a value of 3.0 is suggested for both video and
+                audio. If `None`, defaults to the video value `modality_scale`.
+            audio_guidance_rescale (`float`, *optional*, defaults to `None`):
+                A separate guidance rescale factor for the audio modality. If `None`, defaults to the video value
+                `guidance_rescale`.
+            spatio_temporal_guidance_blocks (`list[int]`, *optional*, defaults to `None`):
+                The zero-indexed transformer block indices at which to apply STG. Must be supplied if STG is used
+                (`stg_scale` or `audio_stg_scale` is greater than `0`). A value of `[29]` is recommended for LTX-2.0
+                and `[28]` is recommended for LTX-2.3.
             noise_scale (`float`, *optional*, defaults to `0.0`):
                 The interpolation factor between random noise and denoised latents at each timestep. Applying noise to
                 the `latents` and `audio_latents` before continue denoising.
@@ -878,6 +952,24 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                 The timestep at which generated video is decoded.
             decode_noise_scale (`float`, defaults to `None`):
                 The interpolation factor between random noise and denoised latents at the decode timestep.
+            use_cross_timestep (`bool` *optional*, defaults to `False`):
+                Whether to use the cross modality (audio is the cross modality of video, and vice versa) sigma when
+                calculating the cross attention modulation parameters. `True` is the newer (e.g. LTX-2.3) behavior;
+                `False` is the legacy LTX-2.0 behavior.
+            system_prompt (`str`, *optional*, defaults to `None`):
+                Optional system prompt to use for prompt enhancement. The system prompt will be used by the current
+                text encoder (by default, a `Gemma3ForConditionalGeneration` model) to generate an enhanced prompt from
+                the original `prompt` to condition generation. If not supplied, prompt enhancement will not be
+                performed.
+            prompt_max_new_tokens (`int`, *optional*, defaults to `512`):
+                The maximum number of new tokens to generate when performing prompt enhancement.
+            prompt_enhancement_kwargs (`dict[str, Any]`, *optional*, defaults to `None`):
+                Keyword arguments for `self.text_encoder.generate`. If not supplied, default arguments of
+                `do_sample=True` and `temperature=0.7` will be used. See
+                https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate
+                for more details.
+            prompt_enhancement_seed (`int`, *optional*, default to `10`):
+                Random seed for any random operations during prompt enhancement.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
@@ -910,6 +1002,11 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
+        audio_guidance_scale = audio_guidance_scale or guidance_scale
+        audio_stg_scale = audio_stg_scale or stg_scale
+        audio_modality_scale = audio_modality_scale or modality_scale
+        audio_guidance_rescale = audio_guidance_rescale or guidance_rescale
+
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
             prompt=prompt,
@@ -920,10 +1017,21 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
             negative_prompt_embeds=negative_prompt_embeds,
             prompt_attention_mask=prompt_attention_mask,
             negative_prompt_attention_mask=negative_prompt_attention_mask,
+            spatio_temporal_guidance_blocks=spatio_temporal_guidance_blocks,
+            stg_scale=stg_scale,
+            audio_stg_scale=audio_stg_scale,
         )
 
+        # Per-modality guidance scales (video, audio)
         self._guidance_scale = guidance_scale
+        self._stg_scale = stg_scale
+        self._modality_scale = modality_scale
         self._guidance_rescale = guidance_rescale
+        self._audio_guidance_scale = audio_guidance_scale
+        self._audio_stg_scale = audio_stg_scale
+        self._audio_modality_scale = audio_modality_scale
+        self._audio_guidance_rescale = audio_guidance_rescale
+
         self._attention_kwargs = attention_kwargs
         self._interrupt = False
         self._current_timestep = None
@@ -939,6 +1047,17 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         device = self._execution_device
 
         # 3. Prepare text embeddings
+        if system_prompt is not None and prompt is not None:
+            prompt = self.enhance_prompt(
+                prompt=prompt,
+                system_prompt=system_prompt,
+                max_new_tokens=prompt_max_new_tokens,
+                seed=prompt_enhancement_seed,
+                generator=generator,
+                generation_kwargs=prompt_enhancement_kwargs,
+                device=device,
+            )
+
         (
             prompt_embeds,
             prompt_attention_mask,
@@ -960,9 +1079,11 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
 
-        additive_attention_mask = (1 - prompt_attention_mask.to(prompt_embeds.dtype)) * -1000000.0
+        tokenizer_padding_side = "left"  # Padding side for default Gemma3-12B text encoder
+        if getattr(self, "tokenizer", None) is not None:
+            tokenizer_padding_side = getattr(self.tokenizer, "padding_side", "left")
         connector_prompt_embeds, connector_audio_prompt_embeds, connector_attention_mask = self.connectors(
-            prompt_embeds, additive_attention_mask, additive_mask=True
+            prompt_embeds, prompt_attention_mask, padding_side=tokenizer_padding_side
         )
 
         # 4. Prepare latent variables
@@ -984,7 +1105,7 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                 raise ValueError(
                     f"Provided `latents` tensor has shape {latents.shape}, but the expected shape is either [batch_size, seq_len, num_features] or [batch_size, latent_dim, latent_frames, latent_height, latent_width]."
                 )
-        video_sequence_length = latent_num_frames * latent_height * latent_width
+        # video_sequence_length = latent_num_frames * latent_height * latent_width
 
         num_channels_latents = self.transformer.config.in_channels
         latents = self.prepare_latents(
@@ -1041,7 +1162,7 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         # 5. Prepare timesteps
         sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
         mu = calculate_shift(
-            video_sequence_length,
+            self.scheduler.config.get("max_image_seq_len", 4096),
             self.scheduler.config.get("base_image_seq_len", 1024),
             self.scheduler.config.get("max_image_seq_len", 4096),
             self.scheduler.config.get("base_shift", 0.95),
@@ -1069,11 +1190,6 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare micro-conditions
-        rope_interpolation_scale = (
-            self.vae_temporal_compression_ratio / frame_rate,
-            self.vae_spatial_compression_ratio,
-            self.vae_spatial_compression_ratio,
-        )
         # Pre-compute video and audio positional ids as they will be the same at each step of the denoising loop
         video_coords = self.transformer.rope.prepare_video_coords(
             latents.shape[0], latent_num_frames, latent_height, latent_width, latents.device, fps=frame_rate
@@ -1111,6 +1227,7 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                         encoder_hidden_states=connector_prompt_embeds,
                         audio_encoder_hidden_states=connector_audio_prompt_embeds,
                         timestep=timestep,
+                        sigma=timestep,  # Used by LTX-2.3
                         encoder_attention_mask=connector_attention_mask,
                         audio_encoder_attention_mask=connector_attention_mask,
                         num_frames=latent_num_frames,
@@ -1120,7 +1237,10 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                         audio_num_frames=audio_num_frames,
                         video_coords=video_coords,
                         audio_coords=audio_coords,
-                        # rope_interpolation_scale=rope_interpolation_scale,
+                        isolate_modalities=False,
+                        spatio_temporal_guidance_blocks=None,
+                        perturbation_mask=None,
+                        use_cross_timestep=use_cross_timestep,
                         attention_kwargs=attention_kwargs,
                         return_dict=False,
                     )
@@ -1128,24 +1248,155 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                 noise_pred_audio = noise_pred_audio.float()
 
                 if self.do_classifier_free_guidance:
-                    noise_pred_video_uncond, noise_pred_video_text = noise_pred_video.chunk(2)
-                    noise_pred_video = noise_pred_video_uncond + self.guidance_scale * (
-                        noise_pred_video_text - noise_pred_video_uncond
+                    noise_pred_video_uncond_text, noise_pred_video = noise_pred_video.chunk(2)
+                    noise_pred_video = self.convert_velocity_to_x0(latents, noise_pred_video, i, self.scheduler)
+                    noise_pred_video_uncond_text = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_text, i, self.scheduler
+                    )
+                    # Use delta formulation as it works more nicely with multiple guidance terms
+                    video_cfg_delta = (self.guidance_scale - 1) * (noise_pred_video - noise_pred_video_uncond_text)
+
+                    noise_pred_audio_uncond_text, noise_pred_audio = noise_pred_audio.chunk(2)
+                    noise_pred_audio = self.convert_velocity_to_x0(audio_latents, noise_pred_audio, i, audio_scheduler)
+                    noise_pred_audio_uncond_text = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_text, i, audio_scheduler
+                    )
+                    audio_cfg_delta = (self.audio_guidance_scale - 1) * (
+                        noise_pred_audio - noise_pred_audio_uncond_text
                     )
 
-                    noise_pred_audio_uncond, noise_pred_audio_text = noise_pred_audio.chunk(2)
-                    noise_pred_audio = noise_pred_audio_uncond + self.guidance_scale * (
-                        noise_pred_audio_text - noise_pred_audio_uncond
+                    # Get positive values from merged CFG inputs in case we need to do other DiT forward passes
+                    if self.do_spatio_temporal_guidance or self.do_modality_isolation_guidance:
+                        if i == 0:
+                            # Only split values that remain constant throughout the loop once
+                            video_prompt_embeds = connector_prompt_embeds.chunk(2, dim=0)[1]
+                            audio_prompt_embeds = connector_audio_prompt_embeds.chunk(2, dim=0)[1]
+                            prompt_attn_mask = connector_attention_mask.chunk(2, dim=0)[1]
+
+                            video_pos_ids = video_coords.chunk(2, dim=0)[0]
+                            audio_pos_ids = audio_coords.chunk(2, dim=0)[0]
+
+                        # Split values that vary each denoising loop iteration
+                        timestep = timestep.chunk(2, dim=0)[0]
+                else:
+                    video_cfg_delta = audio_cfg_delta = 0
+
+                    video_prompt_embeds = connector_prompt_embeds
+                    audio_prompt_embeds = connector_audio_prompt_embeds
+                    prompt_attn_mask = connector_attention_mask
+
+                    video_pos_ids = video_coords
+                    audio_pos_ids = audio_coords
+
+                    noise_pred_video = self.convert_velocity_to_x0(latents, noise_pred_video, i, self.scheduler)
+                    noise_pred_audio = self.convert_velocity_to_x0(audio_latents, noise_pred_audio, i, audio_scheduler)
+
+                if self.do_spatio_temporal_guidance:
+                    with self.transformer.cache_context("uncond_stg"):
+                        noise_pred_video_uncond_stg, noise_pred_audio_uncond_stg = self.transformer(
+                            hidden_states=latents.to(dtype=prompt_embeds.dtype),
+                            audio_hidden_states=audio_latents.to(dtype=prompt_embeds.dtype),
+                            encoder_hidden_states=video_prompt_embeds,
+                            audio_encoder_hidden_states=audio_prompt_embeds,
+                            timestep=timestep,
+                            sigma=timestep,  # Used by LTX-2.3
+                            encoder_attention_mask=prompt_attn_mask,
+                            audio_encoder_attention_mask=prompt_attn_mask,
+                            num_frames=latent_num_frames,
+                            height=latent_height,
+                            width=latent_width,
+                            fps=frame_rate,
+                            audio_num_frames=audio_num_frames,
+                            video_coords=video_pos_ids,
+                            audio_coords=audio_pos_ids,
+                            isolate_modalities=False,
+                            # Use STG at given blocks to perturb model
+                            spatio_temporal_guidance_blocks=spatio_temporal_guidance_blocks,
+                            perturbation_mask=None,
+                            use_cross_timestep=use_cross_timestep,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )
+                    noise_pred_video_uncond_stg = noise_pred_video_uncond_stg.float()
+                    noise_pred_audio_uncond_stg = noise_pred_audio_uncond_stg.float()
+                    noise_pred_video_uncond_stg = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_stg, i, self.scheduler
+                    )
+                    noise_pred_audio_uncond_stg = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_stg, i, audio_scheduler
                     )
 
-                    if self.guidance_rescale > 0:
-                        # Based on 3.4. in https://huggingface.co/papers/2305.08891
-                        noise_pred_video = rescale_noise_cfg(
-                            noise_pred_video, noise_pred_video_text, guidance_rescale=self.guidance_rescale
-                        )
-                        noise_pred_audio = rescale_noise_cfg(
-                            noise_pred_audio, noise_pred_audio_text, guidance_rescale=self.guidance_rescale
+                    video_stg_delta = self.stg_scale * (noise_pred_video - noise_pred_video_uncond_stg)
+                    audio_stg_delta = self.audio_stg_scale * (noise_pred_audio - noise_pred_audio_uncond_stg)
+                else:
+                    video_stg_delta = audio_stg_delta = 0
+
+                if self.do_modality_isolation_guidance:
+                    with self.transformer.cache_context("uncond_modality"):
+                        noise_pred_video_uncond_modality, noise_pred_audio_uncond_modality = self.transformer(
+                            hidden_states=latents.to(dtype=prompt_embeds.dtype),
+                            audio_hidden_states=audio_latents.to(dtype=prompt_embeds.dtype),
+                            encoder_hidden_states=video_prompt_embeds,
+                            audio_encoder_hidden_states=audio_prompt_embeds,
+                            timestep=timestep,
+                            sigma=timestep,  # Used by LTX-2.3
+                            encoder_attention_mask=prompt_attn_mask,
+                            audio_encoder_attention_mask=prompt_attn_mask,
+                            num_frames=latent_num_frames,
+                            height=latent_height,
+                            width=latent_width,
+                            fps=frame_rate,
+                            audio_num_frames=audio_num_frames,
+                            video_coords=video_pos_ids,
+                            audio_coords=audio_pos_ids,
+                            # Turn off A2V and V2A cross attn to isolate video and audio modalities
+                            isolate_modalities=True,
+                            spatio_temporal_guidance_blocks=None,
+                            perturbation_mask=None,
+                            use_cross_timestep=use_cross_timestep,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
                         )
+                    noise_pred_video_uncond_modality = noise_pred_video_uncond_modality.float()
+                    noise_pred_audio_uncond_modality = noise_pred_audio_uncond_modality.float()
+                    noise_pred_video_uncond_modality = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_modality, i, self.scheduler
+                    )
+                    noise_pred_audio_uncond_modality = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_modality, i, audio_scheduler
+                    )
+
+                    video_modality_delta = (self.modality_scale - 1) * (
+                        noise_pred_video - noise_pred_video_uncond_modality
+                    )
+                    audio_modality_delta = (self.audio_modality_scale - 1) * (
+                        noise_pred_audio - noise_pred_audio_uncond_modality
+                    )
+                else:
+                    video_modality_delta = audio_modality_delta = 0
+
+                # Now apply all guidance terms
+                noise_pred_video_g = noise_pred_video + video_cfg_delta + video_stg_delta + video_modality_delta
+                noise_pred_audio_g = noise_pred_audio + audio_cfg_delta + audio_stg_delta + audio_modality_delta
+
+                # Apply LTX-2.X guidance rescaling
+                if self.guidance_rescale > 0:
+                    noise_pred_video = rescale_noise_cfg(
+                        noise_pred_video_g, noise_pred_video, guidance_rescale=self.guidance_rescale
+                    )
+                else:
+                    noise_pred_video = noise_pred_video_g
+
+                if self.audio_guidance_rescale > 0:
+                    noise_pred_audio = rescale_noise_cfg(
+                        noise_pred_audio_g, noise_pred_audio, guidance_rescale=self.audio_guidance_rescale
+                    )
+                else:
+                    noise_pred_audio = noise_pred_audio_g
+
+                # Convert back to velocity for scheduler
+                noise_pred_video = self.convert_x0_to_velocity(latents, noise_pred_video, i, self.scheduler)
+                noise_pred_audio = self.convert_x0_to_velocity(audio_latents, noise_pred_audio, i, audio_scheduler)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred_video, t, latents, return_dict=False)[0]
@@ -1177,9 +1428,6 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
             self.transformer_spatial_patch_size,
             self.transformer_temporal_patch_size,
         )
-        latents = self._denormalize_latents(
-            latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
-        )
 
