ckpt

Fixes the sequence parallel sharding on t, now SP shards on t*h*w

docs/attention/index.md

@@ -6,6 +6,8 @@ FastVideo provides highly optimized custom attention kernels to accelerate video
 
 * **[Video Sparse Attention (VSA)](vsa/index.md)**: Sparse attention mechanism selecting top-k blocks.
 * **[Sliding Tile Attention (STA)](sta/index.md)**: Optimized attention for window-based video generation.
+* **Backend development guide**: See the developer guide at
+  [Attention Backend Development](../contributing/attention_backend.md).
 
 ## General Build Instructions
 

docs/contributing/attention_backend.md

@@ -0,0 +1,176 @@
+# Attention Backend Development
+
+This guide is for contributors adding a new attention backend (kernel or
+implementation) to FastVideo. If you just want to use existing kernels or build
+fastvideo-kernel, see [Attention overview](../attention/index.md).
+
+## When you need this guide
+
+Use this guide when you are:
+
+- Adding a new attention kernel or algorithm.
+- Wiring an existing kernel into FastVideo's attention selection.
+- Extending attention support to a new platform.
+
+## 0) Choose a backend name and scope
+
+Pick a backend name in `UPPER_SNAKE_CASE` and decide where it should run.
+Example: `MY_NEW_ATTN`.
+
+You will use this name in:
+
+- `AttentionBackendEnum` (global list of backends).
+- `get_name()` in your backend class (match the enum name).
+- Platform selectors (CUDA/ROCm/MPS/NPU) to return your backend class.
+
+## 1) Add enum + platform selection
+
+1) Add your backend to `fastvideo/platforms/interface.py`:
+
+```python
+class AttentionBackendEnum(enum.Enum):
+    ...
+    MY_NEW_ATTN = enum.auto()
+```
+
+1) Register it in platform selection (example: CUDA). Update
+`fastvideo/platforms/cuda.py` inside `get_attn_backend_cls`:
+
+```python
+elif selected_backend == AttentionBackendEnum.MY_NEW_ATTN:
+    try:
+        from fastvideo.attention.backends.my_new_attn import MyNewAttnBackend
+        return "fastvideo.attention.backends.my_new_attn.MyNewAttnBackend"
+    except ImportError as e:
+        logger.error("Failed to import MY_NEW_ATTN backend: %s", str(e))
+        raise
+```
+
+If you want support on other platforms, add a similar branch in
+`fastvideo/platforms/rocm.py`, `fastvideo/platforms/mps.py`, or `fastvideo/platforms/npu.py`.
+
+## 2) Implement the backend
+
+Create `fastvideo/attention/backends/my_new_attn.py` and implement the required
+classes.
+
+Minimal skeleton (no custom metadata):
+
+```python
+import torch
+from dataclasses import dataclass
+from fastvideo.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+    AttentionMetadataBuilder,
+)
+
+class MyNewAttnBackend(AttentionBackend):
+    @staticmethod
+    def get_name() -> str:
+        return "MY_NEW_ATTN"
+
+    @staticmethod
+    def get_impl_cls() -> type["MyNewAttnImpl"]:
+        return MyNewAttnImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AttentionMetadata"]:
+        return AttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["AttentionMetadataBuilder"]:
+        return MyNewAttnMetadataBuilder
+
+
+@dataclass
+class MyNewAttnMetadata(AttentionMetadata):
+    current_timestep: int
+
+
+class MyNewAttnMetadataBuilder(AttentionMetadataBuilder):
+    def __init__(self) -> None:
+        pass
+
+    def prepare(self) -> None:
+        pass
+
+    def build(self, current_timestep: int, **kwargs):
+        return MyNewAttnMetadata(current_timestep=current_timestep)
+
+
+class MyNewAttnImpl(AttentionImpl):
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        softmax_scale: float,
+        causal: bool = False,
+        num_kv_heads: int | None = None,
+        prefix: str = "",
+        **extra_impl_args,
+    ) -> None:
+        self.softmax_scale = softmax_scale
+        self.causal = causal
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: MyNewAttnMetadata,
+    ) -> torch.Tensor:
+        # Implement attention
+        return torch.nn.functional.scaled_dot_product_attention(
+            query.transpose(1, 2),
+            key.transpose(1, 2),
+            value.transpose(1, 2),
+            is_causal=self.causal,
+            scale=self.softmax_scale,
+        ).transpose(1, 2)
+```
+
+Optional:
+
+- Implement `preprocess_qkv` / `postprocess_output` if your kernel needs tiling
+  or reshaping.
+- Use `fastvideo.forward_context.get_forward_context()` if you need dynamic
+  per-step data (e.g., window sizes).
+- Set `accept_output_buffer = True` if your backend writes into a provided
+  output buffer.
+
+## 3) Wire into attention layers
+
+Backends are used by `LocalAttention` and `DistributedAttention`. These layers
+accept a `supported_attention_backends` tuple. If your backend should be
+eligible, update the call sites that construct these layers (search for
+`supported_attention_backends=`).
+
+## 4) Add compiled kernels (optional)
+
+If you have a custom CUDA kernel:
+
+1) Add sources in `fastvideo-kernel/csrc/attention/`.
+2) Register bindings in `fastvideo-kernel/csrc/common_extension.cpp`.
+3) Add to `fastvideo-kernel/CMakeLists.txt` (and any feature flags).
+4) Expose in `fastvideo-kernel/python/fastvideo_kernel/ops.py`.
+5) Export in `fastvideo-kernel/python/fastvideo_kernel/__init__.py`.
+
+Keep a Python/Triton fallback so the backend runs even when the kernel is not
+available.
+
+## 5) Testing and debugging
+
+- Add a small parity test or microbenchmark comparing to SDPA.
+- Force your backend with the env var:
+  `FASTVIDEO_ATTENTION_BACKEND=MY_NEW_ATTN`.
+- Check logs from `fastvideo/attention/selector.py` to confirm selection.
+
+## Checklist
+
+- [ ] Added enum entry in `fastvideo/platforms/interface.py`.
+- [ ] Implemented backend in `fastvideo/attention/backends/`.
+- [ ] Registered selection in platform(s).
+- [ ] Updated layer call sites to include the backend where appropriate.
+- [ ] Added tests and documentation.

docs/contributing/coding_agents.md

@@ -0,0 +1,480 @@
+# FastVideo + Coding Agents
+
+Coding agents are now strong at navigating large codebases and iterating fast
+with parity tests and examples. This guide shows how to use them to add new
+model pipelines and ship PRs in a production-grade video diffusion framework.
+
+FastVideo is a great project to contribute to, with production-grade
+infrastructure, active collaborations (including NVIDIA), and a pipeline design
+and inference architecture that has been forked by [SGLang’s
+multimodal generation stack](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen).
+
+Goal: run the new pipeline with a minimal script like
+`examples/inference/basic/basic.py`. In production, FastVideo can download
+models automatically via `HF_HOME`; for development, use local directories so
+agents can run scripts and tests deterministically. We standardize local paths
+as:
+
+- `official_weights/<model_name>/` for official checkpoints
+- `converted_weights/<model_name>/` if conversion is required
+
+## Tips when prompting the agent
+
+When prompting the agent, include:
+
+- This guide and the [FastVideo design overview](../design/overview.md).
+- Exact file paths to edit.
+- A closest reference example file in FastVideo.
+- Expected behavior and acceptance criteria.
+- Repro steps (command, inputs, logs).
+- Constraints (performance, memory, compatibility).
+- Local paths (e.g., `official_weights/<model_name>/` or
+  `converted_weights/<model_name>/`) for parity tests.
+
+## FastVideo structure at a glance
+
+Before diving in, scan these references:
+
+- [Contributing overview](overview.md) for environment/setup context.
+- [FastVideo design overview](../design/overview.md) for pipeline architecture, configs, and HF layout.
+
+FastVideo maps a Diffusers-style repo into a pipeline like:
+
+- `fastvideo/models/*`: model implementations (DiT, VAE, encoders, upsamplers).
+- `fastvideo/configs/models/*`: arch configs and `param_names_mapping` for
+  weight name translation.
+- `fastvideo/configs/pipelines/*`: pipeline wiring (component classes + names).
+- `fastvideo/configs/sample/*`: default runtime sampling parameters.
+- `fastvideo/pipelines/basic/*`: end-to-end pipeline logic built from stages.
+- `model_index.json`: the HF repo entrypoint that maps component names to
+  classes and weight files.
+- Component loading happens in `VideoGenerator.from_pretrained`, which reads
+  `model_index.json`, resolves configs, and loads weights.
+
+Minimal usage example (based on `examples/inference/basic/basic.py`):
+
+```python
+from fastvideo import VideoGenerator
+from fastvideo.configs.sample import SamplingParam
+
+model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # or official_weights/<model_name>/
+generator = VideoGenerator.from_pretrained(model_id, num_gpus=1)
+
+sampling = SamplingParam.from_pretrained(model_id)
+sampling.num_frames = 45
+video = generator.generate_video(
+    "A vibrant city street at sunset.",
+    sampling_param=sampling,
+    output_path="video_samples",
+    save_video=True,
+)
+```
+
+## Some questions to ask yourself before starting
+
+Answering these upfront clarifies the work and speeds up implementation.
+
+### Is the model already supported by SGLang's multimodal generation stack?
+If yes, you can port many components from SGLang. It is a FastVideo fork, so
+interfaces line up, but you still need to swap layers/modules to match
+FastVideo's architecture and attention stack.
+
+If not, implement the model directly in FastVideo.
+
+### Is there an official implementation of the model you are adding?
+
+If yes, use it as the numerical reference. For example, LTX‑2 has an official
+implementation here: https://github.com/Lightricks/LTX-2. Prefer official code
+even if Diffusers also has one.
+
+### Is there a HuggingFace repo for the model you are adding? Is it in Diffusers format?
+
+If yes, load it directly in FastVideo after setting tensor mapping rules in the
+config. Otherwise, convert the weights to Diffusers format. See [Weights and
+Diffusers format](../design/overview.md#weights-and-diffusers-format) for details.
+
+### Do I have official weights + local paths ready?
+
+Standardize local paths as:
+
+- `official_weights/<model_name>/` for official checkpoints
+- `converted_weights/<model_name>/` if conversion is required (can be created later)
+
+### What pipeline components are required for the model you are adding?
+
+Usually you need a transformer (DiT), VAE, text encoder, and tokenizer. Some
+models add extra components.
+
+### What tasks does the model support?
+
+Usually a video diffusion model supports text‑to‑video (T2V),
+image‑to‑video (I2V), and video‑to‑video (V2V). Some add extra tasks (two‑stage
+generation, keyframe interpolation), which require extra components.
+
+It's usually easiest to start with a T2V pipeline and add the other tasks later.
+
+You can refer to the [Pipeline system](../design/overview.md#pipeline-system)
+section for more details.
+
+### Am I able to generate videos with the official implementation?
+
+These videos and prompts are your reference. Once the FastVideo pipeline works,
+compare outputs to the official implementation. Due to seeding and other
+factors, outputs may not match exactly, but they should be comparable.
+
+## Workflow: adding a full pipeline
+
+This is an example workflow for adding a full model pipeline (model + configs +
+examples + tests). This guide is in active development; feedback is welcome.
+
+!!! note
+    If you get stuck, refer to existing models/pipelines in FastVideo or ask in Slack.
+
+### 0) Fetch official model's code and weights
+
+Purpose:
+
+- Keep official checkpoints and source code local so conversion, parity tests,
+  and reference runs are reproducible.
+- Clone the official repo so you can use it as a numerical reference.
+
+Action:
+
+- Download official weights into `official_weights/<model_name>/`
+  (Diffusers format or not).
+- Clone the official repo under the project root (e.g., `FastVideo/LTX-2/`).
+- If a Diffusers-format HF repo already exists, you can skip manual weight
+  handling and download it directly with
+  `scripts/huggingface/download_hf.py`.
+
+!!! note
+    This step is best done manually because large downloads can time out.
+    Example:
+    ```bash
+    python scripts/huggingface/download_hf.py \
+      --repo_id Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+      --local_dir official_weights/Wan2.1-T2V-1.3B-Diffusers \
+      --repo_type model
+    ```
+  
+### 1) Implement the model + config mapping
+
+Purpose:
+
+- Model weights are a dictionary of named tensors (`state_dict`). If the names
+  don’t line up with FastVideo’s module names, weights won’t load correctly (or
+  will silently load into the wrong layer).
+- Official checkpoints often use different prefixes or module layouts than
+  FastVideo, so we translate names via the mapping (during load or conversion).
+- Mapping aligns three things:
+  1. the official implementation’s module names,
+  2. the checkpoint `state_dict` keys,
+  3. FastVideo’s model classes and layer naming conventions.
+- If names don’t align, weights won’t load; implement the FastVideo model and
+  define mapping rules first.
+
+Action:
+
+- Implement the FastVideo model + config mapping.
+  - Add/extend the model in `fastvideo/models/...` and config in
+    `fastvideo/configs/models/...` (including `param_names_mapping`).
+  - Reuse existing FastVideo layers/modules where possible.
+  - Use FastVideo’s attention layers:
+    - `DistributedAttention` only for full‑sequence self‑attention in the DiT.
+    - `LocalAttention` for cross‑attention and other attention layers.
+  - See the “Configuration System” and “Weights and Diffusers format” sections
+    in `docs/design/overview.md` for how these pieces connect.
+  - If you are using an agent, ask it to implement the model, config mapping,
+    and a parity test together so you can validate numerics immediately.
+
+!!! note
+    After the first component is aligned and parity‑tested, open a **DRAFT PR**
+    on FastVideo so the rest of the pipeline work can build on top of it.
+
+!!! note
+    If a Diffusers-format HF repo already exists and loads correctly, you can
+    skip conversion entirely (no conversion script needed) and just download it
+    with `scripts/huggingface/download_hf.py`. Otherwise, you may need a
+    conversion script + a `converted_weights/<model>/` staging directory.
+
+Example (key renaming via arch config mapping, Wan2.1‑style):
+
+```python
+# Official model (simplified) in the upstream repo.
+class OfficialWanTransformer(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.patch_embedding = torch.nn.Conv3d(16, 1536, kernel_size=2, padding=0)
+
+    def forward(self, x):
+        return self.patch_embedding(x)
+
+# FastVideo model (simplified) in fastvideo/models/dits/wanvideo.py
+class PatchEmbed(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.proj = torch.nn.Conv3d(16, 1536, kernel_size=2, padding=0)
+
+    def forward(self, x):
+        return self.proj(x)
+
+class WanTransformer3DModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.patch_embedding = PatchEmbed()
+
+    def forward(self, x):
+        return self.patch_embedding(x)
+
+# Mapping defined in a config (simplified; see the real mapping in
+# fastvideo/configs/models/dits/wanvideo.py)
+param_names_mapping = {
+    r"^patch_embedding\.(.*)$": r"patch_embedding.proj.\1",
+    r"^blocks\.(\d+)\.attn1\.to_q\.(.*)$": r"blocks.\1.to_q.\2",
+}
+
+def apply_regex_map(state_dict, mapping):
+    # Pseudocode: apply regex substitutions in order
+    ...
+
+# Official checkpoint keys (example)
+official = {
+    "patch_embedding.weight": ...,
+    "blocks.0.attn1.to_q.weight": ...,
+}
+
+# Apply mapping so keys match FastVideo modules
+converted = apply_regex_map(official, param_names_mapping)
+
+```
+
+Optional helper (print a few checkpoint keys quickly):
+
+```bash
+python - <<'PY'
+import safetensors.torch as st
+keys = list(st.load_file("official_weights/<model>/transformer/diffusion_pytorch_model.safetensors").keys())
+print(keys[:20])
+PY
+```
+
+Example agent prompt (task request):
+
+```
+Please add the Wan2.1 T2V 1.3B Diffusers pipeline to FastVideo:
+- Add a FastVideo native Wan2.1 DiT implementation + config mapping.
+- Make sure to use the existing FastVideo layers and attention modules where possible.
+- Add a parity test that loads the official model alongside the FastVideo model and compares outputs numerically with fixed seeds and inputs.
+
+Paths:
+  - Official repo: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+  - Local download: official_weights/Wan2.1-T2V-1.3B-Diffusers
+Mapping steps:
+  - Load the official DiT weights from
+    official_weights/Wan2.1-T2V-1.3B-Diffusers/transformer/diffusion_pytorch_model.safetensors.
+  - Instantiate the FastVideo DiT (`WanTransformer3DModel`) and compare
+    its `state_dict().keys()` to the official keys.
+  - Update `param_names_mapping` in
+    fastvideo/configs/models/dits/wanvideo.py to resolve missing/unexpected keys.
+  - Use `load_state_dict(strict=False)` during iteration to surface mismatches.
+```
+
+External examples of the same pattern:
+- SGLang uses prefix-based routing in its weight loader to map checkpoint keys
+  into internal submodules (e.g., stripping a top-level prefix before delegating).
+- vLLM includes model-specific renamers for certain checkpoints that adjust
+  key prefixes so weights match its internal naming.
+
+### 2) Test numerical alignment with the official implementation
+
+Purpose:
+
+- Verify that the FastVideo component is numerically aligned with the official
+  implementation.
+
+Action:
+
+- Add or reuse a numerical parity test that loads the official model and the
+  FastVideo model and compares outputs.
+- See examples in `tests/local_tests/` (e.g., `tests/local_tests/upsamplers/`)
+  and the commands in `tests/local_tests/README.md`.
+- If there are discrepancies, add opt‑in logging to both models and compare
+  activation summaries (layer output sums, per‑stage logs).
+- First align the loaded weights (validate `param_names_mapping`).
+- Then align forward outputs using fixed seeds and inputs.
+  - Start with `atol=1e-4, rtol=1e-4` in `assert_close`.
+  - Keep dtype consistent (bf16 if available; otherwise fp32).
+  - If attention parity is unstable, align backends (e.g.,
+    `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA`).
+
+### 3) Repeat the process for each component
+
+If the model requires additional components, repeat Steps 1–2 for each one.
+For example, implement the VAE in `fastvideo/models/vaes/` and its config in
+`fastvideo/configs/models/vaes/`, then add parity coverage for it.
+
+### 4) Add a pipeline config + sample defaults
+
+Purpose:
+
+- `fastvideo/configs/pipelines/` describes pipeline wiring and model module
+  names.
+- `fastvideo/configs/sample/` defines default runtime parameters.
+
+Action:
+
+- Add a new pipeline config + sampling params.
+- Register them in `fastvideo/configs/pipelines/registry.py` and
+  `fastvideo/configs/sample/registry.py`.
+
+### 5) Wire pipeline stages
+
+Purpose:
+
+- `fastvideo/pipelines/basic/<pipeline>/` contains the actual pipeline logic.
+- `fastvideo/pipelines/stages/` holds reusable, testable stages.
+
+Action:
+
+- Build the pipeline using stages; keep new stages isolated and documented.
+- Prefer opt‑in flags for expensive or optional steps.
+
+### 6) Add pipeline‑level tests
+
+Purpose:
+
+- Ensure the end‑to‑end pipeline works and stays aligned as pieces evolve.
+
+Action:
+
+- Add a pipeline parity test under `tests/local_tests/pipelines/`.
+- See the [Testing Guide](testing.md) for test conventions.
+
+### 7) Add user‑facing examples
+
+Purpose:
+
+- `examples/inference/basic/` is the entry point for simple, runnable scripts.
+
+Action:
+
+- Provide a minimal “hello world” example plus advanced variations.
+- Use fixed seeds and stable prompts.
+- Run the example locally to confirm end‑to‑end behavior.
+
+### 8) Add SSIM tests for CI checks
+
+Purpose:
+
+- Ensure visual similarity stays within expected bounds for regression testing.
+- SSIM tests act as a higher‑level guardrail beyond unit/parity tests.
+
+Action:
+
+- Add SSIM tests under `fastvideo/tests/ssim/` and include reference videos
+  (see the structure in the Testing Guide).
+- Use stable prompts/seeds and document any GPU‑specific requirements.
+- Follow the [Testing Guide](testing.md) for reference video placement and
+  execution details.
+
+### 9) Document it
+
+Purpose:
+
+- `docs/` is where users find the new pipeline usage and limitations.
+
+Action:
+
+- Add a short doc page or update an existing one.
+- Mention any caveats (memory, speed, constraints).
+
+## Common pitfalls when porting models
+
+- **Attention backend mismatch**: parity can fail if the official model uses a
+  different attention backend (e.g., SDPA vs custom). Align backends before
+  debugging deeper issues.
+- **Patchifier shape mistakes**: wrong patchification or reshape lengths can
+  silently corrupt outputs. Validate patch shapes early.
+- **Mask handling**: attention masks must match the official behavior (padding,
+  causal masks, and broadcast shapes).
+- **Scheduler / sigma schedule mismatch**: even small differences in schedules
+  or timestep shapes can cause noticeable drift.
+
+## Diffusers vs manual conversion
+
+If a model already ships in Diffusers format (with a proper `model_index.json`),
+prefer downloading it directly and loading it via FastVideo. In that case:
+
+- You usually **do not need** a conversion script.
+- You still need a correct `param_names_mapping` if the internal module names
+  differ from FastVideo’s implementation.
+
+If the model does **not** have a Diffusers-format repo:
+
+- You will need a conversion script to rewrite `state_dict` keys into FastVideo
+  naming and stage the result (e.g., under `converted_weights/<model>/`).
+- You may still use the official repo for reference parity and debugging.
+
+In both cases, parity testing is required to validate correctness.
+
+If you want to publish a Diffusers‑style repo after conversion, use
+`scripts/checkpoint_conversion/create_hf_repo.py` to assemble a HuggingFace‑ready
+directory before uploading.
+
+## FAQ
+
+**Q: Why do we implement the FastVideo model before conversion?**  
+A: You can’t define the key‑mapping rules until the FastVideo module names are
+known. The implementation determines the target `state_dict` schema.
+
+**Q: Do we always need a conversion script?**  
+A: No. If a Diffusers‑format repo exists and loads correctly, download it and
+skip conversion.
+
+**Q: How do I figure out `param_names_mapping` quickly?**  
+A: Load the official weights, instantiate the FastVideo model, and diff
+`state_dict().keys()` on both sides. Add regex rules until missing/unexpected
+keys are resolved. Agents can help you with this.
+
+**Q: What if parity fails even after mapping?**  
+A: Align attention backends, sigma schedules, and timestep shapes first. Then
+add opt‑in activation logging to locate the first divergent layer.
+
+## Case study: LTX‑2 port (from PLAN.md)
+
+The LTX‑2 port in `PLAN.md` shows the real sequence of steps and backtracking
+that happened during integration. Use it as a reference for how parity work
+actually unfolds:
+
+- Ported components first (transformer, VAE, audio, text encoder).
+- Added parity tests per component; used SDPA for reference parity.
+- Added debug logging to compare per‑block activations and isolate divergence.
+- Fixed cross‑attention reshape and patch grid bounds issues after logging.
+- Aligned sigma schedule and masking behavior to match the official pipeline.
+
+Recommendation:
+
+- Keep raw step‑by‑step logs in your own local `PLAN.md` for large ports.
+
+## Worked example: Wan2.1 T2V 1.3B pipeline
+
+The Wan2.1 T2V 1.3B Diffusers pipeline is a good “standard” example for
+FastVideo integration.
+
+1. Verify model config + mapping.
+   - DiT mapping: `fastvideo/configs/models/dits/wanvideo.py`
+   - VAE: `fastvideo/models/vaes/wanvae.py`
+   - Text encoder: `fastvideo/models/encoders/t5.py`
+
+2. Parity test the core components.
+   - Example tests: `fastvideo/tests/transformers/test_wanvideo.py`,
+     `fastvideo/tests/vaes/test_wan_vae.py`,
+     `fastvideo/tests/encoders/test_t5_encoder.py`
+
+3. Pipeline wiring.
+   - Pipeline: `fastvideo/pipelines/basic/wan/wan_pipeline.py`
+   - Pipeline config: `fastvideo/configs/pipelines/wan.py`
+   - Sampling defaults: `fastvideo/configs/sample/wan.py`
+
+4. Minimal example.
+   - Script: `examples/inference/basic/basic.py`

docs/contributing/developer_env/runpod.md

@@ -1,16 +1,55 @@
 
 # 📦 Developing FastVideo on RunPod
 
-You can easily use the FastVideo Docker image as a custom container on [RunPod](https://www.runpod.io) for development or experimentation.
+You can easily use the FastVideo Pod Template on [RunPod](https://www.runpod.io) for development or experimentation.
 
 ## Creating a new pod
 
-Choose a GPU that supports CUDA 12.8
-
-Pick 1 or 2 L40S GPU(s)
+- Make sure you are using the correct RunPod account.
+![RunPod Account Selection](../../assets/images/runpod_account.png)
 
+- Use "Additional Filters" to select CUDA 12.8.
 ![RunPod CUDA selection](../../assets/images/runpod_cuda.png)
 
+- Click "Deploy" and Pick a single A40 or RTX 4090 GPU.
+![RunPod GPU Selection](../../assets/images/runpod_deploy.png)
+
+- Select the "FastVideo" or "fastvideo-dev" Pod Template.
+![RunPod Pod Template Selection](../../assets/images/runpod_create.png)
+
+- Set the Pod name to "<name>-<FastVideo>-<date>".
+
+- Finally, once the pod is deployed (will take a few minutes as the image is being pulled), you can SSH into it using "SSH exposed over TCP". You'll need to use the matching private ssh key you provided.
+![RunPod SSH](../../assets/images/runpod_ssh.png)
+
+## Working with the pod
+
+After SSH'ing into your pod, you'll find the correct `uv` environment already activated and you should be in /FastVideo/ directory. Make sure to use /FastVideo/ for all your work.
+
+To pull in the latest changes from the GitHub repo:
+
+```bash
+cd /FastVideo
+git pull
+```
+
+Run your development workflows as usual:
+
+```bash
+# Run linters
+pre-commit run --all-files
+
+# Run tests
+pytest tests/
+```
+
+Make sure to push your changes back to the GitHub repo as nothing will be saved to the pod when it is terminated.
+
+After you are done with your work, you can terminate the pod by clicking the "Terminate" and "Delete" buttons. Remember if the pod is not completely deleted, Runpod will keep charging you for it.
+
+## Extra Information:
+If you need to customize the pod template this section has some useful information. For the most part you can leave the defaults of the FastVideo Pod Template.
+
 When creating your pod template, use this image:
 
 ```
@@ -24,30 +63,3 @@ bash -c "apt update;DEBIAN_FRONTEND=noninteractive apt-get install openssh-serve
 ```
 
 ![RunPod template configuration](../../assets/images/runpod_template.png)
-
-After deploying, the pod will take a few minutes to pull the image and start the SSH service.
-
-![RunPod ssh](../../assets/images/runpod_ssh.png)
-
-## Working with the pod
-
-After SSH'ing into your pod, you'll find the `fastvideo-dev` Conda environment already activated.
-
-To pull in the latest changes from the GitHub repo:
-
-```bash
-cd /FastVideo
-git pull
-```
-
-`If you have a persistent volume and want to keep your code changes, you can move /FastVideo to /workspace/FastVideo, or simply clone the repository there.`
-
-Run your development workflows as usual:
-
-```bash
-# Run linters
-pre-commit run --all-files
-
-# Run tests
-pytest tests/
-```

docs/contributing/overview.md

@@ -1,76 +1,84 @@
 
 # 🛠️ Contributing to FastVideo
 
-Thank you for your interest in contributing to FastVideo. We want to make the process as smooth for you as possible and this is a guide to help get you started!
+Thank you for your interest in contributing to FastVideo. We want the process
+to be smooth and beginner‑friendly, whether you are adding a new pipeline,
+improving performance, or fixing a bug.
 
-Our community is open to everyone and welcomes any contributions no matter how large or small.
+## Quick prerequisites
 
-# Developer Environment:
-Do make sure you have CUDA 12.4 installed and supported. FastVideo currently only supports Linux and CUDA GPUs, but we hope to support other platforms in the future.
+- **OS**: Linux is the primary development target (WSL can work).
+- **GPU**: NVIDIA GPU recommended for inference and training workflows.
+- **CUDA**: Use a recent CUDA 12.x toolchain (see the installation guide for
+  the current recommendation).
 
-We recommend using a fresh Python 3.10 Conda environment to develop FastVideo:
+For a full install checklist, see `docs/getting_started/installation/gpu.md`.
+
+## Local development (Conda + editable install)
 
 Install Miniconda:
 
-```
+```bash
 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
 bash Miniconda3-latest-Linux-x86_64.sh
 source ~/.bashrc
 ```
 
-Create and activate a Conda environment for FastVideo:
+Create and activate a Conda environment:
 
-```
+```bash
 conda create -n fastvideo python=3.12 -y
 conda activate fastvideo
 ```
 
 Install `uv` (optional, but recommended):
 
-From instructions on [uv](https://astral.sh/uv/):
-
-```
+```bash
 curl -LsSf https://astral.sh/uv/install.sh | sh
-# or 
+# or
 wget -qO- https://astral.sh/uv/install.sh | sh
 ```
 
-Clone the FastVideo repository and go to the FastVideo directory:
+Clone the repo:
 
-```
+```bash
 git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
-
 ```
 
-Now you can install FastVideo and setup git hooks for running linting. By using `pre-commit`, the linters will run and have to pass before you'll be able to make a commit.
+Install FastVideo in editable mode and set up hooks:
 
 ```bash
 uv pip install -e .[dev]
 
-# Can also install flash-attn (optional)
-uv pip install flash-attn --no-build-isolation 
+# Optional: FlashAttention (builds native kernels)
+uv pip install flash-attn --no-build-isolation
 
-# Linting, formatting and static type checking
+# Linting, formatting, static typing
 pre-commit install --hook-type pre-commit --hook-type commit-msg
-
-# You can manually run pre-commit with
 pre-commit run --all-files
 
 # Unit tests
 pytest tests/
 ```
 
-If you are on a Hopper GPU, you should also install [FA3](https://github.com/Dao-AILab/flash-attention) for much better performance:
+If you are on a Hopper GPU, installing FlashAttention 3 can improve
+performance (see `docs/inference/optimizations.md`).
 
-```
-git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention/hopper
+## Docker development (optional)
 
-# make sure you have ninja installed
-uv pip install ninja
-
-python setup.py install
-```
+If you prefer a containerized environment, use the dev image documented in
+`docs/contributing/developer_env/docker.md`.
 
 ## Testing
 
-Please refer to the [Testing Guide](testing.md) for more information on how to add and run tests in FastVideo.
+See the [Testing Guide](testing.md) for how to add and run tests in FastVideo.
+
+## Attention backend development
+
+If you are adding a new attention kernel or backend, follow
+[Attention Backend Development](attention_backend.md).
+
+## Contributing with coding agents
+
+For a step‑by‑step workflow on adding pipelines or components with coding
+agents, see `docs/contributing/coding_agents.md`.

docs/design/overview.md

@@ -1,424 +1,178 @@
-# 🔍 FastVideo Overview
+# FastVideo Architecture Overview
 
-This document outlines FastVideo's architecture for developers interested in framework internals or contributions. It serves as an onboarding guide for new contributors by providing an overview of the most important directories and files within the `fastvideo/` codebase.
+This document summarizes how FastVideo is structured and how a Diffusers-style
+model repo maps into a runnable pipeline. It is intended for contributors who
+need the high-level layout and key entrypoints, not every internal detail.
 
-## Table of Contents - Directory Structure and Files
+## FastVideo structure at a glance
 
-- [`fastvideo/pipelines/`](#pipeline-system) - Core diffusion pipeline components
-- [`fastvideo/models/`](#model-components) - Model implementations
-  - [`dits/`](#transformer-models) - Transformer-based diffusion models
-  - [`vaes/`](#vae-variational-auto-encoder) - Variational autoencoders
-  - [`encoders/`](#text-and-image-encoders) - Text and image encoders
-  - [`schedulers/`](#schedulers) - Diffusion schedulers
-- [`fastvideo/attention/`](#optimized-attention) - Optimized attention implementations
-- [`fastvideo/distributed/`](#distributed-processing) - Distributed computing utilities
-- [`fastvideo/layers/`](#tensor-parallelism) - Custom neural network layers
-- [`fastvideo/platforms/`](#platforms) - Hardware platform abstractions
-- [`fastvideo/worker/`](#executor-and-worker-system) - Multi-GPU process management
-- [`fastvideo/fastvideo_args.py`](#fastvideoargs) - Argument handling
-- [`fastvideo/forward_context.py`](#forward-context-management) - Forward pass context management
-- `fastvideo/utils.py` - Utility functions
-- [`fastvideo/logger.py`](#logger) - Logging infrastructure
+FastVideo maps a Diffusers-style repo into a pipeline like this:
 
-## Core Architecture
+- `fastvideo/models/*`: model implementations (DiT, VAE, encoders, upsamplers).
+- `fastvideo/configs/models/*`: arch configs and `param_names_mapping` for
+  weight name translation.
+- `fastvideo/configs/pipelines/*`: pipeline wiring (component classes + names).
+- `fastvideo/configs/sample/*`: default runtime sampling parameters.
+- `fastvideo/pipelines/basic/*`: end-to-end pipelines.
+- `fastvideo/pipelines/stages/*`: reusable pipeline stages.
+- `fastvideo/models/loader/*`: component loaders for Diffusers-style repos.
+- `model_index.json`: HF repo entrypoint mapping component names to classes.
 
-FastVideo separates model components from execution logic with these principles:
+Flow:
+`model_index.json` -> component loaders -> model modules -> pipeline stages ->
+sampling params.
 
-- **Component Isolation**: Models (encoders, VAEs, transformers) are isolated from execution (pipelines, stages, distributed processing)
-- **Modular Design**: Components can be independently replaced
-- **Distributed Execution**: Supports various parallelism strategies (Tensor, Sequence)
-- **Custom Attention Backends**: Components can support and use different Attention implementations
-- **Pipeline Abstraction**: Consistent interface across diffusion models
-
-## FastVideoArgs
-
-The `FastVideoArgs` class in `fastvideo/fastvideo_args.py` serves as the central configuration system for FastVideo. It contains all parameters needed to control model loading, inference configuration, performance optimization settings, and more.
-
-Key features include:
-
-- **Command-line Interface**: Automatic conversion between CLI arguments and dataclass fields
-- **Configuration Groups**: Organized by functional areas (model loading, video params, optimization settings)
-- **Context Management**: Global access to current settings via `get_current_fastvideo_args()`
-- **Parameter Validation**: Ensures valid combinations of settings
-
-Common configuration areas:
-
-- **Model paths and loading options**: `model_path`, `trust_remote_code`, `revision`
-- **Distributed execution settings**: `num_gpus`, `tp_size`, `sp_size`
-- **Video generation parameters**: `height`, `width`, `num_frames`, `num_inference_steps`
-- **Precision settings**: Control computation precision for different components
-
-Example usage:
+Minimal usage (from `examples/inference/basic/basic.py`):
 
 ```python
-# Load arguments from command line
-fastvideo_args = prepare_fastvideo_args(sys.argv[1:])
+from fastvideo import VideoGenerator
+from fastvideo.configs.sample import SamplingParam
 
-# Access parameters
-model = load_model(fastvideo_args.model_path)
+model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # or official_weights/<model_name>/
+generator = VideoGenerator.from_pretrained(model_id, num_gpus=1)
 
-# Set as global context
-with set_current_fastvideo_args(fastvideo_args):
-    # Code that requires access to these arguments
-    result = generate_video()
-```
-
-## Pipeline System
-
-### `ComposedPipelineBase`
-
-This foundational class provides:
-
-- **Model Loading**: Automatically loads components from HuggingFace-Diffusers-compatible model directories
-- **Stage Management**: Creates and orchestrates processing stages
-- **Data Flow Coordination**: Ensures proper state flow between stages
-
-```python
-class MyCustomPipeline(ComposedPipelineBase):
-    _required_config_modules = [
-        "text_encoder", "tokenizer", "vae", "transformer", "scheduler"
-    ]
-    
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        # Pipeline-specific initialization
-        pass
-        
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        self.add_stage("input_validation_stage", InputValidationStage())
-        self.add_stage("text_encoding_stage", CLIPTextEncodingStage(
-            text_encoder=self.get_module("text_encoder"),
-            tokenizer=self.get_module("tokenizer")
-        ))
-        # Additional stages...
-```
-
-### Pipeline Stages
-
-Each stage handles a specific diffusion process component:
-
-- **Input Validation**: Parameter verification
-- **Text Encoding**: CLIP, LLaMA, or T5-based encoding
-- **Image Encoding**: Image input processing
-- **Timestep & Latent Preparation**: Setup for diffusion
-- **Denoising**: Core diffusion loop
-- **Decoding**: Latent-to-pixel conversion
-
-Each stage implements a standard interface:
-
-```python
-def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-    # Process batch and update state
-    return batch
-```
-
-![Pipeline execution and data flow](../assets/images/pipeline.png)
-
-### ForwardBatch
-
-Defined in `fastvideo/pipelines/pipeline_batch_info.py`, `ForwardBatch` encapsulates the data payload passed between pipeline stages. It typically holds:
-
-- **Input Data**: Prompts, images, generation parameters
-- **Intermediate State**: Embeddings, latents, timesteps, accumulated during stage execution
-- **Output Storage**: Generated results and metadata
-- **Configuration**: Sampling parameters, precision settings
-
-This structure facilitates clear state transitions between stages.
-
-## Model Components
-
-The `fastvideo/models/` directory contains implementations of the core neural network models used in video diffusion:
-
-### Transformer Models
-
-Transformer networks perform the actual denoising during diffusion:
-
-- **Location**: `fastvideo/models/dits/`
-- **Examples**:
-  - `WanTransformer3DModel`
-  - `HunyuanVideoTransformer3DModel`
-
-Features include:
-
-- Text/image conditioning
-- Standardized interface for model-specific optimizations
-
-```python
-def forward(
-    self, 
-    latents,                    # [B, T, C, H, W]
-    encoder_hidden_states,      # Text embeddings
-    timestep,                   # Current diffusion timestep
-    encoder_hidden_states_image=None,  # Optional image embeddings
-    **kwargs
-):
-    # Perform denoising computation
-    return noise_pred  # Predicted noise residual
-```
-
-### VAE (Variational Auto-Encoder)
-
-VAEs handle conversion between pixel space and latent space:
-
-- **Location**: `fastvideo/models/vaes/`
-- **Examples**:
-  - `AutoencoderKLWan`
-  - `AutoencoderKLHunyuanVideo`
-
-These models compress image/video data to a more efficient latent representation (typically 4x-8x smaller in each dimension).
-
-FastVideo's VAE implementations include:
-
-- Efficient video batch processing
-- Memory optimization
-- Optional tiling for large frames
-- Distributed weight support
-
-### Text and Image Encoders
-
-Encoders process conditioning inputs into embeddings:
-
-- **Location**: `fastvideo/models/encoders/`
-- **Text Encoders**:
-  - `CLIPTextModel`
-  - `LlamaModel`
-  - `UMT5EncoderModel`
-- **Image Encoders**:
-  - `CLIPVisionModel`
-
-FastVideo implements optimizations such as:
-
-- Vocab parallelism for distributed processing
-- Caching for common prompts
-- Precision-tuned computation
-
-### Schedulers
-
-Schedulers manage the diffusion sampling process:
-
-- **Location**: `fastvideo/models/schedulers/`
-- **Examples**:
-  - `UniPCMultistepScheduler`
-  - `FlowMatchEulerDiscreteScheduler`
-
-These components control:
-
-- Diffusion timestep sequences
-- Noise prediction to latent update conversions
-- Quality/speed trade-offs
-
-```python
-def step(
-    self, 
-    model_output: torch.Tensor,
-    timestep: torch.LongTensor,
-    sample: torch.Tensor,
-    **kwargs
-) -> torch.Tensor:
-    # Process model output and update latents
-    # Return updated latents
-    return prev_sample
-```
-
-This diagram shows how models are discovered, validated, and loaded across entrypoints, executors, pipelines, and model loaders.
-
-![Model loading flow](../assets/images/load_models.png)
-
-## Optimized Attention
-
-The `fastvideo/attention/` directory contains optimized attention implementations crucial for efficient video diffusion:
-
-### Attention Backends
-
-Multiple implementations with automatic selection:
-
-- **FLASH_ATTN**: Optimized for supporting hardware
-- **TORCH_SDPA**: Built-in PyTorch scaled dot-product attention
-- **SLIDING_TILE_ATTN**: For very long sequences
-
-```python
-# Configure available attention backends for this layer
-self.attn = LocalAttention(
-    num_heads=num_heads,
-    head_size=head_dim,
-    causal=False,
-    supported_attention_backends=(_Backend.FLASH_ATTN, _Backend.TORCH_SDPA)
-)
-
-# Override via environment variable
-# export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-```
-
-![Attention backend selector design](../assets/images/attention_backend.png)
-
-### Attention Patterns
-
-Supports various patterns with memory optimization techniques:
-
-- **Cross/Self/Temporal/Global-Local Attention**
-- Chunking, progressive computation, optimized masking
-
-## Distributed Processing
-
-The `fastvideo/distributed/` directory contains implementations for distributed model execution:
-
-### Tensor Parallelism
-
-Tensor parallelism splits model weights across devices:
-
-- **Implementation**: Through `RowParallelLinear` and `ColumnParallelLinear` layers
-- **Use cases**: Will be used by encoder models as their sequence lengths are shorter and enables efficient sharding.
-
-```python
-# Tensor-parallel layers in a transformer block
-from fastvideo.layers.linear import ColumnParallelLinear, RowParallelLinear
-
-# Split along output dimension
-self.qkv_proj = ColumnParallelLinear(
-    input_size=hidden_size,
-    output_size=3 * hidden_size,
-    bias=True,
-    gather_output=False
-)
-
-# Split along input dimension
-self.out_proj = RowParallelLinear(
-    input_size=hidden_size,
-    output_size=hidden_size,
-    bias=True,
-    input_is_parallel=True
+sampling = SamplingParam.from_pretrained(model_id)
+sampling.num_frames = 45
+video = generator.generate_video(
+    "A vibrant city street at sunset.",
+    sampling_param=sampling,
+    output_path="video_samples",
+    save_video=True,
 )
 ```
 
-### Sequence Parallelism
+## Configuration system
 
-Sequence parallelism splits sequences across devices:
+FastVideo uses typed configs to keep model definitions, pipeline wiring, and
+runtime parameters consistent:
 
-- **Implementation**: Through `DistributedAttention` and sequence splitting
-- **Use cases**: Long video sequences or high-resolution processing. Used by DiT models.
+- `fastvideo/configs/models/`: architecture definitions, layer shapes, and
+  `param_names_mapping` rules for key renaming.
+- `fastvideo/configs/pipelines/`: pipeline wiring and required components.
+- `fastvideo/configs/sample/`: default sampling parameters (steps, frames,
+  guidance scale, resolution, fps).
+- `fastvideo/configs/registry.py`: pipeline registry.
+- `fastvideo/configs/sample/registry.py`: sampling param registry.
 