         audio_latents = self._denormalize_audio_latents(
             audio_latents, self.audio_vae.latents_mean, self.audio_vae.latents_std
@@ -1187,6 +1435,9 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
         audio_latents = self._unpack_audio_latents(audio_latents, audio_num_frames, num_mel_bins=latent_mel_bins)
 
         if output_type == "latent":
+            latents = self._denormalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
             video = latents
             audio = audio_latents
         else:
@@ -1209,6 +1460,10 @@ class LTX2Pipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
                 ]
                 latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
 
+            latents = self._denormalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
+
             latents = latents.to(self.vae.dtype)
             video = self.vae.decode(latents, timestep, return_dict=False)[0]
             video = self.video_processor.postprocess_video(video, output_type=output_type)

src/diffusers/pipelines/ltx2/pipeline_ltx2_condition.py

@@ -33,7 +33,7 @@ from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
 from .connectors import LTX2TextConnectors
 from .pipeline_output import LTX2PipelineOutput
-from .vocoder import LTX2Vocoder
+from .vocoder import LTX2Vocoder, LTX2VocoderWithBWE
 
 
 if is_torch_xla_available():
@@ -254,7 +254,7 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         tokenizer: GemmaTokenizer | GemmaTokenizerFast,
         connectors: LTX2TextConnectors,
         transformer: LTX2VideoTransformer3DModel,
-        vocoder: LTX2Vocoder,
+        vocoder: LTX2Vocoder | LTX2VocoderWithBWE,
     ):
         super().__init__()
 
@@ -300,74 +300,6 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
             self.tokenizer.model_max_length if getattr(self, "tokenizer", None) is not None else 1024
         )
 
-    @staticmethod
-    # Copied from diffusers.pipelines.ltx2.pipeline_ltx2.LTX2Pipeline._pack_text_embeds
-    def _pack_text_embeds(
-        text_hidden_states: torch.Tensor,
-        sequence_lengths: torch.Tensor,
-        device: str | torch.device,
-        padding_side: str = "left",
-        scale_factor: int = 8,
-        eps: float = 1e-6,
-    ) -> torch.Tensor:
-        """
-        Packs and normalizes text encoder hidden states, respecting padding. Normalization is performed per-batch and
-        per-layer in a masked fashion (only over non-padded positions).
-
-        Args:
-            text_hidden_states (`torch.Tensor` of shape `(batch_size, seq_len, hidden_dim, num_layers)`):
-                Per-layer hidden_states from a text encoder (e.g. `Gemma3ForConditionalGeneration`).
-            sequence_lengths (`torch.Tensor of shape `(batch_size,)`):
-                The number of valid (non-padded) tokens for each batch instance.
-            device: (`str` or `torch.device`, *optional*):
-                torch device to place the resulting embeddings on
-            padding_side: (`str`, *optional*, defaults to `"left"`):
-                Whether the text tokenizer performs padding on the `"left"` or `"right"`.
-            scale_factor (`int`, *optional*, defaults to `8`):
-                Scaling factor to multiply the normalized hidden states by.
-            eps (`float`, *optional*, defaults to `1e-6`):
-                A small positive value for numerical stability when performing normalization.
-
-        Returns:
-            `torch.Tensor` of shape `(batch_size, seq_len, hidden_dim * num_layers)`:
-                Normed and flattened text encoder hidden states.
-        """
-        batch_size, seq_len, hidden_dim, num_layers = text_hidden_states.shape
-        original_dtype = text_hidden_states.dtype
-
-        # Create padding mask
-        token_indices = torch.arange(seq_len, device=device).unsqueeze(0)
-        if padding_side == "right":
-            # For right padding, valid tokens are from 0 to sequence_length-1
-            mask = token_indices < sequence_lengths[:, None]  # [batch_size, seq_len]
-        elif padding_side == "left":
-            # For left padding, valid tokens are from (T - sequence_length) to T-1
-            start_indices = seq_len - sequence_lengths[:, None]  # [batch_size, 1]
-            mask = token_indices >= start_indices  # [B, T]
-        else:
-            raise ValueError(f"padding_side must be 'left' or 'right', got {padding_side}")
-        mask = mask[:, :, None, None]  # [batch_size, seq_len] --> [batch_size, seq_len, 1, 1]
-
-        # Compute masked mean over non-padding positions of shape (batch_size, 1, 1, seq_len)
-        masked_text_hidden_states = text_hidden_states.masked_fill(~mask, 0.0)
-        num_valid_positions = (sequence_lengths * hidden_dim).view(batch_size, 1, 1, 1)
-        masked_mean = masked_text_hidden_states.sum(dim=(1, 2), keepdim=True) / (num_valid_positions + eps)
-
-        # Compute min/max over non-padding positions of shape (batch_size, 1, 1 seq_len)
-        x_min = text_hidden_states.masked_fill(~mask, float("inf")).amin(dim=(1, 2), keepdim=True)
-        x_max = text_hidden_states.masked_fill(~mask, float("-inf")).amax(dim=(1, 2), keepdim=True)
-
-        # Normalization
-        normalized_hidden_states = (text_hidden_states - masked_mean) / (x_max - x_min + eps)
-        normalized_hidden_states = normalized_hidden_states * scale_factor
-
-        # Pack the hidden states to a 3D tensor (batch_size, seq_len, hidden_dim * num_layers)
-        normalized_hidden_states = normalized_hidden_states.flatten(2)
-        mask_flat = mask.squeeze(-1).expand(-1, -1, hidden_dim * num_layers)
-        normalized_hidden_states = normalized_hidden_states.masked_fill(~mask_flat, 0.0)
-        normalized_hidden_states = normalized_hidden_states.to(dtype=original_dtype)
-        return normalized_hidden_states
-
     # Copied from diffusers.pipelines.ltx2.pipeline_ltx2.LTX2Pipeline._get_gemma_prompt_embeds
     def _get_gemma_prompt_embeds(
         self,
@@ -421,16 +353,7 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         )
         text_encoder_hidden_states = text_encoder_outputs.hidden_states
         text_encoder_hidden_states = torch.stack(text_encoder_hidden_states, dim=-1)
-        sequence_lengths = prompt_attention_mask.sum(dim=-1)
-
-        prompt_embeds = self._pack_text_embeds(
-            text_encoder_hidden_states,
-            sequence_lengths,
-            device=device,
-            padding_side=self.tokenizer.padding_side,
-            scale_factor=scale_factor,
-        )
-        prompt_embeds = prompt_embeds.to(dtype=dtype)
+        prompt_embeds = text_encoder_hidden_states.flatten(2, 3).to(dtype=dtype)  # Pack to 3D
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         _, seq_len, _ = prompt_embeds.shape
@@ -541,6 +464,9 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         negative_prompt_attention_mask=None,
         latents=None,
         audio_latents=None,
+        spatio_temporal_guidance_blocks=None,
+        stg_scale=None,
+        audio_stg_scale=None,
     ):
         if height % 32 != 0 or width % 32 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
@@ -597,6 +523,12 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                 f" using the `_unpack_audio_latents` method)."
             )
 
+        if ((stg_scale > 0.0) or (audio_stg_scale > 0.0)) and not spatio_temporal_guidance_blocks:
+            raise ValueError(
+                "Spatio-Temporal Guidance (STG) is specified but no STG blocks are supplied. Please supply a list of"
+                "block indices at which to apply STG in `spatio_temporal_guidance_blocks`"
+            )
+
     @staticmethod
     # Copied from diffusers.pipelines.ltx2.pipeline_ltx2.LTX2Pipeline._pack_latents
     def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
@@ -984,6 +916,24 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         latents = self._pack_audio_latents(latents)
         return latents
 
+    def convert_velocity_to_x0(
+        self, sample: torch.Tensor, denoised_output: torch.Tensor, step_idx: int, scheduler: Any | None = None
+    ) -> torch.Tensor:
+        if scheduler is None:
+            scheduler = self.scheduler
+
+        sample_x0 = sample - denoised_output * scheduler.sigmas[step_idx]
+        return sample_x0
+
+    def convert_x0_to_velocity(
+        self, sample: torch.Tensor, denoised_output: torch.Tensor, step_idx: int, scheduler: Any | None = None
+    ) -> torch.Tensor:
+        if scheduler is None:
+            scheduler = self.scheduler
+
+        sample_v = (sample - denoised_output) / scheduler.sigmas[step_idx]
+        return sample_v
+
     @property
     def guidance_scale(self):
         return self._guidance_scale
@@ -992,9 +942,41 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
     def guidance_rescale(self):
         return self._guidance_rescale
 
+    @property
+    def stg_scale(self):
+        return self._stg_scale
+
+    @property
+    def modality_scale(self):
+        return self._modality_scale
+
+    @property
+    def audio_guidance_scale(self):
+        return self._audio_guidance_scale
+
+    @property
+    def audio_guidance_rescale(self):
+        return self._audio_guidance_rescale
+
+    @property
+    def audio_stg_scale(self):
+        return self._audio_stg_scale
+
+    @property
+    def audio_modality_scale(self):
+        return self._audio_modality_scale
+
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1.0
+        return (self._guidance_scale > 1.0) or (self._audio_guidance_scale > 1.0)
+
+    @property
+    def do_spatio_temporal_guidance(self):
+        return (self._stg_scale > 0.0) or (self._audio_stg_scale > 0.0)
+
+    @property
+    def do_modality_isolation_guidance(self):
+        return (self._modality_scale > 1.0) or (self._audio_modality_scale > 1.0)
 
     @property
     def num_timesteps(self):
@@ -1027,7 +1009,14 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         sigmas: list[float] | None = None,
         timesteps: list[float] | None = None,
         guidance_scale: float = 4.0,
+        stg_scale: float = 0.0,
+        modality_scale: float = 1.0,
         guidance_rescale: float = 0.0,
+        audio_guidance_scale: float | None = None,
+        audio_stg_scale: float | None = None,
+        audio_modality_scale: float | None = None,
+        audio_guidance_rescale: float | None = None,
+        spatio_temporal_guidance_blocks: list[int] | None = None,
         noise_scale: float | None = None,
         num_videos_per_prompt: int | None = 1,
         generator: torch.Generator | list[torch.Generator] | None = None,
@@ -1039,6 +1028,7 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         negative_prompt_attention_mask: torch.Tensor | None = None,
         decode_timestep: float | list[float] = 0.0,
         decode_noise_scale: float | list[float] | None = None,
+        use_cross_timestep: bool = False,
         output_type: str = "pil",
         return_dict: bool = True,
         attention_kwargs: dict[str, Any] | None = None,
@@ -1079,13 +1069,47 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                 Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                 of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
                 `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
-                the text `prompt`, usually at the expense of lower image quality.
+                the text `prompt`, usually at the expense of lower image quality. Used for the video modality (there is
+                a separate value `audio_guidance_scale` for the audio modality).
+            stg_scale (`float`, *optional*, defaults to `0.0`):
+                Video guidance scale for Spatio-Temporal Guidance (STG), proposed in [Spatiotemporal Skip Guidance for
+                Enhanced Video Diffusion Sampling](https://arxiv.org/abs/2411.18664). STG uses a CFG-like estimate
+                where we move the sample away from a weak sample from a perturbed version of the denoising model.
+                Enabling STG will result in an additional denoising model forward pass; the default value of `0.0`
+                means that STG is disabled.
+            modality_scale (`float`, *optional*, defaults to `1.0`):
+                Video guidance scale for LTX-2.X modality isolation guidance, where we move the sample away from a
+                weaker sample generated by the denoising model withy cross-modality (audio-to-video and video-to-audio)
+                cross attention disabled using a CFG-like estimate. Enabling modality guidance will result in an
+                additional denoising model forward pass; the default value of `1.0` means that modality guidance is
+                disabled.
             guidance_rescale (`float`, *optional*, defaults to 0.0):
                 Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                 Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
                 [Common Diffusion Noise Schedules and Sample Steps are
                 Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
-                using zero terminal SNR.
+                using zero terminal SNR. Used for the video modality.
+            audio_guidance_scale (`float`, *optional* defaults to `None`):
+                Audio guidance scale for CFG with respect to the negative prompt. The CFG update rule is the same for
+                video and audio, but they can use different values for the guidance scale. The LTX-2.X authors suggest
+                that the `audio_guidance_scale` should be higher relative to the video `guidance_scale` (e.g. for
+                LTX-2.3 they suggest 3.0 for video and 7.0 for audio). If `None`, defaults to the video value
+                `guidance_scale`.
+            audio_stg_scale (`float`, *optional*, defaults to `None`):
+                Audio guidance scale for STG. As with CFG, the STG update rule is otherwise the same for video and
+                audio. For LTX-2.3, a value of 1.0 is suggested for both video and audio. If `None`, defaults to the
+                video value `stg_scale`.
+            audio_modality_scale (`float`, *optional*, defaults to `None`):
+                Audio guidance scale for LTX-2.X modality isolation guidance. As with CFG, the modality guidance rule
+                is otherwise the same for video and audio. For LTX-2.3, a value of 3.0 is suggested for both video and
+                audio. If `None`, defaults to the video value `modality_scale`.
+            audio_guidance_rescale (`float`, *optional*, defaults to `None`):
+                A separate guidance rescale factor for the audio modality. If `None`, defaults to the video value
+                `guidance_rescale`.
+            spatio_temporal_guidance_blocks (`list[int]`, *optional*, defaults to `None`):
+                The zero-indexed transformer block indices at which to apply STG. Must be supplied if STG is used
+                (`stg_scale` or `audio_stg_scale` is greater than `0`). A value of `[29]` is recommended for LTX-2.0
+                and `[28]` is recommended for LTX-2.3.
             noise_scale (`float`, *optional*, defaults to `None`):
                 The interpolation factor between random noise and denoised latents at each timestep. Applying noise to
                 the `latents` and `audio_latents` before continue denoising. If not set, will be inferred from the
@@ -1117,6 +1141,10 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                 The timestep at which generated video is decoded.
             decode_noise_scale (`float`, defaults to `None`):
                 The interpolation factor between random noise and denoised latents at the decode timestep.
+            use_cross_timestep (`bool` *optional*, defaults to `False`):
+                Whether to use the cross modality (audio is the cross modality of video, and vice versa) sigma when
+                calculating the cross attention modulation parameters. `True` is the newer (e.g. LTX-2.3) behavior;
+                `False` is the legacy LTX-2.0 behavior.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
@@ -1149,6 +1177,11 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
+        audio_guidance_scale = audio_guidance_scale or guidance_scale
+        audio_stg_scale = audio_stg_scale or stg_scale
+        audio_modality_scale = audio_modality_scale or modality_scale
+        audio_guidance_rescale = audio_guidance_rescale or guidance_rescale
+
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
             prompt=prompt,
@@ -1161,10 +1194,21 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
             negative_prompt_attention_mask=negative_prompt_attention_mask,
             latents=latents,
             audio_latents=audio_latents,
+            spatio_temporal_guidance_blocks=spatio_temporal_guidance_blocks,
+            stg_scale=stg_scale,
+            audio_stg_scale=audio_stg_scale,
         )
 
+        # Per-modality guidance scales (video, audio)
         self._guidance_scale = guidance_scale
+        self._stg_scale = stg_scale
+        self._modality_scale = modality_scale
         self._guidance_rescale = guidance_rescale
+        self._audio_guidance_scale = audio_guidance_scale
+        self._audio_stg_scale = audio_stg_scale
+        self._audio_modality_scale = audio_modality_scale
+        self._audio_guidance_rescale = audio_guidance_rescale
+
         self._attention_kwargs = attention_kwargs
         self._interrupt = False
         self._current_timestep = None
@@ -1208,9 +1252,11 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
 
-        additive_attention_mask = (1 - prompt_attention_mask.to(prompt_embeds.dtype)) * -1000000.0
+        tokenizer_padding_side = "left"  # Padding side for default Gemma3-12B text encoder
+        if getattr(self, "tokenizer", None) is not None:
+            tokenizer_padding_side = getattr(self.tokenizer, "padding_side", "left")
         connector_prompt_embeds, connector_audio_prompt_embeds, connector_attention_mask = self.connectors(
-            prompt_embeds, additive_attention_mask, additive_mask=True
+            prompt_embeds, prompt_attention_mask, padding_side=tokenizer_padding_side
         )
 
         # 4. Prepare latent variables
@@ -1222,7 +1268,7 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                 "Got latents of shape [batch_size, latent_dim, latent_frames, latent_height, latent_width], `latent_num_frames`, `latent_height`, `latent_width` will be inferred."
             )
             _, _, latent_num_frames, latent_height, latent_width = latents.shape  # [B, C, F, H, W]
-        video_sequence_length = latent_num_frames * latent_height * latent_width
+        # video_sequence_length = latent_num_frames * latent_height * latent_width
 
         num_channels_latents = self.transformer.config.in_channels
         latents, conditioning_mask, clean_latents = self.prepare_latents(
@@ -1272,7 +1318,7 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         # 5. Prepare timesteps
         sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
         mu = calculate_shift(
-            video_sequence_length,
+            self.scheduler.config.get("max_image_seq_len", 4096),
             self.scheduler.config.get("base_image_seq_len", 1024),
             self.scheduler.config.get("max_image_seq_len", 4096),
             self.scheduler.config.get("base_shift", 0.95),
@@ -1301,11 +1347,6 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare micro-conditions
-        rope_interpolation_scale = (
-            self.vae_temporal_compression_ratio / frame_rate,
-            self.vae_spatial_compression_ratio,
-            self.vae_spatial_compression_ratio,
-        )
         # Pre-compute video and audio positional ids as they will be the same at each step of the denoising loop
         video_coords = self.transformer.rope.prepare_video_coords(
             latents.shape[0], latent_num_frames, latent_height, latent_width, latents.device, fps=frame_rate
@@ -1344,6 +1385,7 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                         audio_encoder_hidden_states=connector_audio_prompt_embeds,
                         timestep=video_timestep,
                         audio_timestep=timestep,
+                        sigma=timestep,  # Used by LTX-2.3
                         encoder_attention_mask=connector_attention_mask,
                         audio_encoder_attention_mask=connector_attention_mask,
                         num_frames=latent_num_frames,
@@ -1353,7 +1395,10 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                         audio_num_frames=audio_num_frames,
                         video_coords=video_coords,
                         audio_coords=audio_coords,
-                        # rope_interpolation_scale=rope_interpolation_scale,
+                        isolate_modalities=False,
+                        spatio_temporal_guidance_blocks=None,
+                        perturbation_mask=None,
+                        use_cross_timestep=use_cross_timestep,
                         attention_kwargs=attention_kwargs,
                         return_dict=False,
                     )
@@ -1361,41 +1406,172 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                 noise_pred_audio = noise_pred_audio.float()
 
                 if self.do_classifier_free_guidance:
-                    noise_pred_video_uncond, noise_pred_video_text = noise_pred_video.chunk(2)
-                    noise_pred_video = noise_pred_video_uncond + self.guidance_scale * (
-                        noise_pred_video_text - noise_pred_video_uncond
+                    noise_pred_video_uncond_text, noise_pred_video = noise_pred_video.chunk(2)
+                    noise_pred_video = self.convert_velocity_to_x0(latents, noise_pred_video, i, self.scheduler)
+                    noise_pred_video_uncond_text = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_text, i, self.scheduler
+                    )
+                    # Use delta formulation as it works more nicely with multiple guidance terms
+                    video_cfg_delta = (self.guidance_scale - 1) * (noise_pred_video - noise_pred_video_uncond_text)
+
+                    noise_pred_audio_uncond_text, noise_pred_audio = noise_pred_audio.chunk(2)
+                    noise_pred_audio = self.convert_velocity_to_x0(audio_latents, noise_pred_audio, i, audio_scheduler)
+                    noise_pred_audio_uncond_text = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_text, i, audio_scheduler
+                    )
+                    audio_cfg_delta = (self.audio_guidance_scale - 1) * (
+                        noise_pred_audio - noise_pred_audio_uncond_text
                     )
 
-                    noise_pred_audio_uncond, noise_pred_audio_text = noise_pred_audio.chunk(2)
-                    noise_pred_audio = noise_pred_audio_uncond + self.guidance_scale * (
-                        noise_pred_audio_text - noise_pred_audio_uncond
+                    # Get positive values from merged CFG inputs in case we need to do other DiT forward passes
+                    if self.do_spatio_temporal_guidance or self.do_modality_isolation_guidance:
+                        if i == 0:
+                            # Only split values that remain constant throughout the loop once
+                            video_prompt_embeds = connector_prompt_embeds.chunk(2, dim=0)[1]
+                            audio_prompt_embeds = connector_audio_prompt_embeds.chunk(2, dim=0)[1]
+                            prompt_attn_mask = connector_attention_mask.chunk(2, dim=0)[1]
+
+                            video_pos_ids = video_coords.chunk(2, dim=0)[0]
+                            audio_pos_ids = audio_coords.chunk(2, dim=0)[0]
+
+                        # Split values that vary each denoising loop iteration
+                        timestep = timestep.chunk(2, dim=0)[0]
+                        video_timestep = video_timestep.chunk(2, dim=0)[0]
+                else:
+                    video_cfg_delta = audio_cfg_delta = 0
+
+                    video_prompt_embeds = connector_prompt_embeds
+                    audio_prompt_embeds = connector_audio_prompt_embeds
+                    prompt_attn_mask = connector_attention_mask
+
+                    video_pos_ids = video_coords
+                    audio_pos_ids = audio_coords
+
+                    noise_pred_video = self.convert_velocity_to_x0(latents, noise_pred_video, i, self.scheduler)
+                    noise_pred_audio = self.convert_velocity_to_x0(audio_latents, noise_pred_audio, i, audio_scheduler)
+
+                if self.do_spatio_temporal_guidance:
+                    with self.transformer.cache_context("uncond_stg"):
+                        noise_pred_video_uncond_stg, noise_pred_audio_uncond_stg = self.transformer(
+                            hidden_states=latents.to(dtype=prompt_embeds.dtype),
+                            audio_hidden_states=audio_latents.to(dtype=prompt_embeds.dtype),
+                            encoder_hidden_states=video_prompt_embeds,
+                            audio_encoder_hidden_states=audio_prompt_embeds,
+                            timestep=video_timestep,
+                            audio_timestep=timestep,
+                            sigma=timestep,  # Used by LTX-2.3
+                            encoder_attention_mask=prompt_attn_mask,
+                            audio_encoder_attention_mask=prompt_attn_mask,
+                            num_frames=latent_num_frames,
+                            height=latent_height,
+                            width=latent_width,
+                            fps=frame_rate,
+                            audio_num_frames=audio_num_frames,
+                            video_coords=video_pos_ids,
+                            audio_coords=audio_pos_ids,
+                            isolate_modalities=False,
+                            # Use STG at given blocks to perturb model
+                            spatio_temporal_guidance_blocks=spatio_temporal_guidance_blocks,
+                            perturbation_mask=None,
+                            use_cross_timestep=use_cross_timestep,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )
+                    noise_pred_video_uncond_stg = noise_pred_video_uncond_stg.float()
+                    noise_pred_audio_uncond_stg = noise_pred_audio_uncond_stg.float()
+                    noise_pred_video_uncond_stg = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_stg, i, self.scheduler
+                    )
+                    noise_pred_audio_uncond_stg = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_stg, i, audio_scheduler
                     )
 
-                    if self.guidance_rescale > 0:
-                        # Based on 3.4. in https://huggingface.co/papers/2305.08891
-                        noise_pred_video = rescale_noise_cfg(
-                            noise_pred_video, noise_pred_video_text, guidance_rescale=self.guidance_rescale
-                        )
-                        noise_pred_audio = rescale_noise_cfg(
-                            noise_pred_audio, noise_pred_audio_text, guidance_rescale=self.guidance_rescale
+                    video_stg_delta = self.stg_scale * (noise_pred_video - noise_pred_video_uncond_stg)
+                    audio_stg_delta = self.audio_stg_scale * (noise_pred_audio - noise_pred_audio_uncond_stg)
+                else:
+                    video_stg_delta = audio_stg_delta = 0
+
+                if self.do_modality_isolation_guidance:
+                    with self.transformer.cache_context("uncond_modality"):
+                        noise_pred_video_uncond_modality, noise_pred_audio_uncond_modality = self.transformer(
+                            hidden_states=latents.to(dtype=prompt_embeds.dtype),
+                            audio_hidden_states=audio_latents.to(dtype=prompt_embeds.dtype),
+                            encoder_hidden_states=video_prompt_embeds,
+                            audio_encoder_hidden_states=audio_prompt_embeds,
+                            timestep=video_timestep,
+                            audio_timestep=timestep,
+                            sigma=timestep,  # Used by LTX-2.3
+                            encoder_attention_mask=prompt_attn_mask,
+                            audio_encoder_attention_mask=prompt_attn_mask,
+                            num_frames=latent_num_frames,
+                            height=latent_height,
+                            width=latent_width,
+                            fps=frame_rate,
+                            audio_num_frames=audio_num_frames,
+                            video_coords=video_pos_ids,
+                            audio_coords=audio_pos_ids,
+                            # Turn off A2V and V2A cross attn to isolate video and audio modalities
+                            isolate_modalities=True,
+                            spatio_temporal_guidance_blocks=None,
+                            perturbation_mask=None,
+                            use_cross_timestep=use_cross_timestep,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
                         )
+                    noise_pred_video_uncond_modality = noise_pred_video_uncond_modality.float()
+                    noise_pred_audio_uncond_modality = noise_pred_audio_uncond_modality.float()
+                    noise_pred_video_uncond_modality = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_modality, i, self.scheduler
+                    )
+                    noise_pred_audio_uncond_modality = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_modality, i, audio_scheduler
+                    )
+
+                    video_modality_delta = (self.modality_scale - 1) * (
+                        noise_pred_video - noise_pred_video_uncond_modality
+                    )
+                    audio_modality_delta = (self.audio_modality_scale - 1) * (
+                        noise_pred_audio - noise_pred_audio_uncond_modality
+                    )
+                else:
+                    video_modality_delta = audio_modality_delta = 0
+
+                # Now apply all guidance terms
+                noise_pred_video_g = noise_pred_video + video_cfg_delta + video_stg_delta + video_modality_delta
+                noise_pred_audio_g = noise_pred_audio + audio_cfg_delta + audio_stg_delta + audio_modality_delta
+
+                # Apply LTX-2.X guidance rescaling
+                if self.guidance_rescale > 0:
+                    noise_pred_video = rescale_noise_cfg(
+                        noise_pred_video_g, noise_pred_video, guidance_rescale=self.guidance_rescale
+                    )
+                else:
+                    noise_pred_video = noise_pred_video_g
+
+                if self.audio_guidance_rescale > 0:
+                    noise_pred_audio = rescale_noise_cfg(
+                        noise_pred_audio_g, noise_pred_audio, guidance_rescale=self.audio_guidance_rescale
+                    )
+                else:
+                    noise_pred_audio = noise_pred_audio_g
 