-```python
-# Distributed attention for long sequences
-from fastvideo.attention import DistributedAttention
+`FastVideoArgs` (in `fastvideo/fastvideo_args.py`) provides runtime settings and
+is passed into pipeline construction and stages.
 
-self.attn = DistributedAttention(
-    num_heads=num_heads,
-    head_size=head_dim,
-    causal=False,
-    supported_attention_backends=(_Backend.SLIDING_TILE_ATTN, _Backend.FLASH_ATTN)
-)
+## Weights and Diffusers format
+
+FastVideo follows the HuggingFace Diffusers repo layout. This keeps loaders
+compatible with HF repos and makes it easy to add new components.
+
+Typical Diffusers repo:
+
+```
+<model-repo>/
+  model_index.json
+  scheduler/
+    scheduler_config.json
+  transformer/               # or unet/ for image models
+    config.json
+    diffusion_pytorch_model.safetensors
+  vae/
+    config.json
+    diffusion_pytorch_model.safetensors
+  text_encoder/
+    config.json
+    model.safetensors
+  tokenizer/
+    tokenizer_config.json
+    tokenizer.json
 ```
 
-### Communication Primitives
+Key points:
 
-Efficient distributed operations via AllGather, AllReduce, and synchronization mechanisms.
+- `model_index.json` is the root map that tells FastVideo which components to
+  load and which classes implement them.
+- Each component lives in its own folder with a `config.json` and weights.
+- Weights are usually in `diffusion_pytorch_model.safetensors`.
 
-Efficient communication primitives minimize distributed overhead:
+Note on tensor names:
 
-- **Sequence-Parallel AllGather**: Collects sequence chunks
-- **Tensor-Parallel AllReduce**: Combines partial results
-- **Distributed Synchronization**: Coordinates execution
+Official checkpoints often use different `state_dict` names than FastVideo's
+module layout. We translate tensor names via the DiT arch config mapping
+(`param_names_mapping` under `fastvideo/configs/models/dits/`). This is similar
+in spirit to name-translation layers used in systems like vLLM and SGLang.
 
-## Forward Context Management
+Example HF repo (Wan 2.1 T2V 1.3B Diffusers):
 
-### ForwardContext
-
-Defined in `fastvideo/forward_context.py`, `ForwardContext` manages execution-specific state *within* a forward pass, particularly for low-level optimizations. It is accessed via `get_forward_context()`.
-
-- **Attention Metadata**: Configuration for optimized attention kernels (`attn_metadata`)
-- **Profiling Data**: Potential hooks for performance metrics collection
-
-This context-based approach enables:
-
-- Dynamic optimization based on execution state (e.g., attention backend selection)
-- Step-specific customizations within model components
-
-Usage example:
-
-```python
-with set_forward_context(current_timestep, attn_metadata, fastvideo_args):
-    # During this forward pass, components can access context
-    # through get_forward_context()
-    output = model(inputs)
+```
+https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers/tree/main
 ```
 
-## Executor and Worker System
+Example `model_index.json` from that repo:
 
-The `fastvideo/worker/` directory contains the distributed execution framework:
-
-### Executor Abstraction
-
-FastVideo implements a flexible execution model for distributed processing:
-
-- **Executor Base Class**: An abstract base class defining the interface for all executors
-- **MultiProcExecutor**: Primary implementation that spawns and manages worker processes
-- **GPU Workers**: Handle actual model execution on individual GPUs
-
-The MultiProcExecutor implementation:
-
-1. Spawns worker processes for each GPU
-2. Establishes communication channels via pipes
-3. Coordinates distributed operations across workers
-4. Handles graceful startup and shutdown of the process group
-
-Each GPU worker:
-
-1. Initializes the distributed environment
-2. Builds the pipeline for the specified model
-3. Executes requested operations on its assigned GPU
-4. Manages local resources and communicates results back to the executor
-
-This design allows FastVideo to efficiently utilize multiple GPUs while providing a simple, unified interface for model execution.
-
-## Platforms
-
-The `fastvideo/platforms/` directory provides hardware platform abstractions that enable FastVideo to run efficiently on different hardware configurations:
-
-### Platform Abstraction
-
-FastVideo's platform abstraction layer enables:
-
-- **Hardware Detection**: Automatic detection of available hardware
-- **Backend Selection**: Appropriate selection of compute kernels
-- **Memory Management**: Efficient utilization of hardware-specific memory features
-
-The primary components include:
-
-- **Platform Interface**: Defines the common API for all platform implementations
-- **CUDA Platform**: Optimized implementation for NVIDIA GPUs
-- **Backend Enum**: Used throughout the codebase for feature selection
-
-Usage example:
-
-```python
-from fastvideo.platforms import current_platform, _Backend
-
-# Check hardware capabilities
-if current_platform.supports_backend(_Backend.FLASH_ATTN):
-    # Use FlashAttention implementation
-else:
-    # Fall back to standard implementation
+```json
+{
+  "_class_name": "WanPipeline",
+  "_diffusers_version": "0.33.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "UniPCMultistepScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "UMT5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "WanTransformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLWan"
+  ]
+}
 ```
 
-The platform system is designed to be extensible for future hardware targets.
+How this maps to FastVideo:
 
-## Logger
+- `WanPipeline` -> `fastvideo/pipelines/basic/wan/wan_pipeline.py`
+- `WanTransformer3DModel` -> `fastvideo/models/dits/wanvideo.py`
+- `AutoencoderKLWan` -> `fastvideo/models/vaes/wanvae.py`
+- `UMT5EncoderModel` -> `fastvideo/models/encoders/t5.py`
+- `T5TokenizerFast` -> loaded via HF in `fastvideo/models/loader/`
+- `UniPCMultistepScheduler` -> loaded via Diffusers scheduler utilities
+- Pipeline defaults -> `fastvideo/configs/pipelines/wan.py`
+- Sampling defaults -> `fastvideo/configs/sample/wan.py`
 
-See [PR](https://github.com/hao-ai-lab/FastVideo/pull/356)
+## Pipeline system
 
-*TODO*: (help wanted) Add an environment variable that disables process-aware logging.
+- `fastvideo/pipelines/basic/*` contains end-to-end pipelines for each model
+  family.
+- `fastvideo/pipelines/stages/*` contains reusable, testable stages.
+- Pipelines subclass `ComposedPipelineBase` and declare required components via
+  `_required_config_modules`.
+- `ForwardBatch` (in `fastvideo/pipelines/pipeline_batch_info.py`) carries
+  prompts, latents, timesteps, and intermediate state across stages.
 
-## Contributing to FastVideo
+## Model components
 
-If you're a new contributor, here are some common areas to explore:
+- DiT models: `fastvideo/models/dits/`
+- VAEs: `fastvideo/models/vaes/`
+- Text/image encoders: `fastvideo/models/encoders/`
+- Schedulers: `fastvideo/models/schedulers/`
+- Upsamplers: `fastvideo/models/upsamplers/`
+- Optional audio models: `fastvideo/models/audio/`
 
-1. **Adding a new model**: Implement new model types in the appropriate subdirectory of `fastvideo/models/`
-2. **Optimizing performance**: Look at attention implementations or memory management
-3. **Adding a new pipeline**: Create a new pipeline subclass in `fastvideo/pipelines/`
-4. **Hardware support**: Extend the `platforms` module for new hardware targets
+## Attention and distributed execution
 
-When adding code, follow these practices:
+- Attention backends live in `fastvideo/attention/` and can be selected via
+  `FASTVIDEO_ATTENTION_BACKEND`.
+- `LocalAttention` is used for cross-attention and most attention layers.
+- `DistributedAttention` is used for full-sequence self-attention in the DiT.
+- Tensor-parallel layers live in `fastvideo/layers/`.
+- Sequence/tensor parallel utilities live in `fastvideo/distributed/`.
 
-- Use type hints for better code readability
-- Add appropriate docstrings
-- Maintain the separation between model components and execution logic
-- Follow existing patterns for distributed processing
+## Related docs
+
+- [Contributing overview](../contributing/overview.md)
+- [Coding agents workflow](../contributing/coding_agents.md)
+- [Testing guide](../contributing/testing.md)

docs/inference/offloading.md

@@ -0,0 +1,140 @@
+# Offloading
+
+This page describes how to use offloading techniques for inference to reduce GPU memory usage while maintaining acceptable performance.
+
+## Default Behavior
+
+```python
+dit_cpu_offload: bool = True
+use_fsdp_inference: bool = False
+dit_layerwise_offload: bool = True
+text_encoder_cpu_offload: bool = True
+image_encoder_cpu_offload: bool = True
+vae_cpu_offload: bool = True
+pin_cpu_memory: bool = True
+```
+
+## Behavior Explanation
+
+!!! note
+    For CLI usage, replace underscores (`_`) with hyphens (`-`).
+
+### `use_fsdp_inference`
+
+Enables [FSDP](https://docs.pytorch.org/tutorials/intermediate/FSDP_tutorial.html) for inference. The model weights are sharded across multiple GPUs to reduce memory usage per GPU, and weights are broadcast to all GPUs layer by layer during inference.
+
+#### Performance Impact
+
+FSDP inference introduces negligible performance overhead due to weight prefetching. Performance overhead may be visible when GPU interconnect is slow (e.g., multiple consumer-level GPUs connected by slow PCIe without GPU P2P support).
+
+#### Usage Recommendation
+
+We recommend enabling this option when multiple GPUs are available.
+
+### `dit_cpu_offload`
+
+Enables CPU offloading for FSDP inference. When enabled, the model weights are offloaded to CPU memory, and the weight of each layer is moved to GPU memory only when that layer is being computed.
+
+#### Performance Impact
+
+The PyTorch FSDP implementation does not overlap computation and data transfer perfectly for inference, so enabling this option will harm performance.
+
+#### Usage Recommendation
+
+This option only takes effect when FSDP is enabled. For single GPU usage, we recommend using `dit_layerwise_offload` instead.
+
+### `dit_layerwise_offload`
+
+This option is similar to `dit_cpu_offload`, but with two key differences:
+
+1. It overlaps computation and PCIe data transfer
+2. It only works for single GPU inference
+
+#### Performance Impact
+
+This option introduces negligible performance overhead.
+
+#### Usage Recommendation
+
+We recommend enabling this option for single GPU usage. This option is not compatible with FSDP.
+
+### `text_encoder_cpu_offload`
+
+When enabled, the text encoder model weights are offloaded to CPU memory, and text encoding is computed on CPU.
+
+#### Performance Impact
+
+This option significantly slows down text encoding computation, but text encoding is usually not the bottleneck.
+
+#### Usage Recommendation
+
+We recommend enabling this option only when OOM happens.
+
+### `image_encoder_cpu_offload` and `vae_cpu_offload`
+
+When enabled, the weights are stored in CPU memory and moved to GPU memory when the corresponding module is being computed. After computation, the weights are moved back to CPU memory.
+
+#### Performance Impact
+
+These options introduce performance overhead due to PCIe data transfer.
+
+#### Usage Recommendation
+
+We recommend enabling these options when OOM happens.
+
+## General Recommendations
+
+### Single GPU Inference
+
+We recommend enabling `dit_layerwise_offload`. If OOM happens, also enable `image_encoder_cpu_offload` and `vae_cpu_offload`. If OOM still happens, consider enabling `text_encoder_cpu_offload`.
+
+### Multi-GPU Inference
+
+We recommend enabling `use_fsdp_inference` and disabling both `dit_layerwise_offload` and `dit_cpu_offload`. If OOM happens, consider enabling `text_encoder_cpu_offload`, `image_encoder_cpu_offload`, and `vae_cpu_offload`. If OOM still happens, consider enabling `dit_cpu_offload`.
+
+## Examples
+
+### Single GPU with Layerwise Offloading
+
+```python
+from fastvideo import VideoGenerator
+
+generator = VideoGenerator.from_pretrained(
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    num_gpus=1,
+    # Recommended for single GPU
+    dit_layerwise_offload=True,
+    # Enable if OOM happens
+    vae_cpu_offload=True,
+    image_encoder_cpu_offload=True,
+    text_encoder_cpu_offload=True,
+    # Speeds up CPU-GPU transfer
+    pin_cpu_memory=True,
+)
+
+prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers."
+video = generator.generate_video(prompt, output_path="output/", save_video=True)
+```
+
+### Multi-GPU with FSDP
+
+```python
+from fastvideo import VideoGenerator
+
+generator = VideoGenerator.from_pretrained(
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    num_gpus=2,
+    # Recommended for multi-GPU
+    use_fsdp_inference=True,
+    dit_layerwise_offload=False,
+    dit_cpu_offload=False,
+    # Enable if OOM happens
+    vae_cpu_offload=True,
+    image_encoder_cpu_offload=True,
+    text_encoder_cpu_offload=True,
+    pin_cpu_memory=True,
+)
+
+prompt = "A majestic lion strides across the golden savanna."
+video = generator.generate_video(prompt, output_path="output/", save_video=True)
+```

examples/distill/SFHunyuan1.5-T2V/distill_dmd.slurm

@@ -0,0 +1,172 @@
+#!/bin/bash
+#SBATCH --job-name=dmd_3333
+#SBATCH --partition=main
+#SBATCH --nodes=4
+#SBATCH --ntasks=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=dmd_3333_output/dmd_3333_%j.out
+#SBATCH --error=dmd_3333_output/dmd_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+source ~/conda/miniconda/bin/activate
+conda activate wei-fv-distill
+export HOME="/mnt/weka/home/hao.zhang/wei"
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=32
+
+# Model paths for Self-Forcing DMD distillation with Hunyuan1.5:
+GENERATOR_MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"  # Updated to Wan2.2
+REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" # Teacher model
+FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
+# REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled"
+# FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled" # Critic model
+
+# DATA_DIR="data/test-text-preprocessing/Node_0_GPU_1_File_1/combined_parquet_dataset/"
+# DATA_DIR="/mnt/weka/home/hao.zhang/matthew/FastVideo/data/test-text-preprocessing"
+DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/vidprom_16k_text_embed"
+DATA_DIR_2="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
+# DATA_DIR=data/crush-smol_processed_t2v/combined_parquet_dataset
+# DATA_DIR="/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/"
+# VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
+
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+training_args=(
+  --tracker_project_name SFhy1.5_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
+  --output_dir "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_3333_1e-5_1e-5_cfg6_corrected_scheduler"
+  --wandb_run_name "self_forcing_3333_context_forcing"
+  --max_train_steps 4000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 848
+  --enable_gradient_checkpointing_type "full"
+  --simulate_generator_forward
+  --log_visualization
+  --num_frames 81
+  --num_frame_per_block 3  # Frame generation block size for self-forcing
+  # --resume-from-checkpoint "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_1333/checkpoint-300"
+  --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_worldplay_tf_init_3333/checkpoint-2800/transformer/diffusion_pytorch_model.safetensors
+  # --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_worldplay_df_init_1333/checkpoint-11600/transformer/diffusion_pytorch_model.safetensors
+  # --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_distilled_ode_init_3333/checkpoint-2400/transformer/diffusion_pytorch_model.safetensors
+)
+
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+model_args=(
+  --model_path $GENERATOR_MODEL_PATH
+  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
+  --real_score_model_path $REAL_SCORE_MODEL_PATH
+  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
+)
+
+dataset_args=(
+  --data_path "$DATA_DIR"
+  # --data_path_2 "$DATA_DIR_2"
+  --dataloader_num_workers 4
+)
+
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 50
+  --validation_sampling_steps "8"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  --text-encoder-cpu-offload
+  # --vae_cpu_offload True
+)
+
+optimizer_args=(
+  --optimizer-type "muon"
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 100
+  --weight_only_checkpointing_steps 500
+  --weight_decay 0.01
+  --betas '0.0,0.999'
+  --max_grad_norm 1.0
+)
+
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.0
+  --dit_precision "fp32"
+  --flow_shift 5
+  --seed 1000
+  --use_ema True
+  --ema_decay 0.99
+  --ema_start_step 200
+)
+
+dmd_args=(
+  --warp_denoising_step
+  # --dmd_denoising_steps '1000,875,750,625,500,375,250,125'
+  # --dmd_denoising_steps '1000,760,520,280'
+  # --dmd_denoising_steps '1000,750,500,250'
+  --min_timestep_ratio 0.02
+  --max_timestep_ratio 0.98
+  --dfake_gen_update_ratio 5
+  --real_score_guidance_scale 3.5
+  --fake_score_learning_rate 5e-6
+  --fake_score_betas '0.0,0.999'
+)
+
+self_forcing_args=(
+  --independent_first_frame False  # Whether to treat first frame independently
+  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
+  --last_step_only False  # Whether to only use the last denoising step
+  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
+  # --use-ode-init True
+  # --use-context-forcing True
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/hy15_self_forcing_distillation_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}" \
+    "${dmd_args[@]}" \
+    "${self_forcing_args[@]}"

examples/distill/SFWan2.1-T2V/distill_dmd.slurm

@@ -0,0 +1,172 @@
+#!/bin/bash
+#SBATCH --job-name=distill_dmd_t2v_1.3b
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=distill_dmd_t2v_1.3b_output/distill_dmd_t2v_1.3b_%j.out
+#SBATCH --error=distill_dmd_t2v_1.3b_output/distill_dmd_t2v_1.3b_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+# export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+export HF_HOME=/mnt/data/huggingface
+export HF_DATASETS_CACHE="/tmp/hf_datasets_${SLURM_JOB_ID}_${SLURM_PROCID}"
+mkdir -p "${HF_DATASETS_CACHE}"
+export CUDA_HOME=/usr/local/cuda
+export PATH="$CUDA_HOME/bin:$PATH"
+export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home/shared-bin/local/usr/lib/aarch64-linux-gnu:$CUDA_HOME/lib64"
+export CUDACXX="$CUDA_HOME/bin/nvcc"
+
+source ~/miniconda3/bin/activate
+conda activate fastvideo
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=64
+
+# Model paths for Self-Forcing DMD distillation:
+GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"  # Teacher model
+FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model
+
+DATA_DIR="/mnt/data/vidprom_filtered_extended_umt5_text_embed"
+# DATA_DIR="/mnt/data/mixkit_wan_1.3b_processed_t2v_distributed"
+DATA_DIR_2="/mnt/data/mixkit_wan_1.3b_processed_t2v_distributed"
+VALIDATION_DATASET_FILE="/mnt/home/zhouw.jerry2017/FastVideo/examples/distill/SFWan2.1-T2V/validation_64.json"
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+training_args=(
+  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd
+  --wandb_run_name causal_forcing_vidprom_extended
+  --output_dir /mnt/data/wan_cf_df_distill_1.3b
+  --max_train_steps 4000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --enable_gradient_checkpointing_type "full"
+  --log_visualization
+  --simulate_generator_forward
+  --num_frames 81
+  --num_frame_per_block 3  # Frame generation block size for self-forcing
+  --enable_gradient_masking
+  --gradient_mask_last_n_frames 21
+)
+
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+model_args=(
+  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
+  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
+  --real_score_model_path $REAL_SCORE_MODEL_PATH
+  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
+)
+
+dataset_args=(
+  --data_path "$DATA_DIR"
+  # --data_path_2 "$DATA_DIR_2"
+  --dataloader_num_workers 4
+)
+
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 100
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "1.0"
+)
+
+optimizer_args=(
+  --optimizer-type "adamw"
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 500
+  --weight_only_checkpointing_steps 500
+  --weight_decay 0.01
+  --betas '0.0,0.999'
+  --max_grad_norm 1.0
+)
+
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.0
+  --dit_precision "fp32"
+  --flow_shift 5
+  --seed 1000
+  --use_ema True
+  --ema_decay 0.99
+  --ema_start_step 200
+#   --init_weights_from_safetensors "/mnt/data/wan_tf_ode_init_3333/checkpoint-1600/transformer/diffusion_pytorch_model.safetensors"
+#   --init_weights_from_safetensors "/mnt/data/wan_tf_init_3333/checkpoint-800/transformer/diffusion_pytorch_model.safetensors"
+  # --init_weights_from_safetensors "/mnt/data/Causal-Forcing-wan1.3b/causal_ode.safetensors"
+  # --init_weights_from_safetensors "/mnt/data/wan_ar_diffusion_3333_mixkit_shift5/checkpoint-7200/transformer/diffusion_pytorch_model.safetensors"
+  --init_weights_from_safetensors "/mnt/data/wan_ar_diffusion_3333_mixkit_shift5/checkpoint-4800/transformer/diffusion_pytorch_model.safetensors"
+  # --init_weights_from_safetensors "/mnt/data/wan_tf_ode_init_3333_mixkit_shift5/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors"
+  # --init_weights_from_safetensors "/mnt/data/wan_ar_df_diffusion_3333_mixkit_shift5/checkpoint-4800/transformer/diffusion_pytorch_model.safetensors"
+)
+
+dmd_args=(
+  --dmd_denoising_steps '1000,750,500,250'
+  --min_timestep_ratio 0.02
+  --max_timestep_ratio 0.98
+  --dfake_gen_update_ratio 5
+  --real_score_guidance_scale 3.0
+  --fake_score_learning_rate 2e-6
+  --fake_score_betas '0.0,0.999'
+  --warp_denoising_step
+  # --use-decoupled-dmd True
+  # --use-gan-loss True
+)
+
+self_forcing_args=(
+  --independent_first_frame False  # Whether to treat first frame independently
+  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
+  --last_step_only False  # Whether to only use the last denoising step
+  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}" \
+    "${dmd_args[@]}" \
+    "${self_forcing_args[@]}"

examples/inference/basic/basic.py

@@ -15,7 +15,7 @@ def main():
         use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=False,
         vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
+        text_encoder_cpu_offload=False,
         pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
         # image_encoder_cpu_offload=False,
     )

examples/inference/basic/basic_hyworld.py

@@ -0,0 +1,224 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Basic example for HYWorld (HY-WorldPlay) video generation using FastVideo.
+
+This example replicates the same functionality as HY-WorldPlay/run.sh,
+demonstrating image-to-video generation with camera trajectory control.
+"""
+
+import time
+import math
+import numpy as np
+import imageio
+import torchvision
+from einops import rearrange
+
+from fastvideo import VideoGenerator
+from fastvideo.pipelines import ForwardBatch
+from fastvideo.utils import shallow_asdict, align_to
+from fastvideo.logger import init_logger
+
+from fastvideo.models.dits.hyworld.pose import pose_to_input, compute_latent_num
+from fastvideo.models.dits.hyworld.resolution_utils import get_resolution_from_image
+
+logger = init_logger(__name__)
+
+class HYWorldVideoGenerator(VideoGenerator):
+    """Extended VideoGenerator that adds HYWorld-specific parameters to batch.extra."""
+
+    def _generate_single_video(self, prompt: str, sampling_param=None, **kwargs):
+        """Override to add viewmats, Ks, and action to batch.extra."""
+        fastvideo_args = self.fastvideo_args
+        pipeline_config = fastvideo_args.pipeline_config
+
+        if sampling_param is None:
+            from fastvideo.configs.sample import SamplingParam
+            sampling_param = SamplingParam.from_pretrained(fastvideo_args.model_path)
+
+        # Update sampling param with kwargs
+        if kwargs:
+            for key, value in kwargs.items():
+                if hasattr(sampling_param, key):
+                    setattr(sampling_param, key, value)
+
+        # Get pose string from sampling_param or kwargs
+        pose = kwargs.get('pose', getattr(sampling_param, 'POSE', 'w-31'))
+        num_frames = kwargs.get('num_frames', getattr(sampling_param, 'num_frames', 125))
+
+        # Calculate number of latents
+        latent_num = compute_latent_num(num_frames)
+
+        # Convert pose to viewmats, Ks, and action
+        viewmats, Ks, action = pose_to_input(pose, latent_num)
+
+        # Convert to tensors and add batch dimension
+        viewmats = viewmats.unsqueeze(0)  # (1, T, 4, 4)
+        Ks = Ks.unsqueeze(0)  # (1, T, 3, 3)
+        action = action.unsqueeze(0)  # (1, T)
+
+        # Validate inputs
+        prompt = prompt.strip()
+        sampling_param = sampling_param.__class__(**shallow_asdict(sampling_param))
+        output_path = kwargs.get("output_path", sampling_param.output_path)
+        sampling_param.prompt = prompt
+
+        if sampling_param.negative_prompt is not None:
+            sampling_param.negative_prompt = sampling_param.negative_prompt.strip()
+
+        # Validate dimensions
+        if (sampling_param.height <= 0 or sampling_param.width <= 0 or
+            sampling_param.num_frames <= 0):
+            raise ValueError(
+                f"Height, width, and num_frames must be positive integers")
+
+        temporal_scale_factor = pipeline_config.vae_config.arch_config.temporal_compression_ratio
+        num_frames = sampling_param.num_frames
+        num_gpus = fastvideo_args.num_gpus
+        use_temporal_scaling_frames = pipeline_config.vae_config.use_temporal_scaling_frames
+
+        # Adjust number of frames based on number of GPUs
+        if use_temporal_scaling_frames:
+            orig_latent_num_frames = (num_frames - 1) // temporal_scale_factor + 1
+        else:
+            orig_latent_num_frames = sampling_param.num_frames // 17 * 3
+
+        if orig_latent_num_frames % fastvideo_args.num_gpus != 0:
+            if use_temporal_scaling_frames:
+                new_num_frames = (orig_latent_num_frames - 1) * temporal_scale_factor + 1
+            else:
+                divisor = math.lcm(3, num_gpus)
+                orig_latent_num_frames = (
+                    (orig_latent_num_frames + divisor - 1) // divisor) * divisor
+                new_num_frames = orig_latent_num_frames // 3 * 17
+
+            logger.info(
+                "Adjusting number of frames from %s to %s based on number of GPUs (%s)",
+                sampling_param.num_frames, new_num_frames, fastvideo_args.num_gpus)
+            sampling_param.num_frames = new_num_frames
+
+        # Calculate sizes
+        target_height = align_to(sampling_param.height, 16)
+        target_width = align_to(sampling_param.width, 16)
+
+        # Calculate latent sizes
+        latents_size = [(sampling_param.num_frames - 1) // 4 + 1,
+                        sampling_param.height // 8, sampling_param.width // 8]
+        n_tokens = latents_size[0] * latents_size[1] * latents_size[2]
+
+        # Prepare batch
+        batch = ForwardBatch(
+            **shallow_asdict(sampling_param),
+            eta=0.0,
+            n_tokens=n_tokens,
+            VSA_sparsity=fastvideo_args.VSA_sparsity,
+        )
+
+        # Add HYWorld-specific parameters to batch.extra
+        batch.extra['viewmats'] = viewmats
+        batch.extra['Ks'] = Ks
+        batch.extra['action'] = action
+        batch.extra['chunk_latent_frames'] = 16  # For bidirectional model
+
+        # Run inference
+        start_time = time.perf_counter()
+        output_batch = self.executor.execute_forward(batch, fastvideo_args)
+        samples = output_batch.output
+        logging_info = output_batch.logging_info
+
+        gen_time = time.perf_counter() - start_time
+        logger.info("Generated successfully in %.2f seconds", gen_time)
+
+        # Process outputs
+        videos = rearrange(samples, "b c t h w -> t b c h w")
+        frames = []
+        for x in videos:
+            x = torchvision.utils.make_grid(x, nrow=6)
+            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+            frames.append((x * 255).numpy().astype(np.uint8))
+
+        # Save video if requested
+        if batch.save_video:
+            imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
+            logger.info("Saved video to %s", output_path)
+
+        if batch.return_frames:
+            return frames
+        else:
+            return {
+                "samples": samples,
+                "frames": frames,
+                "prompts": prompt,
+                "size": (target_height, target_width, batch.num_frames),
+                "generation_time": gen_time,
+                "logging_info": logging_info,
+                "trajectory": output_batch.trajectory_latents,
+                "trajectory_timesteps": output_batch.trajectory_timesteps,
+                "trajectory_decoded": output_batch.trajectory_decoded,
+            }
+
+# Default prompt from HY-WorldPlay run.sh
+DEFAULT_PROMPT = 'A paved pathway leads towards a stone arch bridge spanning a calm body of water.  Lush green trees and foliage line the path and the far bank of the water. A traditional-style pavilion with a tiered, reddish-brown roof sits on the far shore. The water reflects the surrounding greenery and the sky.  The scene is bathed in soft, natural light, creating a tranquil and serene atmosphere. The pathway is composed of large, rectangular stones, and the bridge is constructed of light gray stone.  The overall composition emphasizes the peaceful and harmonious nature of the landscape.'
+DEFAULT_IMAGE = 'https://raw.githubusercontent.com/Tencent-Hunyuan/HY-WorldPlay/main/assets/img/test.png'
+
+def main():
+    import argparse
+
+    # pose: (a, w, s, d) - (15, 31)
+    # num_frames: (61, 125)
+    parser = argparse.ArgumentParser(description="HYWorld video generation with FastVideo")
+    parser.add_argument("--prompt", type=str, default=DEFAULT_PROMPT, help="Text prompt for video generation")
+    parser.add_argument("--image", type=str, default=DEFAULT_IMAGE, help="Path or URL to input image")
+    parser.add_argument("--pose", type=str, default='w-31', help="Pose string (e.g., 'a-31', 'w-31', 's-31', 'd-31')")
+    parser.add_argument("--output_path", type=str, default='video_samples_hyworld', help="Output video path")
+    parser.add_argument("--num-frames", type=int, default=125, help="Number of frames")
+    parser.add_argument("--seed", type=int, default=1, help="Random seed")
+    parser.add_argument("--resolution", type=str, default="480p", help="Only support 480p for now")
+    args = parser.parse_args()
+
+    # Automatically determine resolution from input image
+    HEIGHT, WIDTH = get_resolution_from_image(args.image, args.resolution)
+    print(f"Image: {args.image}")
+    print(f"Pose: {args.pose}")
+    print(f"Resolution: {HEIGHT}x{WIDTH} (from {args.resolution} buckets)")
+    print(f"Num frames: {args.num_frames}")
+    print(f"Output path: {args.output_path}")
+
+    # Initialize generator
+    print("\nInitializing VideoGenerator for HYWorld...")
+    
+    generator = HYWorldVideoGenerator.from_pretrained(
+        "FastVideo/HY-WorldPlay-Bidirectional-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+        image_encoder_cpu_offload=True,
+    )
+
+    # Generate video
+    print("\nGenerating video...")
+    start_time = time.time()
+    video = generator.generate_video(
+        prompt=args.prompt,
+        image_path=args.image,
+        output_path=args.output_path,
+        save_video=True,
+        negative_prompt="",
+        num_frames=args.num_frames,
+        fps=24,
+        height=HEIGHT,
+        width=WIDTH,
+        seed=args.seed,
+        pose=args.pose,
+    )
+    elapsed = time.time() - start_time
+
+    print(f"\nVideo generated successfully!")
+    print(f"Saved to: {args.output_path}")
+    print(f"Time: {elapsed:.2f}s")
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_ltx2.py

@@ -0,0 +1,34 @@
+from fastvideo import VideoGenerator
+
+PROMPT = (
+    "A warm sunny backyard. The camera starts in a tight cinematic close-up "
+    "of a woman and a man in their 30s, facing each other with serious "
+    "expressions. The woman, emotional and dramatic, says softly, \"That's "
+    "it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
+    "annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
+    "then mutters defensively, \"He's just having fun.\" The camera slowly "
+    "pans right, revealing the grandfather in the garden wearing enormous "
+    "butterfly wings, waving his arms in the air like he's trying to take "
+    "off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
+    "The woman covers her face, on the verge of tears. The tone is deadpan, "
+    "absurd, and quietly tragic."
+)
+
+
+def main() -> None:
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LTX2-Distilled-Diffusers",
+        num_gpus=1,
+    )
+
+    output_path = "outputs_video/ltx2_basic/output_ltx2_distilled_t2v.mp4"
+    generator.generate_video(
+        prompt=PROMPT,
+        output_path=output_path,
+        save_video=True,
+    )
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_matrixgame.py

@@ -1,6 +1,5 @@
 from fastvideo import VideoGenerator
-from fastvideo.configs.pipelines.wan import MatrixGameI2V480PConfig
-from fastvideo.models.dits.matrix_game.utils import create_action_presets
+from fastvideo.models.dits.matrixgame.utils import create_action_presets
 
 import torch
 

examples/inference/basic/basic_matrixgame_streaming.py

@@ -1,5 +1,5 @@
 from fastvideo.entrypoints.streaming_generator import StreamingVideoGenerator
-from fastvideo.models.dits.matrix_game.utils import get_current_action_async, expand_action_to_frames
+from fastvideo.models.dits.matrixgame.utils import get_current_action_async, expand_action_to_frames
 
 import torch
 import asyncio

examples/inference/basic/basic_wan2_2_Fun.py

@@ -10,12 +10,13 @@ def main():
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
     generator = VideoGenerator.from_pretrained(
-        "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers",
+        "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers",
+        # "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers",
         # "alibaba-pai/Wan2.2-Fun-A14B-Control",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
         use_fsdp_inference=False, # set to True if GPU is out of memory
-        dit_cpu_offload=True, # DiT need to be offloaded for MoE
+        dit_cpu_offload=False, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,
         # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
@@ -23,14 +24,19 @@ def main():
         # image_encoder_cpu_offload=False,
     )
 
-    prompt = "一位年轻女性穿着一件粉色的连衣裙，裙子上有白色的装饰和粉色的纽扣。她的头发是紫色的，头上戴着一个红色的大蝴蝶结，显得非常可爱和精致。她还戴着一个红色的领结，整体造型充满了少女感和活力。她的表情温柔，双手轻轻交叉放在身前，姿态优雅。背景是简单的灰色，没有任何多余的装饰，使得人物更加突出。她的妆容清淡自然，突显了她的清新气质。整体画面给人一种甜美、梦幻的感觉，仿佛置身于童话世界中。"
-    negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+    # prompt = "一位年轻女性穿着一件粉色的连衣裙，裙子上有白色的装饰和粉色的纽扣。她的头发是紫色的，头上戴着一个红色的大蝴蝶结，显得非常可爱和精致。她还戴着一个红色的领结，整体造型充满了少女感和活力。她的表情温柔，双手轻轻交叉放在身前，姿态优雅。背景是简单的灰色，没有任何多余的装饰，使得人物更加突出。她的妆容清淡自然，突显了她的清新气质。整体画面给人一种甜美、梦幻的感觉，仿佛置身于童话世界中。"
+    # negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
     # prompt                  = "A young woman with beautiful, clear eyes and blonde hair stands in the forest, wearing a white dress and a crown. Her expression is serene, reminiscent of a movie star, with fair and youthful skin. Her brown long hair flows in the wind. The video quality is very high, with a clear view. High quality, masterpiece, best quality, high resolution, ultra-fine, fantastical."
     # negative_prompt         = "Twisted body, limb deformities, text captions, comic, static, ugly, error, messy code."
-    image_path = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/wan_fun/asset_Wan2_2/v1.0/8.png"
-    control_video_path = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/wan_fun/asset_Wan2_2/v1.0/pose.mp4"
-
-    video = generator.generate_video(prompt, negative_prompt=negative_prompt, image_path=image_path, video_path=control_video_path, output_path=OUTPUT_PATH, output_video_name=OUTPUT_NAME, save_video=True)
+    # image_path = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/wan_fun/asset_Wan2_2/v1.0/8.png"
+    # control_video_path = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/wan_fun/asset_Wan2_2/v1.0/pose.mp4"
+    import json, os
+    with open("/mnt/weka/home/hao.zhang/wei/FastVideo/data/mixkit_i2v.jsonl", "r") as f:
+        prompt_image_pairs = json.load(f)
+    for d in prompt_image_pairs:
+        prompt = d["prompt"]
+        image_path = os.path.join("/mnt/weka/home/hao.zhang/wei/FastVideo", d["image_path"])
+        video = generator.generate_video(prompt, image_path=image_path, output_path=OUTPUT_PATH, output_video_name=OUTPUT_NAME, save_video=True)
 
 if __name__ == "__main__":
     main()

examples/inference/basic/basic_wan2_2_ti2v.py

@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator
 
-OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v"
+OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v_720p_77"
 def main():
     # FastVideo will automatically use the optimal default arguments for the
     # model.
@@ -12,17 +12,29 @@ def main():
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
         use_fsdp_inference=False, # set to True if GPU is out of memory
-        dit_cpu_offload=True,
+        dit_cpu_offload=False,
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,
         pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
         # image_encoder_cpu_offload=False,
     )
 
+    import json, os
+    with open("/mnt/weka/home/hao.zhang/wei/FastVideo/data/mixkit_i2v_720p.jsonl", "r") as f:
+        prompt_image_pairs = json.load(f)
+    for d in prompt_image_pairs:
+        prompt = d["prompt"]
+        image_path = os.path.join("/mnt/weka/home/hao.zhang/wei/FastVideo", d["image_path"])
+        video = generator.generate_video(prompt, image_path=image_path, output_path=OUTPUT_PATH, output_video_name=OUTPUT_PATH, save_video=True, num_frames=77)
+    return
+
     # I2V is triggered just by passing in an image_path argument
     prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
     image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+    # prompt = "In the midst of the joyous New Year's Eve celebration, the cheerful group of friends, their spirits lifted by the festivities, decides to immortalize the moment with a vibrant snapshot"
+    # image_path = "images/mixkit-a-cheerful-group-of-friends-celebrate-new-years-eve-and-51525.png"
     video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path)
+    return
 
     # Generate another video with a different prompt, without reloading the
     # model!

examples/inference/gradio/local/gradio_local_demo_matrixgame.py

@@ -10,7 +10,7 @@ from fastapi import FastAPI, Request, HTTPException
 from fastapi.responses import HTMLResponse, FileResponse
 
 from fastvideo.entrypoints.streaming_generator import StreamingVideoGenerator
-from fastvideo.models.dits.matrix_game.utils import expand_action_to_frames
+from fastvideo.models.dits.matrixgame.utils import expand_action_to_frames
 
 
 VARIANT_CONFIG = {

examples/training/consistency_finetune/causal_ode_init/hy15_finetune_ode_init.slurm

@@ -0,0 +1,124 @@
+#!/bin/bash
+#SBATCH --job-name=hy15_ode_3333
+#SBATCH --partition=main
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=hy15_ode_3333_output/hy15_ode_3333_%j.out
+#SBATCH --error=hy15_ode_3333_output/hy15_ode_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=64
+
+MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"
+DATA_DIR="your_data_dir"
+VALIDATION_DATASET_FILE="your_validation_data_dir"
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "hy15_ode_init"
+  --output_dir "your_output_dir"
+  --wandb_run_name "your_wandb_run_name"
+  --warp_denoising_step
+  --dmd_denoising_steps '1000,760,520,280,0'
+  --log_visualization
+  --visualization_steps 100
+  --max_train_steps 4000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 2
+  --num_latent_t 21
+  --num_height 480
+  --num_width 848
+  --num_frames 81
+  --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+  --text-encoder-cpu-offload
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 4
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 400
+  --weight_decay 0.01
+  --max_grad_norm 1.0
+)
+
+validation_args=(
+  # --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  # --vae_cpu_offload True
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 6
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/ode_causal_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/consistency_finetune/causal_ode_init/hy15_finetune_tf_init.slurm

@@ -0,0 +1,133 @@
+#!/bin/bash
+#SBATCH --job-name=hy15_tf_3333
+#SBATCH --partition=main
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=hy15_tf_3333_output/hy15_tf_3333_%j.out
+#SBATCH --error=hy15_tf_3333_output/hy15_tf_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+source ~/conda/miniconda/bin/activate
+conda activate wei-fv-distill
+export HOME="/mnt/weka/home/hao.zhang/wei"
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=64
+
+MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"
+DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "hy15_ode_init"
+  --output_dir "/mnt/weka/home/hao.zhang/wei/hy15_worldplay_tf_init_3333"
+  --wandb_run_name "hy15_worldplay_tf_init_3333"
+  --warp_denoising_step
+  --dmd_denoising_steps '1000,760,520,280,0'
+  --log_visualization
+  --visualization_steps 100
+  --max_train_steps 30000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 848
+  --num_frames 81
+  --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+  --text-encoder-cpu-offload
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 4
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --optimizer-type "muon"
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 400
+  --weight_decay 0.01
+  --max_grad_norm 1.0
+)
+
+validation_args=(
+  # --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  # --vae_cpu_offload True
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 6
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  # --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/hy15_ode_causal_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/consistency_finetune/causal_ode_init/wan_df_init.slurm

@@ -0,0 +1,137 @@
+#!/bin/bash
+#SBATCH --job-name=wan_tf_3333
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=wan_tf_3333_output/wan_tf_3333_%j.out
+#SBATCH --error=wan_tf_3333_output/wan_tf_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+# export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+export HF_HOME=/mnt/data/huggingface
+export CUDA_HOME=/usr/local/cuda
+export PATH="$CUDA_HOME/bin:$PATH"
+export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home/shared-bin/local/usr/lib/aarch64-linux-gnu:$CUDA_HOME/lib64"
+export CUDACXX="$CUDA_HOME/bin/nvcc"
+
+source ~/miniconda3/bin/activate
+conda activate fastvideo
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=64
+
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="/mnt/data/mixkit_wan_1.3b_processed_t2v_distributed"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_tf_init"
+  --output_dir "/mnt/data/wan_ar_df_diffusion_3333_mixkit_shift5"
+  --wandb_run_name "wan_df_init_3333"
+  --warp_denoising_step
+  --dmd_denoising_steps '1000,750,500,250,0'
+  --log_visualization
+  --visualization_steps 100
+  --max_train_steps 30000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 81
+  --override-transformer-cls-name "CausalWanTransformer3DModel"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+  --text-encoder-cpu-offload
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 4
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --optimizer-type "adamw"
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 800
+  --weight_decay 0.01
+  --max_grad_norm 1.0
+)
+
+validation_args=(
+  # --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  # --vae_cpu_offload True
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 6
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/ar_diffusion_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/consistency_finetune/causal_ode_init/wan_tf_init.slurm

@@ -0,0 +1,138 @@
+#!/bin/bash
+#SBATCH --job-name=wan_tf_3333
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=wan_tf_3333_output/wan_tf_3333_%j.out
+#SBATCH --error=wan_tf_3333_output/wan_tf_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+# export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+export HF_HOME=/mnt/data/huggingface
+export CUDA_HOME=/usr/local/cuda
+export PATH="$CUDA_HOME/bin:$PATH"
+export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home/shared-bin/local/usr/lib/aarch64-linux-gnu:$CUDA_HOME/lib64"
+export CUDACXX="$CUDA_HOME/bin/nvcc"
+
+source ~/miniconda3/bin/activate
+conda activate fastvideo
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=64
+
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="/mnt/data/mixkit_wan_1.3b_processed_t2v_distributed"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_tf_init"
+  --output_dir "/mnt/data/wan_ar_diffusion_3333_mixkit_shift5"
+  --wandb_run_name "wan_tf_init_3333"
+  --warp_denoising_step
+  --dmd_denoising_steps '1000,750,500,250,0'
+  --log_visualization
+  --visualization_steps 100
+  --max_train_steps 30000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 81
+  --override-transformer-cls-name "CausalWanTransformer3DModel"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+  --text-encoder-cpu-offload
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 4
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --optimizer-type "adamw"
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 800
+  --weight_decay 0.01
+  --max_grad_norm 1.0
+)
+
+validation_args=(
+  # --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  # --vae_cpu_offload True
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 6
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+  --use-tf True
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/ar_diffusion_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/consistency_finetune/causal_ode_init/wan_tf_ode_init.slurm

@@ -0,0 +1,138 @@
+#!/bin/bash
+#SBATCH --job-name=wan_tf_ode_init_3333
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=wan_tf_ode_init_3333_output/wan_tf_ode_init_3333_%j.out
+#SBATCH --error=wan_tf_ode_init_3333_output/wan_tf_ode_init_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+# export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+export HF_HOME=/mnt/data/huggingface
+export CUDA_HOME=/usr/local/cuda
+export PATH="$CUDA_HOME/bin:$PATH"
+export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home/shared-bin/local/usr/lib/aarch64-linux-gnu:$CUDA_HOME/lib64"
+export CUDACXX="$CUDA_HOME/bin/nvcc"
+
+source ~/miniconda3/bin/activate
+conda activate fastvideo
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=64
+
+MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="/mnt/data/fv-tf-ode-preprocessing-6k-mixkit-wan-1.3b/"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_tf_ode_init"
+  --output_dir "/mnt/data/wan_tf_ode_init_3333_mixkit_shift5"
+  --wandb_run_name "wan_tf_ode_init_3333_mixkit_shift5"
+  --warp_denoising_step
+  --dmd_denoising_steps '1000,750,500,250'
+  --log_visualization
+  --visualization_steps 100
+  --max_train_steps 30000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 81
+  --enable_gradient_checkpointing_type "full"
+  --init_weights_from_safetensors "/mnt/data/wan_ar_diffusion_3333_mixkit_shift5/checkpoint-2400/transformer/diffusion_pytorch_model.safetensors"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+  --text-encoder-cpu-offload
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 4
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --optimizer-type "adamw"
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 500
+  --weight_decay 0.01
+  --max_grad_norm 1.0
+)
+
+validation_args=(
+  # --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  # --vae_cpu_offload True
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 6
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  # --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/ode_causal_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/finetune/MatrixGame2.0/finetune_i2v.sh

@@ -0,0 +1,93 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+MODEL_PATH="FastVideo/Matrix-Game-2.0-Foundation-Diffusers"
+DATA_DIR="footsies-dataset/preprocessed/combined_parquet_dataset"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+NUM_GPUS=8
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "matrixgame_finetune"
+  --output_dir "checkpoints/matrixgame_finetune"
+  --max_train_steps 1500
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 4
+  --num_latent_t 20
+  --num_height 352
+  --num_width 640
+  --num_frames 77
+  --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size 2
+  --tp_size 1
+  --hsdp_replicate_dim 4
+  --hsdp_shard_dim 2
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 100
+  --validation_sampling_steps "40"
+  --validation_guidance_scale "6.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 2e-5
+  --mixed_precision "bf16"
+  --weight_only_checkpointing_steps 100
+  --training_state_checkpointing_steps 100
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/training/matrixgame_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

examples/training/finetune/MatrixGame2.0/preprocess_matrixgame_data_i2v.sh

@@ -0,0 +1,27 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Matrix-Game-2.0-Foundation-Diffusers"
+DATA_MERGE_PATH="footsies-dataset/merge.txt"
+OUTPUT_DIR="footsies-dataset/preprocessed/"
+
+# export CUDA_VISIBLE_DEVICES=0
+export MASTER_ADDR=localhost
+export MASTER_PORT=29500
+export RANK=0
+export WORLD_SIZE=1
+
+python fastvideo/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 4 \
+    --seed 42 \
+    --max_height 352 \
+    --max_width 640 \
+    --num_frames 77 \
+    --dataloader_num_workers 0 \
+    --output_dir=$OUTPUT_DIR \
+    --samples_per_file 4 \
+    --train_fps 25 \
+    --flush_frequency 4 \
+    --preprocess_task matrixgame

examples/training/finetune/wan_t2v_1.3B/mixkit/README.md

@@ -0,0 +1 @@
+The raw mixkit dataset is downloaded and preprocessed following instructions from https://github.com/tianweiy/CausVid/tree/master

examples/training/finetune/wan_t2v_1.3B/mixkit/preprocess_wan_data_t2v.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="/mnt/data/mixkit/merge.txt"
+OUTPUT_DIR="/mnt/data/mixkit_wan_1.3b_processed_t2v/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    fastvideo/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 1 \
+    --seed 42 \
+    --max_height 480 \
+    --max_width 832 \
+    --num_frames 81 \
+    --dataloader_num_workers 0 \
+    --output_dir=$OUTPUT_DIR \
+    --train_fps 16 \
+    --samples_per_file 8 \
+    --flush_frequency 8 \
+    --video_length_tolerance_range 5 \
+    --drop_short_ratio 0.0 \
+    --preprocess_task "t2v" 

examples/training/finetune/wan_t2v_1.3B/mixkit/preprocess_wan_data_t2v.slurm

@@ -0,0 +1,66 @@
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=preprocess_mixkit_wan_1.3b_t2v/preprocess_wan_data_t2v.log
+#SBATCH --error=preprocess_mixkit_wan_1.3b_t2v/preprocess_wan_data_t2v.error
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+# export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+export HF_HOME=/mnt/data/huggingface
+export CUDA_HOME=/usr/local/cuda
+export PATH="$CUDA_HOME/bin:$PATH"
+export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home/shared-bin/local/usr/lib/aarch64-linux-gnu:$CUDA_HOME/lib64"
+export CUDACXX="$CUDA_HOME/bin/nvcc"
+
+source ~/miniconda3/bin/activate
+conda activate fastvideo
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="/mnt/data/mixkit/merge.txt"
+OUTPUT_DIR="/mnt/data/mixkit_wan_1.3b_processed_t2v/"
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $GPU_NUM \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 1 \
+    --seed 42 \
+    --max_height 480 \
+    --max_width 832 \
+    --num_frames 81 \
+    --dataloader_num_workers 1 \
+    --output_dir=$OUTPUT_DIR \
+    --train_fps 16 \
+    --samples_per_file 8 \
+    --flush_frequency 8 \
+    --video_length_tolerance_range 5 \
+    --drop_short_ratio 0.0 \
+    --preprocess_task "t2v" 

fastvideo-kernel/README.md

@@ -40,6 +40,17 @@ out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)
 out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)
 ```
 
+## Benchmark
+
+### VSA (block-sparse) TFLOPs
+
+After building/installing `fastvideo-kernel`, run:
+
+```bash
+cd fastvideo-kernel
+python benchmarks/bench_vsa.py --batch_size 1 --num_heads 16 --head_dim 128 --q_seq_lens 49152 --topk 64
+```
+
 ### TurboDiffusion Kernels
 
 This package also includes kernels from [TurboDiffusion](https://github.com/thu-ml/TurboDiffusion), including INT8 GEMM, Quantization, RMSNorm and LayerNorm.

fastvideo-kernel/benchmarks/bench_vsa.py

@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+"""
+Benchmark VSA *wrapper* performance (forward + backward) and report TFLOPs.
+
+This script benchmarks the autograd-enabled wrapper:
+  - fastvideo_kernel.block_sparse_attn.block_sparse_attn
+
+So measured time includes wrapper overhead (map->index conversion, dispatch) plus kernel time.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import random
+from typing import Tuple, Callable
+
+import numpy as np
+import torch
+
+try:
+    from triton.testing import do_bench
+except Exception as e:  # pragma: no cover
+    raise ImportError("This benchmark requires triton (for triton.testing.do_bench).") from e
+
+
+BLOCK_M = 64
+BLOCK_N = 64
+
+
+def set_seed(seed: int = 42) -> None:
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def parse_arguments() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Benchmark FastVideo VSA block-sparse attention")
+    p.add_argument("--batch_size", type=int, default=1)
+    p.add_argument("--num_heads", type=int, default=12)
+    p.add_argument("--head_dim", type=int, default=128, choices=[64, 128])
+    p.add_argument("--topk", type=int, default=None, help="KV blocks per Q block (default: ~90%% sparsity)")
+    p.add_argument("--q_seq_lens", type=int, nargs="+", default=[49152], help="Q sequence lengths (must be /64)")
+    p.add_argument("--kv_seq_lens", type=int, nargs="+", default=None, help="KV sequence lengths (defaults to q_seq_len)")
+    p.add_argument("--warmup", type=int, default=5)
+    p.add_argument("--rep", type=int, default=20)
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--dtype", type=str, default="bf16", choices=["bf16", "fp16"])
+    p.add_argument("--force_triton", action="store_true", help="Force wrapper to use Triton path (if supported by shapes).")
+    return p.parse_args()
+
+
+def create_qkv(batch: int, heads: int, q_len: int, kv_len: int, d: int, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    q = torch.randn(batch, heads, q_len, d, dtype=dtype, device="cuda")
+    k = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
+    v = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
+    return q, k, v
+
+
+def make_block_map(bs: int, h: int, num_q_blocks: int, num_kv_blocks: int, topk: int) -> torch.Tensor:
+    # block_map: [bs, h, num_q_blocks, num_kv_blocks] bool
+    scores = torch.rand(bs, h, num_q_blocks, num_kv_blocks, device="cuda")
+    topk = min(max(1, topk), num_kv_blocks)
+    idx = torch.topk(scores, topk, dim=-1).indices
+    block_map = torch.zeros(bs, h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device="cuda")
+    block_map.scatter_(-1, idx, True)
+    return block_map
+
+
+def flops_sparse_attention(bs: int, h: int, d: int, q_len: int, topk_blocks: int, block_n: int) -> float:
+    # Approx: QK^T + PV, each is ~2*bs*h*q_len*(topk_blocks*block_n)*d
+    return 4.0 * bs * h * d * q_len * (topk_blocks * block_n)
+
+
+def bench_ms(fn: Callable[[], object], warmup: int, rep: int) -> float:
+    return do_bench(fn, warmup=warmup, rep=rep, quantiles=None)
+
+
+def main() -> None:
+    args = parse_arguments()
+    set_seed(args.seed)
+
+    dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float16
+
+    if args.force_triton:
+        os.environ["FASTVIDEO_KERNEL_VSA_FORCE_TRITON"] = "1"
+
+    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
+
+    bs, h, d = args.batch_size, args.num_heads, args.head_dim
+    kv_seq_lens = args.kv_seq_lens
+    if kv_seq_lens is None:
+        kv_seq_lens = args.q_seq_lens
+    if len(kv_seq_lens) != len(args.q_seq_lens):
+        raise ValueError("kv_seq_lens must have the same number of entries as q_seq_lens (or be omitted).")
+
+    print("VSA Block-Sparse Attention Benchmark (WRAPPER)")
+    print(f"device: {torch.cuda.get_device_name(0)}")
+    print(f"batch={bs}, heads={h}, head_dim={d}, dtype={args.dtype}")
+    print(f"BLOCK_M={BLOCK_M}, BLOCK_N={BLOCK_N}")
+    print("NOTE: timings include wrapper overhead (map->index + dispatch).")
+    if args.force_triton:
+        print("dispatch: forced Triton (FASTVIDEO_KERNEL_VSA_FORCE_TRITON=1)")
+    else:
+        print("dispatch: SM90 if available, else Triton")
+
+    for q_len, kv_len in zip(args.q_seq_lens, kv_seq_lens):
+        if q_len % BLOCK_M != 0 or kv_len % BLOCK_N != 0:
+            print(f"[skip] q_len={q_len}, kv_len={kv_len} must be divisible by 64")
+            continue
+
+        num_q_blocks = q_len // BLOCK_M
+        num_kv_blocks = kv_len // BLOCK_N
+        topk = args.topk if args.topk is not None else max(1, num_kv_blocks // 10)
+        topk = min(topk, num_kv_blocks)
+
+        print("\n" + "=" * 80)
+        print(f"q_len={q_len}, kv_len={kv_len}, num_q_blocks={num_q_blocks}, num_kv_blocks={num_kv_blocks}, topk={topk}")
+
+        q, k, v = create_qkv(bs, h, q_len, kv_len, d, dtype)
+        block_map = make_block_map(bs, h, num_q_blocks, num_kv_blocks, topk)
+
+        # Variable block sizes: default full blocks (64 tokens per KV block)
+        variable_block_sizes = torch.full((num_kv_blocks,), BLOCK_N, dtype=torch.int32, device="cuda")
+
+        def _fwd():
+            return block_sparse_attn(q, k, v, block_map, variable_block_sizes)
+
+        fwd_ms = bench_ms(_fwd, warmup=args.warmup, rep=args.rep)
+
+        # Backward benchmark (wrapper autograd). We build the graph once, then repeatedly run backward
+        # on the retained graph so bwd timing excludes the forward compute.
+        q_ = q.detach().requires_grad_(True)
+        k_ = k.detach().requires_grad_(True)
+        v_ = v.detach().requires_grad_(True)
+        o_, _aux_ = block_sparse_attn(q_, k_, v_, block_map, variable_block_sizes)
+        og = torch.randn_like(o_)
+        loss = (o_ * og).sum()
+
+        for _ in range(max(1, args.warmup // 2)):
+            torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True)
+        torch.cuda.synchronize()
+
+        bwd_ms = bench_ms(
+            lambda: torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True),
+            warmup=0,
+            rep=max(5, args.rep // 2),
+        )
+
+        flops = flops_sparse_attention(bs, h, d, q_len, topk, BLOCK_N)
+        fwd_tflops = flops / fwd_ms * 1e-12 * 1e3
+        # Rough backward multiplier (attention backward typically ~2-3x forward)
+        bwd_tflops = (2.5 * flops) / bwd_ms * 1e-12 * 1e3
+
+        print(f"fwd(wrapper): {fwd_ms:.3f} ms  | {fwd_tflops:.2f} TFLOPs (approx)")
+        print(f"bwd(wrapper): {bwd_ms:.3f} ms  | {bwd_tflops:.2f} TFLOPs (approx)")
+
+
+if __name__ == "__main__":
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required for this benchmark.")
+    main()
+
+

fastvideo-kernel/pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "fastvideo-kernel"
-version = "0.2.4"
+version = "0.2.5"
 description = "Unified CUDA kernels for FastVideo"
 readme = "README.md"
 requires-python = ">=3.10"

fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py

@@ -30,13 +30,12 @@ def _force_triton() -> bool:
     return os.environ.get("FASTVIDEO_KERNEL_VSA_FORCE_TRITON", "0") == "1"
 
 
-def _map_to_index_torch(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+def _map_to_index(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     """
-    Pure-torch (no triton) conversion:
-      block_map: [B, H, Q, KV] bool  (or [H, Q, KV] which will be treated as B=1)
-      returns:
-        index: [B, H, Q, KV] int32 (packed KV indices, -1 padding)
-        num:   [B, H, Q] int32 (#kv blocks per q block)
+    Preferred map->index conversion used by the wrapper.
+
+    This wrapper **requires** the Triton implementation.
+    If Triton (or the Triton map_to_index module) is not available, it raises.
     """
     if block_map.dim() == 3:
         block_map = block_map.unsqueeze(0)
@@ -45,20 +44,17 @@ def _map_to_index_torch(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Te
     if block_map.dtype != torch.bool:
         block_map = block_map.to(torch.bool)
 
-    B, H, Q, KV = block_map.shape
-    index = torch.full((B, H, Q, KV), -1, dtype=torch.int32, device=block_map.device)
-    num = torch.zeros((B, H, Q), dtype=torch.int32, device=block_map.device)
+    if not block_map.is_cuda:
+        raise RuntimeError("block_map must be a CUDA tensor (Triton map_to_index required).")
 
-    # Small sizes in practice (B=1, H<=16, Q/KV<=64), so a Python loop is fine.
-    for b in range(B):
-        for h in range(H):
-            for q in range(Q):
-                kv_idx = torch.nonzero(block_map[b, h, q], as_tuple=False).flatten().to(torch.int32)
-                n = int(kv_idx.numel())
-                if n:
-                    index[b, h, q, :n] = kv_idx
-                num[b, h, q] = n
-    return index, num
+    try:
+        from fastvideo_kernel.triton_kernels.index import map_to_index as triton_map_to_index  # local import
+    except Exception as e:
+        raise ImportError(
+            "Triton map_to_index is required but not available. "
+            "Ensure Triton is installed and fastvideo_kernel.triton_kernels.index is importable."
+        ) from e
+    return triton_map_to_index(block_map)
 
 
 @torch.library.custom_op(
@@ -77,7 +73,7 @@ def block_sparse_attn_triton(
     k = k.contiguous()
     v = v.contiguous()
     block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index_torch(block_map)
+    q2k_idx, q2k_num = _map_to_index(block_map)
 
     from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
         triton_block_sparse_attn_forward,
@@ -87,6 +83,7 @@ def block_sparse_attn_triton(
     return o, M
 
 
+
 @torch.library.register_fake("fastvideo_kernel::block_sparse_attn_triton")
 def _block_sparse_attn_triton_fake(
     q: torch.Tensor,
@@ -117,8 +114,8 @@ def block_sparse_attn_backward_triton(
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     grad_output = grad_output.contiguous()
     block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index_torch(block_map)
-    k2q_idx, k2q_num = _map_to_index_torch(block_map.transpose(-1, -2).contiguous())
+    q2k_idx, q2k_num = _map_to_index(block_map)
+    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
 
     from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
         triton_block_sparse_attn_backward,
@@ -182,7 +179,7 @@ def block_sparse_attn_sm90(
     k_padded = k_padded.contiguous()
     v_padded = v_padded.contiguous()
     block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index_torch(block_map)
+    q2k_idx, q2k_num = _map_to_index(block_map)
 
     o_padded, lse_padded = block_sparse_fwd(
         q_padded, k_padded, v_padded, q2k_idx, q2k_num, variable_block_sizes.int()
@@ -224,7 +221,7 @@ def block_sparse_attn_backward_sm90(
 
     grad_output_padded = grad_output_padded.contiguous()
     block_map = block_map.to(torch.bool)
-    k2q_idx, k2q_num = _map_to_index_torch(block_map.transpose(-1, -2).contiguous())
+    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
 
     dq, dk, dv = block_sparse_bwd(
         q_padded,

fastvideo-kernel/python/fastvideo_kernel/version.py

@@ -1 +1 @@
-__version__ = "0.2.4"
+__version__ = "0.2.5"

fastvideo/configs/models/__init__.py

@@ -2,5 +2,16 @@ from fastvideo.configs.models.base import ModelConfig
 from fastvideo.configs.models.dits.base import DiTConfig
 from fastvideo.configs.models.encoders.base import EncoderConfig
 from fastvideo.configs.models.vaes.base import VAEConfig
+from fastvideo.configs.models.audio import (LTX2AudioDecoderConfig,
+                                            LTX2AudioEncoderConfig,
+                                            LTX2VocoderConfig)
 
-__all__ = ["ModelConfig", "VAEConfig", "DiTConfig", "EncoderConfig"]
+__all__ = [
+    "ModelConfig",
+    "VAEConfig",
+    "DiTConfig",
+    "EncoderConfig",
+    "LTX2AudioEncoderConfig",
+    "LTX2AudioDecoderConfig",
+    "LTX2VocoderConfig",
+]

fastvideo/configs/models/audio/__init__.py

@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from fastvideo.configs.models.audio.ltx2_audio_vae import (
+    LTX2AudioDecoderConfig,
+    LTX2AudioEncoderConfig,
+    LTX2VocoderConfig,
+)
+
+__all__ = [
+    "LTX2AudioEncoderConfig",
+    "LTX2AudioDecoderConfig",
+    "LTX2VocoderConfig",
+]

fastvideo/configs/models/audio/ltx2_audio_vae.py

@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+LTX-2 audio VAE and vocoder configuration.
+"""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.base import ArchConfig, ModelConfig
+
+
+@dataclass
+class LTX2AudioArchConfig(ArchConfig):
+    architectures: list[str] = field(default_factory=list)
+
+
+@dataclass
+class LTX2AudioEncoderConfig(ModelConfig):
+    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
+        architectures=["LTX2AudioEncoder"]))
+
+
+@dataclass
+class LTX2AudioDecoderConfig(ModelConfig):
+    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
+        architectures=["LTX2AudioDecoder"]))
+
+
+@dataclass
+class LTX2VocoderConfig(ModelConfig):
+    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
+        architectures=["LTX2Vocoder"]))

fastvideo/configs/models/dits/__init__.py

@@ -3,11 +3,13 @@ from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
 from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
+from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
 from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
+from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 
 __all__ = [
     "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
     "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig"
+    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
 ]

fastvideo/configs/models/dits/hunyuanvideo15.py

@@ -26,6 +26,9 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
 
     param_names_mapping: dict = field(
         default_factory=lambda: {
+            r"^cond_type_embed\.(.*)$":
+            r"cond_type_embed.\1",
+
             # 1. context_embedder.time_text_embed submodules (specific rules, applied first):
             r"^context_embedder\.time_text_embed\.timestep_embedder\.linear_1\.(.*)$":
             r"txt_in.t_embedder.mlp.fc_in.\1",
@@ -55,6 +58,16 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
             r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
             r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm_out\.linear\.(.*)$":
             r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.self_attn_qkv\.(.*)$":
+            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
+            r"^image_embedder\.linear_1\.(.*)$":
+            r"image_embedder.linear_1.\1",
+            r"^image_embedder\.linear_2\.(.*)$":
+            r"image_embedder.linear_2.\1",
+            r"^image_embedder\.norm_in\.(.*)$":
+            r"image_embedder.norm_in.\1",
+            r"^image_embedder\.norm_out\.(.*)$":
+            r"image_embedder.norm_out.\1",
 
             # 2. txt_in_2 mapping:
             r"^context_embedder_2\.(.*)$":
@@ -144,6 +157,12 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
     exclude_lora_layers: list[str] = field(
         default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
 
+    # Causal HunyuanVideo1.5
+    local_attn_size: int = -1  # Window size for temporal local attention (-1 indicates global attention)
+    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
+    num_frames_per_block: int = 3
+    sliding_window_num_frames: int = 31
+
     def __post_init__(self):
         super().__post_init__()
         self.hidden_size: int = self.attention_head_dim * self.num_attention_heads

fastvideo/configs/models/dits/hyworld.py

@@ -0,0 +1,207 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_double_block(n: str, m) -> bool:
+    return "double" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_single_block(n: str, m) -> bool:
+    return "single" in n and str.isdigit(n.split(".")[-1])
+
+
+# def is_refiner_block(n: str, m) -> bool:
+#     return "refiner" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_txt_in(n: str, m) -> bool:
+    return n.split(".")[-1] == "txt_in"
+
+
+@dataclass
+class HYWorldArchConfig(DiTArchConfig):
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda: [is_double_block, is_single_block])
+
+    _compile_conditions: list = field(
+        default_factory=lambda: [is_double_block, is_single_block, is_txt_in])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            # 1. txt_in submodules (text embedder, refiner blocks):
+            r"^txt_in\.t_embedder\.mlp\.0\.(.*)$":
+            r"txt_in.t_embedder.mlp.fc_in.\1",
+            r"^txt_in\.t_embedder\.mlp\.2\.(.*)$":
+            r"txt_in.t_embedder.mlp.fc_out.\1",
+            r"^txt_in\.c_embedder\.linear_1\.(.*)$":
+            r"txt_in.c_embedder.fc_in.\1",
+            r"^txt_in\.c_embedder\.linear_2\.(.*)$":
+            r"txt_in.c_embedder.fc_out.\1",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.norm1.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm2\.(.*)$":
+            r"txt_in.refiner_blocks.\1.norm2.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_qkv\.(.*)$":
+            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_proj\.(.*)$":
+            r"txt_in.refiner_blocks.\1.self_attn_proj.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc2\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.adaLN_modulation\.1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
+
+            # 2. time_in mappings (HYWorld uses TimestepEmbedder directly,
+            #    but FastVideo model inherits HunyuanVideo15TimeEmbedding with timestep_embedder):
+            r"^time_in\.mlp\.0\.(.*)$":
+            r"time_in.timestep_embedder.mlp.fc_in.\1",
+            r"^time_in\.mlp\.2\.(.*)$":
+            r"time_in.timestep_embedder.mlp.fc_out.\1",
+
+            # 3. action_in mappings:
+            r"^action_in\.mlp\.0\.(.*)$":
+            r"action_in.mlp.fc_in.\1",
+            r"^action_in\.mlp\.2\.(.*)$":
+            r"action_in.mlp.fc_out.\1",
+
+            # 4. byt5_in -> txt_in_2 mappings:
+            r"^byt5_in\.layernorm\.(.*)$":
+            r"txt_in_2.norm.\1",
+            r"^byt5_in\.fc1\.(.*)$":
+            r"txt_in_2.linear_1.\1",
+            r"^byt5_in\.fc2\.(.*)$":
+            r"txt_in_2.linear_2.\1",
+            r"^byt5_in\.fc3\.(.*)$":
+            r"txt_in_2.linear_3.\1",
+
+            # 5. cond_type_embedding -> cond_type_embed:
+            r"^cond_type_embedding\.(.*)$":
+            r"cond_type_embed.\1",
+
+            # 6. vision_in -> image_embedder mappings:
+            r"^vision_in\.proj\.0\.(.*)$":
+            r"image_embedder.norm_in.\1",
+            r"^vision_in\.proj\.1\.(.*)$":
+            r"image_embedder.linear_1.\1",
+            r"^vision_in\.proj\.3\.(.*)$":
+            r"image_embedder.linear_2.\1",
+            r"^vision_in\.proj\.4\.(.*)$":
+            r"image_embedder.norm_out.\1",
+
+            # 7. double_blocks mapping:
+            r"^double_blocks\.(\d+)\.img_attn_q\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 0, 3),
+            r"^double_blocks\.(\d+)\.img_attn_k\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 1, 3),
+            r"^double_blocks\.(\d+)\.img_attn_v\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 2, 3),
+            r"^double_blocks\.(\d+)\.txt_attn_q\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 0, 3),
+            r"^double_blocks\.(\d+)\.txt_attn_k\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 1, 3),
+            r"^double_blocks\.(\d+)\.txt_attn_v\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 2, 3),
+            r"^double_blocks\.(\d+)\.img_mlp\.fc1\.(.*)$":
+            r"double_blocks.\1.img_mlp.fc_in.\2",
+            r"^double_blocks\.(\d+)\.img_mlp\.fc2\.(.*)$":
+            r"double_blocks.\1.img_mlp.fc_out.\2",
+            r"^double_blocks\.(\d+)\.txt_mlp\.fc1\.(.*)$":
+            r"double_blocks.\1.txt_mlp.fc_in.\2",
+            r"^double_blocks\.(\d+)\.txt_mlp\.fc2\.(.*)$":
+            r"double_blocks.\1.txt_mlp.fc_out.\2",
+
+            # 8. Final layer mapping:
+            r"^final_layer\.adaLN_modulation\.1\.(.*)$":
+            r"final_layer.adaLN_modulation.linear.\1",
+        })
+
+    # Reverse mapping for saving checkpoints: custom -> hf
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Parameters from HY-WorldPlay config.json (loaded from checkpoint)
+    patch_size: list | tuple | int = field(default_factory=lambda: [1, 1, 1])
+    # Base latent channels - will be expanded in __post_init__ if concat_condition=True
+    in_channels: int = 32
+    concat_condition: bool = True
+    out_channels: int = 32
+    hidden_size: int = 2048
+    heads_num: int = 16
+    mlp_width_ratio: float = 4.0
+    mlp_act_type: str = "gelu_tanh"
+    mm_double_blocks_depth: int = 54
+    mm_single_blocks_depth: int = 0
+    rope_dim_list: list | tuple = field(default_factory=lambda: [16, 56, 56])
+    qkv_bias: bool = True
+    qk_norm: bool | str = True
+    qk_norm_type: str = "rms"
+    guidance_embed: bool = False
+    use_meanflow: bool = False
+    text_projection: str = "single_refiner"
+    use_attention_mask: bool = True
+    text_states_dim: int = 3584
+    text_states_dim_2: int | None = None
+    text_pool_type: str | None = None
+    rope_theta: float = 256.0
+    attn_mode: str = "flash"
+    attn_param: str | None = None
+    glyph_byT5_v2: bool = True
+    vision_projection: str = "linear"
+    vision_states_dim: int = 1152
+    is_reshape_temporal_channels: bool = False
+    use_cond_type_embedding: bool = True
+    ideal_resolution: str = "480p"
+    ideal_task: str = "i2v"
+    task_type: str = "i2v"
+    exclude_lora_layers: list[str] = field(
+        default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
+
+    def __post_init__(self):
+        super().__post_init__()
+        # Convert HY-WorldPlay naming to FastVideo naming conventions
+        self.num_attention_heads: int = self.heads_num
+        self.attention_head_dim: int = self.hidden_size // self.heads_num
+        self.num_layers: int = self.mm_double_blocks_depth
+        self.num_single_layers: int = self.mm_single_blocks_depth
+        self.num_refiner_layers: int = 2  # Default for HYWorld
+        self.mlp_ratio: float = float(self.mlp_width_ratio)
+        self.text_embed_dim: int = self.text_states_dim
+        self.text_embed_2_dim: int = self.text_states_dim_2 if self.text_states_dim_2 else 1472
+        self.image_embed_dim: int = self.vision_states_dim
+        self.rope_axes_dim: tuple[int, ...] = tuple(self.rope_dim_list)
+        self.num_channels_latents: int = self.out_channels
+        self.target_size: int = 640
+
+        # Handle concat_condition: when True, actual in_channels = base * 2 + 1
+        # (base latent + condition latent + mask channel)
+        # config.json has base in_channels (32), but img_in needs full (65)
+        if self.concat_condition and self.in_channels == 32:
+            if self.is_reshape_temporal_channels:
+                self.in_channels = self.in_channels + self.in_channels // 2 + 1
+            else:
+                self.in_channels = self.in_channels * 2 + 1  # 32 * 2 + 1 = 65
+
+        # Handle patch_size (can be list/tuple or int)
+        if isinstance(self.patch_size, list | tuple):
+            self.patch_size_t: int = self.patch_size[0]
+            # assume square patch size for height and width
+            patch_size_hw: int = self.patch_size[1]
+            object.__setattr__(self, 'patch_size', patch_size_hw)
+        else:
+            self.patch_size_t = 1
+
+        # Convert qk_norm to string format
+        if isinstance(self.qk_norm, bool):
+            if self.qk_norm:
+                self.qk_norm = "rms_norm" if self.qk_norm_type == "rms" else self.qk_norm_type
+            else:
+                self.qk_norm = "none"
+
+
+@dataclass
+class HYWorldConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=HYWorldArchConfig)
+
+    prefix: str = "HYWorld"

fastvideo/configs/models/dits/ltx2.py

@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+LTX-2 Transformer configuration for native FastVideo integration.
+"""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_ltx2_blocks(name: str, _module) -> bool:
+    """FSDP shard condition for LTX-2 transformer blocks."""
+    return "transformer_blocks" in name
+
+
+@dataclass
+class LTX2VideoArchConfig(DiTArchConfig):
+    """Architecture configuration for LTX-2 video transformer."""
+
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda: [is_ltx2_blocks])
+    _compile_conditions: list = field(default_factory=lambda: [is_ltx2_blocks])
+
+    # Parameter name mapping for weight conversion (hf/comfy -> FastVideo)
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^model\.diffusion_model\.(.*)$": r"model.\1",
+            r"^diffusion_model\.(.*)$": r"model.\1",
+            r"^model\.(.*)$": r"model.\1",
+            r"^(.*)$": r"model.\1",
+        })
+
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+    lora_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Core transformer settings (defaults from LTX-2 metadata)
+    num_attention_heads: int = 32
+    attention_head_dim: int = 128
+    num_layers: int = 48
+    cross_attention_dim: int = 4096
+    caption_channels: int = 3840
+    norm_eps: float = 1e-6
+    attention_type: str = "default"
+    rope_type: str = "split"
+    double_precision_rope: bool = True
+
+    positional_embedding_theta: float = 10000.0
+    positional_embedding_max_pos: list[int] = field(
+        default_factory=lambda: [20, 2048, 2048])
+    timestep_scale_multiplier: int = 1000
+    use_middle_indices_grid: bool = True
+
+    # Patchification (video-only path)
+    patch_size: tuple[int, int, int] = (1, 1, 1)
+    num_channels_latents: int = 128
+    in_channels: int | None = None
+    out_channels: int | None = None
+
+    # Audio defaults (reserved for joint AV ports)
+    audio_num_attention_heads: int = 32
+    audio_attention_head_dim: int = 64
+    audio_in_channels: int = 128
+    audio_out_channels: int = 128
+    audio_cross_attention_dim: int = 2048
+    audio_positional_embedding_max_pos: list[int] = field(
+        default_factory=lambda: [20])
+    av_ca_timestep_scale_multiplier: int = 1
+
+    def __post_init__(self):
+        super().__post_init__()
+        patch_volume = self.patch_size[0] * self.patch_size[
+            1] * self.patch_size[2]
+        if self.in_channels is None:
+            self.in_channels = self.num_channels_latents * patch_volume
+        if self.out_channels is None:
+            self.out_channels = self.in_channels
+
+
+@dataclass
+class LTX2VideoConfig(DiTConfig):
+    """Main configuration for LTX-2 transformer."""
+
+    arch_config: DiTArchConfig = field(default_factory=LTX2VideoArchConfig)
+    prefix: str = "ltx2"

fastvideo/configs/models/dits/matrixgame.py

@@ -8,8 +8,8 @@ class MatrixGameWanVideoArchConfig(WanVideoArchConfig):
     # because MatrixGame checkpoints already have patch_embedding.proj format
     param_names_mapping: dict = field(
         default_factory=lambda: {
-            # Removed: r"^patch_embedding\.(.*)$": r"patch_embedding.proj.\1"
-            # because checkpoint already has correct format
+            r"^patch_embedding\.(?!proj\.)(.*)$":
+            r"patch_embedding.proj.\1",
             r"^condition_embedder\.text_embedder\.linear_1\.(.*)$":
             r"condition_embedder.text_embedder.fc_in.\1",
             r"^condition_embedder\.text_embedder\.linear_2\.(.*)$":

fastvideo/configs/models/encoders/__init__.py

@@ -7,11 +7,14 @@ from fastvideo.configs.models.encoders.clip import (
 from fastvideo.configs.models.encoders.llama import LlamaConfig
 from fastvideo.configs.models.encoders.t5 import T5Config, T5LargeConfig
 from fastvideo.configs.models.encoders.qwen2_5 import Qwen2_5_VLConfig
+from fastvideo.configs.models.encoders.siglip import SiglipVisionConfig
 from fastvideo.configs.models.encoders.reason1 import Reason1ArchConfig, Reason1Config
+from fastvideo.configs.models.encoders.gemma import LTX2GemmaConfig
 
 __all__ = [
     "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
     "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
     "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig",
-    "Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config"
+    "Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config", "LTX2GemmaConfig",
+    "SiglipVisionConfig"
 ]

fastvideo/configs/models/encoders/clip.py

@@ -87,10 +87,10 @@ class CLIPVisionConfig(ImageEncoderConfig):
     arch_config: ImageEncoderArchConfig = field(
         default_factory=CLIPVisionArchConfig)
 
-    num_hidden_layers_override: int | None = None
+    num_hidden_layers_override: int | None = 31
     require_post_norm: bool | None = None
-    enable_scale: bool = True
-    is_causal: bool = True
+    enable_scale: bool = False
+    is_causal: bool = False
     prefix: str = "clip"
 
 

fastvideo/configs/models/encoders/gemma.py

@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.encoders.base import (
+    TextEncoderArchConfig,
+    TextEncoderConfig,
+)
+
+
+@dataclass
+class LTX2GemmaArchConfig(TextEncoderArchConfig):
+    architectures: list[str] = field(
+        default_factory=lambda: ["LTX2GemmaTextEncoderModel"])
+    hidden_size: int = 3840
+    num_hidden_layers: int = 48
+    num_attention_heads: int = 30
+    text_len: int = 1024
+    pad_token_id: int = 0
+    eos_token_id: int = 2
+
+    gemma_model_path: str = ""
+    gemma_dtype: str = "bfloat16"
+    padding_side: str = "left"
+
+    feature_extractor_in_features: int = 3840 * 49
+    feature_extractor_out_features: int = 3840
+
+    connector_num_attention_heads: int = 30
+    connector_attention_head_dim: int = 128
+    connector_num_layers: int = 2
+    connector_positional_embedding_theta: float = 10000.0
+    connector_positional_embedding_max_pos: list[int] = field(
+        default_factory=lambda: [4096])
+    connector_rope_type: str = "split"
+    connector_double_precision_rope: bool = False
+    connector_num_learnable_registers: int | None = 128
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        self.tokenizer_kwargs["padding"] = "max_length"
+
+
+@dataclass
+class LTX2GemmaConfig(TextEncoderConfig):
+    arch_config: TextEncoderArchConfig = field(
+        default_factory=LTX2GemmaArchConfig)
+
+    prefix: str = "ltx2_gemma"

fastvideo/configs/models/encoders/siglip.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+"""SigLIP vision encoder configuration for FastVideo."""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.encoders.base import (ImageEncoderArchConfig,
+                                                    ImageEncoderConfig)
+
+
+@dataclass
+class SiglipVisionArchConfig(ImageEncoderArchConfig):
+    """Architecture configuration for SigLIP vision encoder.
+    
+    Fields match the config.json from HuggingFace SigLIP checkpoints.
+    """
+
+    # From config.json
+    architectures: list[str] = field(
+        default_factory=lambda: ["SiglipVisionModel"])
+    attention_dropout: float = 0.0
+    dtype: str | None = None
+    hidden_act: str = "gelu_pytorch_tanh"
+    hidden_size: int = 1152
+    image_size: int = 384
+    intermediate_size: int = 4304
+    layer_norm_eps: float = 1e-6
+    model_type: str = "siglip_vision_model"
+    num_attention_heads: int = 16
+    num_channels: int = 3
+    num_hidden_layers: int = 27
+    patch_size: int = 14
+
+    # FastVideo specific - QKV fusion mapping
+    stacked_params_mapping: list = field(default_factory=lambda: [
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+    ])
+
+
+@dataclass
+class SiglipVisionConfig(ImageEncoderConfig):
+    """Configuration for SigLIP vision encoder."""
+
+    arch_config: ImageEncoderArchConfig = field(
+        default_factory=SiglipVisionArchConfig)
+
+    # FastVideo specific
+    num_hidden_layers_override: int | None = None
+    require_post_norm: bool | None = None
+    enable_scale: bool = True
+    is_causal: bool = False
+    prefix: str = "siglip"

fastvideo/configs/models/vaes/__init__.py

@@ -2,6 +2,7 @@ from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
 from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
+from fastvideo.configs.models.vaes.ltx2vae import LTX2VAEConfig
 from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
 
@@ -12,4 +13,5 @@ __all__ = [
     "CosmosVAEConfig",
     "Cosmos25VAEConfig",
     "Hunyuan15VAEConfig",
+    "LTX2VAEConfig",
 ]

fastvideo/configs/models/vaes/ltx2vae.py

@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+LTX-2 VAE configuration.
+"""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
+
+
+@dataclass
+class LTX2VAEArchConfig(VAEArchConfig):
+    # Mirrors LTX-2 safetensors metadata config under "vae"
+    _class_name: str = "CausalVideoAutoencoder"
+    dims: int = 3
+    in_channels: int = 3
+    out_channels: int = 3
+    latent_channels: int = 128
+    encoder_blocks: list = field(default_factory=list)
+    decoder_blocks: list = field(default_factory=list)
+    patch_size: int = 4
+    norm_layer: str = "pixel_norm"
+    latent_log_var: str = "uniform"
+    encoder_spatial_padding_mode: str = "zeros"
+    decoder_spatial_padding_mode: str = "reflect"
+    causal_decoder: bool = False
+    timestep_conditioning: bool = True
+    use_quant_conv: bool = False
+    scaling_factor: float = 1.0
+    normalize_latent_channels: bool = False
+
+    # Match FastVideo naming for compression ratios (LTX-2 default)
+    temporal_compression_ratio: int = 8
+    spatial_compression_ratio: int = 32
+
+
+@dataclass
+class LTX2VAEConfig(VAEConfig):
+    arch_config: VAEArchConfig = field(default_factory=LTX2VAEArchConfig)
+
+    # LTX-2 tiling defaults (match ltx_core.video_vae.TilingConfig.default()).
+    ltx2_spatial_tile_size_in_pixels: int = 512
+    ltx2_spatial_tile_overlap_in_pixels: int = 64
+    ltx2_temporal_tile_size_in_frames: int = 64
+    ltx2_temporal_tile_overlap_in_frames: int = 24

fastvideo/configs/pipelines/__init__.py

@@ -4,6 +4,8 @@ from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
+from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
 from fastvideo.configs.pipelines.registry import (
     get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
@@ -16,5 +18,6 @@ __all__ = [
     "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
     "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
     "WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
-    "CosmosConfig", "Cosmos25Config", "get_pipeline_config_cls_from_name"
+    "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig", "HYWorldConfig",
+    "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/hunyuan15.py

@@ -127,13 +127,29 @@ class Hunyuan15T2V480PConfig(PipelineConfig):
     vae_tiling: bool = True
 
     def __post_init__(self):
-        self.vae_config.load_encoder = False
+        self.vae_config.load_encoder = True
         self.vae_config.load_decoder = True
 
 
+@dataclass
+class Hunyuan15DistilledI2V480PConfig(Hunyuan15T2V480PConfig):
+    flow_shift: int = 7
+
+
 @dataclass
 class Hunyuan15T2V720PConfig(Hunyuan15T2V480PConfig):
     """Base configuration for HunYuan pipeline architecture."""
 