                 # NOTE: use only the first chunk of conditioning mask in case it is duplicated for CFG
                 bsz = noise_pred_video.size(0)
-                sigma = self.scheduler.sigmas[i]
-                # Convert the noise_pred_video velocity model prediction into a sample (x0) prediction
-                denoised_sample = latents - noise_pred_video * sigma
                 # Apply the (packed) conditioning mask to the denoised (x0) sample and clean conditioning. The
                 # conditioning mask contains conditioning strengths from 0 (always use denoised sample) to 1 (always
                 # use conditions), with intermediate values specifying how strongly to follow the conditions.
+                # NOTE: this operation should be applied in sample (x0) space and not velocity space (which is the
+                # space the denoising model outputs are in)
                 denoised_sample_cond = (
-                    denoised_sample * (1 - conditioning_mask[:bsz]) + clean_latents.float() * conditioning_mask[:bsz]
+                    noise_pred_video * (1 - conditioning_mask[:bsz]) + clean_latents.float() * conditioning_mask[:bsz]
                 ).to(noise_pred_video.dtype)
+
                 # Convert the denoised (x0) sample back to a velocity for the scheduler
-                denoised_latents_cond = ((latents - denoised_sample_cond) / sigma).to(noise_pred_video.dtype)
+                noise_pred_video = self.convert_x0_to_velocity(latents, denoised_sample_cond, i, self.scheduler)
+                noise_pred_audio = self.convert_x0_to_velocity(audio_latents, noise_pred_audio, i, audio_scheduler)
 
                 # Compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(denoised_latents_cond, t, latents, return_dict=False)[0]
+                latents = self.scheduler.step(noise_pred_video, t, latents, return_dict=False)[0]
 
                 # NOTE: for now duplicate scheduler for audio latents in case self.scheduler sets internal state in
                 # the step method (such as _step_index)
@@ -1425,9 +1601,6 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
             self.transformer_spatial_patch_size,
             self.transformer_temporal_patch_size,
         )
-        latents = self._denormalize_latents(
-            latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
-        )
 
         audio_latents = self._denormalize_audio_latents(
             audio_latents, self.audio_vae.latents_mean, self.audio_vae.latents_std
@@ -1435,6 +1608,9 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
         audio_latents = self._unpack_audio_latents(audio_latents, audio_num_frames, num_mel_bins=latent_mel_bins)
 
         if output_type == "latent":
+            latents = self._denormalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
             video = latents
             audio = audio_latents
         else:
@@ -1457,6 +1633,10 @@ class LTX2ConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoad
                 ]
                 latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
 
+            latents = self._denormalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
+
             latents = latents.to(self.vae.dtype)
             video = self.vae.decode(latents, timestep, return_dict=False)[0]
             video = self.video_processor.postprocess_video(video, output_type=output_type)

src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py

@@ -18,7 +18,7 @@ from typing import Any, Callable
 
 import numpy as np
 import torch
-from transformers import Gemma3ForConditionalGeneration, GemmaTokenizer, GemmaTokenizerFast
+from transformers import Gemma3ForConditionalGeneration, Gemma3Processor, GemmaTokenizer, GemmaTokenizerFast
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput
@@ -32,7 +32,7 @@ from ...video_processor import VideoProcessor
 from ..pipeline_utils import DiffusionPipeline
 from .connectors import LTX2TextConnectors
 from .pipeline_output import LTX2PipelineOutput
-from .vocoder import LTX2Vocoder
+from .vocoder import LTX2Vocoder, LTX2VocoderWithBWE
 
 
 if is_torch_xla_available():
@@ -212,7 +212,7 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
     """
 
     model_cpu_offload_seq = "text_encoder->connectors->transformer->vae->audio_vae->vocoder"
-    _optional_components = []
+    _optional_components = ["processor"]
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
 
     def __init__(
@@ -224,7 +224,8 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         tokenizer: GemmaTokenizer | GemmaTokenizerFast,
         connectors: LTX2TextConnectors,
         transformer: LTX2VideoTransformer3DModel,
-        vocoder: LTX2Vocoder,
+        vocoder: LTX2Vocoder | LTX2VocoderWithBWE,
+        processor: Gemma3Processor | None = None,
     ):
         super().__init__()
 
@@ -237,6 +238,7 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
             transformer=transformer,
             vocoder=vocoder,
             scheduler=scheduler,
+            processor=processor,
         )
 
         self.vae_spatial_compression_ratio = (
@@ -271,74 +273,6 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
             self.tokenizer.model_max_length if getattr(self, "tokenizer", None) is not None else 1024
         )
 
-    @staticmethod
-    # Copied from diffusers.pipelines.ltx2.pipeline_ltx2.LTX2Pipeline._pack_text_embeds
-    def _pack_text_embeds(
-        text_hidden_states: torch.Tensor,
-        sequence_lengths: torch.Tensor,
-        device: str | torch.device,
-        padding_side: str = "left",
-        scale_factor: int = 8,
-        eps: float = 1e-6,
-    ) -> torch.Tensor:
-        """
-        Packs and normalizes text encoder hidden states, respecting padding. Normalization is performed per-batch and
-        per-layer in a masked fashion (only over non-padded positions).
-
-        Args:
-            text_hidden_states (`torch.Tensor` of shape `(batch_size, seq_len, hidden_dim, num_layers)`):
-                Per-layer hidden_states from a text encoder (e.g. `Gemma3ForConditionalGeneration`).
-            sequence_lengths (`torch.Tensor of shape `(batch_size,)`):
-                The number of valid (non-padded) tokens for each batch instance.
-            device: (`str` or `torch.device`, *optional*):
-                torch device to place the resulting embeddings on
-            padding_side: (`str`, *optional*, defaults to `"left"`):
-                Whether the text tokenizer performs padding on the `"left"` or `"right"`.
-            scale_factor (`int`, *optional*, defaults to `8`):
-                Scaling factor to multiply the normalized hidden states by.
-            eps (`float`, *optional*, defaults to `1e-6`):
-                A small positive value for numerical stability when performing normalization.
-
-        Returns:
-            `torch.Tensor` of shape `(batch_size, seq_len, hidden_dim * num_layers)`:
-                Normed and flattened text encoder hidden states.
-        """
-        batch_size, seq_len, hidden_dim, num_layers = text_hidden_states.shape
-        original_dtype = text_hidden_states.dtype
-
-        # Create padding mask
-        token_indices = torch.arange(seq_len, device=device).unsqueeze(0)
-        if padding_side == "right":
-            # For right padding, valid tokens are from 0 to sequence_length-1
-            mask = token_indices < sequence_lengths[:, None]  # [batch_size, seq_len]
-        elif padding_side == "left":
-            # For left padding, valid tokens are from (T - sequence_length) to T-1
-            start_indices = seq_len - sequence_lengths[:, None]  # [batch_size, 1]
-            mask = token_indices >= start_indices  # [B, T]
-        else:
-            raise ValueError(f"padding_side must be 'left' or 'right', got {padding_side}")
-        mask = mask[:, :, None, None]  # [batch_size, seq_len] --> [batch_size, seq_len, 1, 1]
-
-        # Compute masked mean over non-padding positions of shape (batch_size, 1, 1, seq_len)
-        masked_text_hidden_states = text_hidden_states.masked_fill(~mask, 0.0)
-        num_valid_positions = (sequence_lengths * hidden_dim).view(batch_size, 1, 1, 1)
-        masked_mean = masked_text_hidden_states.sum(dim=(1, 2), keepdim=True) / (num_valid_positions + eps)
-
-        # Compute min/max over non-padding positions of shape (batch_size, 1, 1 seq_len)
-        x_min = text_hidden_states.masked_fill(~mask, float("inf")).amin(dim=(1, 2), keepdim=True)
-        x_max = text_hidden_states.masked_fill(~mask, float("-inf")).amax(dim=(1, 2), keepdim=True)
-
-        # Normalization
-        normalized_hidden_states = (text_hidden_states - masked_mean) / (x_max - x_min + eps)
-        normalized_hidden_states = normalized_hidden_states * scale_factor
-
-        # Pack the hidden states to a 3D tensor (batch_size, seq_len, hidden_dim * num_layers)
-        normalized_hidden_states = normalized_hidden_states.flatten(2)
-        mask_flat = mask.squeeze(-1).expand(-1, -1, hidden_dim * num_layers)
-        normalized_hidden_states = normalized_hidden_states.masked_fill(~mask_flat, 0.0)
-        normalized_hidden_states = normalized_hidden_states.to(dtype=original_dtype)
-        return normalized_hidden_states
-
     # Copied from diffusers.pipelines.ltx2.pipeline_ltx2.LTX2Pipeline._get_gemma_prompt_embeds
     def _get_gemma_prompt_embeds(
         self,
@@ -392,16 +326,7 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         )
         text_encoder_hidden_states = text_encoder_outputs.hidden_states
         text_encoder_hidden_states = torch.stack(text_encoder_hidden_states, dim=-1)
-        sequence_lengths = prompt_attention_mask.sum(dim=-1)
-
-        prompt_embeds = self._pack_text_embeds(
-            text_encoder_hidden_states,
-            sequence_lengths,
-            device=device,
-            padding_side=self.tokenizer.padding_side,
-            scale_factor=scale_factor,
-        )
-        prompt_embeds = prompt_embeds.to(dtype=dtype)
+        prompt_embeds = text_encoder_hidden_states.flatten(2, 3).to(dtype=dtype)  # Pack to 3D
 
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         _, seq_len, _ = prompt_embeds.shape
@@ -500,6 +425,57 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
 
         return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
 
+    @torch.no_grad()
+    def enhance_prompt(
+        self,
+        image: PipelineImageInput,
+        prompt: str,
+        system_prompt: str,
+        max_new_tokens: int = 512,
+        seed: int = 10,
+        generator: torch.Generator | None = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        device: str | torch.device | None = None,
+    ):
+        """
+        Enhances the supplied `prompt` by generating a new prompt using the current text encoder (default is a
+        `transformers.Gemma3ForConditionalGeneration` model) from it and a system prompt.
+        """
+        device = device or self._execution_device
+        if generation_kwargs is None:
+            # Set to default generation kwargs
+            generation_kwargs = {"do_sample": True, "temperature": 0.7}
+
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": f"User Raw Input Prompt: {prompt}."},
+                ],
+            },
+        ]
+        template = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        model_inputs = self.processor(text=template, images=image, return_tensors="pt").to(device)
+        self.text_encoder.to(device)
+
+        # `transformers.GenerationMixin.generate` does not support using a `torch.Generator` to control randomness,
+        # so manually apply a seed for reproducible generation.
+        if generator is not None:
+            # Overwrite seed to generator's initial seed
+            seed = generator.initial_seed()
+        torch.manual_seed(seed)
+        generated_sequences = self.text_encoder.generate(
+            **model_inputs,
+            max_new_tokens=max_new_tokens,
+            **generation_kwargs,
+        )  # tensor of shape [batch_size, seq_len]
+
+        generated_ids = [seq[len(model_inputs.input_ids[i]) :] for i, seq in enumerate(generated_sequences)]
+        enhanced_prompt = self.processor.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        return enhanced_prompt
+
     # Copied from diffusers.pipelines.ltx2.pipeline_ltx2.LTX2Pipeline.check_inputs
     def check_inputs(
         self,
@@ -511,6 +487,9 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         negative_prompt_embeds=None,
         prompt_attention_mask=None,
         negative_prompt_attention_mask=None,
+        spatio_temporal_guidance_blocks=None,
+        stg_scale=None,
+        audio_stg_scale=None,
     ):
         if height % 32 != 0 or width % 32 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
@@ -554,6 +533,12 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                     f" {negative_prompt_attention_mask.shape}."
                 )
 
+        if ((stg_scale > 0.0) or (audio_stg_scale > 0.0)) and not spatio_temporal_guidance_blocks:
+            raise ValueError(
+                "Spatio-Temporal Guidance (STG) is specified but no STG blocks are supplied. Please supply a list of"
+                "block indices at which to apply STG in `spatio_temporal_guidance_blocks`"
+            )
+
     @staticmethod
     # Copied from diffusers.pipelines.ltx2.pipeline_ltx2.LTX2Pipeline._pack_latents
     def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
@@ -788,7 +773,6 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
             latents = self._create_noised_state(latents, noise_scale, generator)
             return latents.to(device=device, dtype=dtype)
 
-        # TODO: confirm whether this logic is correct
         latent_mel_bins = num_mel_bins // self.audio_vae_mel_compression_ratio
 
         shape = (batch_size, num_channels_latents, audio_latent_length, latent_mel_bins)
@@ -803,6 +787,24 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         latents = self._pack_audio_latents(latents)
         return latents
 
+    def convert_velocity_to_x0(
+        self, sample: torch.Tensor, denoised_output: torch.Tensor, step_idx: int, scheduler: Any | None = None
+    ) -> torch.Tensor:
+        if scheduler is None:
+            scheduler = self.scheduler
+
+        sample_x0 = sample - denoised_output * scheduler.sigmas[step_idx]
+        return sample_x0
+
+    def convert_x0_to_velocity(
+        self, sample: torch.Tensor, denoised_output: torch.Tensor, step_idx: int, scheduler: Any | None = None
+    ) -> torch.Tensor:
+        if scheduler is None:
+            scheduler = self.scheduler
+
+        sample_v = (sample - denoised_output) / scheduler.sigmas[step_idx]
+        return sample_v
+
     @property
     def guidance_scale(self):
         return self._guidance_scale
@@ -811,9 +813,41 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
     def guidance_rescale(self):
         return self._guidance_rescale
 