     # HunyuanConfig-specific parameters with defaults
     flow_shift: int = 9
+
+
+@dataclass
+class SelfForcingHunyuan15T2V480PConfig(Hunyuan15T2V480PConfig):
+    flow_shift: int = 5
+    is_causal: bool = True
+    # dmd_denoising_steps: list[int] | None = field(
+    #     default_factory=lambda: [1000, 750, 500, 250])
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 875, 750, 625, 500, 375, 250, 125])
+    warp_denoising_step: bool = True

fastvideo/configs/pipelines/hyworld.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig
+from fastvideo.configs.models.dits import HYWorldConfig as HYWorldDiTConfig
+from fastvideo.configs.models.encoders import SiglipVisionConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig
+
+
+@dataclass
+class HYWorldConfig(Hunyuan15T2V480PConfig):
+    """Base configuration for HYWorld pipeline architecture."""
+
+    # HYWorldConfig-specific parameters with defaults
+    dit_config: DiTConfig = field(default_factory=HYWorldDiTConfig)
+
+    # SigLIP image encoder for I2V
+    image_encoder_config: EncoderConfig = field(
+        default_factory=SiglipVisionConfig)
+    image_encoder_precision: str = "fp16"
+    # vae_precision: str = "fp32"
+
+    # Text encoding
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("fp16", "fp32"))
+
+    def __post_init__(self):
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/ltx2.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+from collections.abc import Callable
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
+                                      LTX2AudioDecoderConfig, LTX2VocoderConfig,
+                                      VAEConfig)
+from fastvideo.configs.models.dits import LTX2VideoConfig
+from fastvideo.configs.models.encoders import BaseEncoderOutput, LTX2GemmaConfig
+from fastvideo.configs.models.vaes import LTX2VAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
+
+
+def ltx2_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
+    return outputs.last_hidden_state
+
+
+@dataclass
+class LTX2T2VConfig(PipelineConfig):
+    """Configuration for LTX-2 T2V pipeline."""
+
+    dit_config: DiTConfig = field(default_factory=LTX2VideoConfig)
+    vae_config: VAEConfig = field(default_factory=LTX2VAEConfig)
+    vae_tiling: bool = True
+    vae_sp: bool = False
+
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (LTX2GemmaConfig(), ))
+    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
+        default_factory=lambda: (preprocess_text, ))
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
+                                  ...] = field(default_factory=lambda:
+                                               (ltx2_postprocess_text, ))
+
+    dit_precision: str = "bf16"
+    vae_precision: str = "bf16"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("bf16", ))
+
+    audio_decoder_config: ModelConfig = field(
+        default_factory=LTX2AudioDecoderConfig)
+    vocoder_config: ModelConfig = field(default_factory=LTX2VocoderConfig)
+    audio_decoder_precision: str = "bf16"
+    vocoder_precision: str = "bf16"
+
+    def __post_init__(self) -> None:
+        self.vae_config.load_encoder = False
+        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/registry.py

@@ -8,7 +8,9 @@ from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
-from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
+from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig, SelfForcingHunyuan15T2V480PConfig, Hunyuan15DistilledI2V480PConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
 from fastvideo.configs.pipelines.turbodiffusion import (
@@ -33,10 +35,15 @@ logger = init_logger(__name__)
 PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
     "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
+    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
+    SelfForcingHunyuan15T2V480PConfig,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
+    Hunyuan15DistilledI2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15T2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15T2V720PConfig,
+    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorldConfig,
     "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers": WANV2VConfig,
@@ -64,6 +71,9 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/LongCat-Video-T2V-Diffusers": LongCatT2V480PConfig,
     "FastVideo/LongCat-Video-I2V-Diffusers": LongCatT2V480PConfig,
     "FastVideo/LongCat-Video-VC-Diffusers": LongCatT2V480PConfig,
+    # LTX-2 models
+    "Lightricks/LTX-2": LTX2T2VConfig,
+    "converted/ltx2_diffusers": LTX2T2VConfig,
     # TurboDiffusion models
     "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers": TurboDiffusionT2V_1_3B_Config,
     "loayrashid/TurboWan2.1-T2V-14B-Diffusers": TurboDiffusionT2V_14B_Config,
@@ -83,6 +93,8 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
+    "hyworld":
+    lambda id: "hyworld" in id.lower(),
     "matrixgame":
     lambda id: "matrix-game" in id.lower() or "matrixgame" in id.lower(),
     "wanpipeline":
@@ -102,6 +114,8 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "cosmos25" in id.lower(),
     "turbodiffusion":
     lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
+    "ltx2":
+    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -116,6 +130,8 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "matrixgame": MatrixGameI2V480PConfig,
     "hunyuan15":
     Hunyuan15T2V480PConfig,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
+    "hyworld":
+    HYWorldConfig,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V480PConfig,
@@ -123,6 +139,7 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
     "stepvideo": StepVideoT2VConfig,
     "turbodiffusion": TurboDiffusionT2V_1_3B_Config,
+    "ltx2": LTX2T2VConfig,
     # Other fallbacks by architecture
 }
 

fastvideo/configs/pipelines/wan.py

@@ -41,7 +41,7 @@ class WanT2V480PConfig(PipelineConfig):
     vae_sp: bool = False
 
     # Denoising stage
-    flow_shift: float | None = 3.0
+    flow_shift: float | None = 5.0
 
     # Text encoding stage
     text_encoder_configs: tuple[EncoderConfig, ...] = field(
@@ -176,6 +176,8 @@ class SelfForcingWanT2V480PConfig(WanT2V480PConfig):
     flow_shift: float | None = 5.0
     dmd_denoising_steps: list[int] | None = field(
         default_factory=lambda: [1000, 750, 500, 250])
+    # dmd_denoising_steps: list[int] | None = field(
+    #     default_factory=lambda: [1000, 680, 360, 180])
     warp_denoising_step: bool = True
 
 
@@ -196,6 +198,12 @@ class SelfForcingWan2_2_T2V480PConfig(Wan2_2_T2V_A14B_Config):
 # =============================================
 # ============= Matrix Game ===================
 # =============================================
+@dataclass
+class MatrixGameBaseI2V480PConfig(WanI2V480PConfig):
+    dit_config: DiTConfig = field(default_factory=MatrixGameWanVideoConfig)
+    flow_shift: float | None = 5.0
+
+
 @dataclass
 class MatrixGameI2V480PConfig(WanI2V480PConfig):
     dit_config: DiTConfig = field(default_factory=MatrixGameWanVideoConfig)

fastvideo/configs/sample/hunyuan15.py

@@ -17,11 +17,24 @@ class Hunyuan15_480P_SamplingParam(SamplingParam):
     guidance_scale: float = 6.0
     prompt_attention_mask: list = field(default_factory=list)
     negative_attention_mask: list = field(default_factory=list)
-    sigmas: list[float] | None = field(
-        default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+    # sigmas: list[float] | None = field(
+    #     default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
 
     negative_prompt: str = ""
 
+    def __post_init__(self):
+        super().__post_init__()
+        self.sigmas = list(
+            np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])
+
+
+@dataclass
+class Hunyuan15_480P_Distilled_SamplingParam(Hunyuan15_480P_SamplingParam):
+    num_inference_steps: int = 8
+    fps: int = 24
+
+    guidance_scale: float = 1.0
+
 
 @dataclass
 class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):

fastvideo/configs/sample/hyworld.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+import numpy as np
+
+
+@dataclass
+class HYWorld_SamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 125
+    height: int = 480
+    width: int = 832
+    fps: int = 24
+
+    # Camera trajectory: pose string (e.g., 'w-31' means generating [1 + 31] latents) or JSON file path
+    pose: str = 'w-31'
+
+    guidance_scale: float = 6.0
+    prompt_attention_mask: list = field(default_factory=list)
+    negative_attention_mask: list = field(default_factory=list)
+    sigmas: list[float] | None = field(
+        default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+
+    negative_prompt: str = ""

fastvideo/configs/sample/ltx2.py

@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class LTX2SamplingParam(SamplingParam):
+    """Default sampling parameters for LTX-2 distilled T2V.
+    """
+
+    seed: int = 10
+    num_frames: int = 121
+    height: int = 1024
+    width: int = 1536
+    fps: int = 24
+    num_inference_steps: int = 8
+    guidance_scale: float = 1.0
+    # No default negative_prompt for distilled models
+    negative_prompt: str = ""

fastvideo/configs/sample/registry.py

@@ -5,11 +5,13 @@ from typing import Any
 
 from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                               HunyuanSamplingParam)
-from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam
+from fastvideo.configs.sample.hyworld import HYWorld_SamplingParam
+from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam, Hunyuan15_480P_Distilled_SamplingParam
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
 
 from fastvideo.configs.sample.cosmos import Cosmos_Predict2_2B_Video2World_SamplingParam
 from fastvideo.configs.sample.cosmos2_5 import Cosmos_Predict2_5_2B_Diffusers_SamplingParam
+from fastvideo.configs.sample.ltx2 import LTX2SamplingParam
 
 # isort: off
 from fastvideo.configs.sample.wan import (
@@ -40,48 +42,41 @@ from fastvideo.utils import (maybe_download_model_index,
 logger = init_logger(__name__)
 # Registry maps specific model weights to their config classes
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
-    "FastVideo/FastHunyuan-diffusers":
-    FastHunyuanSamplingParam,
-    "hunyuanvideo-community/HunyuanVideo":
-    HunyuanSamplingParam,
+    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
+    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
+    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
+    Hunyuan15_480P_SamplingParam,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
+    Hunyuan15_480P_Distilled_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15_480P_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15_720P_SamplingParam,
-    "FastVideo/stepvideo-t2v-diffusers":
-    StepVideoT2VSamplingParam,
+    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorld_SamplingParam,
+    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
 
     # Wan2.1
-    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers":
-    WanT2V_1_3B_SamplingParam,
-    "Wan-AI/Wan2.1-T2V-14B-Diffusers":
-    WanT2V_14B_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers":
-    WanI2V_14B_480P_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers":
-    WanI2V_14B_720P_SamplingParam,
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
+    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
     Wan2_1_Fun_1_3B_InP_SamplingParam,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers":
     Wan2_1_Fun_1_3B_Control_SamplingParam,
 
     # Wan2.2
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers":
-    Wan2_2_TI2V_5B_SamplingParam,
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
     "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers":
     Wan2_2_TI2V_5B_SamplingParam,
-    "Wan-AI/Wan2.2-T2V-A14B-Diffusers":
-    Wan2_2_T2V_A14B_SamplingParam,
-    "Wan-AI/Wan2.2-I2V-A14B-Diffusers":
-    Wan2_2_I2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
 
     # FastWan2.1
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers":
-    FastWanT2V480P_SamplingParam,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480P_SamplingParam,
 
     # FastWan2.2
-    "FastVideo/FastWan2.2-TI2V-5B-Diffusers":
-    Wan2_2_TI2V_5B_SamplingParam,
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
 
     # Causal Self-Forcing Wan2.1
     "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers":
@@ -102,12 +97,9 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
 
     # MatrixGame2.0 models
-    "FastVideo/Matrix-Game-2.0-Base-Diffusers":
-    MatrixGame2_SamplingParam,
-    "FastVideo/Matrix-Game-2.0-GTA-Diffusers":
-    MatrixGame2_SamplingParam,
-    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers":
-    MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGame2_SamplingParam,
 
     # TurboDiffusion models
     "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers":
@@ -117,6 +109,10 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "loayrashid/TurboWan2.2-I2V-A14B-Diffusers":
     TurboDiffusionI2V_A14B_SamplingParam,
 
+    # LTX-2 models
+    "Lightricks/LTX-2": LTX2SamplingParam,
+    "FastVideo/LTX2-Distilled-Diffusers": LTX2SamplingParam,
+
     # Add other specific weight variants
 }
 
@@ -126,6 +122,8 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
+    "hyworld":
+    lambda id: "hyworld" in id.lower(),
     "wanpipeline":
     lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo":
@@ -144,6 +142,8 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "cosmos2_5" in id.lower(),
     "cosmos":
     lambda id: "cosmos" in id.lower() and "2_5" not in id.lower(),
+    "ltx2":
+    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -153,6 +153,8 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
     "hunyuan15":
     Hunyuan15_480P_SamplingParam,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
+    "hyworld":
+    HYWorld_SamplingParam,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V_1_3B_SamplingParam,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V_14B_480P_SamplingParam,
@@ -164,13 +166,13 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     TurboDiffusionT2V_1_3B_SamplingParam,  # Default to T2V for fallback
     "cosmos25": Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
     "cosmos": Cosmos_Predict2_2B_Video2World_SamplingParam,
+    "ltx2": LTX2SamplingParam,
     # Other fallbacks by architecture
 }
 
 
 def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
     """Get the appropriate sampling param for specific pretrained weights."""
-
     # First try exact match for specific weights
     if pipeline_name_or_path in SAMPLING_PARAM_REGISTRY:
         return SAMPLING_PARAM_REGISTRY[pipeline_name_or_path]

fastvideo/configs/sample/wan.py

@@ -15,7 +15,8 @@ class WanT2V_1_3B_SamplingParam(SamplingParam):
 
     # Denoising stage
     guidance_scale: float = 3.0
-    negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+    negative_prompt: str | None = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+    # negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
     num_inference_steps: int = 50
 
     teacache_params: WanTeaCacheParams = field(

fastvideo/dataset/__init__.py

@@ -10,7 +10,7 @@ from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
 from fastvideo.dataset.validation_dataset import ValidationDataset
 
 
-def getdataset(args) -> VideoCaptionMergedDataset:
+def getdataset(args, start_idx: int = 0) -> VideoCaptionMergedDataset:
     if args.do_temporal_sample:
         temporal_sample = TemporalRandomCrop(args.num_frames)  # 16 x
     else:
@@ -36,6 +36,7 @@ def getdataset(args) -> VideoCaptionMergedDataset:
                                      transform=transform,
                                      temporal_sample=temporal_sample,
                                      transform_topcrop=transform_topcrop,
+                                     start_idx=start_idx,
                                      seed=args.seed)
                                     
 

fastvideo/dataset/dataloader/record_schema.py

@@ -165,6 +165,25 @@ def ode_text_only_record_creator(
         "trajectory_timesteps_dtype": str(trajectory_timesteps.dtype),
     })
 
+    # if text_embedding_2 is not None:
+    #     record.update({
+    #         "text_embedding_2_bytes": text_embedding_2.tobytes(),
+    #         "text_embedding_2_shape": list(text_embedding_2.shape),
+    #         "text_embedding_2_dtype": str(text_embedding_2.dtype),
+    #     })
+    # if text_mask is not None:
+    #     record.update({
+    #         "text_mask_bytes": text_mask.tobytes(),
+    #         "text_mask_shape": list(text_mask.shape),
+    #         "text_mask_dtype": str(text_mask.dtype),
+    #     })
+    # if text_mask_2 is not None:
+    #     record.update({
+    #         "text_mask_2_bytes": text_mask_2.tobytes(),
+    #         "text_mask_2_shape": list(text_mask_2.shape),
+    #         "text_mask_2_dtype": str(text_mask_2.dtype),
+    #     })
+        
     return record
 
 
@@ -187,4 +206,4 @@ def text_only_record_creator(text_name: str, text_embedding: np.ndarray,
         "text_embedding_dtype": str(text_embedding.dtype),
         "caption": caption,
     }
-    return record
+    return record

fastvideo/dataset/dataloader/schema.py

@@ -90,6 +90,15 @@ pyarrow_schema_ode_trajectory_text_only = pa.schema([
     pa.field("text_embedding_shape", pa.list_(pa.int64())),
     # e.g., 'bfloat16' or 'float32'
     pa.field("text_embedding_dtype", pa.string()),
+    # pa.field("text_embedding_2_bytes", pa.binary()),
+    # pa.field("text_embedding_2_shape", pa.list_(pa.int64())),
+    # pa.field("text_embedding_2_dtype", pa.string()),
+    # pa.field("text_mask_bytes", pa.binary()),
+    # pa.field("text_mask_shape", pa.list_(pa.int64())),
+    # pa.field("text_mask_dtype", pa.string()),
+    # pa.field("text_mask_2_bytes", pa.binary()),
+    # pa.field("text_mask_2_shape", pa.list_(pa.int64())),
+    # pa.field("text_mask_2_dtype", pa.string()),
     # --- ODE Trajectory ---
     pa.field("trajectory_latents_bytes", pa.binary()),
     pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
@@ -116,3 +125,43 @@ pyarrow_schema_text_only = pa.schema([
     # --- Metadata ---
     pa.field("caption", pa.string()),
 ])
+
+pyarrow_schema_matrixgame = pa.schema([
+    pa.field("id", pa.string()),
+    # --- Image/Video VAE latents ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("vae_latent_bytes", pa.binary()),
+    # e.g., [C, T, H, W] or [C, H, W]
+    pa.field("vae_latent_shape", pa.list_(pa.int64())),
+    # e.g., 'float32'
+    pa.field("vae_latent_dtype", pa.string()),
+    #I2V
+    pa.field("clip_feature_bytes", pa.binary()),
+    pa.field("clip_feature_shape", pa.list_(pa.int64())),
+    pa.field("clip_feature_dtype", pa.string()),
+    pa.field("first_frame_latent_bytes", pa.binary()),
+    pa.field("first_frame_latent_shape", pa.list_(pa.int64())),
+    pa.field("first_frame_latent_dtype", pa.string()),
+    # --- Action ---
+    pa.field("mouse_cond_bytes", pa.binary()),
+    pa.field("mouse_cond_shape", pa.list_(pa.int64())),  # [T, 2]
+    pa.field("mouse_cond_dtype", pa.string()),
+    pa.field("keyboard_cond_bytes", pa.binary()),
+    pa.field("keyboard_cond_shape", pa.list_(pa.int64())),  # [T, 4]
+    pa.field("keyboard_cond_dtype", pa.string()),
+    # I2V Validation
+    pa.field("pil_image_bytes", pa.binary()),
+    pa.field("pil_image_shape", pa.list_(pa.int64())),
+    pa.field("pil_image_dtype", pa.string()),
+    # --- Metadata ---
+    pa.field("file_name", pa.string()),
+    pa.field("caption", pa.string()),
+    pa.field("media_type", pa.string()),  # 'image' or 'video'
+    pa.field("width", pa.int64()),
+    pa.field("height", pa.int64()),
+    # -- Video-specific (can be null/default for images) ---
+    # Number of frames processed (e.g., 1 for image, N for video)
+    pa.field("num_frames", pa.int64()),
+    pa.field("duration_sec", pa.float64()),
+    pa.field("fps", pa.float64()),
+])

fastvideo/dataset/preprocessing_datasets.py

@@ -17,6 +17,7 @@ from PIL import Image
 from transformers import AutoTokenizer
 
 from fastvideo.logger import init_logger
+from fastvideo.utils import maybe_download_model
 
 logger = init_logger(__name__)
 
@@ -469,15 +470,14 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
         self.seed = seed
 
         # Initialize tokenizer
-        tokenizer_path = os.path.join(args.model_path, "tokenizer")
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
-                                                  cache_dir=args.cache_dir)
+        tokenizer_path = os.path.join(maybe_download_model(args.model_path), "tokenizer")
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
 
         # Initialize processing stages
         self._init_stages(args, transform, transform_topcrop, tokenizer)
 
         # Process metadata
-        self.processed_batches = self._process_metadata()
+        self.processed_batches = self._process_metadata(start_idx=start_idx)
 
     def _init_stages(self, args, transform, transform_topcrop,
                      tokenizer) -> None:
@@ -495,13 +495,16 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
         self.video_transform_stage = VideoTransformStage(transform)
         self.image_transform_stage = ImageTransformStage(
             transform, transform_topcrop)
-        self.text_encoding_stage = TextEncodingStage(
-            tokenizer=tokenizer,
-            text_max_length=args.text_max_length,
-            cfg_rate=args.training_cfg_rate,
-            seed=self.seed)
+        if tokenizer is not None:
+            self.text_encoding_stage = TextEncodingStage(
+                tokenizer=tokenizer,
+                text_max_length=args.text_max_length,
+                cfg_rate=args.training_cfg_rate,
+                seed=self.seed)
+        else:
+            self.text_encoding_stage = None
 
-    def _load_raw_data(self) -> list[dict]:
+    def _load_raw_data(self, start_idx: int = 0) -> list[dict]:
         """Load raw data from JSON files."""
         # Read folder-annotation pairs
         with open(self.data_merge_path) as f:
@@ -522,11 +525,11 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
         for item in data_items:
             item["path"] = opj(folder, item["path"])
 
-        return data_items
+        return data_items[start_idx:]
 
-    def _process_metadata(self) -> list[PreprocessBatch]:
+    def _process_metadata(self, start_idx: int = 0) -> list[PreprocessBatch]:
         """Process the raw metadata through all filtering stages."""
-        raw_data = self._load_raw_data()
+        raw_data = self._load_raw_data(start_idx=start_idx)
         processed_batches = []
 
         # Initialize counters
@@ -604,17 +607,27 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
         # Apply transformation stages
         batch = self.video_transform_stage.process(batch)
         batch = self.image_transform_stage.process(batch)
-        batch = self.text_encoding_stage.process(batch)
+        if self.text_encoding_stage is not None:
+            batch = self.text_encoding_stage.process(batch)
+        else:
+            raise ValueError("Text encoding stage is not initialized")
 
         # Build result dictionary
         result = {
             "pixel_values": batch.pixel_values,
-            "text": batch.text,
-            "input_ids": batch.input_ids,
-            "cond_mask": batch.cond_mask,
+            # "text": batch.text,
+            # "input_ids": batch.input_ids,
+            # "cond_mask": batch.cond_mask,
             "path": batch.path,
         }
 
+        if batch.text is not None:
+            result["text"] = batch.text
+            result["input_ids"] = batch.input_ids
+            result["cond_mask"] = batch.cond_mask
+        else:
+            raise ValueError("Text encoding stage is not initialized")
+            
         # Add video-specific fields
         if batch.is_video:
             result.update({"fps": batch.fps, "duration": batch.duration})
@@ -655,7 +668,7 @@ class TextDataset(torch.utils.data.IterableDataset,
         self.seed = seed
 
         # Initialize tokenizer
-        tokenizer_path = os.path.join(args.model_path, "tokenizer")
+        tokenizer_path = os.path.join(maybe_download_model(args.model_path), "tokenizer")
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
                                                   cache_dir=args.cache_dir)
 
@@ -758,4 +771,4 @@ class TextDataset(torch.utils.data.IterableDataset,
 
     def load_state_dict(self, state_dict: dict[str, Any]) -> None:
         """Load state dict from checkpoint."""
-        self.processed_batches = state_dict["processed_batches"]
+        self.processed_batches = state_dict["processed_batches"]

fastvideo/dataset/utils.py

@@ -156,6 +156,14 @@ def collate_rows_from_parquet_schema(rows,
                     else:
                         data = np.frombuffer(
                             bytes_data, dtype=np.float32).reshape(shape).copy()
+                        # if row[f"{tensor_name}_dtype"] == "float32":
+                        #     data = np.frombuffer(
+                        #         bytes_data, dtype=np.float32).reshape(shape).copy()
+                        # elif row[f"{tensor_name}_dtype"] == "int64":
+                        #     data = np.frombuffer(
+                        #         bytes_data, dtype=np.int64).reshape(shape).copy()
+                        # else:
+                        #     raise ValueError(f"Unsupported dtype: {row[f"{tensor_name}_dtype"]}")
                     tensor = torch.from_numpy(data)
                     # if len(data.shape) == 3:
                     #     B, L, D = tensor.shape
@@ -175,6 +183,8 @@ def collate_rows_from_parquet_schema(rows,
 
             for tensor in tensor_list:
                 if tensor.numel() > 0:
+                    if tensor.ndim == 3:
+                        tensor = tensor.squeeze(0)
                     padded_tensor, mask = pad(tensor, text_padding_length)
                     padded_tensors.append(padded_tensor)
                     attention_masks.append(mask)
@@ -222,4 +232,4 @@ def collate_rows_from_parquet_schema(rows,
     if info_list and 'caption' in info_list[0]:
         batch_data['caption_text'] = [info['caption'] for info in info_list]
 
-    return batch_data
+    return batch_data

fastvideo/dataset/validation_dataset.py

@@ -2,6 +2,7 @@
 # adapted from: https://github.com/a-r-r-o-w/finetrainers/blob/main/finetrainers/data/dataset.py
 import os
 import pathlib
+import json
 
 import datasets
 from torch.utils.data import IterableDataset
@@ -32,10 +33,7 @@ class ValidationDataset(IterableDataset):
                                          data_files=self.filename.as_posix(),
                                          split="train")
         elif self.filename.suffix == ".json":
-            data = datasets.load_dataset("json",
-                                         data_files=self.filename.as_posix(),
-                                         split="train",
-                                         field="data")
+            data = self._load_json_without_hf_locking(self.filename)
         elif self.filename.suffix == ".parquet":
             data = datasets.load_dataset("parquet",
                                          data_files=self.filename.as_posix(),
@@ -162,3 +160,33 @@ class ValidationDataset(IterableDataset):
 
             sample = {k: v for k, v in sample.items() if v is not None}
             yield sample
+
+    @staticmethod
+    def _load_json_without_hf_locking(filename: pathlib.Path) -> list[dict]:
+        """Load JSON validation files without using datasets.load_dataset lock files.
+
+        This avoids distributed lock contention when many ranks initialize
+        validation simultaneously on shared filesystems.
+        """
+        with filename.open("r", encoding="utf-8") as f:
+            payload = json.load(f)
+
+        if isinstance(payload, dict):
+            if "data" not in payload:
+                raise ValueError(
+                    f"Validation JSON {filename.as_posix()} must contain a 'data' field when top-level is an object."
+                )
+            records = payload["data"]
+        elif isinstance(payload, list):
+            records = payload
+        else:
+            raise ValueError(
+                f"Validation JSON {filename.as_posix()} must be either a list of samples or an object containing a 'data' list."
+            )
+
+        if not isinstance(records, list):
+            raise ValueError(
+                f"Validation JSON records in {filename.as_posix()} must be a list."
+            )
+
+        return records

fastvideo/distributed/device_communicators/base_device_communicator.py

@@ -57,6 +57,9 @@ class DistributedAutograd:
             ctx.dim = dim
             ctx.input_shape = input_.shape
 
+            # NCCL all_gather_into_tensor requires contiguous tensors.
+            if not input_.is_contiguous():
+                input_ = input_.contiguous()
             input_size = input_.size()
             output_size = (input_size[0] * world_size, ) + input_size[1:]
             output_tensor = torch.empty(output_size,

fastvideo/entrypoints/video_generator.py

@@ -18,6 +18,8 @@ import numpy as np
 import torch
 import torchvision
 from einops import rearrange
+import shutil
+import tempfile
 
 from fastvideo.configs.sample import SamplingParam
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -389,6 +391,11 @@ class VideoGenerator:
         if batch.save_video:
             imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
             logger.info("Saved video to %s", output_path)
+            audio = output_batch.extra.get("audio")
+            audio_sample_rate = output_batch.extra.get("audio_sample_rate")
+            if (audio is not None and audio_sample_rate is not None and
+                    not self._mux_audio(output_path, audio, audio_sample_rate)):
+                logger.warning("Audio mux failed; saved video without audio.")
 
         if batch.return_frames:
             return frames
@@ -396,6 +403,7 @@ class VideoGenerator:
             return {
                 "samples": samples,
                 "frames": frames,
+                "audio": output_batch.extra.get("audio"),
                 "prompts": prompt,
                 "size": (target_height, target_width, batch.num_frames),
                 "generation_time": gen_time,
@@ -405,6 +413,98 @@ class VideoGenerator:
                 "trajectory_decoded": output_batch.trajectory_decoded,
             }
 
+    @staticmethod
+    def _mux_audio(
+        video_path: str,
+        audio: torch.Tensor | np.ndarray,
+        sample_rate: int,
+    ) -> bool:
+        """Mux audio into video using PyAV."""
+        try:
+            import av
+        except ImportError:
+            logger.warning("PyAV not installed; cannot mux audio. "
+                           "Install with: pip install av")
+            return False
+
+        if torch.is_tensor(audio):
+            audio_np = audio.detach().cpu().float().numpy()
+        else:
+            audio_np = np.asarray(audio, dtype=np.float32)
+
+        if audio_np.ndim == 1:
+            audio_np = audio_np[:, None]
+        elif audio_np.ndim == 2:
+            if audio_np.shape[0] <= 8 and audio_np.shape[1] > audio_np.shape[0]:
+                audio_np = audio_np.T
+        else:
+            logger.warning("Unexpected audio shape %s; skipping mux.",
+                           audio_np.shape)
+            return False
+
+        audio_np = np.clip(audio_np, -1.0, 1.0)
+        audio_int16 = (audio_np * 32767.0).astype(np.int16)
+        num_channels = audio_int16.shape[1]
+        layout = "stereo" if num_channels == 2 else "mono"
+
+        try:
+            import wave
+            with tempfile.TemporaryDirectory() as tmpdir:
+                out_path = os.path.join(tmpdir, "muxed.mp4")
+                wav_path = os.path.join(tmpdir, "audio.wav")
+
+                # Write audio to WAV file
+                with wave.open(wav_path, "wb") as wav_file:
+                    wav_file.setnchannels(num_channels)
+                    wav_file.setsampwidth(2)
+                    wav_file.setframerate(sample_rate)
+                    wav_file.writeframes(audio_int16.tobytes())
+
+                # Open input video and audio
+                input_video = av.open(video_path)
+                input_audio = av.open(wav_path)
+
+                # Create output with both streams
+                output = av.open(out_path, mode="w")
+
+                # Add video stream (copy codec from input)
+                in_video_stream = input_video.streams.video[0]
+                out_video_stream = output.add_stream(
+                    codec_name=in_video_stream.codec_context.name,
+                    rate=in_video_stream.average_rate,
+                )
+                out_video_stream.width = in_video_stream.width
+                out_video_stream.height = in_video_stream.height
+                out_video_stream.pix_fmt = in_video_stream.pix_fmt
+
+                # Add audio stream (AAC)
+                out_audio_stream = output.add_stream("aac", rate=sample_rate)
+                out_audio_stream.layout = layout
+
+                # Remux video (decode and re-encode to be safe)
+                for frame in input_video.decode(video=0):
+                    for packet in out_video_stream.encode(frame):
+                        output.mux(packet)
+                for packet in out_video_stream.encode():
+                    output.mux(packet)
+
+                # Encode audio
+                for frame in input_audio.decode(audio=0):
+                    frame.pts = None  # Let encoder assign PTS
+                    for packet in out_audio_stream.encode(frame):
+                        output.mux(packet)
+                for packet in out_audio_stream.encode():
+                    output.mux(packet)
+
+                input_video.close()
+                input_audio.close()
+                output.close()
+                shutil.move(out_path, video_path)
+            return True
+        except Exception as e:
+            logger.warning("Audio mux failed: %s", e)
+            return False
+
     def set_lora_adapter(self,
                          lora_nickname: str,
                          lora_path: str | None = None) -> None:

fastvideo/fastvideo_args.py

@@ -166,6 +166,14 @@ class FastVideoArgs:
     # Prompt text file for batch processing
     prompt_txt: str | None = None
 
+    # LTX-2 VAE tiling overrides
+    ltx2_vae_tiling: bool | None = None
+    ltx2_vae_spatial_tile_size_in_pixels: int | None = None
+    ltx2_vae_spatial_tile_overlap_in_pixels: int | None = None
+    ltx2_vae_temporal_tile_size_in_frames: int | None = None
+    ltx2_vae_temporal_tile_overlap_in_frames: int | None = None
+    ltx2_initial_latent_path: str | None = None
+
     # model paths for correct deallocation
     model_paths: dict[str, str] = field(default_factory=dict)
     model_loaded: dict[str, bool] = field(default_factory=lambda: {
@@ -203,8 +211,44 @@ class FastVideoArgs:
                 logger.error("Failed to load V-MoBA config from %s: %s",
                              self.moba_config_path, e)
                 raise
+        self._apply_ltx2_vae_overrides()
         self.check_fastvideo_args()
 
+    def _apply_ltx2_vae_overrides(self) -> None:
+        if self.pipeline_config is None:
+            return
+        vae_config = self.pipeline_config.vae_config
+        has_any = any(value is not None for value in (
+            self.ltx2_vae_spatial_tile_size_in_pixels,
+            self.ltx2_vae_spatial_tile_overlap_in_pixels,
+            self.ltx2_vae_temporal_tile_size_in_frames,
+            self.ltx2_vae_temporal_tile_overlap_in_frames,
+        ))
+        if self.ltx2_vae_tiling is not None and hasattr(self.pipeline_config,
+                                                        "vae_tiling"):
+            self.pipeline_config.vae_tiling = self.ltx2_vae_tiling
+        elif has_any and hasattr(self.pipeline_config, "vae_tiling"):
+            self.pipeline_config.vae_tiling = True
+
+        if hasattr(vae_config, "ltx2_spatial_tile_size_in_pixels"
+                   ) and self.ltx2_vae_spatial_tile_size_in_pixels is not None:
+            vae_config.ltx2_spatial_tile_size_in_pixels = (
+                self.ltx2_vae_spatial_tile_size_in_pixels)
+        if hasattr(
+                vae_config, "ltx2_spatial_tile_overlap_in_pixels"
+        ) and self.ltx2_vae_spatial_tile_overlap_in_pixels is not None:
+            vae_config.ltx2_spatial_tile_overlap_in_pixels = (
+                self.ltx2_vae_spatial_tile_overlap_in_pixels)
+        if hasattr(vae_config, "ltx2_temporal_tile_size_in_frames"
+                   ) and self.ltx2_vae_temporal_tile_size_in_frames is not None:
+            vae_config.ltx2_temporal_tile_size_in_frames = (
+                self.ltx2_vae_temporal_tile_size_in_frames)
+        if hasattr(
+                vae_config, "ltx2_temporal_tile_overlap_in_frames"
+        ) and self.ltx2_vae_temporal_tile_overlap_in_frames is not None:
+            vae_config.ltx2_temporal_tile_overlap_in_frames = (
+                self.ltx2_vae_temporal_tile_overlap_in_frames)
+
     @staticmethod
     def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         # Model and path configuration
@@ -325,6 +369,44 @@ class FastVideoArgs:
             "Path to a text file containing prompts (one per line) for batch processing",
         )
 
+        # LTX-2 VAE tiling overrides
+        parser.add_argument(
+            "--ltx2-vae-tiling",
+            action=StoreBoolean,
+            default=FastVideoArgs.ltx2_vae_tiling,
+            help="Enable LTX-2 VAE tiling overrides.",
+        )
+        parser.add_argument(
+            "--ltx2-vae-spatial-tile-size-in-pixels",
+            type=int,
+            default=FastVideoArgs.ltx2_vae_spatial_tile_size_in_pixels,
+            help="LTX-2 VAE spatial tile size in pixels.",
+        )
+        parser.add_argument(
+            "--ltx2-vae-spatial-tile-overlap-in-pixels",
+            type=int,
+            default=FastVideoArgs.ltx2_vae_spatial_tile_overlap_in_pixels,
+            help="LTX-2 VAE spatial tile overlap in pixels.",
+        )
+        parser.add_argument(
+            "--ltx2-vae-temporal-tile-size-in-frames",
+            type=int,
+            default=FastVideoArgs.ltx2_vae_temporal_tile_size_in_frames,
+            help="LTX-2 VAE temporal tile size in frames.",
+        )
+        parser.add_argument(
+            "--ltx2-vae-temporal-tile-overlap-in-frames",
+            type=int,
+            default=FastVideoArgs.ltx2_vae_temporal_tile_overlap_in_frames,
+            help="LTX-2 VAE temporal tile overlap in frames.",
+        )
+        parser.add_argument(
+            "--ltx2-initial-latent-path",
+            type=str,
+            default=FastVideoArgs.ltx2_initial_latent_path,
+            help="Path to load/save a precomputed LTX-2 initial latent.",
+        )
+
         # LoRA parameters (inference-time adapter loading)
         parser.add_argument(
             "--lora-path",
@@ -748,6 +830,7 @@ class TrainingArgs(FastVideoArgs):
     precedence.
     """
     data_path: str = ""
+    data_path_2: str | None = None
     dataloader_num_workers: int = 0
     num_height: int = 0
     num_width: int = 0
@@ -777,6 +860,7 @@ class TrainingArgs(FastVideoArgs):
     validation_sampling_steps: str = ""
     validation_guidance_scale: str = ""
     validation_steps: float = 0.0
+    visualization_steps: float = 0.0
     log_validation: bool = False
     trackers: list[str] = dataclasses.field(default_factory=list)
     tracker_project_name: str = ""
@@ -792,6 +876,7 @@ class TrainingArgs(FastVideoArgs):
     num_train_epochs: int = 0
     max_train_steps: int = 0
     gradient_accumulation_steps: int = 0
+    optimizer_type: str = "adamw"
     learning_rate: float = 0.0
     scale_lr: bool = False
     lr_scheduler: str = "constant"
@@ -852,6 +937,9 @@ class TrainingArgs(FastVideoArgs):
     # simulate generator forward to match inference
     simulate_generator_forward: bool = False
     warp_denoising_step: bool = False
+    use_tf: bool = False
+    use_decoupled_dmd: bool = False  # use decoupled DMD
+    use_gan_loss: bool = False
 
     # Self-forcing specific arguments
     num_frame_per_block: int = 3
@@ -861,6 +949,8 @@ class TrainingArgs(FastVideoArgs):
     same_step_across_blocks: bool = False  # Use same exit timestep for all blocks
     last_step_only: bool = False  # Only use the last timestep for training
     context_noise: int = 0  # Context noise level for cache updates
+    use_context_forcing: bool = False
+    use_ode_init: bool = False
 
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
@@ -916,6 +1006,10 @@ class TrainingArgs(FastVideoArgs):
                             type=str,
                             required=True,
                             help="Path to parquet files")
+        parser.add_argument("--data-path-2",
+                            type=str,
+                            required=False,
+                            help="Path to parquet files")
         parser.add_argument("--dataloader-num-workers",
                             type=int,
                             required=True,
@@ -1009,6 +1103,9 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--validation-steps",
                             type=float,
                             help="Number of validation steps")
+        parser.add_argument("--visualization-steps",
+                            type=float,
+                            help="Number of visualization steps")
         parser.add_argument("--log-validation",
                             action=StoreBoolean,
                             help="Whether to log validation results")
@@ -1057,6 +1154,10 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--gradient-accumulation-steps",
                             type=int,
                             help="Number of steps to accumulate gradients")
+        parser.add_argument("--optimizer-type",
+                            type=str,
+                            choices=["adamw", "muon"],
+                            help="Optimizer type")
         parser.add_argument("--learning-rate",
                             type=float,
                             required=True,
@@ -1247,6 +1348,21 @@ class TrainingArgs(FastVideoArgs):
             help=
             "Whether to warp denoising step according to the scheduler time shift"
         )
+        parser.add_argument(
+            "--use-tf",
+            action=StoreBoolean,
+            help="Whether to use Teacher-forcing for finetuning"
+        )
+        parser.add_argument(
+            "--use-decoupled-dmd",
+            action=StoreBoolean,
+            help="Whether to use decoupled DMD"
+        )
+        parser.add_argument(
+            "--use-gan-loss",
+            action=StoreBoolean,
+            help="Whether to use GAN loss"
+        )
 
         # Self-forcing specific arguments
         parser.add_argument(
@@ -1283,6 +1399,12 @@ class TrainingArgs(FastVideoArgs):
                             type=int,
                             default=TrainingArgs.context_noise,
                             help="Context noise level for cache updates")
+        parser.add_argument("--use-context-forcing",
+                            action=StoreBoolean,
+                            help="Whether to use context forcing")
+        parser.add_argument("--use-ode-init",
+                            action=StoreBoolean,
+                            help="Whether to use ODE init")
 
         return parser
 

fastvideo/layers/layernorm.py

@@ -168,9 +168,15 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
         else:
             raise NotImplementedError(f"Norm type {norm_type} not implemented")
 
-    def forward(self, residual: torch.Tensor, x: torch.Tensor,
-                gate: torch.Tensor | int, shift: torch.Tensor,
-                scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    def forward(
+        self,
+        residual: torch.Tensor,
+        x: torch.Tensor,
+        gate: torch.Tensor | int,
+        shift: torch.Tensor,
+        scale: torch.Tensor,
+        convert_modulation_dtype: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Apply gated residual connection, followed by layernorm and 
         scale/shift in a single fused operation.
@@ -205,6 +211,11 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
 
         # Apply normalization
         normalized = self.norm(residual_output)
+
+        if convert_modulation_dtype:
+            scale = scale.to(normalized.dtype)
+            shift = shift.to(normalized.dtype)
+
         # Apply scale and shift
         if isinstance(scale, torch.Tensor) and scale.dim() == 4:
             # scale.shape: [batch_size, num_frames, 1, inner_dim]
@@ -254,14 +265,21 @@ class LayerNormScaleShift(nn.Module):
         else:
             raise NotImplementedError(f"Norm type {norm_type} not implemented")
 
-    def forward(self, x: torch.Tensor, shift: torch.Tensor,
-                scale: torch.Tensor) -> torch.Tensor:
+    def forward(self,
+                x: torch.Tensor,
+                shift: torch.Tensor,
+                scale: torch.Tensor,
+                convert_modulation_dtype: bool = False) -> torch.Tensor:
         """Apply ln followed by scale and shift in a single fused operation."""
         # x.shape: [batch_size, seq_len, inner_dim]
         normalized = self.norm(x)
         if self.compute_dtype == torch.float32:
             normalized = normalized.float()
 
+        if convert_modulation_dtype:
+            scale = scale.to(normalized.dtype)
+            shift = shift.to(normalized.dtype)
+
         if scale.dim() == 4:
             # scale.shape: [batch_size, num_frames, 1, inner_dim]
             num_frames = scale.shape[1]

fastvideo/models/audio/__init__.py

@@ -0,0 +1,9 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from fastvideo.models.audio.ltx2_audio_vae import (
+    LTX2AudioDecoder,
+    LTX2AudioEncoder,
+    LTX2Vocoder,
+)
+
+__all__ = ["LTX2AudioEncoder", "LTX2AudioDecoder", "LTX2Vocoder"]

fastvideo/models/dits/causal_hunyuanvideo15.py

@@ -0,0 +1,948 @@
+# Copyright 2025 The Hunyuan Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, Optional, List
+import math
+
+import torch
+# import torch._dynamo
+# torch._dynamo.config.cache_size_limit = 128
+# try:
+#     torch._dynamo.config.recompile_limit = 128
+# except AttributeError:
+#     pass
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch.nn.attention.flex_attention import create_block_mask, flex_attention
+from torch.nn.attention.flex_attention import BlockMask
+flex_attention = torch.compile(
+    flex_attention, dynamic=False, mode="default")
+import torch.distributed as dist
+
+from fastvideo.attention import LocalAttention
+from fastvideo.forward_context import set_forward_context
+from fastvideo.configs.models.dits import HunyuanVideo15Config
+from fastvideo.layers.layernorm import (LayerNormScaleShift, ScaleResidual,
+                                        ScaleResidualLayerNormScaleShift)
+from fastvideo.layers.linear import ReplicatedLinear
+# TODO(will-PY-refactor): RMSNorm ....
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import get_rotary_pos_embed, _apply_rotary_emb
+from fastvideo.layers.visual_embedding import (ModulateProjection, PatchEmbed,
+                                               unpatchify)
+from fastvideo.models.dits.base import CachableDiT
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.logger import init_logger
+
+from fastvideo.models.dits.hunyuanvideo15 import (
+    HunyuanRMSNorm, 
+    HunyuanVideo15TimeEmbedding, 
+    HunyuanVideo15ByT5TextProjection,
+    HunyuanVideo15ImageProjection,
+    SingleTokenRefiner,
+    FinalLayer)
+
+logger = init_logger(__name__)
+
+class MMDoubleStreamBlock(nn.Module):
+    """
+    A multimodal DiT block with separate modulation for text and image/video,
+    using distributed attention and linear layers.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        mlp_ratio: float,
+        local_attn_size: int = -1,
+        sink_size: int = 0,
+        dtype: torch.dtype | None = None,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...]
+        | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.deterministic = False
+        self.num_attention_heads = num_attention_heads
+        self.head_dim = hidden_size // num_attention_heads
+        self.local_attn_size = local_attn_size
+        self.sink_size = sink_size
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+
+        # Image modulation components
+        self.img_mod = ModulateProjection(
+            hidden_size,
+            factor=6,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.img_mod",
+        )
+
+        # Fused operations for image stream
+        self.img_attn_norm = LayerNormScaleShift(hidden_size,
+                                                 norm_type="layer",
+                                                 elementwise_affine=False,
+                                                 dtype=dtype)
+        self.img_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
+            hidden_size,
+            norm_type="layer",
+            elementwise_affine=False,
+            dtype=dtype)
+        self.img_mlp_residual = ScaleResidual()
+
+        # Image attention components
+        self.img_attn_qkv = ReplicatedLinear(hidden_size,
+                                             hidden_size * 3,
+                                             bias=True,
+                                             params_dtype=dtype,
+                                             prefix=f"{prefix}.img_attn_qkv")
+
+        self.img_attn_q_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
+        self.img_attn_k_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
+
+        self.img_attn_proj = ReplicatedLinear(hidden_size,
+                                              hidden_size,
+                                              bias=True,
+                                              params_dtype=dtype,
+                                              prefix=f"{prefix}.img_attn_proj")
+
+        self.img_mlp = MLP(hidden_size,
+                           mlp_hidden_dim,
+                           bias=True,
+                           dtype=dtype,
+                           prefix=f"{prefix}.img_mlp")
+
+        # Text modulation components
+        self.txt_mod = ModulateProjection(
+            hidden_size,
+            factor=6,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.txt_mod",
+        )
+
+        # Fused operations for text stream
+        self.txt_attn_norm = LayerNormScaleShift(hidden_size,
+                                                 norm_type="layer",
+                                                 elementwise_affine=False,
+                                                 dtype=dtype)
+        self.txt_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
+            hidden_size,
+            norm_type="layer",
+            elementwise_affine=False,
+            dtype=dtype)
+        self.txt_mlp_residual = ScaleResidual()
+
+        # Text attention components
+        self.txt_attn_qkv = ReplicatedLinear(hidden_size,
+                                             hidden_size * 3,
+                                             bias=True,
+                                             params_dtype=dtype)
+
+        # QK norm layers for text
+        self.txt_attn_q_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
+        self.txt_attn_k_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
+
+        self.txt_attn_proj = ReplicatedLinear(hidden_size,
+                                              hidden_size,
+                                              bias=True,
+                                              params_dtype=dtype)
+
+        self.txt_mlp = MLP(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype)
+
+        self.max_attention_size = 21 * 1590 if local_attn_size == -1 else local_attn_size * 1590
+
+        self.attn = LocalAttention(
+            num_heads=self.num_attention_heads,
+            head_size=self.head_dim,
+            supported_attention_backends=supported_attention_backends,
+            prefix=f"{prefix}.attn"
+        )
+
+    def forward_txt(
+        self,
+        txt: torch.Tensor,
+        vec: torch.Tensor,
+        cache_txt: bool = False,
+    ):
+        txt_mod_outputs = self.txt_mod(vec)
+        (
+            txt_attn_shift,
+            txt_attn_scale,
+            txt_attn_gate,
+            txt_mlp_shift,
+            txt_mlp_scale,
+            txt_mlp_gate,
+        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)
+
+        # Prepare text for attention using fused operation
+        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale)
+
+        # Get QKV for text
+        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
+        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]
+
+        # Split QKV
+        txt_qkv = txt_qkv.view(batch_size, text_seq_len, 3,
+                               self.num_attention_heads, -1)
+        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :,
+                                                                          2]
+        # Apply QK-Norm if needed
+        txt_q = self.txt_attn_q_norm(txt_q).to(txt_q.dtype)
+        txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
+
+        t_kv = {}
+        if cache_txt:
+            t_kv["k_txt"] = txt_k
+            t_kv["v_txt"] = txt_v
+
+        txt_attn = self.attn(txt_q, txt_k, txt_v)
+        # Process text attention output
+        txt_attn_out, _ = self.txt_attn_proj(
+            txt_attn.reshape(batch_size, text_seq_len, -1))
+
+        # Use fused operation for residual connection, normalization, and modulation
+        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
+            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale)
+
+        # Process text MLP
+        txt_mlp_out = self.txt_mlp(txt_mlp_input)
+        txt = self.txt_mlp_residual(txt_residual, txt_mlp_out, txt_mlp_gate)
+
+        return txt, t_kv
+
+    def forward_vision(
+        self,
+        img: torch.Tensor,
+        vec: torch.Tensor,
+        freqs_cis: tuple,
+        block_mask: BlockMask,
+        kv_cache: dict | None = None,
+        txt_kv_cache: list | None = None,
+        current_start: int = 0,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Process modulation vectors
+        if vec.dim() == 3:
+            img_mod_outputs = self.img_mod(vec).unflatten(dim=-1, sizes=(6, -1))
+            (
+                img_attn_shift,
+                img_attn_scale,
+                img_attn_gate,
+                img_mlp_shift,
+                img_mlp_scale,
+                img_mlp_gate,
+            ) = torch.chunk(img_mod_outputs, 6, dim=2)
+        else:
+            img_mod_outputs = self.img_mod(vec)
+            (
+                img_attn_shift,
+                img_attn_scale,
+                img_attn_gate,
+                img_mlp_shift,
+                img_mlp_scale,
+                img_mlp_gate,
+            ) = torch.chunk(img_mod_outputs, 6, dim=-1)
+
+        # Prepare image for attention using fused operation
+        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale)
+        # Get QKV for image
+        img_qkv, _ = self.img_attn_qkv(img_attn_input)
+        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]
+
+        # Split QKV
+        img_qkv = img_qkv.view(batch_size, image_seq_len, 3,
+                               self.num_attention_heads, -1)
+        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :,
+                                                                          2]
+
+        # Apply QK-Norm if needed
+        img_q = self.img_attn_q_norm(img_q).to(img_v)
+        img_k = self.img_attn_k_norm(img_k).to(img_v)
+
+        # Apply rotary embeddings
+        cos, sin = freqs_cis
+
+        # Apply flex_attention
+        # Does not support SP padding for now
+        if kv_cache is None:
+            is_tf = (img_q.shape[1] == cos.shape[0] * 2)
+            if is_tf:
+                q_chunk = torch.chunk(img_q, 2, dim=1)
+                k_chunk = torch.chunk(img_k, 2, dim=1)
+                roped_query = []
+                roped_key = []
+                for ii in range(2):
+                    rq = _apply_rotary_emb(q_chunk[ii], cos, sin, is_neox_style=False)
+                    rk = _apply_rotary_emb(k_chunk[ii], cos, sin, is_neox_style=False)
+                    roped_query.append(rq)
+                    roped_key.append(rk)
+                img_q = torch.cat(roped_query, dim=1)
+                img_k = torch.cat(roped_key, dim=1)
+            else:
+                img_q = _apply_rotary_emb(img_q, cos, sin, is_neox_style=False)
+                img_k = _apply_rotary_emb(img_k, cos, sin, is_neox_style=False)
+
+            q = img_q
+            k = torch.cat([img_k, txt_kv_cache["k_txt"]], dim=1)
+            v = torch.cat([img_v, txt_kv_cache["v_txt"]], dim=1)
+            # Padding for flex attention
+            padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
+            padded_kv_length = math.ceil(k.shape[1] / 128) * 128 - k.shape[1]
+            padded_roped_query = torch.cat(
+                [q,
+                    torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
+                                device=q.device, dtype=v.dtype)],
+                dim=1
+            )
+
+            padded_roped_key = torch.cat(
+                [k, torch.zeros([k.shape[0], padded_kv_length, k.shape[2], k.shape[3]],
+                                        device=k.device, dtype=v.dtype)],
+                dim=1
+            )
+
+            padded_v = torch.cat(
+                [v, torch.zeros([v.shape[0], padded_kv_length, v.shape[2], v.shape[3]],
+                                device=v.device, dtype=v.dtype)],
+                dim=1
+            )
+
+            img_attn = flex_attention(
+                query=padded_roped_query.transpose(2, 1),
+                key=padded_roped_key.transpose(2, 1),
+                value=padded_v.transpose(2, 1),
+                block_mask=block_mask
+            )[:, :, :-padded_length].transpose(2, 1)
+
+            assert img_attn.shape[1] == image_seq_len
+            updated_kv_cache = None
+        else:
+            img_q = _apply_rotary_emb(img_q, cos, sin, is_neox_style=False)
+            img_k = _apply_rotary_emb(img_k, cos, sin, is_neox_style=False)
+
+            current_end = current_start + img_q.shape[1]
+            num_new_tokens = img_q.shape[1]
+            sink_tokens = self.sink_size * 1590
+            # If we are using local attention and the current KV cache size is larger than the local attention size, we need to truncate the KV cache
+            kv_cache_size = self.max_attention_size
+            
+            # Clone cache to avoid in-place modification during gradient checkpointing
+            k_cache = kv_cache["k"].clone()
+            v_cache = kv_cache["v"].clone()
+            
+            if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
+                    num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
+                # Calculate the number of new tokens added in this step
+                # Shift existing cache content left to discard oldest tokens
+                # Clone the source slice to avoid overlapping memory error
+                num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
+                num_rolled_tokens = kv_cache_size - num_new_tokens - sink_tokens
+                k_cache[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                    k_cache[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
+                v_cache[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                    v_cache[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
+                # Insert the new keys/values at the end
+                local_end_index = kv_cache["local_end_index"].item() + current_end - \
+                    kv_cache["global_end_index"].item() - num_evicted_tokens
+                local_start_index = local_end_index - num_new_tokens
+                assert local_end_index == self.max_attention_size
+            else:
+                # Assign new keys/values directly up to current_end
+                local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
+                local_start_index = local_end_index - num_new_tokens
+
+            assert local_start_index >= 0
+            q = img_q
+            k = torch.cat([k_cache[:, :local_start_index], img_k, txt_kv_cache["k_txt"]], dim=1)
+            v = torch.cat([v_cache[:, :local_start_index], img_v, txt_kv_cache["v_txt"]], dim=1)
+            img_attn = self.attn(q, k, v)
+
+            k_cache[:, local_start_index:local_end_index] = img_k
+            v_cache[:, local_start_index:local_end_index] = img_v
+            
+            updated_kv_cache = {
+                "k": k_cache,
+                "v": v_cache,
+                "global_end_index": torch.tensor([current_end], dtype=torch.long, device=k_cache.device),
+                "local_end_index": torch.tensor([local_end_index], dtype=torch.long, device=k_cache.device)
+            }
+        
+        img_attn_out, _ = self.img_attn_proj(
+            img_attn.view(batch_size, image_seq_len, -1))
+        # Use fused operation for residual connection, normalization, and modulation
+        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
+            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale)
+
+        # Process image MLP
+        img_mlp_out = self.img_mlp(img_mlp_input)
+        img = self.img_mlp_residual(img_residual, img_mlp_out, img_mlp_gate)
+
+        return img, updated_kv_cache
+
+    def forward(
+        self,
+        txt_inference=False,
+        vision_inference=False,
+        **kwargs
+    ):
+        if txt_inference:
+            return self.forward_txt(**kwargs)
+        elif vision_inference:
+            return self.forward_vision(**kwargs)
+        else:
+            raise ValueError("txt_inference and vision_inference cannot be both False")
+
+
+class CausalHunyuanVideo15Transformer3DModel(CachableDiT):
+    r"""
+    A Transformer model for video-like data used in [HunyuanVideo1.5](https://huggingface.co/tencent/HunyuanVideo1.5).
+    """
+
+    # shard single stream, double stream blocks, and refiner_blocks
+    _fsdp_shard_conditions = HunyuanVideo15Config()._fsdp_shard_conditions
+    _compile_conditions = HunyuanVideo15Config()._compile_conditions
+    _supported_attention_backends = HunyuanVideo15Config(
+    )._supported_attention_backends
+    param_names_mapping = HunyuanVideo15Config().param_names_mapping
+    reverse_param_names_mapping = HunyuanVideo15Config(
+    ).reverse_param_names_mapping
+    lora_param_names_mapping = HunyuanVideo15Config().lora_param_names_mapping
+
+    def __init__(
+        self,
+        config: HunyuanVideo15Config,
+        hf_config: dict[str, Any],
+    ) -> None:
+        super().__init__(config=config, hf_config=hf_config)
+
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.num_channels_latents = config.num_channels_latents
+        self.out_channels = config.out_channels or config.in_channels
+        self.patch_size = (config.patch_size_t, config.patch_size, config.patch_size)
+
+        # 1. Latent and condition embedders
+        self.img_in = PatchEmbed(self.patch_size, 
+                                    config.in_channels, 
+                                    self.hidden_size,
+                                    prefix=f"{config.prefix}.img_in")
+        self.image_embedder = HunyuanVideo15ImageProjection(config.image_embed_dim, self.hidden_size)
+
+        self.txt_in = SingleTokenRefiner(config.text_embed_dim,
+                                                   self.hidden_size,
+                                                   config.num_attention_heads,
+                                                   depth=config.num_refiner_layers,
+                                                   dtype=None,
+                                                   prefix=f"{config.prefix}.txt_in")
+
+        self.txt_in_2 = HunyuanVideo15ByT5TextProjection(config.text_embed_2_dim, 2048, self.hidden_size)
+
+        self.time_in = HunyuanVideo15TimeEmbedding(self.hidden_size, use_meanflow=config.use_meanflow)
+
+        self.cond_type_embed = nn.Embedding(3, self.hidden_size)
+
+        # 3. Dual stream transformer blocks
+
+        self.double_blocks = nn.ModuleList(
+            [
+                MMDoubleStreamBlock(
+                    hidden_size=self.hidden_size,
+                    num_attention_heads=config.num_attention_heads,
+                    mlp_ratio=config.mlp_ratio,
+                    local_attn_size=config.local_attn_size,
+                    sink_size=config.sink_size,
+                    dtype=None,
+                    supported_attention_backends=self._supported_attention_backends,
+                    prefix=f"{config.prefix}.double_blocks.{i}"
+                )
+                for i in range(config.num_layers)
+            ]
+        )
+
+        # 5. Output projection
+        self.final_layer = FinalLayer(self.hidden_size,
+                                self.patch_size,
+                                self.out_channels,
+                                prefix=f"{config.prefix}.final_layer")
+
+        self.gradient_checkpointing = False
+
+        self.num_frame_per_block = config.num_frames_per_block
+        self.local_attn_size = config.local_attn_size
+        self.block_mask = None
+
+        self.__post_init__()
+
+    def get_text_and_mask(
+        self,
+        encoder_hidden_states: torch.Tensor,
+        encoder_hidden_states_2: torch.Tensor,
+        encoder_attention_mask: torch.Tensor,
+        encoder_attention_mask_2: torch.Tensor,
+        encoder_hidden_states_image: torch.Tensor,
+        timestep: torch.Tensor,
+    ):
+        batch_size, txt_seq_len = encoder_hidden_states.shape[0], encoder_hidden_states.shape[1]
+        # qwen text embedding
+        encoder_hidden_states = self.txt_in(encoder_hidden_states, timestep, encoder_attention_mask)
+
+        encoder_hidden_states_cond_emb = self.cond_type_embed(
+            torch.zeros_like(encoder_hidden_states[:, :, 0], dtype=torch.long)
+        )
+        encoder_hidden_states = encoder_hidden_states + encoder_hidden_states_cond_emb
+
+        # byt5 text embedding
+        encoder_hidden_states_2 = self.txt_in_2(encoder_hidden_states_2)
+
+        encoder_hidden_states_2_cond_emb = self.cond_type_embed(
+            torch.ones_like(encoder_hidden_states_2[:, :, 0], dtype=torch.long)
+        )
+        encoder_hidden_states_2 = encoder_hidden_states_2 + encoder_hidden_states_2_cond_emb
+
+        # image embed
+        encoder_hidden_states_3 = self.image_embedder(encoder_hidden_states_image)
+        is_t2v = torch.all(encoder_hidden_states_image == 0)
+        if is_t2v:
+            encoder_hidden_states_3 = encoder_hidden_states_3 * 0.0
+            encoder_attention_mask_3 = torch.zeros(
+                (batch_size, encoder_hidden_states_3.shape[1]),
+                dtype=encoder_attention_mask.dtype,
+                device=encoder_attention_mask.device,
+            )
+        else:
+            encoder_attention_mask_3 = torch.ones(
+                (batch_size, encoder_hidden_states_3.shape[1]),
+                dtype=encoder_attention_mask.dtype,
+                device=encoder_attention_mask.device,
+            )
+        encoder_hidden_states_3_cond_emb = self.cond_type_embed(
+            2
+            * torch.ones_like(
+                encoder_hidden_states_3[:, :, 0],
+                dtype=torch.long,
+            )
+        )
+        encoder_hidden_states_3 = encoder_hidden_states_3 + encoder_hidden_states_3_cond_emb
+
+        # reorder and combine text tokens: combine valid tokens first, then padding
+        encoder_attention_mask = encoder_attention_mask.bool()
+        encoder_attention_mask_2 = encoder_attention_mask_2.bool()
+        encoder_attention_mask_3 = encoder_attention_mask_3.bool()
+        new_encoder_hidden_states = []
+        new_encoder_attention_mask = []
+
+        for text, text_mask, text_2, text_mask_2, image, image_mask in zip(
+            encoder_hidden_states,
+            encoder_attention_mask,
+            encoder_hidden_states_2,
+            encoder_attention_mask_2,
+            encoder_hidden_states_3,
+            encoder_attention_mask_3,
+        ):
+            # Concatenate: [valid_image, valid_byt5, valid_mllm, invalid_image, invalid_byt5, invalid_mllm]
+            new_encoder_hidden_states.append(
+                torch.cat(
+                    [
+                        image[image_mask],  # valid image
+                        text_2[text_mask_2],  # valid byt5
+                        text[text_mask],  # valid mllm
+                        image[~image_mask],  # invalid image (zeroed)
+                        torch.zeros_like(text_2[~text_mask_2]),  # invalid byt5 (zeroed)
+                        torch.zeros_like(text[~text_mask]),  # invalid mllm (zeroed)
+                    ],
+                    dim=0,
+                )
+            )
+            # Apply same reordering to attention masks
+            new_encoder_attention_mask.append(
+                torch.cat(
+                    [
+                        image_mask[image_mask],
+                        text_mask_2[text_mask_2],
+                        text_mask[text_mask],
+                        image_mask[~image_mask],
+                        text_mask_2[~text_mask_2],
+                        text_mask[~text_mask],
+                    ],
+                    dim=0,
+                )
+            )
+
+        encoder_hidden_states = torch.stack(new_encoder_hidden_states)
+        encoder_attention_mask = torch.stack(new_encoder_attention_mask)
+        assert encoder_hidden_states.shape[0] == 1
+        return encoder_hidden_states, encoder_attention_mask
+
+
+    @staticmethod
+    def _prepare_teacher_forcing_mask(
+        device: torch.device | str, num_frames: int = 21,
+        frame_seqlen: int = 1560, num_frame_per_block=1,
+        text_seq_len: int = 0
+    ) -> BlockMask:
+        """
+        we will divide the token sequence into the following format
+        [1 latent frame] [1 latent frame] ... [1 latent frame]
+        We use flexattention to construct the attention mask
+        """
+        # debug
+        DEBUG = False
+        if DEBUG:
+            num_frames = 9
+            frame_seqlen = 256
+
+        total_length = num_frames * frame_seqlen * 2
+        total_kv_length = total_length + text_seq_len
+
+        # we do right padding to get to a multiple of 128
+        padded_length = math.ceil(total_length / 128) * 128 - total_length
+        kv_padded_length = math.ceil(total_kv_length / 128) * 128 - total_kv_length
+
+        total_length_tensor = torch.tensor(total_length, device=device)
+        total_kv_length_tensor = torch.tensor(total_kv_length, device=device)
+
+        clean_ends = num_frames * frame_seqlen
+        # for clean context frames, we can construct their flex attention mask based on a [start, end] interval
+        context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        # for noisy frames, we need two intervals to construct the flex attention mask [context_start, context_end] [noisy_start, noisy_end]
+        noise_context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_noise_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_noise_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+
+        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
+        attention_block_size = frame_seqlen * num_frame_per_block
+        frame_indices = torch.arange(
+            start=0,
+            end=num_frames * frame_seqlen,
+            step=attention_block_size,
+            device=device, dtype=torch.long
+        )
+
+        # attention for clean context frames
+        for start in frame_indices:
+            context_ends[start:start + attention_block_size] = start + attention_block_size
+
+        noisy_image_start_list = torch.arange(
+            num_frames * frame_seqlen, total_length,
+            step=attention_block_size,
+            device=device, dtype=torch.long
+        )
+        noisy_image_end_list = noisy_image_start_list + attention_block_size
+
+        # attention for noisy frames
+        for block_index, (start, end) in enumerate(zip(noisy_image_start_list, noisy_image_end_list)):
+            # attend to noisy tokens within the same block
+            noise_noise_starts[start:end] = start
+            noise_noise_ends[start:end] = end
+            # attend to context tokens in previous blocks
+            # noise_context_starts[start:end] = 0
+            noise_context_ends[start:end] = block_index * attention_block_size
+
+        def attention_mask(b, h, q_idx, kv_idx):
+            # first design the mask for clean frames
+            clean_mask = (q_idx < clean_ends) & (kv_idx < context_ends[q_idx])
+            # then design the mask for noisy frames
+            # noisy frames will attend to all clean preceeding clean frames + itself
+            C1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx])
+            C2 = (kv_idx < noise_context_ends[q_idx]) & (kv_idx >= noise_context_starts[q_idx])
+            noise_mask = (q_idx >= clean_ends) & (C1 | C2)
+
+            eye_mask = q_idx == kv_idx
+            text_mask = (kv_idx >= total_length_tensor) & (kv_idx < total_kv_length_tensor)
+            return eye_mask | clean_mask | noise_mask | text_mask
+
+        block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
+                                       KV_LEN=total_kv_length + kv_padded_length, _compile=False, device=device)
+
+        if DEBUG:
+            print(block_mask)
+            import imageio
+            import numpy as np
+            from torch.nn.attention.flex_attention import create_mask
+
+            mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
+                                padded_length, KV_LEN=total_kv_length + kv_padded_length, device=device)
+            import cv2
+            mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
+            imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
+
+        return block_mask
+    
+    @staticmethod
+    def _prepare_blockwise_causal_attn_mask(
+        device: torch.device | str, num_frames: int = 21,
+        frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1,
+        text_seq_len: int = 0
+    ) -> BlockMask:
+        """
+        we will divide the token sequence into the following format
+        [1 latent frame] [1 latent frame] ... [1 latent frame]
+        We use flexattention to construct the attention mask
+        """
+        total_length = num_frames * frame_seqlen
+        total_kv_length = total_length + text_seq_len
+
+        total_length_tensor = torch.tensor(total_length, device=device)
+        total_kv_length_tensor = torch.tensor(total_kv_length, device=device)
+
+        # we do right padding to get to a multiple of 128
+        padded_length = math.ceil(total_length / 128) * 128 - total_length
+        kv_padded_length = math.ceil(total_kv_length / 128) * 128 - total_kv_length
+
+        ends = torch.zeros(total_length + padded_length,
+                           device=device, dtype=torch.long)
+
+        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
+        frame_indices = torch.arange(
+            # start=frame_seqlen,
+            start=0,
+            end=total_length,
+            step=frame_seqlen * num_frame_per_block,
+            device=device
+        )
+        # frame_indices = torch.cat([torch.tensor([0], device=device), frame_indices])
+
+        for i, tmp in enumerate(frame_indices):
+            # if i == 0:
+            #     ends[tmp:tmp + frame_seqlen] = tmp + frame_seqlen
+            # else:
+            ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \
+                frame_seqlen * num_frame_per_block
+
+        def attention_mask(b, h, q_idx, kv_idx):
+            if local_attn_size == -1:
+                return (kv_idx < ends[q_idx]) | (q_idx == kv_idx) | ((kv_idx >= total_length_tensor) & (kv_idx < total_kv_length_tensor))
+            else:
+                return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | (q_idx == kv_idx) | ((kv_idx >= total_length_tensor) & (kv_idx < total_kv_length_tensor))
+            # return ((kv_idx < total_length) & (q_idx < total_length))  | (q_idx == kv_idx) # bidirectional mask
+
+        block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
+                                       KV_LEN=total_kv_length + kv_padded_length, _compile=False, device=device)
+
+        # if not dist.is_initialized() or dist.get_rank() == 0:
+        #     print(
+        #         f" cache a block wise causal mask with block size of {num_frame_per_block} frames")
+        #     print(block_mask)
+
+        # import imageio
+        # import numpy as np
+        # from torch.nn.attention.flex_attention import create_mask
+
+        # mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
+        #                    padded_length, KV_LEN=total_length + padded_length, device=device)
+        # import cv2
+        # mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
+        # imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
+
+        return block_mask
+
+    def forward_txt(
+        self,
+        encoder_hidden_states: List[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: Optional[List[torch.Tensor]] = None,
+        encoder_attention_mask: Optional[List[torch.Tensor]] = None,
+        guidance: Optional[torch.Tensor] = None,
+        timestep_r: Optional[torch.LongTensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        cache_txt: bool = False,
+        **kwargs
+    ):
+        # Check that the timestep is only consisted of 0s
+        assert torch.all(timestep == 0), "Timestep for txt must be only consisted of 0s"
+
+        if cache_txt:
+            _kv_cache_new = []
+            transformer_num_layers = len(self.double_blocks)
+            for _ in range(transformer_num_layers):
+                _kv_cache_new.append(
+                    {"k_vision": None, "v_vision": None, "k_txt": None, "v_txt": None}
+                )
+
+        encoder_hidden_states_image = encoder_hidden_states_image[0]
+        encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
+        encoder_attention_mask, encoder_attention_mask_2 = encoder_attention_mask
+
+        # 2. Conditional embeddings
+        if timestep.dim() == 2:
+            ts_seq_len = timestep.shape[1]
+            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
+        else:
+            temb = self.time_in(timestep, timestep_r=timestep_r)
+        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
+
+        encoder_hidden_states, encoder_attention_mask = self.get_text_and_mask(
+            encoder_hidden_states,
+            encoder_hidden_states_2,
+            encoder_attention_mask,
+            encoder_attention_mask_2,
+            encoder_hidden_states_image,
+            timestep
+        )
+        
+        encoder_hidden_states = encoder_hidden_states[encoder_attention_mask.bool().to(encoder_hidden_states.device)].unsqueeze(0)
+
+        # 4. Transformer blocks
+        for index, block in enumerate(self.double_blocks):
+            encoder_hidden_states, t_kv = block(
+                txt_inference=True,
+                vision_inference=False,
+                txt=encoder_hidden_states,
+                vec=temb,
+                cache_txt=cache_txt,
+            )
+
+            if cache_txt:
+                _kv_cache_new[index]["k_txt"] = t_kv["k_txt"]
+                _kv_cache_new[index]["v_txt"] = t_kv["v_txt"]
+
+        if cache_txt:
+            return _kv_cache_new
+
+    def forward_vision(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        guidance: Optional[torch.Tensor] = None,
+        timestep_r: Optional[torch.LongTensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        kv_cache: dict | None = None,
+        txt_kv_cache: list | None = None,
+        current_start: int = 0,
+        rope_start_idx: int = 0,
+        clean_hidden_states: Optional[torch.Tensor] = None,
+        aug_timestep: Optional[torch.Tensor] = None,
+    ):
+        assert txt_kv_cache is not None, "txt_kv_cache must be provided"
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # 1. RoPE
+        # Get rotary embeddings
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames, post_patch_height, post_patch_width), self.hidden_size,
+            self.num_attention_heads, self.config.rope_axes_dim, self.config.rope_theta, start_frame=rope_start_idx)
+        freqs_cos = freqs_cos.to(hidden_states.device)
+        freqs_sin = freqs_sin.to(hidden_states.device)
+        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
+
+        # 2. Conditional embeddings
+        if timestep.dim() == 2:
+            ts_seq_len = timestep.shape[1]
+            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
+        else:
+            temb = self.time_in(timestep, timestep_r=timestep_r)
+        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
+
+        hidden_states = self.img_in(hidden_states)
+
+        if clean_hidden_states is not None:
+            clean_hidden_states = self.img_in(clean_hidden_states)
+            hidden_states = torch.cat([clean_hidden_states, hidden_states], dim=1)
+
+            if aug_timestep is None:
+                aug_timestep = torch.zeros_like(timestep)
+            if aug_timestep.dim() == 2:
+                ts_seq_len = aug_timestep.shape[1]
+                aug_temb = self.time_in(aug_timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
+            else:
+                aug_temb = self.time_in(aug_timestep, timestep_r=timestep_r)
+            temb = torch.cat([aug_temb, temb], dim=1)
+
+        # Prepare block-wise causal attention mask
+        if kv_cache is None:
+            if clean_hidden_states is not None:
+                self.block_mask = self._prepare_teacher_forcing_mask(
+                    device=hidden_states.device,
+                    num_frames=num_frames,
+                    frame_seqlen=post_patch_height * post_patch_width,
+                    num_frame_per_block=self.num_frame_per_block,
+                    text_seq_len=txt_kv_cache[0]["k_txt"].shape[1]
+                )
+            else:
+                self.block_mask = self._prepare_blockwise_causal_attn_mask(
+                    device=hidden_states.device,
+                    num_frames=num_frames,
+                    frame_seqlen=post_patch_height * post_patch_width,
+                    num_frame_per_block=self.num_frame_per_block,
+                    local_attn_size=self.local_attn_size,
+                    text_seq_len=txt_kv_cache[0]["k_txt"].shape[1]
+                )
+
+        # 4. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block_index, block in enumerate(self.double_blocks):
+                hidden_states, new_cache = self._gradient_checkpointing_func(
+                    block,
+                    txt_inference=False,
+                    vision_inference=True,
+                    img=hidden_states,
+                    vec=temb,
+                    freqs_cis=freqs_cis,
+                    block_mask=self.block_mask,
+                    kv_cache=kv_cache[block_index] if kv_cache is not None else None,
+                    txt_kv_cache=txt_kv_cache[block_index],
+                    current_start=current_start
+                )
+                if new_cache is not None and kv_cache is not None:
+                    for k in new_cache.keys():
+                        kv_cache[block_index][k] = new_cache[k].clone()
+
+        else:
+            for block_index, block in enumerate(self.double_blocks):
+                hidden_states, new_cache = block(
+                    txt_inference=False,
+                    vision_inference=True,
+                    img=hidden_states,
+                    vec=temb,
+                    freqs_cis=freqs_cis,
+                    block_mask=self.block_mask,
+                    kv_cache=kv_cache[block_index] if kv_cache is not None else None,
+                    txt_kv_cache=txt_kv_cache[block_index],
+                    current_start=current_start
+                )
+                if new_cache is not None and kv_cache is not None:
+                    for k in new_cache.keys():
+                        kv_cache[block_index][k] = new_cache[k].clone()
+
+        if clean_hidden_states is not None:
+            hidden_states = hidden_states[:, hidden_states.shape[1] // 2:]
+
+        # Final layer processing
+        hidden_states = self.final_layer(hidden_states, temb)
+        # Unpatchify to get original shape
+        hidden_states = unpatchify(hidden_states, post_patch_num_frames, post_patch_height, post_patch_width, self.patch_size, self.out_channels)
+
+        return hidden_states, kv_cache
+
+    def forward(
+        self,
+        txt_inference=False,
+        vision_inference=False,
+        **kwargs,
+    ):
+        if txt_inference:
+            return self.forward_txt(**kwargs)
+        elif vision_inference:
+            return self.forward_vision(**kwargs)
+        else:
+            raise ValueError("txt_inference and vision_inference cannot be both False")

fastvideo/models/dits/causal_wanvideo.py

@@ -33,7 +33,7 @@ from fastvideo.layers.visual_embedding import (PatchEmbed)
 from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
 from fastvideo.models.dits.wanvideo import WanT2VCrossAttention, WanTimeTextImageEmbedding
-from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.platforms import AttentionBackendEnum, current_platform
 
 logger = init_logger(__name__)
 class CausalWanSelfAttention(nn.Module):
@@ -88,10 +88,25 @@ class CausalWanSelfAttention(nn.Module):
             cache_start = current_start
 
         cos, sin = freqs_cis
-        roped_query = _apply_rotary_emb(q, cos, sin, is_neox_style=False).type_as(v)
-        roped_key = _apply_rotary_emb(k, cos, sin, is_neox_style=False).type_as(v)
 
         if kv_cache is None:
+            is_tf = (q.shape[1] == cos.shape[0] * 2)
+            if is_tf:
+                q_chunk = torch.chunk(q, 2, dim=1)
+                k_chunk = torch.chunk(k, 2, dim=1)
+                roped_query = []
+                roped_key = []
+                for ii in range(2):
+                    rq = _apply_rotary_emb(q_chunk[ii], cos, sin, is_neox_style=False).type_as(v)
+                    rk = _apply_rotary_emb(k_chunk[ii], cos, sin, is_neox_style=False).type_as(v)
+                    roped_query.append(rq)
+                    roped_key.append(rk)
+                roped_query = torch.cat(roped_query, dim=1)
+                roped_key = torch.cat(roped_key, dim=1)
+            else:
+                roped_query = _apply_rotary_emb(q, cos, sin, is_neox_style=False).type_as(v)
+                roped_key = _apply_rotary_emb(k, cos, sin, is_neox_style=False).type_as(v)
+
             # Padding for flex attention
             padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
             padded_roped_query = torch.cat(
@@ -120,6 +135,8 @@ class CausalWanSelfAttention(nn.Module):
                 block_mask=block_mask
             )[:, :, :-padded_length].transpose(2, 1)
         else:
+            roped_query = _apply_rotary_emb(q, cos, sin, is_neox_style=False).type_as(v)
+            roped_key = _apply_rotary_emb(k, cos, sin, is_neox_style=False).type_as(v)
             frame_seqlen = q.shape[1]
             current_end = current_start + roped_query.shape[1]
             sink_tokens = self.sink_size * frame_seqlen
@@ -286,6 +303,8 @@ class CausalWanTransformerBlock(nn.Module):
         null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
         norm_hidden_states, hidden_states = self.self_attn_residual_norm(
             hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 2. Cross-attention
         attn_output = self.attn2(norm_hidden_states,
@@ -294,10 +313,13 @@ class CausalWanTransformerBlock(nn.Module):
                                  crossattn_cache=crossattn_cache)
         norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
             hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
 