+    @property
+    def stg_scale(self):
+        return self._stg_scale
+
+    @property
+    def modality_scale(self):
+        return self._modality_scale
+
+    @property
+    def audio_guidance_scale(self):
+        return self._audio_guidance_scale
+
+    @property
+    def audio_guidance_rescale(self):
+        return self._audio_guidance_rescale
+
+    @property
+    def audio_stg_scale(self):
+        return self._audio_stg_scale
+
+    @property
+    def audio_modality_scale(self):
+        return self._audio_modality_scale
+
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1.0
+        return (self._guidance_scale > 1.0) or (self._audio_guidance_scale > 1.0)
+
+    @property
+    def do_spatio_temporal_guidance(self):
+        return (self._stg_scale > 0.0) or (self._audio_stg_scale > 0.0)
+
+    @property
+    def do_modality_isolation_guidance(self):
+        return (self._modality_scale > 1.0) or (self._audio_modality_scale > 1.0)
 
     @property
     def num_timesteps(self):
@@ -846,7 +880,14 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         sigmas: list[float] | None = None,
         timesteps: list[int] | None = None,
         guidance_scale: float = 4.0,
+        stg_scale: float = 0.0,
+        modality_scale: float = 1.0,
         guidance_rescale: float = 0.0,
+        audio_guidance_scale: float | None = None,
+        audio_stg_scale: float | None = None,
+        audio_modality_scale: float | None = None,
+        audio_guidance_rescale: float | None = None,
+        spatio_temporal_guidance_blocks: list[int] | None = None,
         noise_scale: float = 0.0,
         num_videos_per_prompt: int = 1,
         generator: torch.Generator | list[torch.Generator] | None = None,
@@ -858,6 +899,11 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         negative_prompt_attention_mask: torch.Tensor | None = None,
         decode_timestep: float | list[float] = 0.0,
         decode_noise_scale: float | list[float] | None = None,
+        use_cross_timestep: bool = False,
+        system_prompt: str | None = None,
+        prompt_max_new_tokens: int = 512,
+        prompt_enhancement_kwargs: dict[str, Any] | None = None,
+        prompt_enhancement_seed: int = 10,
         output_type: str = "pil",
         return_dict: bool = True,
         attention_kwargs: dict[str, Any] | None = None,
@@ -898,13 +944,47 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                 Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
                 of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
                 `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
-                the text `prompt`, usually at the expense of lower image quality.
+                the text `prompt`, usually at the expense of lower image quality. Used for the video modality (there is
+                a separate value `audio_guidance_scale` for the audio modality).
+            stg_scale (`float`, *optional*, defaults to `0.0`):
+                Video guidance scale for Spatio-Temporal Guidance (STG), proposed in [Spatiotemporal Skip Guidance for
+                Enhanced Video Diffusion Sampling](https://arxiv.org/abs/2411.18664). STG uses a CFG-like estimate
+                where we move the sample away from a weak sample from a perturbed version of the denoising model.
+                Enabling STG will result in an additional denoising model forward pass; the default value of `0.0`
+                means that STG is disabled.
+            modality_scale (`float`, *optional*, defaults to `1.0`):
+                Video guidance scale for LTX-2.X modality isolation guidance, where we move the sample away from a
+                weaker sample generated by the denoising model withy cross-modality (audio-to-video and video-to-audio)
+                cross attention disabled using a CFG-like estimate. Enabling modality guidance will result in an
+                additional denoising model forward pass; the default value of `1.0` means that modality guidance is
+                disabled.
             guidance_rescale (`float`, *optional*, defaults to 0.0):
                 Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                 Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
                 [Common Diffusion Noise Schedules and Sample Steps are
                 Flawed](https://huggingface.co/papers/2305.08891). Guidance rescale factor should fix overexposure when
-                using zero terminal SNR.
+                using zero terminal SNR. Used for the video modality.
+            audio_guidance_scale (`float`, *optional* defaults to `None`):
+                Audio guidance scale for CFG with respect to the negative prompt. The CFG update rule is the same for
+                video and audio, but they can use different values for the guidance scale. The LTX-2.X authors suggest
+                that the `audio_guidance_scale` should be higher relative to the video `guidance_scale` (e.g. for
+                LTX-2.3 they suggest 3.0 for video and 7.0 for audio). If `None`, defaults to the video value
+                `guidance_scale`.
+            audio_stg_scale (`float`, *optional*, defaults to `None`):
+                Audio guidance scale for STG. As with CFG, the STG update rule is otherwise the same for video and
+                audio. For LTX-2.3, a value of 1.0 is suggested for both video and audio. If `None`, defaults to the
+                video value `stg_scale`.
+            audio_modality_scale (`float`, *optional*, defaults to `None`):
+                Audio guidance scale for LTX-2.X modality isolation guidance. As with CFG, the modality guidance rule
+                is otherwise the same for video and audio. For LTX-2.3, a value of 3.0 is suggested for both video and
+                audio. If `None`, defaults to the video value `modality_scale`.
+            audio_guidance_rescale (`float`, *optional*, defaults to `None`):
+                A separate guidance rescale factor for the audio modality. If `None`, defaults to the video value
+                `guidance_rescale`.
+            spatio_temporal_guidance_blocks (`list[int]`, *optional*, defaults to `None`):
+                The zero-indexed transformer block indices at which to apply STG. Must be supplied if STG is used
+                (`stg_scale` or `audio_stg_scale` is greater than `0`). A value of `[29]` is recommended for LTX-2.0
+                and `[28]` is recommended for LTX-2.3.
             noise_scale (`float`, *optional*, defaults to `0.0`):
                 The interpolation factor between random noise and denoised latents at each timestep. Applying noise to
                 the `latents` and `audio_latents` before continue denoising.
@@ -935,6 +1015,24 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                 The timestep at which generated video is decoded.
             decode_noise_scale (`float`, defaults to `None`):
                 The interpolation factor between random noise and denoised latents at the decode timestep.
+            use_cross_timestep (`bool` *optional*, defaults to `False`):
+                Whether to use the cross modality (audio is the cross modality of video, and vice versa) sigma when
+                calculating the cross attention modulation parameters. `True` is the newer (e.g. LTX-2.3) behavior;
+                `False` is the legacy LTX-2.0 behavior.
+            system_prompt (`str`, *optional*, defaults to `None`):
+                Optional system prompt to use for prompt enhancement. The system prompt will be used by the current
+                text encoder (by default, a `Gemma3ForConditionalGeneration` model) to generate an enhanced prompt from
+                the original `prompt` to condition generation. If not supplied, prompt enhancement will not be
+                performed.
+            prompt_max_new_tokens (`int`, *optional*, defaults to `512`):
+                The maximum number of new tokens to generate when performing prompt enhancement.
+            prompt_enhancement_kwargs (`dict[str, Any]`, *optional*, defaults to `None`):
+                Keyword arguments for `self.text_encoder.generate`. If not supplied, default arguments of
+                `do_sample=True` and `temperature=0.7` will be used. See
+                https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate
+                for more details.
+            prompt_enhancement_seed (`int`, *optional*, default to `10`):
+                Random seed for any random operations during prompt enhancement.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
@@ -967,6 +1065,11 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
+        audio_guidance_scale = audio_guidance_scale or guidance_scale
+        audio_stg_scale = audio_stg_scale or stg_scale
+        audio_modality_scale = audio_modality_scale or modality_scale
+        audio_guidance_rescale = audio_guidance_rescale or guidance_rescale
+
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
             prompt=prompt,
@@ -977,10 +1080,21 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
             negative_prompt_embeds=negative_prompt_embeds,
             prompt_attention_mask=prompt_attention_mask,
             negative_prompt_attention_mask=negative_prompt_attention_mask,
+            spatio_temporal_guidance_blocks=spatio_temporal_guidance_blocks,
+            stg_scale=stg_scale,
+            audio_stg_scale=audio_stg_scale,
         )
 
+        # Per-modality guidance scales (video, audio)
         self._guidance_scale = guidance_scale
+        self._stg_scale = stg_scale
+        self._modality_scale = modality_scale
         self._guidance_rescale = guidance_rescale
+        self._audio_guidance_scale = audio_guidance_scale
+        self._audio_stg_scale = audio_stg_scale
+        self._audio_modality_scale = audio_modality_scale
+        self._audio_guidance_rescale = audio_guidance_rescale
+
         self._attention_kwargs = attention_kwargs
         self._interrupt = False
         self._current_timestep = None
@@ -996,6 +1110,18 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         device = self._execution_device
 
         # 3. Prepare text embeddings
+        if system_prompt is not None and prompt is not None:
+            prompt = self.enhance_prompt(
+                image=image,
+                prompt=prompt,
+                system_prompt=system_prompt,
+                max_new_tokens=prompt_max_new_tokens,
+                seed=prompt_enhancement_seed,
+                generator=generator,
+                generation_kwargs=prompt_enhancement_kwargs,
+                device=device,
+            )
+
         (
             prompt_embeds,
             prompt_attention_mask,
@@ -1017,9 +1143,11 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
 
-        additive_attention_mask = (1 - prompt_attention_mask.to(prompt_embeds.dtype)) * -1000000.0
+        tokenizer_padding_side = "left"  # Padding side for default Gemma3-12B text encoder
+        if getattr(self, "tokenizer", None) is not None:
+            tokenizer_padding_side = getattr(self.tokenizer, "padding_side", "left")
         connector_prompt_embeds, connector_audio_prompt_embeds, connector_attention_mask = self.connectors(
-            prompt_embeds, additive_attention_mask, additive_mask=True
+            prompt_embeds, prompt_attention_mask, padding_side=tokenizer_padding_side
         )
 
         # 4. Prepare latent variables
@@ -1041,7 +1169,7 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                 raise ValueError(
                     f"Provided `latents` tensor has shape {latents.shape}, but the expected shape is either [batch_size, seq_len, num_features] or [batch_size, latent_dim, latent_frames, latent_height, latent_width]."
                 )
-        video_sequence_length = latent_num_frames * latent_height * latent_width
+        # video_sequence_length = latent_num_frames * latent_height * latent_width
 
         if latents is None:
             image = self.video_processor.preprocess(image, height=height, width=width)
@@ -1105,7 +1233,7 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         # 5. Prepare timesteps
         sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
         mu = calculate_shift(
-            video_sequence_length,
+            self.scheduler.config.get("max_image_seq_len", 4096),
             self.scheduler.config.get("base_image_seq_len", 1024),
             self.scheduler.config.get("max_image_seq_len", 4096),
             self.scheduler.config.get("base_shift", 0.95),
@@ -1134,11 +1262,6 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare micro-conditions
-        rope_interpolation_scale = (
-            self.vae_temporal_compression_ratio / frame_rate,
-            self.vae_spatial_compression_ratio,
-            self.vae_spatial_compression_ratio,
-        )
         # Pre-compute video and audio positional ids as they will be the same at each step of the denoising loop
         video_coords = self.transformer.rope.prepare_video_coords(
             latents.shape[0], latent_num_frames, latent_height, latent_width, latents.device, fps=frame_rate
@@ -1177,6 +1300,7 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                         audio_encoder_hidden_states=connector_audio_prompt_embeds,
                         timestep=video_timestep,
                         audio_timestep=timestep,
+                        sigma=timestep,  # Used by LTX-2.3
                         encoder_attention_mask=connector_attention_mask,
                         audio_encoder_attention_mask=connector_attention_mask,
                         num_frames=latent_num_frames,
@@ -1186,7 +1310,10 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                         audio_num_frames=audio_num_frames,
                         video_coords=video_coords,
                         audio_coords=audio_coords,
-                        # rope_interpolation_scale=rope_interpolation_scale,
+                        isolate_modalities=False,
+                        spatio_temporal_guidance_blocks=None,
+                        perturbation_mask=None,
+                        use_cross_timestep=use_cross_timestep,
                         attention_kwargs=attention_kwargs,
                         return_dict=False,
                     )
@@ -1194,24 +1321,154 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                 noise_pred_audio = noise_pred_audio.float()
 
                 if self.do_classifier_free_guidance:
-                    noise_pred_video_uncond, noise_pred_video_text = noise_pred_video.chunk(2)
-                    noise_pred_video = noise_pred_video_uncond + self.guidance_scale * (
-                        noise_pred_video_text - noise_pred_video_uncond
+                    noise_pred_video_uncond_text, noise_pred_video = noise_pred_video.chunk(2)
+                    noise_pred_video = self.convert_velocity_to_x0(latents, noise_pred_video, i, self.scheduler)
+                    noise_pred_video_uncond_text = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_text, i, self.scheduler
+                    )
+                    # Use delta formulation as it works more nicely with multiple guidance terms
+                    video_cfg_delta = (self.guidance_scale - 1) * (noise_pred_video - noise_pred_video_uncond_text)
+
+                    noise_pred_audio_uncond_text, noise_pred_audio = noise_pred_audio.chunk(2)
+                    noise_pred_audio = self.convert_velocity_to_x0(audio_latents, noise_pred_audio, i, audio_scheduler)
+                    noise_pred_audio_uncond_text = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_text, i, audio_scheduler
+                    )
+                    audio_cfg_delta = (self.audio_guidance_scale - 1) * (
+                        noise_pred_audio - noise_pred_audio_uncond_text
                     )
 