         # 3. Feed-forward
         ff_output = self.ffn(norm_hidden_states)
         hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)
 
         return hidden_states
 
@@ -376,6 +398,94 @@ class CausalWanTransformer3DModel(BaseDiT):
 
         self.__post_init__()
 
+    @staticmethod
+    def _prepare_teacher_forcing_mask(
+        device: torch.device | str, num_frames: int = 21,
+        frame_seqlen: int = 1560, num_frame_per_block=1
+    ) -> BlockMask:
+        """
+        we will divide the token sequence into the following format
+        [1 latent frame] [1 latent frame] ... [1 latent frame]
+        We use flexattention to construct the attention mask
+        """
+        # debug
+        DEBUG = False
+        if DEBUG:
+            num_frames = 9
+            frame_seqlen = 256
+
+        total_length = num_frames * frame_seqlen * 2
+
+        # we do right padding to get to a multiple of 128
+        padded_length = math.ceil(total_length / 128) * 128 - total_length
+
+        clean_ends = num_frames * frame_seqlen
+        # for clean context frames, we can construct their flex attention mask based on a [start, end] interval
+        context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        # for noisy frames, we need two intervals to construct the flex attention mask [context_start, context_end] [noisy_start, noisy_end]
+        noise_context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_noise_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+        noise_noise_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
+
+        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
+        attention_block_size = frame_seqlen * num_frame_per_block
+        frame_indices = torch.arange(
+            start=0,
+            end=num_frames * frame_seqlen,
+            step=attention_block_size,
+            device=device, dtype=torch.long
+        )
+
+        # attention for clean context frames
+        for start in frame_indices:
+            context_ends[start:start + attention_block_size] = start + attention_block_size
+
+        noisy_image_start_list = torch.arange(
+            num_frames * frame_seqlen, total_length,
+            step=attention_block_size,
+            device=device, dtype=torch.long
+        )
+        noisy_image_end_list = noisy_image_start_list + attention_block_size
+
+        # attention for noisy frames
+        for block_index, (start, end) in enumerate(zip(noisy_image_start_list, noisy_image_end_list)):
+            # attend to noisy tokens within the same block
+            noise_noise_starts[start:end] = start
+            noise_noise_ends[start:end] = end
+            # attend to context tokens in previous blocks
+            # noise_context_starts[start:end] = 0
+            noise_context_ends[start:end] = block_index * attention_block_size
+
+        def attention_mask(b, h, q_idx, kv_idx):
+            # first design the mask for clean frames
+            clean_mask = (q_idx < clean_ends) & (kv_idx < context_ends[q_idx])
+            # then design the mask for noisy frames
+            # noisy frames will attend to all clean preceeding clean frames + itself
+            C1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx])
+            C2 = (kv_idx < noise_context_ends[q_idx]) & (kv_idx >= noise_context_starts[q_idx])
+            noise_mask = (q_idx >= clean_ends) & (C1 | C2)
+
+            eye_mask = q_idx == kv_idx
+            return eye_mask | clean_mask | noise_mask
+
+        block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
+                                       KV_LEN=total_length + padded_length, _compile=False, device=device)
+
+        if DEBUG:
+            print(block_mask)
+            import imageio
+            import numpy as np
+            from torch.nn.attention.flex_attention import create_mask
+
+            mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
+                                padded_length, KV_LEN=total_length + padded_length, device=device)
+            import cv2
+            mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
+            imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
+
+        return block_mask
+
     @staticmethod
     def _prepare_blockwise_causal_attn_mask(
         device: torch.device | str, num_frames: int = 21,
@@ -551,6 +661,7 @@ class CausalWanTransformer3DModel(BaseDiT):
                 encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
                 | None = None,
                 start_frame: int = 0,
+                clean_hidden_states: torch.Tensor | None = None,
                 **kwargs) -> torch.Tensor:
 
         orig_dtype = hidden_states.dtype
@@ -562,6 +673,10 @@ class CausalWanTransformer3DModel(BaseDiT):
         else:
             encoder_hidden_states_image = None
 
+        if timestep.ndim == 1:
+            # [B] -> [B, F]
+            timestep = timestep.unsqueeze(1).repeat(1, hidden_states.shape[2])
+
         batch_size, num_channels, num_frames, height, width = hidden_states.shape
         p_t, p_h, p_w = self.patch_size
         post_patch_num_frames = num_frames // p_t
@@ -588,13 +703,21 @@ class CausalWanTransformer3DModel(BaseDiT):
 
         # Construct blockwise causal attn mask
         if self.block_mask is None:
-            self.block_mask = self._prepare_blockwise_causal_attn_mask(
-                device=hidden_states.device,
-                num_frames=num_frames,
-                frame_seqlen=post_patch_height * post_patch_width,
-                num_frame_per_block=self.num_frame_per_block,
-                local_attn_size=self.local_attn_size
-            )
+            if clean_hidden_states is not None:
+                self.block_mask = self._prepare_teacher_forcing_mask(
+                    device=hidden_states.device,
+                    num_frames=num_frames,
+                    frame_seqlen=post_patch_height * post_patch_width,
+                    num_frame_per_block=self.num_frame_per_block,
+                )
+            else:
+                self.block_mask = self._prepare_blockwise_causal_attn_mask(
+                    device=hidden_states.device,
+                    num_frames=num_frames,
+                    frame_seqlen=post_patch_height * post_patch_width,
+                    num_frame_per_block=self.num_frame_per_block,
+                    local_attn_size=self.local_attn_size
+                )
 
         hidden_states = self.patch_embedding(hidden_states)
         grid_sizes = torch.stack(
@@ -607,6 +730,17 @@ class CausalWanTransformer3DModel(BaseDiT):
                         timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
         timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
 
+        if clean_hidden_states is not None:
+            clean_hidden_states = self.patch_embedding(clean_hidden_states)
+            clean_hidden_states = clean_hidden_states.flatten(2).transpose(1, 2)
+            hidden_states = torch.cat([clean_hidden_states, hidden_states], dim=1)
+
+            aug_timestep = torch.zeros_like(timestep)
+            temb_clean = self.condition_embedder.time_embedder(aug_timestep.flatten(), None)
+            timestep_proj_clean = self.condition_embedder.time_modulation(temb_clean)
+            timestep_proj_clean = timestep_proj_clean.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=aug_timestep.shape)
+            timestep_proj = torch.cat([timestep_proj_clean, timestep_proj], dim=1)
+
         if encoder_hidden_states_image is not None:
             encoder_hidden_states = torch.concat(
                 [encoder_hidden_states_image, encoder_hidden_states], dim=1)
@@ -630,6 +764,9 @@ class CausalWanTransformer3DModel(BaseDiT):
                                         timestep_proj, freqs_cis,
                                         block_mask=self.block_mask)
 
+        if clean_hidden_states is not None:
+            hidden_states = hidden_states[:, hidden_states.shape[1] // 2:]
+
         # 5. Output norm, projection & unpatchify
         temb = temb.unflatten(dim=0, sizes=timestep.shape).unsqueeze(2)
         shift, scale = (self.scale_shift_table.unsqueeze(1) + temb).chunk(2,
@@ -644,12 +781,13 @@ class CausalWanTransformer3DModel(BaseDiT):
     def forward(
         self,
         *args,
+        clean_hidden_states: torch.Tensor | None = None,
         **kwargs
     ):
         if kwargs.get('kv_cache', None) is not None:
             return self._forward_inference(*args, **kwargs)
         else:
-            return self._forward_train(*args, **kwargs)
+            return self._forward_train(*args, clean_hidden_states=clean_hidden_states, **kwargs)
 
 
     def unpatchify(self, x, grid_sizes):

fastvideo/models/dits/hunyuanvideo15.py

@@ -127,8 +127,9 @@ class HunyuanVideo15TimeEmbedding(nn.Module):
         self,
         timestep: torch.Tensor,
         timestep_r: Optional[torch.Tensor] = None,
+        timestep_seq_len: int | None = None,
     ) -> torch.Tensor:
-        timesteps_emb = self.timestep_embedder(timestep)
+        timesteps_emb = self.timestep_embedder(timestep, timestep_seq_len)
 
         if timestep_r is not None:
             timesteps_emb_r = self.timestep_embedder_r(timestep_r)
@@ -473,6 +474,7 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
         guidance: Optional[torch.Tensor] = None,
         timestep_r: Optional[torch.LongTensor] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs
     ):
         encoder_hidden_states_image = encoder_hidden_states_image[0]
         encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
@@ -494,7 +496,12 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
         freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
 
         # 2. Conditional embeddings
-        temb = self.time_in(timestep, timestep_r=timestep_r)
+        if timestep.dim() == 2:
+            ts_seq_len = timestep.shape[1]
+            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
+        else:
+            temb = self.time_in(timestep, timestep_r=timestep_r)
+        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
 
         hidden_states = self.img_in(hidden_states)
         hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
@@ -626,6 +633,8 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
                 )
 
         # Final layer processing
+        if get_sp_world_size() > 1:
+            hidden_states = hidden_states.contiguous()
         hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
         hidden_states = self.final_layer(hidden_states, temb)
         # Unpatchify to get original shape
@@ -696,6 +705,7 @@ class SingleTokenRefiner(nn.Module):
         else:
             mask_float = mask.float().unsqueeze(-1)  # [B, L, 1]
             context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
+        context_aware_representations = context_aware_representations.to(original_dtype)
 
         context_aware_representations = self.c_embedder(
             context_aware_representations)
@@ -848,6 +858,13 @@ class FinalLayer(nn.Module):
     def forward(self, x, c):
         # What the heck HF? Why you change the scale and shift order here???
         scale, shift = self.adaLN_modulation(c).chunk(2, dim=-1)
-        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        if c.dim() == 3:
+            # [bs, seq_len, inner_dim]
+            num_frames = scale.shape[1]
+            frame_seqlen = x.shape[1] // num_frames
+            x = (self.norm_final(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale.unsqueeze(2)) + shift.unsqueeze(2)).flatten(1, 2)
+        else:
+            # [bs, inner_dim]
+            x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
         x, _ = self.linear(x)
         return x

fastvideo/models/dits/hyworld/__init__.py

@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+HYWorld (HY-WorldPlay) model components for FastVideo.
+
+This module provides:
+- HYWorldTransformer3DModel: The main transformer model with ProPE and action conditioning
+- HYWorldVideoGenerator: Extended VideoGenerator for HYWorld inference
+- Utilities for pose processing and camera trajectory generation
+"""
+
+from .hyworld import HYWorldTransformer3DModel, HYWorldDoubleStreamBlock
+
+# Inference utilities (used by examples)
+from .resolution_utils import (
+    get_resolution_from_image,
+)
+
+__all__ = [
+    # Model (used by model registry)
+    "HYWorldTransformer3DModel",
+    "HYWorldDoubleStreamBlock",
+    # Inference utilities (used by examples)
+    "get_resolution_from_image",
+]

fastvideo/models/dits/hyworld/camera_rope.py

@@ -0,0 +1,261 @@
+# HY-WorldPlay/hyvideo/prope/camera_rope.py
+
+# MIT License
+#
+# Copyright (c) Authors of
+# "PRoPE: Projective Positional Encoding for Multiview Transformers"
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# How to use PRoPE attention for self-attention:
+#
+# 1. Easiest way (fast):
+#    attn = PropeDotProductAttention(...)
+#    o = attn(q, k, v, viewmats, Ks)
+#
+# 2. More flexible way (fast):
+#    attn = PropeDotProductAttention(...)
+#    attn._precompute_and_cache_apply_fns(viewmats, Ks)
+#    q = attn._apply_to_q(q)
+#    k = attn._apply_to_kv(k)
+#    v = attn._apply_to_kv(v)
+#    o = F.scaled_dot_product_attention(q, k, v, **kwargs)
+#    o = attn._apply_to_o(o)
+#
+# 3. The most flexible way (but slower because repeated computation of RoPE coefficients):
+#    o = prope_dot_product_attention(q, k, v, ...)
+#
+# How to use PRoPE attention for cross-attention:
+#
+#    attn_src = PropeDotProductAttention(...)
+#    attn_tgt = PropeDotProductAttention(...)
+#    attn_src._precompute_and_cache_apply_fns(viewmats_src, Ks_src)
+#    attn_tgt._precompute_and_cache_apply_fns(viewmats_tgt, Ks_tgt)
+#    q_src = attn_src._apply_to_q(q_src)
+#    k_tgt = attn_tgt._apply_to_kv(k_tgt)
+#    v_tgt = attn_tgt._apply_to_kv(v_tgt)
+#    o_src = F.scaled_dot_product_attention(q_src, k_tgt, v_tgt, **kwargs)
+#    o_src = attn_src._apply_to_o(o_src)
+
+from functools import partial
+from typing import Callable, Optional, Tuple, List
+
+import torch
+import torch.nn.functional as F
+
+
+def prope_qkv(
+    q: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    k: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    v: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    *,
+    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
+    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
+    patches_x: int = None,  # How many patches wide is each image?
+    patches_y: int = None,  # How many patches tall is each image?
+    image_width: int = None,  # Width of the image. Used to normalize intrinsics.
+    image_height: int = None,  # Height of the image. Used to normalize intrinsics.
+    coeffs_x: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    coeffs_y: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    mask: Optional[torch.Tensor] = None,
+    kv_cache=None,
+    is_cache: bool = False,
+    **kwargs,
+) -> torch.Tensor:
+    """Similar to torch.nn.functional.scaled_dot_product_attention, but applies PRoPE-style
+    positional encoding.
+
+    Currently, we assume that the sequence length is equal to:
+
+        cameras * patches_x * patches_y
+
+    And token ordering allows the `(seqlen,)` axis to be reshaped into
+    `(cameras, patches_x, patches_y)`.
+    """
+    # We're going to assume self-attention: all inputs are the same shape.
+    (batch, num_heads, seqlen, head_dim) = q.shape
+    cameras = viewmats.shape[1]
+    assert q.shape == k.shape == v.shape
+    assert viewmats.shape == (batch, cameras, 4, 4)
+    assert Ks is None or Ks.shape == (batch, cameras, 3, 3)
+    # assert seqlen == cameras * patches_x * patches_y
+
+    apply_fn_q, apply_fn_kv, apply_fn_o = _prepare_apply_fns_all_dim(
+        head_dim=head_dim,
+        viewmats=viewmats,
+        Ks=Ks,
+        patches_x=patches_x,
+        patches_y=patches_y,
+        image_width=image_width,
+        image_height=image_height,
+        coeffs_x=coeffs_x,
+        coeffs_y=coeffs_y,
+    )
+
+    query = apply_fn_q(q)
+    key = apply_fn_kv(k)
+    value = apply_fn_kv(v)
+
+    return query, key, value, apply_fn_o
+
+
+def _prepare_apply_fns_all_dim(
+    head_dim: int,  # Q/K/V will have this last dimension
+    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
+    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
+    patches_x: int,  # How many patches wide is each image?
+    patches_y: int,  # How many patches tall is each image?
+    image_width: int,  # Width of the image. Used to normalize intrinsics.
+    image_height: int,  # Height of the image. Used to normalize intrinsics.
+    coeffs_x: Optional[torch.Tensor] = None,
+    coeffs_y: Optional[torch.Tensor] = None,
+) -> Tuple[
+    Callable[[torch.Tensor], torch.Tensor],
+    Callable[[torch.Tensor], torch.Tensor],
+    Callable[[torch.Tensor], torch.Tensor],
+]:
+    """Prepare transforms for PRoPE-style positional encoding."""
+    device = viewmats.device
+    (batch, cameras, _, _) = viewmats.shape
+
+    # Normalize camera intrinsics.
+    if Ks is not None:
+        Ks_norm = torch.zeros_like(Ks)
+        Ks_norm[..., 0, 0] = Ks[..., 0, 0]
+        Ks_norm[..., 1, 1] = Ks[..., 1, 1]
+        Ks_norm[..., 0, 2] = 0
+        Ks_norm[..., 1, 2] = 0
+        Ks_norm[..., 2, 2] = 1.0
+        Ks_norm = Ks_norm.to(dtype=Ks.dtype)
+        del Ks
+
+        # Compute the camera projection matrices we use in PRoPE.
+        # - K is an `image<-camera` transform.
+        # - viewmats is a `camera<-world` transform.
+        # - P = lift(K) @ viewmats is an `image<-world` transform.
+        P = torch.einsum("...ij,...jk->...ik", _lift_K(Ks_norm), viewmats)
+        P_T = P.transpose(-1, -2).to(dtype=viewmats.dtype)
+        P_inv = torch.einsum(
+            "...ij,...jk->...ik",
+            _invert_SE3(viewmats),
+            _lift_K(_invert_K(Ks_norm)),
+        ).to(dtype=viewmats.dtype)
+
+    else:
+        # GTA formula. P is `camera<-world` transform.
+        P = viewmats
+        P_T = P.transpose(-1, -2)
+        P_inv = _invert_SE3(viewmats)
+
+    assert P.shape == P_inv.shape == (batch, cameras, 4, 4)
+
+    # Block-diagonal transforms to the inputs and outputs of the attention operator.
+    assert head_dim % 4 == 0
+    transforms_q = [
+        (partial(_apply_tiled_projmat, matrix=P_T), head_dim),
+    ]
+    transforms_kv = [
+        (partial(_apply_tiled_projmat, matrix=P_inv), head_dim),
+    ]
+    transforms_o = [
+        (partial(_apply_tiled_projmat, matrix=P), head_dim),
+    ]
+
+    apply_fn_q = partial(_apply_block_diagonal, func_size_pairs=transforms_q)
+    apply_fn_kv = partial(_apply_block_diagonal, func_size_pairs=transforms_kv)
+    apply_fn_o = partial(_apply_block_diagonal, func_size_pairs=transforms_o)
+    return apply_fn_q, apply_fn_kv, apply_fn_o
+
+
+def _apply_tiled_projmat(
+    feats: torch.Tensor,  # (batch, num_heads, seqlen, feat_dim)
+    matrix: torch.Tensor,  # (batch, cameras, D, D)
+) -> torch.Tensor:
+    """Apply projection matrix to features."""
+    # - seqlen => (cameras, patches_x * patches_y)
+    # - feat_dim => (feat_dim // 4, 4)
+    (batch, num_heads, seqlen, feat_dim) = feats.shape
+    cameras = matrix.shape[1]
+    assert seqlen >= cameras and seqlen % cameras == 0
+    D = matrix.shape[-1]
+    assert matrix.shape == (batch, cameras, D, D)
+    assert feat_dim % D == 0
+    return torch.einsum(
+        "bcij,bncpkj->bncpki",
+        matrix,
+        feats.reshape((batch, num_heads, cameras, -1, feat_dim // D, D)),
+    ).reshape(feats.shape)
+
+
+def _apply_block_diagonal(
+    feats: torch.Tensor,  # (..., dim)
+    func_size_pairs: List[Tuple[Callable[[torch.Tensor], torch.Tensor], int]],
+) -> torch.Tensor:
+    """Apply a block-diagonal function to an input array.
+
+    Each function is specified as a tuple with form:
+
+        ((Tensor) -> Tensor, int)
+
+    Where the integer is the size of the input to the function.
+    """
+    funcs, block_sizes = zip(*func_size_pairs)
+    assert feats.shape[-1] == sum(block_sizes)
+    x_blocks = torch.split(feats, block_sizes, dim=-1)
+    out = torch.cat(
+        [f(x_block) for f, x_block in zip(funcs, x_blocks)],
+        dim=-1,
+    )
+    assert out.shape == feats.shape, "Input/output shapes should match."
+    return out
+
+
+def _invert_SE3(transforms: torch.Tensor) -> torch.Tensor:
+    """Invert a 4x4 SE(3) matrix."""
+    assert transforms.shape[-2:] == (4, 4)
+    Rinv = transforms[..., :3, :3].transpose(-1, -2)
+    out = torch.zeros_like(transforms)
+    out[..., :3, :3] = Rinv
+    out[..., :3, 3] = -torch.einsum("...ij,...j->...i", Rinv, transforms[..., :3, 3])
+    out[..., 3, 3] = 1.0
+    out = out.to(dtype=transforms.dtype)
+    return out
+
+
+def _lift_K(Ks: torch.Tensor) -> torch.Tensor:
+    """Lift 3x3 matrices to homogeneous 4x4 matrices."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = torch.zeros(Ks.shape[:-2] + (4, 4), device=Ks.device)
+    out[..., :3, :3] = Ks
+    out[..., 3, 3] = 1.0
+    out = out.to(dtype=Ks.dtype)
+    return out
+
+
+def _invert_K(Ks: torch.Tensor) -> torch.Tensor:
+    """Invert 3x3 intrinsics matrices. Assumes no skew."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = torch.zeros_like(Ks)
+    out[..., 0, 0] = 1.0 / Ks[..., 0, 0]
+    out[..., 1, 1] = 1.0 / Ks[..., 1, 1]
+    out[..., 0, 2] = -Ks[..., 0, 2] / Ks[..., 0, 0]
+    out[..., 1, 2] = -Ks[..., 1, 2] / Ks[..., 1, 1]
+    out[..., 2, 2] = 1.0
+    out = out.to(dtype=Ks.dtype)
+    return out

fastvideo/models/dits/hyworld/data_utils.py

@@ -0,0 +1,76 @@
+# HY-WorldPlay/hyvideo/utils/data_utils.py
+
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
+#
+# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
+# output and results therefrom are provided "AS IS" without any express or implied warranties of
+# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
+# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
+# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
+# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
+# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
+# of rights and permissions under this agreement.
+# See the License for the specific language governing permissions and limitations under the License.
+
+import numpy as np
+from PIL import Image
+
+
+def resize_and_center_crop(image, target_width, target_height):
+    if target_height == image.shape[0] and target_width == image.shape[1]:
+        return image
+
+    pil_image = Image.fromarray(image)
+    original_width, original_height = pil_image.size
+    scale_factor = max(target_width / original_width, target_height / original_height)
+    resized_width = int(round(original_width * scale_factor))
+    resized_height = int(round(original_height * scale_factor))
+    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
+    left = (resized_width - target_width) / 2
+    top = (resized_height - target_height) / 2
+    right = (resized_width + target_width) / 2
+    bottom = (resized_height + target_height) / 2
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return np.array(cropped_image)
+
+
+def get_closest_ratio(height: float, width: float, ratios: list, buckets: list):
+    """
+    Get the closest ratio in the buckets.
+
+    Args:
+        height (float): video height
+        width (float): video width
+        ratios (list): video aspect ratio
+        buckets (list): buckets generated by `generate_crop_size_list`
+
+    Returns:
+        the closest size in the buckets and the corresponding ratio
+    """
+    aspect_ratio = float(height) / float(width)
+
+    ratios_array = np.array(ratios)
+    closest_ratio_id = np.abs(ratios_array - aspect_ratio).argmin()
+    closest_size = buckets[closest_ratio_id]
+    closest_ratio = ratios_array[closest_ratio_id]
+
+    return closest_size, closest_ratio
+
+
+def generate_crop_size_list(base_size=256, patch_size=16, max_ratio=4.0):
+    num_patches = round((base_size / patch_size) ** 2)
+    assert max_ratio >= 1.0
+    crop_size_list = []
+    wp, hp = num_patches, 1
+    while wp > 0:
+        if max(wp, hp) / min(wp, hp) <= max_ratio:
+            crop_size_list.append((wp * patch_size, hp * patch_size))
+        if (hp + 1) * wp <= num_patches:
+            hp += 1
+        else:
+            wp -= 1
+    return crop_size_list

fastvideo/models/dits/hyworld/hyworld.py

@@ -0,0 +1,569 @@
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
+#
+# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
+# output and results therefrom are provided "AS IS" without any express or implied warranties of
+# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
+# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
+# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
+# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
+# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
+# of rights and permissions under this agreement.
+# See the License for the specific language governing permissions and limitations under the License.
+
+from typing import Any, Optional
+
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+from fastvideo.distributed.communication_op import (
+    sequence_model_parallel_all_gather_with_unpad,
+    sequence_model_parallel_shard)
+from fastvideo.configs.models.dits import HYWorldConfig
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
+from fastvideo.layers.visual_embedding import TimestepEmbedder, unpatchify
+from fastvideo.models.dits.hunyuanvideo15 import (
+    MMDoubleStreamBlock,
+    HunyuanVideo15Transformer3DModel,
+)
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.logger import init_logger
+from fastvideo.forward_context import set_forward_context
+from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.distributed.utils import create_attention_mask_for_padding
+
+from .camera_rope import prope_qkv
+
+logger = init_logger(__name__)
+
+
+class HYWorldDoubleStreamBlock(MMDoubleStreamBlock):
+    """
+    Extended MMDoubleStreamBlock with ProPE (Projective Positional Encoding) support
+    for camera-aware attention in HY-World/WorldPlay models.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        mlp_ratio: float,
+        dtype: torch.dtype | None = None,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+        prefix: str = "",
+    ):
+        super().__init__(
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            mlp_ratio=mlp_ratio,
+            dtype=dtype,
+            supported_attention_backends=supported_attention_backends,
+            prefix=prefix,
+        )
+        self.hidden_size = hidden_size
+
+        # Add ProPE projection layer for camera-aware attention
+        self.img_attn_prope_proj = ReplicatedLinear(
+            hidden_size,
+            hidden_size,
+            bias=True,
+            params_dtype=dtype,
+            prefix=f"{prefix}.img_attn_prope_proj"
+        )
+        # Zero-initialize ProPE projection (starts as identity)
+        nn.init.zeros_(self.img_attn_prope_proj.weight)
+        if self.img_attn_prope_proj.bias is not None:
+            nn.init.zeros_(self.img_attn_prope_proj.bias)
+
+    def forward(
+        self,
+        img: torch.Tensor,
+        txt: torch.Tensor,
+        encoder_attention_mask: torch.Tensor,
+        vec: torch.Tensor,
+        vec_txt: torch.Tensor,
+        freqs_cis: tuple,
+        seq_attention_mask: torch.Tensor,
+        viewmats: torch.Tensor,
+        Ks: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Forward pass with ProPE camera conditioning.
+
+        Args:
+            img: Image/video tokens
+            txt: Text tokens
+            encoder_attention_mask: Text attention mask
+            vec: Modulation vector
+            freqs_cis: Rotary embedding frequencies
+            seq_attention_mask: Sequence attention mask
+            viewmats: Camera view matrices for ProPE [B, T, 4, 4]
+            Ks: Camera intrinsics for ProPE [B, T, 3, 3]
+
+        Returns:
+            Tuple of (img, txt) output tokens
+        """
+        # Process modulation vectors (inherited from parent)
+        img_mod_outputs = self.img_mod(vec)
+        (
+            img_attn_shift,
+            img_attn_scale,
+            img_attn_gate,
+            img_mlp_shift,
+            img_mlp_scale,
+            img_mlp_gate,
+        ) = torch.chunk(img_mod_outputs, 6, dim=-1)
+
+        txt_mod_outputs = self.txt_mod(vec_txt)
+        (
+            txt_attn_shift,
+            txt_attn_scale,
+            txt_attn_gate,
+            txt_mlp_shift,
+            txt_mlp_scale,
+            txt_mlp_gate,
+        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)
+
+        # Prepare image for attention using fused operation
+        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale, convert_modulation_dtype=True)
+        # Get QKV for image
+        img_qkv, _ = self.img_attn_qkv(img_attn_input)
+        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]
+
+        # Split QKV
+        img_qkv = img_qkv.view(batch_size, image_seq_len, 3,
+                               self.num_attention_heads, -1)
+        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :,
+                                                                          2]
+
+        # Apply QK-Norm if needed
+        img_q = self.img_attn_q_norm(img_q).to(img_v)
+        img_k = self.img_attn_k_norm(img_k).to(img_v)
+
+        # Prepare text for attention using fused operation
+        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale, convert_modulation_dtype=True)
+
+        # Get QKV for text
+        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
+        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]
+
+        # Split QKV
+        txt_qkv = txt_qkv.view(batch_size, text_seq_len, 3,
+                               self.num_attention_heads, -1)
+        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :,
+                                                                          2]
+        # Apply QK-Norm if needed
+        txt_q = self.txt_attn_q_norm(txt_q).to(txt_q.dtype)
+        txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
+
+        # begin hyworld: add camera pose through prope
+        img_q_prope, img_k_prope, img_v_prope, apply_fn_o = prope_qkv(
+            img_q.permute(0, 2, 1, 3),
+            img_k.permute(0, 2, 1, 3),
+            img_v.permute(0, 2, 1, 3),
+            viewmats=viewmats,
+            Ks=Ks,
+        )  # [batch, num_heads, seqlen, head_dim]
+        img_q_prope = img_q_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
+        img_k_prope = img_k_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
+        img_v_prope = img_v_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
+        # end hyworld
+
+        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+        attn_metadata = FlashAttnMetadataBuilder().build(
+            current_timestep=0,
+            attn_mask=encoder_attention_mask,
+        )
+        # Run distributed attention
+        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
+            img_attn, txt_attn = self.attn(img_q, img_k, img_v, txt_q, txt_k, txt_v, freqs_cis=freqs_cis, attention_mask=seq_attention_mask)
+        
+        # begin hyworld
+        # attention with prope
+        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+        attn_metadata_prope = FlashAttnMetadataBuilder().build(
+            current_timestep=0,
+            attn_mask=encoder_attention_mask,
+        )       
+        # NOTE: Do NOT pass freqs_cis to prope attention - HY-WorldPlay does not apply RoPE to prope
+        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata_prope):
+            img_attn_prope, _ = self.attn(
+                img_q_prope, img_k_prope, img_v_prope, txt_q, txt_k, txt_v, 
+                freqs_cis=None, attention_mask=seq_attention_mask  # No RoPE for prope attention
+            )
+            img_attn_prope = img_attn_prope.reshape(batch_size, image_seq_len, -1)
+            img_attn_prope = rearrange(
+                img_attn_prope, "B L (H D) -> B H L D", H=self.num_attention_heads
+            )
+            img_attn_prope = apply_fn_o(img_attn_prope) # [batch, num_heads, seqlen, head_dim]
+            img_attn_prope = rearrange(img_attn_prope, "B H L D -> B L (H D)")
+
+        # add prope to img_attn
+        img_attn_out, _ = self.img_attn_proj(img_attn.view(batch_size, image_seq_len, -1))
+        img_attn_prope_out, _ = self.img_attn_prope_proj(img_attn_prope)
+        img_attn_out = img_attn_out + img_attn_prope_out
+        # end hyworld
+
+        # Use fused operation for residual connection, normalization, and modulation
+        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
+            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale, convert_modulation_dtype=True)
+
+        # Process image MLP
+        img_mlp_out = self.img_mlp(img_mlp_input)
+        img = self.img_mlp_residual(img_residual, img_mlp_out, img_mlp_gate)
+
+        # Process text attention output
+        txt_attn_out, _ = self.txt_attn_proj(
+            txt_attn.reshape(batch_size, text_seq_len, -1))
+
+        # Use fused operation for residual connection, normalization, and modulation
+        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
+            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale, convert_modulation_dtype=True)
+
+        # Process text MLP
+        txt_mlp_out = self.txt_mlp(txt_mlp_input)
+        txt = self.txt_mlp_residual(txt_residual, txt_mlp_out, txt_mlp_gate)
+
+        return img, txt
+
+
+class HYWorldFinalLayer(nn.Module):
+    """
+    Final layer for HYWorld that uses modulate() to handle per-token conditioning.
+    This matches HY-WorldPlay's FinalLayer behavior.
+    """
+
+    def __init__(self,
+                 hidden_size,
+                 patch_size,
+                 out_channels,
+                 dtype=None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        from fastvideo.layers.linear import ReplicatedLinear
+        from fastvideo.layers.visual_embedding import ModulateProjection
+        from fastvideo.layers.layernorm import LayerNormScaleShift
+
+        self.norm_final = LayerNormScaleShift(
+            hidden_size,
+            norm_type="layer",
+            eps=1e-6,
+            elementwise_affine=False,
+            dtype=dtype,
+            prefix=f"{prefix}.norm_final")
+
+        output_dim = patch_size[0] * patch_size[1] * patch_size[2] * out_channels
+
+        self.linear = ReplicatedLinear(hidden_size,
+                                       output_dim,
+                                       bias=True,
+                                       params_dtype=dtype,
+                                       prefix=f"{prefix}.linear")
+
+        # Modulation projection to get shift/scale
+        self.adaLN_modulation = ModulateProjection(
+            hidden_size,
+            factor=2,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.adaLN_modulation")
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
+        x = self.norm_final(x, shift, scale, convert_modulation_dtype=True)
+        x, _ = self.linear(x)
+        return x
+
+
+class HYWorldTransformer3DModel(HunyuanVideo15Transformer3DModel):
+    r"""
+    HY-World Transformer extending HunyuanVideo15 with:
+    - ProPE (Projective Positional Encoding) for camera-aware attention
+    - Action conditioning for interactive video generation
+    """
+    
+    # Class attributes for weight loading - use HYWorld-specific mapping
+    _fsdp_shard_conditions = HYWorldConfig().arch_config._fsdp_shard_conditions
+    _compile_conditions = HYWorldConfig().arch_config._compile_conditions
+    param_names_mapping = HYWorldConfig().arch_config.param_names_mapping
+    reverse_param_names_mapping = HYWorldConfig().arch_config.reverse_param_names_mapping
+
+    def __init__(
+        self,
+        config: HYWorldConfig,
+        hf_config: dict[str, Any],
+    ) -> None:
+        super().__init__(config=config, hf_config=hf_config)
+
+        # Replace double_blocks with HY-World version that supports ProPE
+        self.double_blocks = nn.ModuleList([
+            HYWorldDoubleStreamBlock(
+                hidden_size=self.hidden_size,
+                num_attention_heads=self.num_attention_heads,
+                mlp_ratio=config.arch_config.mlp_ratio,
+                dtype=None,
+                supported_attention_backends=self._supported_attention_backends,
+                prefix=f"{config.prefix}.double_blocks.{i}"
+            )
+            for i in range(config.arch_config.num_layers)
+        ])
+
+        # Add action conditioning module
+        self.action_in = TimestepEmbedder(
+            self.hidden_size,
+            act_layer="silu",
+            dtype=None,
+            prefix=f"{config.prefix}.action_in"
+        )
+        # Zero-initialize action embedding (starts with no effect)
+        nn.init.zeros_(self.action_in.mlp.fc_out.weight)
+        if self.action_in.mlp.fc_out.bias is not None:
+            nn.init.zeros_(self.action_in.mlp.fc_out.bias)
+
+        # Override final_layer with HYWorld version that uses per-token modulate()
+        self.final_layer = HYWorldFinalLayer(
+            hidden_size=self.hidden_size,
+            patch_size=self.patch_size,
+            out_channels=self.out_channels,
+            dtype=None,
+            prefix=f"{config.prefix}.final_layer"
+        )
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: list[torch.Tensor],
+        encoder_attention_mask: list[torch.Tensor],
+        action: torch.Tensor,
+        viewmats: torch.Tensor,
+        Ks: torch.Tensor,
+        timestep_txt: torch.LongTensor,
+        guidance: Optional[torch.Tensor] = None,
+        timestep_r: Optional[torch.LongTensor] = None,
+        attention_kwargs: Optional[dict[str, Any]] = None,
+    ):
+        """
+        Forward pass with action and camera conditioning.
+
+        Args:
+            action: Action tensor for action conditioning [B, T] or [B*T]
+            viewmats: Camera view matrices [B, T, 4, 4]
+            Ks: Camera intrinsics [B, T, 3, 3]
+            ... (other args same as parent)
+        """
+        encoder_hidden_states_image = encoder_hidden_states_image[0]
+        encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
+        encoder_attention_mask, encoder_attention_mask_2 = encoder_attention_mask
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # 1. RoPE
+        # Get rotary embeddings
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames, post_patch_height, post_patch_width),
+            self.hidden_size,
+            self.num_attention_heads,
+            self.config.rope_axes_dim,
+            self.config.rope_theta
+        )
+        freqs_cos = freqs_cos.to(hidden_states.device)
+        freqs_sin = freqs_sin.to(hidden_states.device)
+        # NOTE: freqs_cis does NOT need sharding because FastVideo's DistributedAttention
+        # uses all-to-all to gather the full sequence before applying RoPE
+        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
+
+        # 2. Conditional embeddings
+        temb = self.time_in(timestep, timestep_r=timestep_r)
+        temb_txt = self.time_in(timestep_txt, timestep_r=timestep_r)
+
+        # Add action conditioning if provided
+        # temb shape: [B*T, C] where T = num_frames
+        temb = temb + self.action_in(action.reshape(-1))
+        
+        # Broadcast timestep embedding for transformer blocks (one per spatial token)
+        # [B*T, C] -> [B, T*H*W, C] -> [B*T*H*W, C]
+        temb = repeat(temb, "(B T) C -> B (T H W) C", B=batch_size, H=post_patch_height, W=post_patch_width)
+
+        hidden_states = self.img_in(hidden_states)
+        hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
+
+        current_seq_len = hidden_states.shape[1]
+        sp_world_size = get_sp_world_size()
+        padded_seq_len = current_seq_len * sp_world_size
+
+        if padded_seq_len > original_seq_len:
+            seq_attention_mask = create_attention_mask_for_padding(
+                seq_len=original_seq_len,
+                padded_seq_len=padded_seq_len,
+                batch_size=batch_size,
+                device=hidden_states.device,
+            )
+        else:
+            seq_attention_mask = None
+
+        viewmats_seq = repeat(
+            viewmats, "B T M N->B (T H W) M N",
+            H=post_patch_height,
+            W=post_patch_width
+        )
+        Ks_seq = repeat(
+            Ks, "B T M N->B (T H W) M N",
+            H=post_patch_height,
+            W=post_patch_width
+        )
+        
+        # Shard viewmats, Ks, and temb for sequence parallelism (shard along sequence dim=1)
+        # Note that temb in HY1.5 does not need sharding because it is per-sample modulation
+        # In HYWorld, temb is per-token modulation.
+        if sp_world_size > 1:
+            viewmats_seq, _ = sequence_model_parallel_shard(viewmats_seq, dim=1)
+            Ks_seq, _ = sequence_model_parallel_shard(Ks_seq, dim=1)
+            temb, _ = sequence_model_parallel_shard(temb, dim=1)
+        
+        # Rearrange temb after sharding to match expected shape
+        temb = rearrange(temb, "B S C -> (B S) C")
+
+        # qwen text embedding
+        encoder_hidden_states = self.txt_in(encoder_hidden_states, timestep_txt, encoder_attention_mask)
+
+        encoder_hidden_states_cond_emb = self.cond_type_embed(
+            torch.zeros_like(encoder_hidden_states[:, :, 0], dtype=torch.long)
+        )
+        encoder_hidden_states = encoder_hidden_states + encoder_hidden_states_cond_emb
+
+        # byt5 text embedding
+        encoder_hidden_states_2 = self.txt_in_2(encoder_hidden_states_2)
+
+        encoder_hidden_states_2_cond_emb = self.cond_type_embed(
+            torch.ones_like(encoder_hidden_states_2[:, :, 0], dtype=torch.long)
+        )
+        encoder_hidden_states_2 = encoder_hidden_states_2 + encoder_hidden_states_2_cond_emb
+
+        # image embed
+        encoder_hidden_states_3 = self.image_embedder(encoder_hidden_states_image)
+        is_t2v = torch.all(encoder_hidden_states_image == 0)
+        if is_t2v:
+            encoder_hidden_states_3 = encoder_hidden_states_3 * 0.0
+            encoder_attention_mask_3 = torch.zeros(
+                (batch_size, encoder_hidden_states_3.shape[1]),
+                dtype=encoder_attention_mask.dtype,
+                device=encoder_attention_mask.device,
+            )
+        else:
+            encoder_attention_mask_3 = torch.ones(
+                (batch_size, encoder_hidden_states_3.shape[1]),
+                dtype=encoder_attention_mask.dtype,
+                device=encoder_attention_mask.device,
+            )
+        encoder_hidden_states_3_cond_emb = self.cond_type_embed(
+            2
+            * torch.ones_like(
+                encoder_hidden_states_3[:, :, 0],
+                dtype=torch.long,
+            )
+        )
+        encoder_hidden_states_3 = encoder_hidden_states_3 + encoder_hidden_states_3_cond_emb
+
+        # reorder and combine text tokens: combine valid tokens first, then padding
+        encoder_attention_mask = encoder_attention_mask.bool()
+        encoder_attention_mask_2 = encoder_attention_mask_2.bool()
+        encoder_attention_mask_3 = encoder_attention_mask_3.bool()
+        new_encoder_hidden_states = []
+        new_encoder_attention_mask = []
+
+        for text, text_mask, text_2, text_mask_2, image, image_mask in zip(
+            encoder_hidden_states,
+            encoder_attention_mask,
+            encoder_hidden_states_2,
+            encoder_attention_mask_2,
+            encoder_hidden_states_3,
+            encoder_attention_mask_3,
+        ):
+            # Concatenate: [valid_image, valid_byt5, valid_mllm, invalid_image, invalid_byt5, invalid_mllm]
+            new_encoder_hidden_states.append(
+                torch.cat(
+                    [
+                        image[image_mask],  # valid image
+                        text_2[text_mask_2],  # valid byt5
+                        text[text_mask],  # valid mllm
+                        image[~image_mask],  # invalid image (zeroed)
+                        torch.zeros_like(text_2[~text_mask_2]),  # invalid byt5 (zeroed)
+                        torch.zeros_like(text[~text_mask]),  # invalid mllm (zeroed)
+                    ],
+                    dim=0,
+                )
+            )
+            # Apply same reordering to attention masks
+            new_encoder_attention_mask.append(
+                torch.cat(
+                    [
+                        image_mask[image_mask],
+                        text_mask_2[text_mask_2],
+                        text_mask[text_mask],
+                        image_mask[~image_mask],
+                        text_mask_2[~text_mask_2],
+                        text_mask[~text_mask],
+                    ],
+                    dim=0,
+                )
+            )
+
+        encoder_hidden_states = torch.stack(new_encoder_hidden_states)
+        encoder_attention_mask = torch.stack(new_encoder_attention_mask)
+
+        # 4. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block in self.double_blocks:
+                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    temb,
+                    temb_txt,
+                    freqs_cis,
+                    seq_attention_mask,
+                    viewmats_seq, # hyworld
+                    Ks_seq, # hyworld
+                )
+        else:
+            for block in self.double_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    temb,
+                    temb_txt,
+                    freqs_cis,
+                    seq_attention_mask,
+                    viewmats=viewmats_seq, # hyworld
+                    Ks=Ks_seq, # hyworld
+                )
+
+
+        # Final layer processing (per-token conditioning via HYWorldFinalLayer)
+        # Apply final_layer on sharded data first, then gather
+        hidden_states = self.final_layer(hidden_states, temb)
+        
+        # Gather the output from all ranks
+        if get_sp_world_size() > 1:
+            hidden_states = hidden_states.contiguous()
+        hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
+        
+        # Unpatchify to get original shape
+        hidden_states = unpatchify(hidden_states, post_patch_num_frames, post_patch_height, post_patch_width, self.patch_size, self.out_channels)
+
+        return hidden_states

fastvideo/models/dits/hyworld/pose.py

@@ -0,0 +1,413 @@
+# Some functions from HY-WorldPlay/hyvideo/generate.py
+
+"""
+Pose processing utilities for HYWorld video generation.
+
+This module provides functions to convert camera poses to model input tensors,
+including viewmats, intrinsics, and action labels.
+
+Adapted from HY-WorldPlay: https://github.com/Tencent-Hunyuan/HY-WorldPlay
+"""
+
+import json
+import numpy as np
+import torch
+from scipy.spatial.transform import Rotation as R
+from typing import Union, Optional
+
+from .trajectory import generate_camera_trajectory_local
+
+
+# Mapping from one-hot action encoding to single label
+mapping = {
+    (0, 0, 0, 0): 0,
+    (1, 0, 0, 0): 1,
+    (0, 1, 0, 0): 2,
+    (0, 0, 1, 0): 3,
+    (0, 0, 0, 1): 4,
+    (1, 0, 1, 0): 5,
+    (1, 0, 0, 1): 6,
+    (0, 1, 1, 0): 7,
+    (0, 1, 0, 1): 8,
+}
+
+# Default camera intrinsic matrix (for 1920x1080 resolution)
+DEFAULT_INTRINSIC = [
+    [969.6969696969696, 0.0, 960.0],
+    [0.0, 969.6969696969696, 540.0],
+    [0.0, 0.0, 1.0],
+]
+
+# Default movement speeds
+DEFAULT_FORWARD_SPEED = 0.08  # units per frame
+DEFAULT_YAW_SPEED = np.deg2rad(3)  # radians per frame
+DEFAULT_PITCH_SPEED = np.deg2rad(3)  # radians per frame
+
+
+def one_hot_to_one_dimension(one_hot: torch.Tensor) -> torch.Tensor:
+    """Convert one-hot action encoding to single dimension labels."""
+    return torch.tensor([mapping[tuple(row.tolist())] for row in one_hot])
+
+
+def parse_pose_string(
+    pose_string: str,
+    forward_speed: float = DEFAULT_FORWARD_SPEED,
+    yaw_speed: float = DEFAULT_YAW_SPEED,
+    pitch_speed: float = DEFAULT_PITCH_SPEED,
+) -> list[dict]:
+    """
+    Parse pose string to motions list.
+    
+    Format: "w-3, right-0.5, d-4"
+    - w: forward movement
+    - s: backward movement
+    - a: left movement
+    - d: right movement
+    - up: pitch up rotation
+    - down: pitch down rotation
+    - left: yaw left rotation
+    - right: yaw right rotation
+    - number after dash: duration in frames/latents
+    
+    Args:
+        pose_string: Comma-separated pose commands
+        forward_speed: Movement amount per frame
+        yaw_speed: Yaw rotation amount per frame (radians)
+        pitch_speed: Pitch rotation amount per frame (radians)
+        
+    Returns:
+        List of motion dictionaries for generate_camera_trajectory_local
+    """
+    motions = []
+    commands = [cmd.strip() for cmd in pose_string.split(",")]
+
+    for cmd in commands:
+        if not cmd:
+            continue
+
+        parts = cmd.split("-")
+        if len(parts) != 2:
+            raise ValueError(
+                f"Invalid pose command: {cmd}. Expected format: 'action-duration'"
+            )
+
+        action = parts[0].strip()
+        try:
+            duration = float(parts[1].strip())
+        except ValueError:
+            raise ValueError(f"Invalid duration in command: {cmd}")
+
+        num_frames = int(duration)
+
+        # Parse action and create motion dicts
+        if action == "w":
+            # Forward
+            for _ in range(num_frames):
+                motions.append({"forward": forward_speed})
+        elif action == "s":
+            # Backward
+            for _ in range(num_frames):
+                motions.append({"forward": -forward_speed})
+        elif action == "a":
+            # Left
+            for _ in range(num_frames):
+                motions.append({"right": -forward_speed})
+        elif action == "d":
+            # Right
+            for _ in range(num_frames):
+                motions.append({"right": forward_speed})
+        elif action == "up":
+            # Pitch up
+            for _ in range(num_frames):
+                motions.append({"pitch": pitch_speed})
+        elif action == "down":
+            # Pitch down
+            for _ in range(num_frames):
+                motions.append({"pitch": -pitch_speed})
+        elif action == "left":
+            # Yaw left
+            for _ in range(num_frames):
+                motions.append({"yaw": -yaw_speed})
+        elif action == "right":
+            # Yaw right
+            for _ in range(num_frames):
+                motions.append({"yaw": yaw_speed})
+        else:
+            raise ValueError(
+                f"Unknown action: {action}. "
+                f"Supported actions: w, s, a, d, up, down, left, right"
+            )
+
+    return motions
+
+def pose_string_to_json(
+    pose_string: str,
+    intrinsic: Optional[list[list[float]]] = None,
+) -> dict:
+    """
+    Convert pose string to pose JSON format.
+    
+    Args:
+        pose_string: Comma-separated pose commands
+        intrinsic: Camera intrinsic matrix (default: DEFAULT_INTRINSIC from trajectory)
+        
+    Returns:
+        Dict with frame indices as keys, containing extrinsic and K (intrinsic) matrices
+    """
+    if intrinsic is None:
+        intrinsic = DEFAULT_INTRINSIC
+    
+    motions = parse_pose_string(pose_string)
+    poses = generate_camera_trajectory_local(motions)
+
+    pose_json = {}
+    for i, p in enumerate(poses):
+        pose_json[str(i)] = {"extrinsic": p.tolist(), "K": intrinsic}
+
+    return pose_json
+
+def pose_to_input(
+    pose_data: Union[str, dict],
+    latent_num: int,
+    tps: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    Convert pose data to model input tensors.
+    
+    Args:
+        pose_data: One of:
+            - str ending with '.json': path to JSON file
+            - str: pose string (e.g., "w-3, right-0.5, d-4")
+            - dict: pose JSON data
+        latent_num: Number of latents (frames in latent space)
+        tps: Third-person mode flag
+        
+    Returns:
+        Tuple of (viewmats, intrinsics, action_labels):
+            - viewmats: World-to-camera matrices [T, 4, 4]
+            - intrinsics: Normalized camera intrinsics [T, 3, 3]
+            - action_labels: Action labels for each frame [T]
+    """
+    # Handle different input types
+    if isinstance(pose_data, str):
+        if pose_data.endswith(".json"):
+            # Load from JSON file
+            with open(pose_data, "r") as f:
+                pose_json = json.load(f)
+        else:
+            # Parse pose string
+            pose_json = pose_string_to_json(pose_data)
+    elif isinstance(pose_data, dict):
+        pose_json = pose_data
+    else:
+        raise ValueError(
+            f"Invalid pose_data type: {type(pose_data)}. Expected str or dict."
+        )
+
+    pose_keys = list(pose_json.keys())
+    latent_num_from_pose = len(pose_keys)
+    assert latent_num_from_pose == latent_num, (
+        f"pose corresponds to {latent_num_from_pose * 4 - 3} frames, num_frames "
+        f"must be set to {latent_num_from_pose * 4 - 3} to ensure alignment."
+    )
+
+    intrinsic_list = []
+    w2c_list = []
+    for i in range(latent_num):
+        t_key = pose_keys[i]
+        c2w = np.array(pose_json[t_key]["extrinsic"])
+        w2c = np.linalg.inv(c2w)
+        w2c_list.append(w2c)
+        
+        # Normalize intrinsics
+        intrinsic = np.array(pose_json[t_key]["K"])
+        intrinsic[0, 0] /= intrinsic[0, 2] * 2
+        intrinsic[1, 1] /= intrinsic[1, 2] * 2
+        intrinsic[0, 2] = 0.5
+        intrinsic[1, 2] = 0.5
+        intrinsic_list.append(intrinsic)
+
+    w2c_list = np.array(w2c_list)
+    intrinsic_list = torch.tensor(np.array(intrinsic_list))
+
+    # Compute relative camera-to-world transforms
+    c2ws = np.linalg.inv(w2c_list)
+    C_inv = np.linalg.inv(c2ws[:-1])
+    relative_c2w = np.zeros_like(c2ws)
+    relative_c2w[0, ...] = c2ws[0, ...]
+    relative_c2w[1:, ...] = C_inv @ c2ws[1:, ...]
+    
+    # Initialize one-hot action encodings
+    trans_one_hot = np.zeros((relative_c2w.shape[0], 4), dtype=np.int32)
+    rotate_one_hot = np.zeros((relative_c2w.shape[0], 4), dtype=np.int32)
+
+    move_norm_valid = 0.0001
+    for i in range(1, relative_c2w.shape[0]):
+        move_dirs = relative_c2w[i, :3, 3]  # direction vector
+        move_norms = np.linalg.norm(move_dirs)
+        
+        if move_norms > move_norm_valid:  # threshold for movement
+            move_norm_dirs = move_dirs / move_norms
+            angles_rad = np.arccos(move_norm_dirs.clip(-1.0, 1.0))
+            trans_angles_deg = angles_rad * (180.0 / np.pi)  # convert to degrees
+        else:
+            trans_angles_deg = np.zeros(3)
+
+        R_rel = relative_c2w[i, :3, :3]
+        r = R.from_matrix(R_rel)
+        rot_angles_deg = r.as_euler("xyz", degrees=True)
+
+        # Determine movement and rotation actions
+        if move_norms > move_norm_valid:  # threshold for movement
+            if (not tps) or (
+                tps and abs(rot_angles_deg[1]) < 5e-2 and abs(rot_angles_deg[0]) < 5e-2
+            ):
+                if trans_angles_deg[2] < 60:
+                    trans_one_hot[i, 0] = 1  # forward
+                elif trans_angles_deg[2] > 120:
+                    trans_one_hot[i, 1] = 1  # backward
+
+                if trans_angles_deg[0] < 60:
+                    trans_one_hot[i, 2] = 1  # right
+                elif trans_angles_deg[0] > 120:
+                    trans_one_hot[i, 3] = 1  # left
+
+        if rot_angles_deg[1] > 5e-2:
+            rotate_one_hot[i, 0] = 1  # right
+        elif rot_angles_deg[1] < -5e-2:
+            rotate_one_hot[i, 1] = 1  # left
+
+        if rot_angles_deg[0] > 5e-2:
+            rotate_one_hot[i, 2] = 1  # up
+        elif rot_angles_deg[0] < -5e-2:
+            rotate_one_hot[i, 3] = 1  # down
+
+    trans_one_hot = torch.tensor(trans_one_hot)
+    rotate_one_hot = torch.tensor(rotate_one_hot)
+
+    # Convert one-hot to single-dimension labels
+    trans_one_label = one_hot_to_one_dimension(trans_one_hot)
+    rotate_one_label = one_hot_to_one_dimension(rotate_one_hot)
+    action_one_label = trans_one_label * 9 + rotate_one_label
+
+    return (
+        torch.as_tensor(w2c_list),
+        torch.as_tensor(intrinsic_list),
+        action_one_label,
+    )
+
+
+def camera_center_normalization(w2c: np.ndarray) -> np.ndarray:
+    """Normalize camera centers relative to the first camera."""
+    c2w = np.linalg.inv(w2c)
+    C0_inv = np.linalg.inv(c2w[0])
+    c2w_aligned = np.array([C0_inv @ C for C in c2w])
+    return np.linalg.inv(c2w_aligned)
+
+
+
+def parse_pose_string_to_actions(pose_string: str, fps: int = 24) -> list[dict]:
+    """
+    Parse pose string to frame-level action timeline.
+    
+    Format: pose string uses latent counts, where:
+    - 1 latent = 4 frames
+    - Special rule: first frame of entire video is extra (frame 0)
+    - Example: "w-4,d-4" means:
+      - w-4: forward for frames 0-16 (17 frames total: 1 extra + 4*4)
+      - d-4: right for frames 17-32 (16 frames total: 4*4)
+    
+    Args:
+        pose_string: Comma-separated pose commands (e.g., "w-4,d-4")
+        fps: Frames per second for video (default: 24)
+        
+    Returns:
+        List of dicts with action values for each frame
+    """
+    commands = [cmd.strip() for cmd in pose_string.split(",")]
+
+    frame_actions = []
+    is_first_command = True
+
+    for cmd in commands:
+        if not cmd:
+            continue
+
+        parts = cmd.split("-")
+        if len(parts) != 2:
+            raise ValueError(
+                f"Invalid pose command: {cmd}. Expected format: 'action-duration'"
+            )
+
+        action = parts[0].strip()
+        try:
+            num_latents = int(parts[1].strip())
+        except ValueError:
+            raise ValueError(f"Invalid duration in command: {cmd}")
+
+        # Convert latents to frames
+        # First command gets 1 extra frame (the special frame 0)
+        if is_first_command:
+            num_frames = 1 + num_latents * 4
+            is_first_command = False
+        else:
+            num_frames = num_latents * 4
+
+        # Map action to action values
+        action_values = {"forward": 0, "left": 0, "yaw": 0, "pitch": 0}
+
+        if action == "w":
+            action_values["forward"] = 1
+        elif action == "s":
+            action_values["forward"] = -1
+        elif action == "a":
+            action_values["left"] = 1
+        elif action == "d":
+            action_values["left"] = -1
+        elif action == "up":
+            action_values["pitch"] = 1
+        elif action == "down":
+            action_values["pitch"] = -1
+        elif action == "left":
+            action_values["yaw"] = -1
+        elif action == "right":
+            action_values["yaw"] = 1
+        else:
+            raise ValueError(f"Unknown action: {action}")
+
+        # Add frame-level actions
+        for _ in range(num_frames):
+            frame_actions.append(action_values.copy())
+
+    return frame_actions
+
+
+def compute_latent_num(num_frames: int) -> int:
+    """
+    Compute the number of latents from number of frames.
+    
+    Formula: num_frames = (latent_num - 1) * 4 + 1
+    So: latent_num = (num_frames - 1) // 4 + 1
+    
+    Args:
+        num_frames: Number of video frames
+        
+    Returns:
+        Number of latents
+    """
+    return (num_frames - 1) // 4 + 1
+
+
+def compute_num_frames(latent_num: int) -> int:
+    """
+    Compute the number of frames from number of latents.
+    
+    Formula: num_frames = (latent_num - 1) * 4 + 1
+    
+    Args:
+        latent_num: Number of latents
+        
+    Returns:
+        Number of video frames
+    """
+    return (latent_num - 1) * 4 + 1

fastvideo/models/dits/hyworld/resolution_utils.py

@@ -0,0 +1,64 @@
+import numpy as np
+from PIL import Image
+import requests
+from io import BytesIO
+
+from fastvideo.models.dits.hyworld.data_utils import generate_crop_size_list
+
+# Target resolution configs (matching HY-WorldPlay)
+TARGET_SIZE_CONFIG = {
+    "360p": {"bucket_hw_base_size": 480, "bucket_hw_bucket_stride": 16},
+    "480p": {"bucket_hw_base_size": 640, "bucket_hw_bucket_stride": 16},
+    "720p": {"bucket_hw_base_size": 960, "bucket_hw_bucket_stride": 16},
+    "1080p": {"bucket_hw_base_size": 1440, "bucket_hw_bucket_stride": 16},
+}
+
+
+def get_closest_resolution(image_height, image_width, target_resolution="480p"):
+    """
+    Get closest supported resolution for given image dimensions.
+    
+    Args:
+        image_height: Height of input image
+        image_width: Width of input image  
+        target_resolution: Target resolution string (e.g., "480p", "720p")
+        
+    Returns:
+        tuple[int, int]: (height, width) of closest supported resolution
+    """
+    config = TARGET_SIZE_CONFIG[target_resolution]
+    bucket_hw_base_size = config["bucket_hw_base_size"]
+    bucket_hw_bucket_stride = config["bucket_hw_bucket_stride"]
+    
+    crop_size_list = generate_crop_size_list(bucket_hw_base_size, bucket_hw_bucket_stride)
+    aspect_ratios = np.array([round(float(h) / float(w), 5) for h, w in crop_size_list])
+    
+    # Find closest aspect ratio
+    image_ratio = float(image_height) / float(image_width)
+    closest_idx = np.abs(aspect_ratios - image_ratio).argmin()
+    closest_size = crop_size_list[closest_idx]
+    
+    return closest_size[0], closest_size[1]  # (height, width)
+
+
+def get_resolution_from_image(image_path, target_resolution="480p"):
+    """
+    Automatically determine resolution from input image.
+    
+    Args:
+        image_path: Path or URL to input image
+        target_resolution: Target resolution tier ("480p", "720p", etc.)
+        
+    Returns:
+        tuple[int, int]: (height, width) matching HY-WorldPlay's bucket selection
+    """
+    # Handle URL inputs
+    if isinstance(image_path, str) and image_path.startswith(('http://', 'https://')):
+        response = requests.get(image_path)
+        response.raise_for_status()
+        img = Image.open(BytesIO(response.content))
+    else:
+        img = Image.open(image_path)
+    img_width, img_height = img.size
+    return get_closest_resolution(img_height, img_width, target_resolution)
+

fastvideo/models/dits/hyworld/retrieval_context.py

@@ -0,0 +1,316 @@
+# HY-WorldPlay/hyvideo/utils/retrieval_context.py
+
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
+#
+# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
+# output and results therefrom are provided "AS IS" without any express or implied warranties of
+# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
+# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
+# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
+# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
+# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
+# of rights and permissions under this agreement.
+# See the License for the specific language governing permissions and limitations under the License.
+
+import torch
+import numpy as np
+from typing import List, Tuple, Dict
+import math
+
+
+def generate_points_in_sphere(n_points: int, radius: float) -> torch.Tensor:
+    """
+    Uniformly sample points within a sphere of a specified radius.
+
+    :param n_points: The number of points to generate.
+    :param radius: The radius of the sphere.
+    :return: A tensor of shape (n_points, 3), representing the (x, y, z) coordinates of the points.
+    """
+    samples_r = torch.rand(n_points)
+    samples_phi = torch.rand(n_points)
+    samples_u = torch.rand(n_points)
+
+    r = radius * torch.pow(samples_r, 1 / 3)
+    phi = 2 * math.pi * samples_phi
+    theta = torch.acos(1 - 2 * samples_u)
+
+    # transfer the coordinates from spherical to cartesian
+    x = r * torch.sin(theta) * torch.cos(phi)
+    y = r * torch.sin(theta) * torch.sin(phi)
+    z = r * torch.cos(theta)
+
+    points = torch.stack((x, y, z), dim=1)
+    return points
+
+
+def rotation_matrix_to_angles(R: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Estimate the Pitch and Yaw angles from a 3x3 rotation matrix R in the camera coordinate system.
+
+    Assumed Camera Coordinate System: X=Right, Y=Up, Z=Backward
+    (or NeRF style: X=Right, Y=Down, Z=Forward).
+    Here we adopt the common Computer Vision convention: Z-axis is Forward.
+
+    Note: The angle calculations here are directly based on the conventions of your `is_inside_fov_3d_hv` function:
+    - Yaw/Azimuth angle is in the XZ plane (atan2(x, z)).
+    - Pitch/Elevation angle is relative to the horizontal plane (atan2(y, sqrt(x^2 + z^2))).
+
+    For the third column R[:, 2] of the W2C matrix R (the direction of the World Z-axis in the Camera frame),
+    this typically corresponds to the direction the camera is looking
+    (the representation of the world Z-axis in the camera frame).
+
+    To simplify and match your `is_inside_fov` logic, we directly use the camera's Z-axis vector:
+    Camera Z-axis direction in World Frame (Forward Vector): fwd = R_w2c_inv @ [0, 0, 1]
+    More simply, the Z-axis vector of the C2W matrix is the camera's forward vector in the world frame.
+    C2W = W2C_inv
+    """
+
+    R_c2w = R.T
+    fwd = R_c2w[:, 2]
+
+    x = fwd[0]
+    y = fwd[1]
+    z = fwd[2]
+
+    # compute yaw and pitch
+    yaw_rad = torch.atan2(x, z)
+    yaw_deg = yaw_rad * (180.0 / math.pi)
+    pitch_rad = torch.atan2(y, torch.sqrt(x**2 + z**2))
+    pitch_deg = pitch_rad * (180.0 / math.pi)
+
+    return pitch_deg, yaw_deg
+
+
+def is_inside_fov_3d_hv(
+    points: torch.Tensor,
+    center: torch.Tensor,
+    center_pitch: torch.Tensor,
+    center_yaw: torch.Tensor,
+    fov_half_h: torch.Tensor,
+    fov_half_v: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Check whether points are inside a 3D view frustum defined by a center coordinate, pitch angle, and yaw angle.
+
+    :param points: Tensor of shape (N, 3) or (N, B, 3) representing the coordinates of the sampled points.
+    :param center: Tensor of shape (3) or (B, 3) representing the camera center coordinates.
+    :param center_pitch: Tensor of shape (1) or (B) representing the pitch angle of center view direction.
+    :param center_yaw: Tensor of shape (1) or (B) representing the yaw angle of the center view direction.
+    :param fov_half_h: The horizontal half field-of-view angle (in degrees).
+    :param fov_half_v: The vertical half field-of-view angle (in degrees).
+    :return: Boolean tensor of shape (N) or (N, B), indicating whether each point is inside the FOV.
+    """
+    if points.ndim == 2:  # N, 3
+        vectors = points - center[None, :]
+        C = 1
+    elif points.ndim == 3:  # N, B, 3
+        vectors = points - center[None, ...]
+        center_pitch = center_pitch[None, :] if center_pitch.ndim == 1 else center_pitch
+        center_yaw = center_yaw[None, :] if center_yaw.ndim == 1 else center_yaw
+    else:
+        raise ValueError("points' shape should be (N, 3) or (N, B, 3)")
+
+    x = vectors[..., 0]
+    y = vectors[..., 1]
+    z = vectors[..., 2]
+
+    # Calculate the horizontal angle (yaw/azimuth), assuming the Z-axis is forward.
+    azimuth = torch.atan2(x, z) * (180 / math.pi)
+
+    # Calculate the vertical angle (pitch/elevation).
+    elevation = torch.atan2(y, torch.sqrt(x**2 + z**2)) * (180 / math.pi)
+
+    # Calculate the angular difference from the center view direction (handling angle wrapping).
+    diff_azimuth = azimuth - center_yaw
+    diff_azimuth = torch.remainder(diff_azimuth + 180, 360) - 180
+
+    diff_elevation = elevation - center_pitch
+    diff_elevation = torch.remainder(diff_elevation + 180, 360) - 180
+
+    # Check if within FOV
+    in_fov_h = diff_azimuth.abs() < fov_half_h
+    in_fov_v = diff_elevation.abs() < fov_half_v
+
+    return in_fov_h & in_fov_v
+
+
+def calculate_fov_overlap_similarity(
+    w2c_matrix_curr: torch.Tensor,
+    w2c_matrix_hist: torch.Tensor,
+    fov_h_deg: float = 105.0,
+    fov_v_deg: float = 75.0,
+    device=None,
+    points_local=None,
+) -> float:
+    """
+    Calculate the Field-of-View (FOV) overlap similarity between two W2C poses using Monte Carlo sampling.
+
+    Similarity = (Number of points in Curr_FOV ∩ Hist_FOV) / (Number of points in Curr_FOV).
+
+    :param w2c_matrix_curr: The (4, 4) W2C matrix for the current frame.
+    :param w2c_matrix_hist: The (4, 4) W2C matrix for the historical frame.
+    :param num_samples, radius, fov_h_deg, fov_v_deg: Sampling and FOV parameters.
+    :return: The overlap ratio (a float between 0.0 and 1.0).
+    """
+    w2c_matrix_curr = torch.tensor(w2c_matrix_curr, device=device)
+    w2c_matrix_hist = torch.tensor(w2c_matrix_hist, device=device)
+
+    c2w_matrix_curr = torch.linalg.inv(w2c_matrix_curr)
+    c2w_matrix_hist = torch.linalg.inv(w2c_matrix_hist)
+    C_inv = w2c_matrix_curr
+
+    w2c_matrix_curr = torch.linalg.inv(C_inv @ c2w_matrix_curr)
+    w2c_matrix_hist = torch.linalg.inv(C_inv @ c2w_matrix_hist)
+
+    R_curr, t_curr = w2c_matrix_curr[:3, :3], w2c_matrix_curr[:3, 3]
+    R_hist, t_hist = w2c_matrix_hist[:3, :3], w2c_matrix_hist[:3, 3]
+    P_w_curr = -R_curr.T @ t_curr
+    P_w_hist = -R_hist.T @ t_hist
+
+    # pitch, yaw
+    pitch_curr, yaw_curr = rotation_matrix_to_angles(R_curr)
+    pitch_hist, yaw_hist = rotation_matrix_to_angles(R_hist)
+
+    fov_half_h = torch.tensor(fov_h_deg / 2.0, device=device)
+    fov_half_v = torch.tensor(fov_v_deg / 2.0, device=device)
+
+    # move to P_w_curr (N, 3)
+    points_world = points_local + P_w_curr[None, :]
+
+    in_fov_curr = is_inside_fov_3d_hv(
+        points_world,
+        P_w_curr[None, :],
+        pitch_curr[None],
+        yaw_curr[None],
+        fov_half_h,
+        fov_half_v,
+    )
+
+    # compute based on angle
+    in_fov_hist = is_inside_fov_3d_hv(
+        points_world,
+        P_w_hist[None, :],
+        pitch_hist[None],
+        yaw_hist[None],
+        fov_half_h,
+        fov_half_v,
+    )
+
+    # compute based on distance
+    dist = torch.norm(points_world - P_w_hist.reshape(1, -1), dim=1) < 8.0
+    in_fov_hist = in_fov_hist.bool() & dist.reshape(1, -1).bool()
+
+    overlap_count = (in_fov_curr.bool() & in_fov_hist.bool()).sum().float()
+    fov_curr_count = in_fov_curr.sum().float()
+
+    if fov_curr_count == 0:
+        return 0.0
+
+    overlap_ratio = overlap_count / fov_curr_count
+
+    return overlap_ratio.item()
+
+
+def select_aligned_memory_frames(
+    w2c_list: List[np.ndarray],
+    current_frame_idx: int,
+    memory_frames: int,
+    temporal_context_size: int,
+    pred_latent_size: int,
+    pos_weight: float = 1.0,
+    ang_weight: float = 1.0,
+    device=None,
+    points_local=None,
+) -> List[int]:
+    """
+    Selects memory and context frames for a given frame based on a four-frame segment distance calculation.
+
+    :param w2c_list: List of all N 4x4 World-to-Camera (W2C) extrinsic matrices (np.ndarray).
+    :param current_frame_idx: The index of the current frame to be processed.
+    :param memory_frames: The total number of memory frames to select.
+    :param context_size: The total number of context frames to select.
+    :param pos_weight: The weight applied to the spatial (position) distance component.
+    :param ang_weight: The weight applied to the angular distance component.
+
+    :return: List[int]: A list containing the indices of the selected memory frames and context frames.
+    """
+    if current_frame_idx <= memory_frames:
+        return list(range(0, current_frame_idx))
+
+    num_total_frames = len(w2c_list)
+    if current_frame_idx >= num_total_frames or current_frame_idx < 3:
+        raise ValueError(
+            f"The current frame index must be within the valid range of w2c_list and must be at least 3."
+            f"{current_frame_idx}, {len(w2c_list)}"
+        )
+
+    start_context_idx = max(0, current_frame_idx - temporal_context_size)
+    context_frames_indices = list(range(start_context_idx, current_frame_idx))
+
+    candidate_distances = []
+    query_clip_indices = list(
+        range(
+            current_frame_idx,
+            (
+                current_frame_idx + pred_latent_size
+                if current_frame_idx + pred_latent_size <= num_total_frames
+                else num_total_frames
+            ),
+        )
+    )
+
+    historical_clip_indices = list(
+        range(4, current_frame_idx - temporal_context_size, 4)
+    )
+
+    memory_frames_indices = [0, 1, 2, 3]  # add the first chunk as context
+    memory_frames = memory_frames - temporal_context_size
+
+    for hist_idx in historical_clip_indices:
+        total_dist = 0
+        hist_w2c_1 = w2c_list[hist_idx]
+        hist_w2c_2 = w2c_list[hist_idx + 2]
+        for query_idx in query_clip_indices:
+            dist_1_for_query_idx = 1.0 - calculate_fov_overlap_similarity(
+                w2c_list[query_idx],
+                hist_w2c_1,
+                fov_h_deg=60.0,
+                fov_v_deg=35.0,
+                device=device,
+                points_local=points_local,
+            )
+            dist_2_for_query_idx = 1.0 - calculate_fov_overlap_similarity(
+                w2c_list[query_idx],
+                hist_w2c_2,
+                fov_h_deg=60.0,
+                fov_v_deg=35.0,
+                device=device,
+                points_local=points_local,
+            )
+            dist_for_query_idx = (dist_1_for_query_idx + dist_2_for_query_idx) / 2.0
+            total_dist += dist_for_query_idx
+
+        final_clip_distance = total_dist / len(query_clip_indices)
+        candidate_distances.append((hist_idx, final_clip_distance))
+
+    candidate_distances.sort(key=lambda x: x[1])
+
+    for start_idx, _ in candidate_distances:
+        # check the memory frame number
+        if len(memory_frames_indices) >= memory_frames:
+            break
+
+        if start_idx not in memory_frames_indices:
+            memory_frames_indices.extend(range(start_idx, start_idx + 4))
+
+    # exclude the repeated frames
+    selected_frames_set = set(context_frames_indices)
+    selected_frames_set.update(memory_frames_indices)
+
+    final_selected_frames = sorted(list(selected_frames_set))
+
+    return final_selected_frames

fastvideo/models/dits/hyworld/trajectory.py

@@ -0,0 +1,112 @@
+# HY-WorldPlay/hyvideo/generate_custom_trajectory.py
+
+import numpy as np
+import json
+
+
+def rot_x(theta):
+    c, s = np.cos(theta), np.sin(theta)
+    return np.array([[1, 0, 0], [0, c, -s], [0, s, c]])
+
+
+def rot_y(theta):
+    c, s = np.cos(theta), np.sin(theta)
+    return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])
+
+
+def rot_z(theta):
+    c, s = np.cos(theta), np.sin(theta)
+    return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])
+
+
+def generate_camera_trajectory_local(motions):
+    """
+    motions: list of dict
+             {"forward": 1.0}, {"yaw": np.pi/2}, {"pitch": np.pi/6}, {"right": 1.0}
+             - forward: Translation (Forward or Backward)
+             - yaw:   Rotate (Left or Right)
+             - pitch: Rotate (Up or Down)
+             - right: Translation (Right or Left)
+             - third_yaw: Third Perspective Rotate (Left or Right)
+    """
+
+    poses = []
+    T = np.eye(4)
+    poses.append(T.copy())
+
+    for move in motions:
+        # Rotate (Left or Right)
+        if "yaw" in move:
+            R = rot_y(move["yaw"])
+            T[:3, :3] = T[:3, :3] @ R
+
+        # Rotate (Up or Down)
+        if "pitch" in move:
+            R = rot_x(move["pitch"])
+            T[:3, :3] = T[:3, :3] @ R
+
+        # Translation (Z-direction of the camera's local coordinate system)
+        forward = move.get("forward", 0.0)
+        if forward != 0:
+            local_t = np.array([0, 0, forward])
+            world_t = T[:3, :3] @ local_t
+            T[:3, 3] += world_t
+
+        # Translation (Z-direction of the camera's local coordinate system)
+        right = move.get("right", 0.0)
+        if right != 0:
+            local_t = np.array([right, 0, 0])
+            world_t = T[:3, :3] @ local_t
+            T[:3, 3] += world_t
+
+        # Third Perspective Rotate (Left or Right)
+        third_yaw = move.get("third_yaw", 0.0)
+        if third_yaw != 0:
+            theta = -third_yaw
+            C = np.array([[1, 0.0, 0, 0], [0, 1, 0, 0], [0, 0, 1, -1.0], [0, 0, 0, 1]])
+            c_origin = C.copy()
+            # Rotation around the Y-axis
+            R_y = np.array(
+                [
+                    [np.cos(theta), 0, np.sin(theta)],
+                    [0, 1, 0],
+                    [-np.sin(theta), 0, np.cos(theta)],
+                ]
+            )
+            # Translation
+            C[:3, :3] = C[:3, :3] @ R_y
+            C[:3, 3] = R_y @ C[:3, 3]
+            c_inv = np.linalg.inv(c_origin)
+            c_relative = c_inv @ C
+            T = T @ c_relative
+
+        poses.append(T.copy())
+
+    return poses
+
+
+if __name__ == "__main__":
+    # Examples: Forward 0.08 * 16 -> Right Rotate 3 degree * 16
+    motions = []
+    for i in range(15):
+        motions.append({"forward": 0.08})
+
+    for i in range(16):
+        motions.append({"yaw": np.deg2rad(3)})
+
+    intrinsic = [
+        [969.6969696969696, 0.0, 960.0],
+        [0.0, 969.6969696969696, 540.0],
+        [0.0, 0.0, 1.0],
+    ]
+
+    poses = generate_camera_trajectory_local(motions)
+    custom_c2w = {}
+    for i, p in enumerate(poses):
+        custom_c2w[str(i)] = {"extrinsic": p.tolist(), "K": intrinsic}
+        json.dump(
+            custom_c2w,
+            open("./assets/pose/pose.json", "w"),
+            indent=4,
+            ensure_ascii=False,
+        )

fastvideo/models/dits/matrixgame/action_module.py

@@ -257,6 +257,17 @@ class ActionModule(nn.Module):
         '''
         assert use_rope_keyboard
 
+        target_device = x.device
+        target_dtype = x.dtype
+        if mouse_condition is not None:
+            mouse_condition = mouse_condition.to(device=target_device,
+                                                 dtype=target_dtype)
+        if keyboard_condition is not None:
+            keyboard_condition = keyboard_condition.to(
+                device=target_device, dtype=target_dtype)
+        else:
+            return x
+
         B, N_frames, C = keyboard_condition.shape
         assert tt*th*tw == x.shape[1]
         assert ((N_frames - 1) + self.vae_time_compression_ratio) % self.vae_time_compression_ratio == 0
@@ -272,7 +283,9 @@ class ActionModule(nn.Module):
         # Defined freqs_cis early so it's available for both mouse and keyboard
         freqs_cis = (self._freqs_cos, self._freqs_sin)
 
-        assert (N_feats == tt and ((is_causal and kv_cache_mouse is None) or not is_causal)) or ((N_frames - 1) // self.vae_time_compression_ratio + 1 == start_frame + num_frame_per_block and is_causal)
+        if is_causal:
+            assert (N_feats == tt and kv_cache_mouse is None) or ((N_frames - 1) // self.vae_time_compression_ratio + 1 == start_frame + num_frame_per_block)
+        # For non-causal (training), we trust that the caller provides correctly shaped inputs
         
         if self.enable_mouse and mouse_condition is not None:
             hidden_states = rearrange(x, "B (T S) C -> (B S) T C", T=tt, S=th*tw) # 65*272*480 -> 17*(272//16)*(480//16) -> 8670
@@ -289,13 +302,15 @@ class ActionModule(nn.Module):
                 mouse_condition = mouse_condition[:, self.vae_time_compression_ratio*(N_feats - num_frame_per_block - self.windows_size) + pad_t:, :] 
                 group_mouse = [mouse_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(num_frame_per_block)]
             else:
-                group_mouse = [mouse_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(N_feats)]
+                local_num_frames = tt
+                group_mouse = [mouse_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(local_num_frames)]
                 
             group_mouse = torch.stack(group_mouse, dim = 1)
+            actual_num_frames = group_mouse.shape[1]  # Use actual stacked frame count
 
             S = th * tw 
-            group_mouse = group_mouse.unsqueeze(-1).expand(B, num_frame_per_block, pad_t, C, S)
-            group_mouse = group_mouse.permute(0, 4, 1, 2, 3).reshape(B * S, num_frame_per_block, pad_t * C) 
+            group_mouse = group_mouse.unsqueeze(-1).expand(B, actual_num_frames, pad_t, C, S)
+            group_mouse = group_mouse.permute(0, 4, 1, 2, 3).reshape(B * S, actual_num_frames, pad_t * C) 
 
             group_mouse = torch.cat([hidden_states, group_mouse], dim = -1)
             group_mouse = self.mouse_mlp(group_mouse)
@@ -314,7 +329,7 @@ class ActionModule(nn.Module):
             ## TODO: adding cache here
             if is_causal:
                 if kv_cache_mouse is None:
-                    assert q.shape[0] ==  k.shape[0] and q.shape[0] % 880 == 0 # == 880, f"{q.shape[0]},{k.shape[0]}"
+                    assert q.shape[0] ==  k.shape[0] and q.shape[0] % S == 0 # == 880, f"{q.shape[0]},{k.shape[0]}"
                     padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
                     padded_q = torch.cat(
                         [q,
@@ -395,7 +410,8 @@ class ActionModule(nn.Module):
                 group_keyboard = [keyboard_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(num_frame_per_block)]
             else:
                 keyboard_condition = self.keyboard_embed(keyboard_condition)
-                group_keyboard = [keyboard_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(N_feats)]
+                local_num_frames = tt
+                group_keyboard = [keyboard_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(local_num_frames)]
             group_keyboard = torch.stack(group_keyboard, dim = 1) # B F RW C
             group_keyboard = group_keyboard.reshape(shape=(group_keyboard.shape[0],group_keyboard.shape[1],-1))
             # apply cross attn
@@ -415,7 +431,7 @@ class ActionModule(nn.Module):
             q = self.key_attn_q_norm(q).to(v)
             k = self.key_attn_k_norm(k).to(v)
             S = th * tw
-            assert S == 880
+            # assert S == 880
             # position embed 
             if use_rope_keyboard: 
                 B, TS, H, D = q.shape
@@ -424,13 +440,13 @@ class ActionModule(nn.Module):
                 q, k = _apply_rotary_emb_qk(q, k, freqs_cis[0], freqs_cis[1], start_offset=start_frame)
 
                 k1, k2, k3, k4 = k.shape
-                k = k.expand(S, k2, k3, k4)
-                v = v.expand(S, k2, k3, k4)
+                k = k.repeat_interleave(S, dim=0)  
+                v = v.repeat_interleave(S, dim=0)
 
 
                 if is_causal:
                     if kv_cache_keyboard is None:
-                        assert q.shape[0] == k.shape[0] and q.shape[0] % 880 == 0 
+                        assert q.shape[0] == k.shape[0] and q.shape[0] % S == 0 
 
                         padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
                         padded_q = torch.cat(
@@ -480,7 +496,7 @@ class ActionModule(nn.Module):
                         else:
                             local_end_index = kv_cache_keyboard["local_end_index"].item() + current_end - kv_cache_keyboard["global_end_index"].item()
                             local_start_index = local_end_index - num_new_tokens
-                        assert k.shape[0] == 880 # BS == 1 or the cache should not be saved/ load method should be modified
+                        assert k.shape[0] == S # BS == 1 or the cache should not be saved/ load method should be modified
                         kv_cache_keyboard["k"][:, local_start_index:local_end_index] = k[:1]
                         kv_cache_keyboard["v"][:, local_start_index:local_end_index] = v[:1]
 

fastvideo/models/dits/matrixgame/causal_model.py

@@ -1096,4 +1096,4 @@ class CausalMatrixGameWanModel(BaseDiT):
                     if block.action_model.proj_keyboard.bias is not None:
                         nn.init.zeros_(block.action_model.proj_keyboard.bias)
                 except AttributeError:
-                    pass
+                    pass

fastvideo/models/dits/matrixgame/model.py

@@ -61,14 +61,17 @@ class MatrixGameTimeImageEmbedding(nn.Module):
         timestep_proj = self.time_modulation(temb)
 
         # MatrixGame does not use text embeddings, so we ignore encoder_hidden_states
-        # and return None for the text embedding part
         
         if encoder_hidden_states_image is not None:
             assert self.image_embedder is not None
             encoder_hidden_states_image = self.image_embedder(
                 encoder_hidden_states_image)
 
-        return temb, timestep_proj, None, encoder_hidden_states_image
+        encoder_hidden_states = torch.zeros((timestep.shape[0], 0, temb.shape[-1]),
+                                            device=temb.device,
+                                            dtype=temb.dtype)
+
+        return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
 
 
 class MatrixGameCrossAttention(WanSelfAttention):
@@ -313,6 +316,7 @@ class MatrixGameWanModel(BaseDiT):
         inner_dim = config.num_attention_heads * config.attention_head_dim
         self.hidden_size = config.hidden_size
         self.num_attention_heads = config.num_attention_heads
+        self.num_channels_latents = config.out_channels
         self.patch_size = config.patch_size
 
         # 1. Patch & position embedding
@@ -395,7 +399,8 @@ class MatrixGameWanModel(BaseDiT):
             self.num_attention_heads,
             rope_dim_list,
             dtype=torch.float32 if current_platform.is_mps() else torch.float64,
-            rope_theta=10000)
+            rope_theta=10000,
+            do_sp_sharding=True)
         freqs_cos = freqs_cos.to(hidden_states.device)
         freqs_sin = freqs_sin.to(hidden_states.device)
         freqs_cis = (freqs_cos.float(),

fastvideo/models/dits/matrixgame/utils.py

@@ -157,8 +157,8 @@ def load_initial_image(image_path: str = None) -> Image.Image:
     return Image.new("RGB", (640, 352), (128, 128, 128))
 
 def create_action_presets(num_frames: int, keyboard_dim: int = 4, seed: int = None):
-    if keyboard_dim not in (2, 4, 7):
-        raise ValueError(f"keyboard_dim must be 2, 4, or 7, got {keyboard_dim}")
+    if keyboard_dim not in (2, 4, 6, 7):
+        raise ValueError(f"keyboard_dim must be 2, 4, 6, or 7, got {keyboard_dim}")
     if num_frames % 4 != 1:
         raise ValueError("Matrix-Game conditioning expects num_frames to be 4k+1.")
 