-                    noise_pred_audio_uncond, noise_pred_audio_text = noise_pred_audio.chunk(2)
-                    noise_pred_audio = noise_pred_audio_uncond + self.guidance_scale * (
-                        noise_pred_audio_text - noise_pred_audio_uncond
+                    # Get positive values from merged CFG inputs in case we need to do other DiT forward passes
+                    if self.do_spatio_temporal_guidance or self.do_modality_isolation_guidance:
+                        if i == 0:
+                            # Only split values that remain constant throughout the loop once
+                            video_prompt_embeds = connector_prompt_embeds.chunk(2, dim=0)[1]
+                            audio_prompt_embeds = connector_audio_prompt_embeds.chunk(2, dim=0)[1]
+                            prompt_attn_mask = connector_attention_mask.chunk(2, dim=0)[1]
+
+                            video_pos_ids = video_coords.chunk(2, dim=0)[0]
+                            audio_pos_ids = audio_coords.chunk(2, dim=0)[0]
+
+                        # Split values that vary each denoising loop iteration
+                        timestep = timestep.chunk(2, dim=0)[0]
+                        video_timestep = video_timestep.chunk(2, dim=0)[0]
+                else:
+                    video_cfg_delta = audio_cfg_delta = 0
+
+                    video_prompt_embeds = connector_prompt_embeds
+                    audio_prompt_embeds = connector_audio_prompt_embeds
+                    prompt_attn_mask = connector_attention_mask
+
+                    video_pos_ids = video_coords
+                    audio_pos_ids = audio_coords
+
+                    noise_pred_video = self.convert_velocity_to_x0(latents, noise_pred_video, i, self.scheduler)
+                    noise_pred_audio = self.convert_velocity_to_x0(audio_latents, noise_pred_audio, i, audio_scheduler)
+
+                if self.do_spatio_temporal_guidance:
+                    with self.transformer.cache_context("uncond_stg"):
+                        noise_pred_video_uncond_stg, noise_pred_audio_uncond_stg = self.transformer(
+                            hidden_states=latents.to(dtype=prompt_embeds.dtype),
+                            audio_hidden_states=audio_latents.to(dtype=prompt_embeds.dtype),
+                            encoder_hidden_states=video_prompt_embeds,
+                            audio_encoder_hidden_states=audio_prompt_embeds,
+                            timestep=video_timestep,
+                            audio_timestep=timestep,
+                            sigma=timestep,  # Used by LTX-2.3
+                            encoder_attention_mask=prompt_attn_mask,
+                            audio_encoder_attention_mask=prompt_attn_mask,
+                            num_frames=latent_num_frames,
+                            height=latent_height,
+                            width=latent_width,
+                            fps=frame_rate,
+                            audio_num_frames=audio_num_frames,
+                            video_coords=video_pos_ids,
+                            audio_coords=audio_pos_ids,
+                            isolate_modalities=False,
+                            # Use STG at given blocks to perturb model
+                            spatio_temporal_guidance_blocks=spatio_temporal_guidance_blocks,
+                            perturbation_mask=None,
+                            use_cross_timestep=use_cross_timestep,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )
+                    noise_pred_video_uncond_stg = noise_pred_video_uncond_stg.float()
+                    noise_pred_audio_uncond_stg = noise_pred_audio_uncond_stg.float()
+                    noise_pred_video_uncond_stg = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_stg, i, self.scheduler
+                    )
+                    noise_pred_audio_uncond_stg = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_stg, i, audio_scheduler
                     )
 
-                    if self.guidance_rescale > 0:
-                        # Based on 3.4. in https://huggingface.co/papers/2305.08891
-                        noise_pred_video = rescale_noise_cfg(
-                            noise_pred_video, noise_pred_video_text, guidance_rescale=self.guidance_rescale
-                        )
-                        noise_pred_audio = rescale_noise_cfg(
-                            noise_pred_audio, noise_pred_audio_text, guidance_rescale=self.guidance_rescale
+                    video_stg_delta = self.stg_scale * (noise_pred_video - noise_pred_video_uncond_stg)
+                    audio_stg_delta = self.audio_stg_scale * (noise_pred_audio - noise_pred_audio_uncond_stg)
+                else:
+                    video_stg_delta = audio_stg_delta = 0
+
+                if self.do_modality_isolation_guidance:
+                    with self.transformer.cache_context("uncond_modality"):
+                        noise_pred_video_uncond_modality, noise_pred_audio_uncond_modality = self.transformer(
+                            hidden_states=latents.to(dtype=prompt_embeds.dtype),
+                            audio_hidden_states=audio_latents.to(dtype=prompt_embeds.dtype),
+                            encoder_hidden_states=video_prompt_embeds,
+                            audio_encoder_hidden_states=audio_prompt_embeds,
+                            timestep=video_timestep,
+                            audio_timestep=timestep,
+                            sigma=timestep,  # Used by LTX-2.3
+                            encoder_attention_mask=prompt_attn_mask,
+                            audio_encoder_attention_mask=prompt_attn_mask,
+                            num_frames=latent_num_frames,
+                            height=latent_height,
+                            width=latent_width,
+                            fps=frame_rate,
+                            audio_num_frames=audio_num_frames,
+                            video_coords=video_pos_ids,
+                            audio_coords=audio_pos_ids,
+                            # Turn off A2V and V2A cross attn to isolate video and audio modalities
+                            isolate_modalities=True,
+                            spatio_temporal_guidance_blocks=None,
+                            perturbation_mask=None,
+                            use_cross_timestep=use_cross_timestep,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
                         )
+                    noise_pred_video_uncond_modality = noise_pred_video_uncond_modality.float()
+                    noise_pred_audio_uncond_modality = noise_pred_audio_uncond_modality.float()
+                    noise_pred_video_uncond_modality = self.convert_velocity_to_x0(
+                        latents, noise_pred_video_uncond_modality, i, self.scheduler
+                    )
+                    noise_pred_audio_uncond_modality = self.convert_velocity_to_x0(
+                        audio_latents, noise_pred_audio_uncond_modality, i, audio_scheduler
+                    )
+
+                    video_modality_delta = (self.modality_scale - 1) * (
+                        noise_pred_video - noise_pred_video_uncond_modality
+                    )
+                    audio_modality_delta = (self.audio_modality_scale - 1) * (
+                        noise_pred_audio - noise_pred_audio_uncond_modality
+                    )
+                else:
+                    video_modality_delta = audio_modality_delta = 0
+
+                # Now apply all guidance terms
+                noise_pred_video_g = noise_pred_video + video_cfg_delta + video_stg_delta + video_modality_delta
+                noise_pred_audio_g = noise_pred_audio + audio_cfg_delta + audio_stg_delta + audio_modality_delta
+
+                # Apply LTX-2.X guidance rescaling
+                if self.guidance_rescale > 0:
+                    noise_pred_video = rescale_noise_cfg(
+                        noise_pred_video_g, noise_pred_video, guidance_rescale=self.guidance_rescale
+                    )
+                else:
+                    noise_pred_video = noise_pred_video_g
+
+                if self.audio_guidance_rescale > 0:
+                    noise_pred_audio = rescale_noise_cfg(
+                        noise_pred_audio_g, noise_pred_audio, guidance_rescale=self.audio_guidance_rescale
+                    )
+                else:
+                    noise_pred_audio = noise_pred_audio_g
 
                 # compute the previous noisy sample x_t -> x_t-1
                 noise_pred_video = self._unpack_latents(
@@ -1231,6 +1488,10 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                     self.transformer_temporal_patch_size,
                 )
 
+                # Convert back to velocity for scheduler
+                noise_pred_video = self.convert_x0_to_velocity(latents, noise_pred_video, i, self.scheduler)
+                noise_pred_audio = self.convert_x0_to_velocity(audio_latents, noise_pred_audio, i, audio_scheduler)
+
                 noise_pred_video = noise_pred_video[:, :, 1:]
                 noise_latents = latents[:, :, 1:]
                 pred_latents = self.scheduler.step(noise_pred_video, t, noise_latents, return_dict=False)[0]
@@ -1268,9 +1529,6 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
             self.transformer_spatial_patch_size,
             self.transformer_temporal_patch_size,
         )
-        latents = self._denormalize_latents(
-            latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
-        )
 
         audio_latents = self._denormalize_audio_latents(
             audio_latents, self.audio_vae.latents_mean, self.audio_vae.latents_std
@@ -1278,6 +1536,9 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         audio_latents = self._unpack_audio_latents(audio_latents, audio_num_frames, num_mel_bins=latent_mel_bins)
 
         if output_type == "latent":
+            latents = self._denormalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
             video = latents
             audio = audio_latents
         else:
@@ -1300,6 +1561,10 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                 ]
                 latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
 
+            latents = self._denormalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
+
             latents = latents.to(self.vae.dtype)
             video = self.vae.decode(latents, timestep, return_dict=False)[0]
             video = self.video_processor.postprocess_video(video, output_type=output_type)

src/diffusers/pipelines/ltx2/vocoder.py

@@ -8,6 +8,209 @@ from ...configuration_utils import ConfigMixin, register_to_config
 from ...models.modeling_utils import ModelMixin
 
 
+def kaiser_sinc_filter1d(cutoff: float, half_width: float, kernel_size: int) -> torch.Tensor:
+    """
+    Creates a Kaiser sinc kernel for low-pass filtering.
+
+    Args:
+        cutoff (`float`):
+            Normalized frequency cutoff (relative to the sampling rate). Must be between 0 and 0.5 (the Nyquist
+            frequency).
+        half_width (`float`):
+            Used to determine the Kaiser window's beta parameter.
+        kernel_size:
+            Size of the Kaiser window (and ultimately the Kaiser sinc kernel).
+
+    Returns:
+        `torch.Tensor` of shape `(kernel_size,)`:
+            The Kaiser sinc kernel.
+    """
+    delta_f = 4 * half_width
+    half_size = kernel_size // 2
+    amplitude = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if amplitude > 50.0:
+        beta = 0.1102 * (amplitude - 8.7)
+    elif amplitude >= 21.0:
+        beta = 0.5842 * (amplitude - 21) ** 0.4 + 0.07886 * (amplitude - 21.0)
+    else:
+        beta = 0.0
+
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    even = kernel_size % 2 == 0
+    time = torch.arange(-half_size, half_size) + 0.5 if even else torch.arange(kernel_size) - half_size
+
+    if cutoff == 0.0:
+        filter = torch.zeros_like(time)
+    else:
+        time = 2 * cutoff * time
+        sinc = torch.where(
+            time == 0,
+            torch.ones_like(time),
+            torch.sin(math.pi * time) / math.pi / time,
+        )
+        filter = 2 * cutoff * window * sinc
+        filter = filter / filter.sum()
+    return filter
+
+
+class DownSample1d(nn.Module):
+    """1D low-pass filter for antialias downsampling."""
+
+    def __init__(
+        self,
+        ratio: int = 2,
+        kernel_size: int | None = None,
+        use_padding: bool = True,
+        padding_mode: str = "replicate",
+        persistent: bool = True,
+    ):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = kernel_size or int(6 * ratio // 2) * 2
+        self.pad_left = self.kernel_size // 2 + (self.kernel_size % 2) - 1
+        self.pad_right = self.kernel_size // 2
+        self.use_padding = use_padding
+        self.padding_mode = padding_mode
+
+        cutoff = 0.5 / ratio
+        half_width = 0.6 / ratio
+        low_pass_filter = kaiser_sinc_filter1d(cutoff, half_width, self.kernel_size)
+        self.register_buffer("filter", low_pass_filter.view(1, 1, self.kernel_size), persistent=persistent)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x expected shape: [batch_size, num_channels, hidden_dim]
+        num_channels = x.shape[1]
+        if self.use_padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        x_filtered = F.conv1d(x, self.filter.expand(num_channels, -1, -1), stride=self.ratio, groups=num_channels)
+        return x_filtered
+
+
+class UpSample1d(nn.Module):
+    def __init__(
+        self,
+        ratio: int = 2,
+        kernel_size: int | None = None,
+        window_type: str = "kaiser",
+        padding_mode: str = "replicate",
+        persistent: bool = True,
+    ):
+        super().__init__()
+        self.ratio = ratio
+        self.padding_mode = padding_mode
+
+        if window_type == "hann":
+            rolloff = 0.99
+            lowpass_filter_width = 6
+            width = math.ceil(lowpass_filter_width / rolloff)
+            self.kernel_size = 2 * width * ratio + 1
+            self.pad = width
+            self.pad_left = 2 * width * ratio
+            self.pad_right = self.kernel_size - ratio
+
+            time_axis = (torch.arange(self.kernel_size) / ratio - width) * rolloff
+            time_clamped = time_axis.clamp(-lowpass_filter_width, lowpass_filter_width)
+            window = torch.cos(time_clamped * math.pi / lowpass_filter_width / 2) ** 2
+            sinc_filter = (torch.sinc(time_axis) * window * rolloff / ratio).view(1, 1, -1)
+        else:
+            # Kaiser sinc filter is BigVGAN default
+            self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+            self.pad = self.kernel_size // ratio - 1
+            self.pad_left = self.pad * self.ratio + (self.kernel_size - self.ratio) // 2
+            self.pad_right = self.pad * self.ratio + (self.kernel_size - self.ratio + 1) // 2
+
+            sinc_filter = kaiser_sinc_filter1d(
+                cutoff=0.5 / ratio,
+                half_width=0.6 / ratio,
+                kernel_size=self.kernel_size,
+            )
+
+        self.register_buffer("filter", sinc_filter.view(1, 1, self.kernel_size), persistent=persistent)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x expected shape: [batch_size, num_channels, hidden_dim]
+        num_channels = x.shape[1]
+        x = F.pad(x, (self.pad, self.pad), mode=self.padding_mode)
+        low_pass_filter = self.filter.to(dtype=x.dtype, device=x.device).expand(num_channels, -1, -1)
+        x = self.ratio * F.conv_transpose1d(x, low_pass_filter, stride=self.ratio, groups=num_channels)
+        return x[..., self.pad_left : -self.pad_right]
+
+
+class AntiAliasAct1d(nn.Module):
+    """
+    Antialiasing activation for a 1D signal: upsamples, applies an activation (usually snakebeta), and then downsamples
+    to avoid aliasing.
+    """
+
+    def __init__(
+        self,
+        act_fn: str | nn.Module,
+        ratio: int = 2,
+        kernel_size: int = 12,
+        **kwargs,
+    ):
+        super().__init__()
+        self.upsample = UpSample1d(ratio=ratio, kernel_size=kernel_size)
+        if isinstance(act_fn, str):
+            if act_fn == "snakebeta":
+                act_fn = SnakeBeta(**kwargs)
+            elif act_fn == "snake":
+                act_fn = SnakeBeta(**kwargs)
+            else:
+                act_fn = nn.LeakyReLU(**kwargs)
+        self.act = act_fn
+        self.downsample = DownSample1d(ratio=ratio, kernel_size=kernel_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+        return x
+
+
+class SnakeBeta(nn.Module):
+    """
+    Implements the Snake and SnakeBeta activations, which help with learning periodic patterns.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        alpha: float = 1.0,
+        eps: float = 1e-9,
+        trainable_params: bool = True,
+        logscale: bool = True,
+        use_beta: bool = True,
+    ):
+        super().__init__()
+        self.eps = eps
+        self.logscale = logscale
+        self.use_beta = use_beta
+
+        self.alpha = nn.Parameter(torch.zeros(channels) if self.logscale else torch.ones(channels) * alpha)
+        self.alpha.requires_grad = trainable_params
+        if use_beta:
+            self.beta = nn.Parameter(torch.zeros(channels) if self.logscale else torch.ones(channels) * alpha)
+            self.beta.requires_grad = trainable_params
+
+    def forward(self, hidden_states: torch.Tensor, channel_dim: int = 1) -> torch.Tensor:
+        broadcast_shape = [1] * hidden_states.ndim
+        broadcast_shape[channel_dim] = -1
+        alpha = self.alpha.view(broadcast_shape)
+        if self.use_beta:
+            beta = self.beta.view(broadcast_shape)
+
+        if self.logscale:
+            alpha = torch.exp(alpha)
+            if self.use_beta:
+                beta = torch.exp(beta)
+
+        amplitude = beta if self.use_beta else alpha
+        hidden_states = hidden_states + (1.0 / (amplitude + self.eps)) * torch.sin(hidden_states * alpha).pow(2)
+        return hidden_states
+
+
 class ResBlock(nn.Module):
     def __init__(
         self,
@@ -15,12 +218,15 @@ class ResBlock(nn.Module):
         kernel_size: int = 3,
         stride: int = 1,
         dilations: tuple[int, ...] = (1, 3, 5),
+        act_fn: str = "leaky_relu",
         leaky_relu_negative_slope: float = 0.1,
+        antialias: bool = False,
+        antialias_ratio: int = 2,
+        antialias_kernel_size: int = 12,
         padding_mode: str = "same",
     ):
         super().__init__()
         self.dilations = dilations
-        self.negative_slope = leaky_relu_negative_slope
 
         self.convs1 = nn.ModuleList(
             [
@@ -28,6 +234,18 @@ class ResBlock(nn.Module):
                 for dilation in dilations
             ]
         )
+        self.acts1 = nn.ModuleList()
+        for _ in range(len(self.convs1)):
+            if act_fn == "snakebeta":
+                act = SnakeBeta(channels, use_beta=True)
+            elif act_fn == "snake":
+                act = SnakeBeta(channels, use_beta=False)
+            else:
+                act = nn.LeakyReLU(negative_slope=leaky_relu_negative_slope)
+
+            if antialias:
+                act = AntiAliasAct1d(act, ratio=antialias_ratio, kernel_size=antialias_kernel_size)
+            self.acts1.append(act)
 
         self.convs2 = nn.ModuleList(
             [
@@ -35,12 +253,24 @@ class ResBlock(nn.Module):
                 for _ in range(len(dilations))
             ]
         )
+        self.acts2 = nn.ModuleList()
+        for _ in range(len(self.convs2)):
+            if act_fn == "snakebeta":
+                act = SnakeBeta(channels, use_beta=True)
+            elif act_fn == "snake":
+                act = SnakeBeta(channels, use_beta=False)
+            else:
+                act_fn = nn.LeakyReLU(negative_slope=leaky_relu_negative_slope)
+
+            if antialias:
+                act = AntiAliasAct1d(act, ratio=antialias_ratio, kernel_size=antialias_kernel_size)
+            self.acts2.append(act)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        for conv1, conv2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, negative_slope=self.negative_slope)
+        for act1, conv1, act2, conv2 in zip(self.acts1, self.convs1, self.acts2, self.convs2):
+            xt = act1(x)
             xt = conv1(xt)
-            xt = F.leaky_relu(xt, negative_slope=self.negative_slope)
+            xt = act2(xt)
             xt = conv2(xt)
             x = x + xt
         return x
@@ -61,7 +291,13 @@ class LTX2Vocoder(ModelMixin, ConfigMixin):
         upsample_factors: list[int] = [6, 5, 2, 2, 2],
         resnet_kernel_sizes: list[int] = [3, 7, 11],
         resnet_dilations: list[list[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+        act_fn: str = "leaky_relu",
         leaky_relu_negative_slope: float = 0.1,
+        antialias: bool = False,
+        antialias_ratio: int = 2,
+        antialias_kernel_size: int = 12,
+        final_act_fn: str | None = "tanh",  # tanh, clamp, None
+        final_bias: bool = True,
         output_sampling_rate: int = 24000,
     ):
         super().__init__()
@@ -69,7 +305,9 @@ class LTX2Vocoder(ModelMixin, ConfigMixin):
         self.resnets_per_upsample = len(resnet_kernel_sizes)
         self.out_channels = out_channels
         self.total_upsample_factor = math.prod(upsample_factors)
+        self.act_fn = act_fn
         self.negative_slope = leaky_relu_negative_slope
+        self.final_act_fn = final_act_fn
 
         if self.num_upsample_layers != len(upsample_factors):
             raise ValueError(
@@ -83,6 +321,13 @@ class LTX2Vocoder(ModelMixin, ConfigMixin):
                 f" {len(self.resnets_per_upsample)} and {len(resnet_dilations)}, respectively."
             )
 
+        supported_act_fns = ["snakebeta", "snake", "leaky_relu"]
+        if self.act_fn not in supported_act_fns:
+            raise ValueError(
+                f"Unsupported activation function: {self.act_fn}. Currently supported values of `act_fn` are "
+                f"{supported_act_fns}."
+            )
+
         self.conv_in = nn.Conv1d(in_channels, hidden_channels, kernel_size=7, stride=1, padding=3)
 
         self.upsamplers = nn.ModuleList()
@@ -103,15 +348,27 @@ class LTX2Vocoder(ModelMixin, ConfigMixin):
             for kernel_size, dilations in zip(resnet_kernel_sizes, resnet_dilations):
                 self.resnets.append(
                     ResBlock(
-                        output_channels,
-                        kernel_size,
+                        channels=output_channels,
+                        kernel_size=kernel_size,
                         dilations=dilations,
+                        act_fn=act_fn,
                         leaky_relu_negative_slope=leaky_relu_negative_slope,
+                        antialias=antialias,
+                        antialias_ratio=antialias_ratio,
+                        antialias_kernel_size=antialias_kernel_size,
                     )
                 )
             input_channels = output_channels
 
-        self.conv_out = nn.Conv1d(output_channels, out_channels, 7, stride=1, padding=3)
+        if act_fn == "snakebeta" or act_fn == "snake":
+            # Always use antialiasing
+            act_out = SnakeBeta(channels=output_channels, use_beta=True)
+            self.act_out = AntiAliasAct1d(act_out, ratio=antialias_ratio, kernel_size=antialias_kernel_size)
+        elif act_fn == "leaky_relu":
+            # NOTE: does NOT use self.negative_slope, following the original code
+            self.act_out = nn.LeakyReLU()
+
+        self.conv_out = nn.Conv1d(output_channels, out_channels, 7, stride=1, padding=3, bias=final_bias)
 
     def forward(self, hidden_states: torch.Tensor, time_last: bool = False) -> torch.Tensor:
         r"""
@@ -139,7 +396,9 @@ class LTX2Vocoder(ModelMixin, ConfigMixin):
         hidden_states = self.conv_in(hidden_states)
 
         for i in range(self.num_upsample_layers):
-            hidden_states = F.leaky_relu(hidden_states, negative_slope=self.negative_slope)
+            if self.act_fn == "leaky_relu":
+                # Other activations are inside each upsampling block
+                hidden_states = F.leaky_relu(hidden_states, negative_slope=self.negative_slope)
             hidden_states = self.upsamplers[i](hidden_states)
 
             # Run all resnets in parallel on hidden_states
@@ -149,10 +408,190 @@ class LTX2Vocoder(ModelMixin, ConfigMixin):
 
             hidden_states = torch.mean(resnet_outputs, dim=0)
 
-        # NOTE: unlike the first leaky ReLU, this leaky ReLU is set to use the default F.leaky_relu negative slope of
-        # 0.01 (whereas the others usually use a slope of 0.1). Not sure if this is intended
-        hidden_states = F.leaky_relu(hidden_states, negative_slope=0.01)
+        hidden_states = self.act_out(hidden_states)
         hidden_states = self.conv_out(hidden_states)
-        hidden_states = torch.tanh(hidden_states)
+        if self.final_act_fn == "tanh":
+            hidden_states = torch.tanh(hidden_states)
+        elif self.final_act_fn == "clamp":
+            hidden_states = torch.clamp(hidden_states, -1, 1)
 
         return hidden_states
+
+
+class CausalSTFT(nn.Module):
+    """
+    Performs a causal short-time Fourier transform (STFT) using causal Hann windows on a waveform. The DFT bases
+    multiplied by the Hann windows are pre-calculated and stored as buffers. For exact parity with training, the exact
+    buffers should be loaded from the checkpoint in bfloat16.
+    """
+
+    def __init__(self, filter_length: int = 512, hop_length: int = 80, window_length: int = 512):
+        super().__init__()
+        self.hop_length = hop_length
+        self.window_length = window_length
+        n_freqs = filter_length // 2 + 1
+
+        self.register_buffer("forward_basis", torch.zeros(n_freqs * 2, 1, filter_length), persistent=True)
+        self.register_buffer("inverse_basis", torch.zeros(n_freqs * 2, 1, filter_length), persistent=True)
+
+    def forward(self, waveform: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        if waveform.ndim == 2:
+            waveform = waveform.unsqueeze(1)  # [B, num_channels, num_samples]
+
+        left_pad = max(0, self.window_length - self.hop_length)  # causal: left-only
+        waveform = F.pad(waveform, (left_pad, 0))
+
+        spec = F.conv1d(waveform, self.forward_basis, stride=self.hop_length, padding=0)
+        n_freqs = spec.shape[1] // 2
+        real, imag = spec[:, :n_freqs], spec[:, n_freqs:]
+        magnitude = torch.sqrt(real**2 + imag**2)
+        phase = torch.atan2(imag.float(), real.float()).to(dtype=real.dtype)
+        return magnitude, phase
+
+
+class MelSTFT(nn.Module):
+    """
+    Calculates a causal log-mel spectrogram from a waveform. Uses a pre-calculated mel filterbank, which should be
+    loaded from the checkpoint in bfloat16.
+    """
+
+    def __init__(
+        self,
+        filter_length: int = 512,
+        hop_length: int = 80,
+        window_length: int = 512,
+        num_mel_channels: int = 64,
+    ):
+        super().__init__()
+        self.stft_fn = CausalSTFT(filter_length, hop_length, window_length)
+
+        num_freqs = filter_length // 2 + 1
+        self.register_buffer("mel_basis", torch.zeros(num_mel_channels, num_freqs), persistent=True)
+
+    def forward(self, waveform: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        magnitude, phase = self.stft_fn(waveform)
+        energy = torch.norm(magnitude, dim=1)
+        mel = torch.matmul(self.mel_basis.to(magnitude.dtype), magnitude)
+        log_mel = torch.log(torch.clamp(mel, min=1e-5))
+        return log_mel, magnitude, phase, energy
+
+
+class LTX2VocoderWithBWE(ModelMixin, ConfigMixin):
+    """
+    LTX-2.X vocoder with bandwidth extension (BWE) upsampling. The vocoder and the BWE module run in sequence, with the
+    BWE module upsampling the vocoder output waveform to a higher sampling rate. The BWE module itself has the same
+    architecture as the original vocoder.
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 128,
+        hidden_channels: int = 1536,
+        out_channels: int = 2,
+        upsample_kernel_sizes: list[int] = [11, 4, 4, 4, 4, 4],
+        upsample_factors: list[int] = [5, 2, 2, 2, 2, 2],
+        resnet_kernel_sizes: list[int] = [3, 7, 11],
+        resnet_dilations: list[list[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+        act_fn: str = "snakebeta",
+        leaky_relu_negative_slope: float = 0.1,
+        antialias: bool = True,
+        antialias_ratio: int = 2,
+        antialias_kernel_size: int = 12,
+        final_act_fn: str | None = None,
+        final_bias: bool = False,
+        bwe_in_channels: int = 128,
+        bwe_hidden_channels: int = 512,
+        bwe_out_channels: int = 2,
+        bwe_upsample_kernel_sizes: list[int] = [12, 11, 4, 4, 4],
+        bwe_upsample_factors: list[int] = [6, 5, 2, 2, 2],
+        bwe_resnet_kernel_sizes: list[int] = [3, 7, 11],
+        bwe_resnet_dilations: list[list[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+        bwe_act_fn: str = "snakebeta",
+        bwe_leaky_relu_negative_slope: float = 0.1,
+        bwe_antialias: bool = True,
+        bwe_antialias_ratio: int = 2,
+        bwe_antialias_kernel_size: int = 12,
+        bwe_final_act_fn: str | None = None,
+        bwe_final_bias: bool = False,
+        filter_length: int = 512,
+        hop_length: int = 80,
+        window_length: int = 512,
+        num_mel_channels: int = 64,
+        input_sampling_rate: int = 16000,
+        output_sampling_rate: int = 48000,
+    ):
+        super().__init__()
+
+        self.vocoder = LTX2Vocoder(
+            in_channels=in_channels,
+            hidden_channels=hidden_channels,
+            out_channels=out_channels,
+            upsample_kernel_sizes=upsample_kernel_sizes,
+            upsample_factors=upsample_factors,
+            resnet_kernel_sizes=resnet_kernel_sizes,
+            resnet_dilations=resnet_dilations,
+            act_fn=act_fn,
+            leaky_relu_negative_slope=leaky_relu_negative_slope,
+            antialias=antialias,
+            antialias_ratio=antialias_ratio,
+            antialias_kernel_size=antialias_kernel_size,
+            final_act_fn=final_act_fn,
+            final_bias=final_bias,
+            output_sampling_rate=input_sampling_rate,
+        )
+        self.bwe_generator = LTX2Vocoder(
+            in_channels=bwe_in_channels,
+            hidden_channels=bwe_hidden_channels,
+            out_channels=bwe_out_channels,
+            upsample_kernel_sizes=bwe_upsample_kernel_sizes,
+            upsample_factors=bwe_upsample_factors,
+            resnet_kernel_sizes=bwe_resnet_kernel_sizes,
+            resnet_dilations=bwe_resnet_dilations,
+            act_fn=bwe_act_fn,
+            leaky_relu_negative_slope=bwe_leaky_relu_negative_slope,
+            antialias=bwe_antialias,
+            antialias_ratio=bwe_antialias_ratio,
+            antialias_kernel_size=bwe_antialias_kernel_size,
+            final_act_fn=bwe_final_act_fn,
+            final_bias=bwe_final_bias,
+            output_sampling_rate=output_sampling_rate,
+        )
+
+        self.mel_stft = MelSTFT(
+            filter_length=filter_length,
+            hop_length=hop_length,
+            window_length=window_length,
+            num_mel_channels=num_mel_channels,
+        )
+
+        self.resampler = UpSample1d(
+            ratio=output_sampling_rate // input_sampling_rate,
+            window_type="hann",
+            persistent=False,
+        )
+
+    def forward(self, mel_spec: torch.Tensor) -> torch.Tensor:
+        # 1. Run stage 1 vocoder to get low sampling rate waveform
+        x = self.vocoder(mel_spec)
+        batch_size, num_channels, num_samples = x.shape
+
+        # Pad to exact multiple of hop_length for exact mel frame count
+        remainder = num_samples % self.config.hop_length
+        if remainder != 0:
+            x = F.pad(x, (0, self.hop_length - remainder))
+
+        # 2. Compute mel spectrogram on vocoder output
+        mel, _, _, _ = self.mel_stft(x.flatten(0, 1))
+        mel = mel.unflatten(0, (-1, num_channels))
+
+        # 3. Run bandwidth extender (BWE) on new mel spectrogram
+        mel_for_bwe = mel.transpose(2, 3)  # [B, C, num_mel_bins, num_frames] --> [B, C, num_frames, num_mel_bins]
+        residual = self.bwe_generator(mel_for_bwe)
+
+        # 4. Residual connection with resampler
+        skip = self.resampler(x)
+        waveform = torch.clamp(residual + skip, -1, 1)
+        output_samples = num_samples * self.config.output_sampling_rate // self.config.input_sampling_rate
+        waveform = waveform[..., :output_samples]
+        return waveform

src/diffusers/pipelines/paint_by_example/image_encoder.py

@@ -35,6 +35,8 @@ class PaintByExampleImageEncoder(CLIPPreTrainedModel):
         # uncondition for scaling
         self.uncond_vector = nn.Parameter(torch.randn((1, 1, self.proj_size)))
 
+        self.post_init()
+
     def forward(self, pixel_values, return_uncond_vector=False):
         clip_output = self.model(pixel_values=pixel_values)
         latent_states = clip_output.pooler_output

src/diffusers/pipelines/sana_video/pipeline_sana_video.py

@@ -24,7 +24,7 @@ from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFa
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...loaders import SanaLoraLoaderMixin
-from ...models import AutoencoderDC, AutoencoderKLWan, SanaVideoTransformer3DModel
+from ...models import AutoencoderDC, AutoencoderKLLTX2Video, AutoencoderKLWan, SanaVideoTransformer3DModel
 from ...schedulers import DPMSolverMultistepScheduler
 from ...utils import (
     BACKENDS_MAPPING,
@@ -194,7 +194,7 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             The tokenizer used to tokenize the prompt.
         text_encoder ([`Gemma2PreTrainedModel`]):
             Text encoder model to encode the input prompts.
-        vae ([`AutoencoderKLWan` or `AutoencoderDCAEV`]):
+        vae ([`AutoencoderKLWan`, `AutoencoderDC`, or `AutoencoderKLLTX2Video`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
         transformer ([`SanaVideoTransformer3DModel`]):
             Conditional Transformer to denoise the input latents.
@@ -213,7 +213,7 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
         self,
         tokenizer: GemmaTokenizer | GemmaTokenizerFast,
         text_encoder: Gemma2PreTrainedModel,
-        vae: AutoencoderDC | AutoencoderKLWan,
+        vae: AutoencoderDC | AutoencoderKLLTX2Video | AutoencoderKLWan,
         transformer: SanaVideoTransformer3DModel,
         scheduler: DPMSolverMultistepScheduler,
     ):
@@ -223,8 +223,19 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
         )
 
-        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
-        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
+        if getattr(self, "vae", None):
+            if isinstance(self.vae, AutoencoderKLLTX2Video):
+                self.vae_scale_factor_temporal = self.vae.config.temporal_compression_ratio
+                self.vae_scale_factor_spatial = self.vae.config.spatial_compression_ratio
+            elif isinstance(self.vae, (AutoencoderDC, AutoencoderKLWan)):
+                self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal
+                self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial
+            else:
+                self.vae_scale_factor_temporal = 4
+                self.vae_scale_factor_spatial = 8
+        else:
+            self.vae_scale_factor_temporal = 4
+            self.vae_scale_factor_spatial = 8
 
         self.vae_scale_factor = self.vae_scale_factor_spatial
 
@@ -985,14 +996,21 @@ class SanaVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
                 if is_torch_version(">=", "2.5.0")
                 else torch_accelerator_module.OutOfMemoryError
             )
-            latents_mean = (
-                torch.tensor(self.vae.config.latents_mean)
-                .view(1, self.vae.config.z_dim, 1, 1, 1)
-                .to(latents.device, latents.dtype)
-            )
-            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
-                latents.device, latents.dtype
-            )
+            if isinstance(self.vae, AutoencoderKLLTX2Video):
+                latents_mean = self.vae.latents_mean
+                latents_std = self.vae.latents_std
+                z_dim = self.vae.config.latent_channels
+            elif isinstance(self.vae, AutoencoderKLWan):
+                latents_mean = torch.tensor(self.vae.config.latents_mean)
+                latents_std = torch.tensor(self.vae.config.latents_std)
+                z_dim = self.vae.config.z_dim
+            else:
+                latents_mean = torch.zeros(latents.shape[1], device=latents.device, dtype=latents.dtype)
+                latents_std = torch.ones(latents.shape[1], device=latents.device, dtype=latents.dtype)
+                z_dim = latents.shape[1]
+
+            latents_mean = latents_mean.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
+            latents_std = 1.0 / latents_std.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
             latents = latents / latents_std + latents_mean
             try:
                 video = self.vae.decode(latents, return_dict=False)[0]

src/diffusers/pipelines/sana_video/pipeline_sana_video_i2v.py

@@ -26,7 +26,7 @@ from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFa
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput
 from ...loaders import SanaLoraLoaderMixin
-from ...models import AutoencoderDC, AutoencoderKLWan, SanaVideoTransformer3DModel
+from ...models import AutoencoderDC, AutoencoderKLLTX2Video, AutoencoderKLWan, SanaVideoTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
     BACKENDS_MAPPING,
@@ -184,7 +184,7 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             The tokenizer used to tokenize the prompt.
         text_encoder ([`Gemma2PreTrainedModel`]):
             Text encoder model to encode the input prompts.
-        vae ([`AutoencoderKLWan` or `AutoencoderDCAEV`]):
+        vae ([`AutoencoderKLWan`, `AutoencoderDC`, or `AutoencoderKLLTX2Video`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
         transformer ([`SanaVideoTransformer3DModel`]):
             Conditional Transformer to denoise the input latents.
@@ -203,7 +203,7 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
         self,
         tokenizer: GemmaTokenizer | GemmaTokenizerFast,
         text_encoder: Gemma2PreTrainedModel,
-        vae: AutoencoderDC | AutoencoderKLWan,
+        vae: AutoencoderDC | AutoencoderKLLTX2Video | AutoencoderKLWan,
         transformer: SanaVideoTransformer3DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,
     ):
@@ -213,8 +213,19 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
         )
 
-        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
-        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
+        if getattr(self, "vae", None):
+            if isinstance(self.vae, AutoencoderKLLTX2Video):
+                self.vae_scale_factor_temporal = self.vae.config.temporal_compression_ratio
+                self.vae_scale_factor_spatial = self.vae.config.spatial_compression_ratio
+            elif isinstance(self.vae, (AutoencoderDC, AutoencoderKLWan)):
+                self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal
+                self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial
+            else:
+                self.vae_scale_factor_temporal = 4
+                self.vae_scale_factor_spatial = 8
+        else:
+            self.vae_scale_factor_temporal = 4
+            self.vae_scale_factor_spatial = 8
 
         self.vae_scale_factor = self.vae_scale_factor_spatial
 
@@ -687,14 +698,18 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             image_latents = retrieve_latents(self.vae.encode(image), sample_mode="argmax")
             image_latents = image_latents.repeat(batch_size, 1, 1, 1, 1)
 
-        latents_mean = (
-            torch.tensor(self.vae.config.latents_mean)
-            .view(1, -1, 1, 1, 1)
-            .to(image_latents.device, image_latents.dtype)
-        )
-        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, -1, 1, 1, 1).to(
-            image_latents.device, image_latents.dtype
-        )
+        if isinstance(self.vae, AutoencoderKLLTX2Video):
+            _latents_mean = self.vae.latents_mean
+            _latents_std = self.vae.latents_std
+        elif isinstance(self.vae, AutoencoderKLWan):
+            _latents_mean = torch.tensor(self.vae.config.latents_mean)
+            _latents_std = torch.tensor(self.vae.config.latents_std)
+        else:
+            _latents_mean = torch.zeros(image_latents.shape[1], device=image_latents.device, dtype=image_latents.dtype)
+            _latents_std = torch.ones(image_latents.shape[1], device=image_latents.device, dtype=image_latents.dtype)
+
+        latents_mean = _latents_mean.view(1, -1, 1, 1, 1).to(image_latents.device, image_latents.dtype)
+        latents_std = 1.0 / _latents_std.view(1, -1, 1, 1, 1).to(image_latents.device, image_latents.dtype)
         image_latents = (image_latents - latents_mean) * latents_std
 
         latents[:, :, 0:1] = image_latents.to(dtype)
@@ -1034,14 +1049,21 @@ class SanaImageToVideoPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
                 if is_torch_version(">=", "2.5.0")
                 else torch_accelerator_module.OutOfMemoryError
             )
-            latents_mean = (
-                torch.tensor(self.vae.config.latents_mean)
-                .view(1, self.vae.config.z_dim, 1, 1, 1)
-                .to(latents.device, latents.dtype)
-            )
-            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
-                latents.device, latents.dtype
-            )
+            if isinstance(self.vae, AutoencoderKLLTX2Video):
+                latents_mean = self.vae.latents_mean
+                latents_std = self.vae.latents_std
+                z_dim = self.vae.config.latent_channels
+            elif isinstance(self.vae, AutoencoderKLWan):
+                latents_mean = torch.tensor(self.vae.config.latents_mean)
+                latents_std = torch.tensor(self.vae.config.latents_std)
+                z_dim = self.vae.config.z_dim
+            else:
+                latents_mean = torch.zeros(latents.shape[1], device=latents.device, dtype=latents.dtype)
+                latents_std = torch.ones(latents.shape[1], device=latents.device, dtype=latents.dtype)
+                z_dim = latents.shape[1]
+
+            latents_mean = latents_mean.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
+            latents_std = 1.0 / latents_std.view(1, z_dim, 1, 1, 1).to(latents.device, latents.dtype)
             latents = latents / latents_std + latents_mean
             try:
                 video = self.vae.decode(latents, return_dict=False)[0]

tests/lora/test_lora_layers_wanvace.py

@@ -28,7 +28,6 @@ from diffusers.utils.import_utils import is_peft_available
 
 from ..testing_utils import (
     floats_tensor,
-    is_flaky,
     require_peft_backend,
     require_peft_version_greater,
     skip_mps,
@@ -46,7 +45,6 @@ from .utils import PeftLoraLoaderMixinTests  # noqa: E402
 
 @require_peft_backend
 @skip_mps
-@is_flaky(max_attempts=10, description="very flaky class")
 class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     pipeline_class = WanVACEPipeline
     scheduler_cls = FlowMatchEulerDiscreteScheduler
@@ -73,8 +71,8 @@ class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
         "base_dim": 3,
         "z_dim": 4,
         "dim_mult": [1, 1, 1, 1],
-        "latents_mean": torch.randn(4).numpy().tolist(),
-        "latents_std": torch.randn(4).numpy().tolist(),
+        "latents_mean": [-0.7571, -0.7089, -0.9113, -0.7245],
+        "latents_std": [2.8184, 1.4541, 2.3275, 2.6558],
         "num_res_blocks": 1,
         "temperal_downsample": [False, True, True],
     }

tests/pipelines/helios/test_helios.py

@@ -139,9 +139,9 @@ class HeliosPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
         self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
 
-    # Override to set a more lenient max diff threshold.
+    @unittest.skip("Helios uses a lot of mixed precision internally, which is not suitable for this test case")
     def test_save_load_float16(self):
-        super().test_save_load_float16(expected_max_diff=0.03)
+        pass
 
     @unittest.skip("Test not supported")
     def test_attention_slicing_forward_pass(self):

tests/pipelines/ltx2/test_ltx2.py

@@ -171,6 +171,7 @@ class LTX2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "tokenizer": tokenizer,
             "connectors": connectors,
             "vocoder": vocoder,
+            "processor": None,
         }
 
         return components

tests/pipelines/ltx2/test_ltx2_image2video.py

@@ -171,6 +171,7 @@ class LTX2ImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "tokenizer": tokenizer,
             "connectors": connectors,
             "vocoder": vocoder,
+            "processor": None,
         }
 
         return components