@@ -181,6 +181,11 @@ def create_action_presets(num_frames: int, keyboard_dim: int = 4, seed: int = No
         actions_double_action = []
         actions_single_camera = ["camera_l", "camera_r"]
         keyboard_idx = {"forward": 0, "back": 1}
+    elif keyboard_dim == 6:
+        actions_single_action = ["forward", "back", "left", "right"]
+        actions_double_action = ["forward_left", "forward_right"]
+        actions_single_camera = ["camera_l", "camera_r"]
+        keyboard_idx = {"forward": 0, "back": 1, "left": 2, "right": 3, "t1": 4, "t2": 5}
     else:  # keyboard_dim == 7
         # Temple Run model: still, w, s, left, right, a, d (no mouse)
         actions_single_action = ["forward", "back", "left", "right"]

fastvideo/models/dits/wanvideo.py

@@ -8,6 +8,8 @@ import numpy as np
 import torch
 import torch.nn as nn
 
+from einops import repeat
+
 import fastvideo.envs as envs
 from fastvideo.attention import (DistributedAttention, DistributedAttention_VSA,
                                  LocalAttention)
@@ -76,7 +78,7 @@ class WanTimeTextImageEmbedding(nn.Module):
                                  dim,
                                  dim,
                                  bias=True,
-                                 act_type="gelu_pytorch_tanh")
+                                 act_type="gelu_pytorch_tanh") if text_embed_dim > 0 else None
 
         self.image_embedder = None
         if image_embed_dim is not None:
@@ -92,7 +94,10 @@ class WanTimeTextImageEmbedding(nn.Module):
         temb = self.time_embedder(timestep, timestep_seq_len)
         timestep_proj = self.time_modulation(temb)
 
-        encoder_hidden_states = self.text_embedder(encoder_hidden_states)
+        if self.text_embedder is not None:
+            encoder_hidden_states = self.text_embedder(encoder_hidden_states)
+        else:
+            encoder_hidden_states = torch.zeros((timestep.shape[0], 0, temb.shape[-1]), device=temb.device, dtype=temb.dtype)
         if encoder_hidden_states_image is not None:
             assert self.image_embedder is not None
             encoder_hidden_states_image = self.image_embedder(
@@ -149,6 +154,32 @@ class WanSelfAttention(nn.Module):
         """
         pass
 
+class WanGanCrossAttention(WanSelfAttention):
+
+    def forward(self, x, context, context_lens=None, crossattn_cache=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C]
+            context_lens(Tensor): Shape [B]
+            crossattn_cache (List[dict], *optional*): Contains the cached key and value tensors for context embedding.
+        """
+        b, n, d = x.size(0), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        qq = self.norm_q(self.to_q(context)[0]).view(b, 1, -1, d)
+
+        kk = self.norm_k(self.to_k(x)[0]).view(b, -1, n, d)
+        vv = self.to_v(x)[0].view(b, -1, n, d)
+
+        # compute attention
+        x = self.attn(qq, kk, vv)
+
+        # output
+        x = x.flatten(2)
+        x, _ = self.to_out(x)
+        return x
+
 
 class WanT2VCrossAttention(WanSelfAttention):
 
@@ -179,7 +210,10 @@ class WanT2VCrossAttention(WanSelfAttention):
             v = self.to_v(context)[0].view(b, -1, n, d)
 
         # compute attention
-        x = self.attn(q, k, v)
+        if k.size(1) > 0:
+            x = self.attn(q, k, v)
+        else:
+            x = torch.zeros_like(q)
 
         # output
         x = x.flatten(2)
@@ -227,7 +261,10 @@ class WanI2VCrossAttention(WanSelfAttention):
         v_img = self.add_v_proj(context_img)[0].view(b, -1, n, d)
         img_x = self.attn(q, k_img, v_img)
         # compute attention
-        x = self.attn(q, k, v)
+        if k.size(1) > 0:
+            x = self.attn(q, k, v)
+        else:
+            x = torch.zeros_like(q)
 
         # output
         x = x.flatten(2)
@@ -236,6 +273,98 @@ class WanI2VCrossAttention(WanSelfAttention):
         x, _ = self.to_out(x)
         return x
 
+class GanAttentionBlock(nn.Module):
+
+    def __init__(self,
+                 dim=1536,
+                 ffn_dim=8192,
+                 num_heads=12,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6):
+        super().__init__()
+        self.dim = dim
+        self.ffn_dim = ffn_dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.qk_norm = qk_norm
+        self.cross_attn_norm = cross_attn_norm
+        self.eps = eps
+
+        # layers
+        # self.norm1 = WanLayerNorm(dim, eps)
+        # self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
+        #   eps)
+        self.norm3 = FP32LayerNorm(
+            dim, eps,
+            elementwise_affine=True) if cross_attn_norm else nn.Identity()
+
+        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.ffn = nn.Sequential(
+            nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
+            nn.Linear(ffn_dim, dim))
+
+        self.cross_attn = WanGanCrossAttention(dim, num_heads,
+                                               (-1, -1),
+                                               qk_norm,
+                                               eps)
+
+        # modulation
+        # self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+
+    def forward(
+        self,
+        hidden_states,
+        encoder_hidden_states,
+        # seq_lens,
+        # grid_sizes,
+        # freqs,
+        # context,
+        # context_lens,
+    ):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, C]
+            e(Tensor): Shape [B, 6, C]
+            seq_lens(Tensor): Shape [B], length of each sequence in batch
+            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        # assert e.dtype == torch.float32
+        # with amp.autocast(dtype=torch.float32):
+        # e = (self.modulation + e).chunk(6, dim=1)
+        # assert e[0].dtype == torch.float32
+
+        # # self-attention
+        # y = self.self_attn(
+        #     self.norm1(x) * (1 + e[1]) + e[0], seq_lens, grid_sizes,
+        #     freqs)
+        # # with amp.autocast(dtype=torch.float32):
+        # x = x + y * e[2]
+
+        # cross-attention & ffn function
+        def cross_attn_ffn(x, context):
+            token = context + self.cross_attn(self.norm3(x), context)
+            y = self.ffn(self.norm2(token)) + token  # * (1 + e[4]) + e[3])
+            # with amp.autocast(dtype=torch.float32):
+            # x = x + y * e[5]
+            return y
+
+        x = cross_attn_ffn(hidden_states, encoder_hidden_states)
+        return x
+
+class RegisterTokens(nn.Module):
+    def __init__(self, num_registers: int, dim: int):
+        super().__init__()
+        self.register_tokens = nn.Parameter(torch.randn(num_registers, dim) * 0.02)
+        self.rms_norm = RMSNorm(dim, eps=1e-6)
+
+    def forward(self):
+        return self.rms_norm(self.register_tokens)
+
+    def reset_parameters(self):
+        nn.init.normal_(self.register_tokens, std=0.02)
 
 class WanTransformerBlock(nn.Module):
 
@@ -621,6 +750,26 @@ class WanTransformer3DModel(CachableDiT):
         self.cnt = 0
         self.__post_init__()
 
+    def adding_cls_branch(self, atten_dim=1536, num_class=4, time_embed_dim=0) -> None:
+        self._cls_pred_branch = nn.Sequential(
+            # Input: [B, 384, 21, 60, 104]
+            nn.LayerNorm(atten_dim * 3 + time_embed_dim),
+            nn.Linear(atten_dim * 3 + time_embed_dim, 1536),
+            nn.SiLU(),
+            nn.Linear(atten_dim, num_class)
+        )
+        self._cls_pred_branch.requires_grad_(True)
+        num_registers = 3
+        self._register_tokens = RegisterTokens(num_registers=num_registers, dim=atten_dim)
+        self._register_tokens.requires_grad_(True)
+        
+        gan_ca_blocks = []
+        for _ in range(num_registers):
+            block = GanAttentionBlock()
+            gan_ca_blocks.append(block)
+        self._gan_ca_blocks = nn.ModuleList(gan_ca_blocks)
+        self._gan_ca_blocks.requires_grad_(True)
+
     def forward(self,
                 hidden_states: torch.Tensor,
                 encoder_hidden_states: torch.Tensor | list[torch.Tensor],
@@ -628,12 +777,14 @@ class WanTransformer3DModel(CachableDiT):
                 encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
                 | None = None,
                 guidance=None,
+                classify_mode=False,
+                concat_time_embeddings=False,
                 **kwargs) -> torch.Tensor:
         forward_batch = get_forward_context().forward_batch
         enable_teacache = forward_batch is not None and forward_batch.enable_teacache
 
         orig_dtype = hidden_states.dtype
-        if not isinstance(encoder_hidden_states, torch.Tensor):
+        if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
             encoder_hidden_states = encoder_hidden_states[0]
         if isinstance(encoder_hidden_states_image,
                       list) and len(encoder_hidden_states_image) > 0:
@@ -705,8 +856,11 @@ class WanTransformer3DModel(CachableDiT):
             timestep_proj = timestep_proj.unflatten(1, (6, -1))
 
         if encoder_hidden_states_image is not None:
-            encoder_hidden_states = torch.concat(
-                [encoder_hidden_states_image, encoder_hidden_states], dim=1)
+            if encoder_hidden_states is not None:
+                encoder_hidden_states = torch.concat(
+                    [encoder_hidden_states_image, encoder_hidden_states], dim=1)
+            else:
+                encoder_hidden_states = encoder_hidden_states_image
 
         if current_platform.is_mps() or current_platform.is_npu():
             encoder_hidden_states = encoder_hidden_states.to(orig_dtype)
@@ -715,6 +869,16 @@ class WanTransformer3DModel(CachableDiT):
 
         assert encoder_hidden_states.dtype == orig_dtype
 
+        final_x = None
+        if classify_mode:
+            assert self._register_tokens is not None
+            assert self._gan_ca_blocks is not None
+            assert self._cls_pred_branch is not None
+
+            final_x = []
+            registers = repeat(self._register_tokens(), "n d -> b n d", b=hidden_states.shape[0])
+
+        gan_idx = 0
         # 4. Transformer blocks
         # if caching is enabled, we might be able to skip the forward pass
         should_skip_forward = self.should_skip_forward_for_cached_states(
@@ -728,19 +892,33 @@ class WanTransformer3DModel(CachableDiT):
             if enable_teacache:
                 original_hidden_states = hidden_states.clone()
 
-            if torch.is_grad_enabled() and self.gradient_checkpointing:
-                for block in self.blocks:
+            for ii, block in enumerate(self.blocks):
+                if torch.is_grad_enabled() and self.gradient_checkpointing:
                     hidden_states = self._gradient_checkpointing_func(
                         block, hidden_states, encoder_hidden_states,
                         timestep_proj, freqs_cis, attention_mask)
-            else:
-                for block in self.blocks:
+                else:
                     hidden_states = block(hidden_states, encoder_hidden_states,
-                                              timestep_proj, freqs_cis, attention_mask)
+                                            timestep_proj, freqs_cis, attention_mask)
+
+                if classify_mode and ii in [13, 21, 29]:
+                    gan_token = registers[:, gan_idx: gan_idx + 1]
+                    final_x.append(self._gan_ca_blocks[gan_idx](hidden_states, gan_token))
+                    gan_idx += 1
             # if teacache is enabled, we need to cache the original hidden states
 
             if enable_teacache:
                 self.maybe_cache_states(hidden_states, original_hidden_states)
+
+        if classify_mode:
+            final_x = torch.cat(final_x, dim=1)
+            if concat_time_embeddings:
+                cls_input = torch.cat([final_x.float(), (10 * temb[:, None, :]).float()], dim=1).view(final_x.shape[0], -1)
+                final_x = self._cls_pred_branch(cls_input)
+            else:
+                cls_input = final_x.float().view(final_x.shape[0], -1)
+                final_x = self._cls_pred_branch(cls_input)
+
         # 5. Output norm, projection & unpatchify
         if temb.dim() == 3:
             # batch_size, seq_len, inner_dim (wan 2.2 ti2v)
@@ -766,6 +944,8 @@ class WanTransformer3DModel(CachableDiT):
         hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
         output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
 
+        if classify_mode:
+            return output, torch.nan_to_num(final_x)
         return output
 
     def maybe_cache_states(self, hidden_states: torch.Tensor,

fastvideo/models/encoders/gemma.py

@@ -0,0 +1,563 @@
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from dataclasses import dataclass
+import os
+from typing import Iterable
+
+import torch
+from torch import nn
+from transformers import Gemma3ForConditionalGeneration
+
+from fastvideo.configs.models.encoders import BaseEncoderOutput, TextEncoderConfig
+from fastvideo.models.encoders.base import TextEncoder
+from fastvideo.models.dits.ltx2 import (
+    FeedForward,
+    LTXRopeType,
+    apply_ltx_rotary_emb,
+    generate_ltx_freq_grid_np,
+    generate_ltx_freq_grid_pytorch,
+    precompute_ltx_freqs_cis,
+)
+from fastvideo.models.loader.weight_utils import default_weight_loader
+from fastvideo.platforms import AttentionBackendEnum
+
+
+def _debug_log_line(message: str) -> None:
+    if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") != "1":
+        return
+    log_path = os.getenv("LTX2_PIPELINE_DEBUG_PATH", "")
+    if not log_path:
+        return
+    log_dir = os.path.dirname(log_path)
+    if log_dir:
+        os.makedirs(log_dir, exist_ok=True)
+    with open(log_path, "a", encoding="utf-8") as f:
+        f.write(message + "\n")
+
+
+def _debug_gemma_log_line(message: str) -> None:
+    log_path = os.getenv("LTX2_FASTVIDEO_GEMMA_LOG", "")
+    if not log_path:
+        return
+    log_dir = os.path.dirname(log_path)
+    if log_dir:
+        os.makedirs(log_dir, exist_ok=True)
+    with open(log_path, "a", encoding="utf-8") as f:
+        f.write(message + "\n")
+
+
+@dataclass(frozen=True)
+class GemmaConnectorConfig:
+    num_attention_heads: int
+    attention_head_dim: int
+    num_layers: int
+    positional_embedding_theta: float
+    positional_embedding_max_pos: list[int]
+    rope_type: LTXRopeType
+    double_precision_rope: bool
+    num_learnable_registers: int | None
+
+
+class GemmaFeaturesExtractorProjLinear(nn.Module):
+    """Linear projection that aggregates stacked Gemma hidden states."""
+
+    def __init__(self, in_features: int, out_features: int) -> None:
+        super().__init__()
+        self.aggregate_embed = nn.Linear(in_features, out_features, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.aggregate_embed(x)
+
+
+class _BasicTransformerBlock1D(nn.Module):
+    """1D transformer block for connector processing."""
+
+    def __init__(
+        self,
+        dim: int,
+        heads: int,
+        dim_head: int,
+        rope_type: LTXRopeType,
+        norm_eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+        self.attn1 = _GemmaAttention(
+            query_dim=dim,
+            context_dim=None,
+            heads=heads,
+            dim_head=dim_head,
+            norm_eps=norm_eps,
+            rope_type=rope_type,
+        )
+        self.ff = FeedForward(dim, dim_out=dim)
+        self.norm_eps = norm_eps
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        pe: tuple[torch.Tensor, torch.Tensor] | None = None,
+    ) -> torch.Tensor:
+        norm_hidden_states = torch.nn.functional.rms_norm(
+            hidden_states, (hidden_states.shape[-1],), eps=self.norm_eps
+        )
+        if norm_hidden_states.ndim == 4:
+            norm_hidden_states = norm_hidden_states.squeeze(1)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            mask=attention_mask,
+            pe=pe,
+        )
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        norm_hidden_states = torch.nn.functional.rms_norm(
+            hidden_states, (hidden_states.shape[-1],), eps=self.norm_eps
+        )
+        ff_output = self.ff(norm_hidden_states)
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+        return hidden_states
+
+
+class _GemmaAttention(nn.Module):
+    """Attention implementation aligned with LTX-2 text encoder."""
+
+    def __init__(
+        self,
+        query_dim: int,
+        context_dim: int | None,
+        heads: int,
+        dim_head: int,
+        norm_eps: float,
+        rope_type: LTXRopeType,
+    ) -> None:
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = query_dim if context_dim is None else context_dim
+
+        self.heads = heads
+        self.dim_head = dim_head
+        self.rope_type = rope_type
+
+        self.q_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
+        self.k_norm = torch.nn.RMSNorm(inner_dim, eps=norm_eps)
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=True)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=True)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=True)
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim, bias=True), nn.Identity())
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: torch.Tensor | None = None,
+        mask: torch.Tensor | None = None,
+        pe: tuple[torch.Tensor, torch.Tensor] | None = None,
+        k_pe: tuple[torch.Tensor, torch.Tensor] | None = None,
+    ) -> torch.Tensor:
+        q = self.to_q(x)
+        context = x if context is None else context
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        if pe is not None:
+            q = apply_ltx_rotary_emb(q, pe, self.rope_type)
+            k = apply_ltx_rotary_emb(k, pe if k_pe is None else k_pe, self.rope_type)
+
+        b, q_len, _ = q.shape
+        k_len = k.shape[1]
+        q = q.view(b, q_len, self.heads, self.dim_head).transpose(1, 2)
+        k = k.view(b, k_len, self.heads, self.dim_head).transpose(1, 2)
+        v = v.view(b, k_len, self.heads, self.dim_head).transpose(1, 2)
+
+        if mask is not None:
+            if mask.ndim == 2:
+                mask = mask.unsqueeze(0)
+            if mask.ndim == 3:
+                mask = mask.unsqueeze(1)
+
+        out = torch.nn.functional.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            attn_mask=mask,
+            dropout_p=0.0,
+            is_causal=False,
+        )
+        out = out.transpose(1, 2).reshape(b, q_len, -1)
+        return self.to_out(out)
+
+
+class Embeddings1DConnector(nn.Module):
+    """Transformer connector that refines Gemma embeddings for LTX-2."""
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(self, config: GemmaConnectorConfig) -> None:
+        super().__init__()
+        self.num_attention_heads = config.num_attention_heads
+        self.inner_dim = config.num_attention_heads * config.attention_head_dim
+        self.positional_embedding_theta = config.positional_embedding_theta
+        self.positional_embedding_max_pos = config.positional_embedding_max_pos
+        self.rope_type = config.rope_type
+        self.double_precision_rope = config.double_precision_rope
+        self.transformer_1d_blocks = nn.ModuleList(
+            [
+                _BasicTransformerBlock1D(
+                    dim=self.inner_dim,
+                    heads=config.num_attention_heads,
+                    dim_head=config.attention_head_dim,
+                    rope_type=config.rope_type,
+                )
+                for _ in range(config.num_layers)
+            ]
+        )
+        self.num_learnable_registers = config.num_learnable_registers
+        if self.num_learnable_registers:
+            self.learnable_registers = nn.Parameter(
+                torch.rand(
+                    self.num_learnable_registers,
+                    self.inner_dim,
+                    dtype=torch.bfloat16,
+                )
+                * 2.0
+                - 1.0
+            )
+
+    def _replace_padded_with_learnable_registers(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert hidden_states.shape[1] % self.num_learnable_registers == 0, (
+            f"Hidden states sequence length {hidden_states.shape[1]} must be divisible by "
+            f"num_learnable_registers {self.num_learnable_registers}."
+        )
+
+        num_registers_duplications = (
+            hidden_states.shape[1] // self.num_learnable_registers
+        )
+        learnable_registers = torch.tile(
+            self.learnable_registers, (num_registers_duplications, 1)
+        )
+        attention_mask_binary = (
+            attention_mask.squeeze(1).squeeze(1).unsqueeze(-1) >= -9000.0
+        ).int()
+
+        non_zero_hidden_states = hidden_states[
+            :, attention_mask_binary.squeeze().bool(), :
+        ]
+        non_zero_nums = non_zero_hidden_states.shape[1]
+        pad_length = hidden_states.shape[1] - non_zero_nums
+        adjusted_hidden_states = torch.nn.functional.pad(
+            non_zero_hidden_states, pad=(0, 0, 0, pad_length), value=0
+        )
+        flipped_mask = torch.flip(attention_mask_binary, dims=[1])
+        hidden_states = flipped_mask * adjusted_hidden_states + (
+            1 - flipped_mask
+        ) * learnable_registers
+
+        attention_mask = torch.full_like(
+            attention_mask,
+            0.0,
+            dtype=attention_mask.dtype,
+            device=attention_mask.device,
+        )
+
+        return hidden_states, attention_mask
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if self.num_learnable_registers:
+            hidden_states, attention_mask = (
+                self._replace_padded_with_learnable_registers(
+                    hidden_states, attention_mask
+                )
+            )
+
+        indices_grid = torch.arange(
+            hidden_states.shape[1],
+            dtype=torch.float32,
+            device=hidden_states.device,
+        )
+        indices_grid = indices_grid[None, None, :]
+        freq_grid_generator = (
+            generate_ltx_freq_grid_np
+            if self.double_precision_rope
+            else generate_ltx_freq_grid_pytorch
+        )
+        freqs_cis = precompute_ltx_freqs_cis(
+            indices_grid=indices_grid,
+            dim=self.inner_dim,
+            out_dtype=hidden_states.dtype,
+            theta=self.positional_embedding_theta,
+            max_pos=self.positional_embedding_max_pos,
+            num_attention_heads=self.num_attention_heads,
+            rope_type=self.rope_type,
+            freq_grid_generator=freq_grid_generator,
+        )
+
+        for block in self.transformer_1d_blocks:
+            hidden_states = block(
+                hidden_states, attention_mask=attention_mask, pe=freqs_cis
+            )
+
+        hidden_states = torch.nn.functional.rms_norm(
+            hidden_states, (hidden_states.shape[-1],), eps=1e-6
+        )
+
+        return hidden_states, attention_mask
+
+
+class LTX2GemmaTextEncoderModel(TextEncoder):
+    _supported_attention_backends = (
+        AttentionBackendEnum.FLASH_ATTN,
+        AttentionBackendEnum.TORCH_SDPA,
+    )
+
+    def __init__(self, config: TextEncoderConfig) -> None:
+        super().__init__(config)
+        arch = config.arch_config
+
+        self.feature_extractor_linear = GemmaFeaturesExtractorProjLinear(
+            in_features=arch.feature_extractor_in_features,
+            out_features=arch.feature_extractor_out_features,
+        )
+
+        connector_config = GemmaConnectorConfig(
+            num_attention_heads=arch.connector_num_attention_heads,
+            attention_head_dim=arch.connector_attention_head_dim,
+            num_layers=arch.connector_num_layers,
+            positional_embedding_theta=arch.connector_positional_embedding_theta,
+            positional_embedding_max_pos=arch.connector_positional_embedding_max_pos,
+            rope_type=LTXRopeType(arch.connector_rope_type),
+            double_precision_rope=arch.connector_double_precision_rope,
+            num_learnable_registers=arch.connector_num_learnable_registers,
+        )
+        self.embeddings_connector = Embeddings1DConnector(connector_config)
+        self.audio_embeddings_connector = Embeddings1DConnector(connector_config)
+
+        self.gemma_model_path = arch.gemma_model_path
+        self.gemma_dtype = arch.gemma_dtype
+        self.padding_side = arch.padding_side
+        self._gemma_model: Gemma3ForConditionalGeneration | None = None
+
+    def named_parameters(self, prefix: str = "", recurse: bool = True):
+        for name, param in super().named_parameters(
+            prefix=prefix, recurse=recurse
+        ):
+            if name.startswith("gemma_model."):
+                continue
+            yield name, param
+
+    @property
+    def gemma_model(self) -> Gemma3ForConditionalGeneration:
+        if self._gemma_model is None:
+            gemma_path = self.gemma_model_path
+            if not gemma_path:
+                raise ValueError(
+                    "gemma_model_path must be set (expected text_encoder/gemma)."
+                )
+            dtype = getattr(torch, self.gemma_dtype, torch.bfloat16)
+            self._gemma_model = Gemma3ForConditionalGeneration.from_pretrained(
+                gemma_path,
+                local_files_only=True,
+                torch_dtype=dtype,
+            )
+            # Configure model-level attention implementation when using TORCH_SDPA.
+            # Note: torch.backends.cuda.enable_*_sdp() settings should be configured
+            # at application/pipeline initialization level, not here, to avoid
+            # unexpected side effects across the application.
+            if os.getenv("FASTVIDEO_ATTENTION_BACKEND") == "TORCH_SDPA":
+                if hasattr(self._gemma_model.config, "attn_implementation"):
+                    self._gemma_model.config.attn_implementation = "sdpa"
+                if hasattr(self._gemma_model.config, "_attn_implementation"):
+                    self._gemma_model.config._attn_implementation = "sdpa"
+            device = next(self.feature_extractor_linear.parameters()).device
+            self._gemma_model.to(device=device)
+            self._gemma_model.eval()
+        return self._gemma_model
+
+    def _run_feature_extractor(
+        self,
+        hidden_states: tuple[torch.Tensor, ...],
+        attention_mask: torch.Tensor,
+        padding_side: str,
+    ) -> torch.Tensor:
+        encoded_text_features = torch.stack(hidden_states, dim=-1)
+        if os.getenv("LTX2_FASTVIDEO_GEMMA_LOG", ""):
+            for idx, layer in enumerate(hidden_states):
+                _debug_gemma_log_line(
+                    f"fastvideo:gemma_hidden_state_{idx}"
+                    f":sum={layer.float().sum().item():.6f}"
+                )
+            _debug_gemma_log_line(
+                "fastvideo:gemma_hidden_states_stack"
+                f":sum={encoded_text_features.float().sum().item():.6f}"
+            )
+        encoded_text_features_dtype = encoded_text_features.dtype
+        sequence_lengths = attention_mask.sum(dim=-1)
+        normed_text_features = _norm_and_concat_padded_batch(
+            encoded_text_features, sequence_lengths, padding_side=padding_side
+        )
+        return self.feature_extractor_linear(
+            normed_text_features.to(encoded_text_features_dtype)
+        )
+
+    def _convert_to_additive_mask(
+        self, attention_mask: torch.Tensor, dtype: torch.dtype
+    ) -> torch.Tensor:
+        return (attention_mask - 1).to(dtype).reshape(
+            (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
+        ) * torch.finfo(dtype).max
+
+    def _run_connectors(
+        self,
+        encoded_input: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        connector_attention_mask = self._convert_to_additive_mask(
+            attention_mask, encoded_input.dtype
+        )
+        encoded, encoded_connector_attention_mask = self.embeddings_connector(
+            encoded_input, connector_attention_mask
+        )
+
+        attention_mask = (encoded_connector_attention_mask < 0.000001).to(
+            torch.int64
+        )
+        attention_mask = attention_mask.reshape(
+            [encoded.shape[0], encoded.shape[1], 1]
+        )
+        encoded = encoded * attention_mask
+
+        encoded_for_audio, _ = self.audio_embeddings_connector(
+            encoded_input, connector_attention_mask
+        )
+
+        return encoded, encoded_for_audio, attention_mask.squeeze(-1)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor | None,
+        position_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        output_hidden_states: bool | None = None,
+        **kwargs,
+    ) -> BaseEncoderOutput:
+        if input_ids is None:
+            raise ValueError("input_ids is required for Gemma text encoding.")
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+
+        model = self.gemma_model
+        input_ids = input_ids.to(device=model.device)
+        attention_mask = attention_mask.to(device=model.device)
+        outputs = model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+
+        encoded_inputs = self._run_feature_extractor(
+            outputs.hidden_states,
+            attention_mask,
+            padding_side=self.padding_side,
+        )
+        if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
+            _debug_log_line(
+                "fastvideo:gemma_feature"
+                f":sum={encoded_inputs.float().sum().item():.6f} "
+                f"shape={tuple(encoded_inputs.shape)}"
+            )
+        video_encoding, audio_encoding, attention_mask = self._run_connectors(
+            encoded_inputs, attention_mask
+        )
+        if os.getenv("LTX2_PIPELINE_DEBUG_LOG", "0") == "1":
+            _debug_log_line(
+                "fastvideo:gemma_video_encoding"
+                f":sum={video_encoding.float().sum().item():.6f} "
+                f"shape={tuple(video_encoding.shape)}"
+            )
+            _debug_log_line(
+                "fastvideo:gemma_audio_encoding"
+                f":sum={audio_encoding.float().sum().item():.6f} "
+                f"shape={tuple(audio_encoding.shape)}"
+            )
+
+        hidden_states = (audio_encoding, ) if output_hidden_states else None
+        return BaseEncoderOutput(
+            last_hidden_state=video_encoding,
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+        )
+
+    def load_weights(
+        self, weights: Iterable[tuple[str, torch.Tensor]]
+    ) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if name == "aggregate_embed.weight":
+                name = "feature_extractor_linear.aggregate_embed.weight"
+            if name not in params_dict:
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+def _norm_and_concat_padded_batch(
+    encoded_text: torch.Tensor,
+    sequence_lengths: torch.Tensor,
+    padding_side: str = "right",
+) -> torch.Tensor:
+    b, t, d, l = encoded_text.shape
+    device = encoded_text.device
+
+    token_indices = torch.arange(t, device=device)[None, :]
+    if padding_side == "right":
+        mask = token_indices < sequence_lengths[:, None]
+    elif padding_side == "left":
+        start_indices = t - sequence_lengths[:, None]
+        mask = token_indices >= start_indices
+    else:
+        raise ValueError(
+            f"padding_side must be 'left' or 'right', got {padding_side}"
+        )
+
+    mask = mask.reshape(b, t, 1, 1)
+    eps = 1e-6
+
+    masked = encoded_text.masked_fill(~mask, 0.0)
+    denom = (sequence_lengths * d).view(b, 1, 1, 1)
+    mean = masked.sum(dim=(1, 2), keepdim=True) / (denom + eps)
+
+    x_min = encoded_text.masked_fill(~mask, float("inf")).amin(
+        dim=(1, 2), keepdim=True
+    )
+    x_max = encoded_text.masked_fill(~mask, float("-inf")).amax(
+        dim=(1, 2), keepdim=True
+    )
+    range_ = x_max - x_min
+
+    normed = 8 * (encoded_text - mean) / (range_ + eps)
+    normed = normed.reshape(b, t, -1)
+
+    mask_flattened = mask.reshape(b, t, 1).expand(-1, -1, d * l)
+    normed = normed.masked_fill(~mask_flattened, 0.0)
+    return normed

fastvideo/models/encoders/siglip.py

@@ -0,0 +1,419 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+SigLIP Vision Encoder for FastVideo.
+
+SigLIP (Sigmoid Loss for Language-Image Pre-training) is similar to CLIP
+but uses sigmoid loss instead of contrastive loss, and doesn't use a CLS token.
+"""
+
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+
+from fastvideo.attention import LocalAttention
+from fastvideo.configs.models.encoders import BaseEncoderOutput
+from fastvideo.configs.models.encoders.siglip import SiglipVisionArchConfig, SiglipVisionConfig
+from fastvideo.distributed import divide, get_tp_world_size
+from fastvideo.layers.activation import get_act_fn
+from fastvideo.layers.linear import (ColumnParallelLinear, QKVParallelLinear,
+                                     RowParallelLinear)
+from fastvideo.layers.quantization import QuantizationConfig
+from fastvideo.logger import init_logger
+from fastvideo.models.encoders.base import ImageEncoder
+from fastvideo.models.loader.weight_utils import default_weight_loader
+
+logger = init_logger(__name__)
+
+
+class SiglipVisionEmbeddings(nn.Module):
+    """
+    SigLIP vision embeddings - similar to CLIP but without class embedding.
+    """
+
+    def __init__(self, arch_config: SiglipVisionArchConfig):
+        super().__init__()
+        self.arch_config = arch_config
+        self.embed_dim = arch_config.hidden_size
+        self.image_size = arch_config.image_size
+        self.patch_size = arch_config.patch_size
+        # SigLIP uses valid padding, so non-divisible sizes work (edge pixels are ignored)
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=arch_config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",  # SigLIP uses valid padding
+        )
+
+        # Integer division - with valid padding, edge pixels are ignored
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer(
+            "position_ids",
+            torch.arange(self.num_positions).expand((1, -1)),
+            persistent=False,
+        )
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(
+            pixel_values.to(dtype=target_dtype)
+        )  # shape = [*, embed_dim, grid, grid]
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+        embeddings = embeddings + self.position_embedding(self.position_ids)
+        return embeddings
+
+
+class SiglipAttention(nn.Module):
+    """Multi-headed attention for SigLIP."""
+
+    def __init__(
+        self,
+        arch_config: SiglipVisionArchConfig,
+        enable_scale: bool = True,
+        is_causal: bool = False,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.arch_config = arch_config
+        self.embed_dim = arch_config.hidden_size
+        self.num_heads = arch_config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads "
+                f"(got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+            )
+        
+        self.scale = self.head_dim**-0.5 if enable_scale else None
+        self.dropout = arch_config.attention_dropout
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.embed_dim,
+            head_size=self.head_dim,
+            total_num_heads=self.num_heads,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.out_proj = RowParallelLinear(
+            input_size=self.embed_dim,
+            output_size=self.embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        self.tp_size = get_tp_world_size()
+        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
+
+        self.attn = LocalAttention(
+            self.num_heads_per_partition,
+            self.head_dim,
+            self.num_heads_per_partition,
+            softmax_scale=self.scale,
+            causal=is_causal,
+            supported_attention_backends=arch_config._supported_attention_backends,
+        )
+
+    def forward(self, hidden_states: torch.Tensor):
+        """Input shape: Batch x Time x Channel"""
+        qkv_states, _ = self.qkv_proj(hidden_states)
+        query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
+        
+        query_states = query_states.reshape(
+            query_states.shape[0], query_states.shape[1],
+            self.num_heads_per_partition, self.head_dim
+        )
+        key_states = key_states.reshape(
+            key_states.shape[0], key_states.shape[1],
+            self.num_heads_per_partition, self.head_dim
+        )
+        value_states = value_states.reshape(
+            value_states.shape[0], value_states.shape[1],
+            self.num_heads_per_partition, self.head_dim
+        )
+        
+        attn_output = self.attn(query_states, key_states, value_states)
+        attn_output = attn_output.reshape(
+            attn_output.shape[0], attn_output.shape[1],
+            self.num_heads_per_partition * self.head_dim
+        )
+        attn_output, _ = self.out_proj(attn_output)
+        return attn_output, None
+
+
+class SiglipMLP(nn.Module):
+
+    def __init__(
+        self,
+        arch_config: SiglipVisionArchConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.arch_config = arch_config
+        self.activation_fn = get_act_fn(arch_config.hidden_act)
+        self.fc1 = ColumnParallelLinear(
+            arch_config.hidden_size,
+            arch_config.intermediate_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.fc2 = RowParallelLinear(
+            arch_config.intermediate_size,
+            arch_config.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+        return hidden_states
+
+
+class SiglipEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        arch_config: SiglipVisionArchConfig,
+        enable_scale: bool = True,
+        is_causal: bool = False,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.self_attn = SiglipAttention(
+            arch_config,
+            enable_scale=enable_scale,
+            is_causal=is_causal,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.layer_norm1 = nn.LayerNorm(
+            arch_config.hidden_size, eps=arch_config.layer_norm_eps
+        )
+        self.mlp = SiglipMLP(
+            arch_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+        self.layer_norm2 = nn.LayerNorm(
+            arch_config.hidden_size, eps=arch_config.layer_norm_eps
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # SigLIP uses post-norm (like original ViT)
+        residual = hidden_states
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class SiglipEncoder(nn.Module):
+    """SigLIP encoder consisting of transformer layers."""
+
+    def __init__(
+        self,
+        arch_config: SiglipVisionArchConfig,
+        enable_scale: bool = True,
+        is_causal: bool = False,
+        quant_config: QuantizationConfig | None = None,
+        num_hidden_layers_override: int | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.arch_config = arch_config
+
+        if num_hidden_layers_override is None:
+            num_hidden_layers = arch_config.num_hidden_layers
+        else:
+            num_hidden_layers = num_hidden_layers_override
+            
+        self.layers = nn.ModuleList([
+            SiglipEncoderLayer(
+                arch_config=arch_config,
+                enable_scale=enable_scale,
+                is_causal=is_causal,
+                quant_config=quant_config,
+                prefix=f"{prefix}.layers.{layer_idx}",
+            )
+            for layer_idx in range(num_hidden_layers)
+        ])
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        return_all_hidden_states: bool,
+    ) -> torch.Tensor | list[torch.Tensor]:
+        hidden_states_pool = [inputs_embeds]
+        hidden_states = inputs_embeds
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states)
+            if return_all_hidden_states:
+                hidden_states_pool.append(hidden_states)
+
+        if return_all_hidden_states:
+            return hidden_states_pool
+        return [hidden_states]
+
+
+class SiglipVisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: SiglipVisionConfig,
+        quant_config: QuantizationConfig | None = None,
+        num_hidden_layers_override: int | None = None,
+        require_post_norm: bool | None = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        arch_config = config.arch_config
+        embed_dim = arch_config.hidden_size
+
+        self.embeddings = SiglipVisionEmbeddings(arch_config)
+
+        self.encoder = SiglipEncoder(
+            arch_config=arch_config,
+            enable_scale=config.enable_scale,
+            is_causal=config.is_causal,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.encoder",
+        )
+
+        num_hidden_layers = arch_config.num_hidden_layers
+        if len(self.encoder.layers) > arch_config.num_hidden_layers:
+            raise ValueError(
+                f"The original encoder only has {num_hidden_layers} "
+                f"layers, but you requested {len(self.encoder.layers)} layers."
+            )
+
+        # Post layer norm (applied to output)
+        if require_post_norm is None:
+            require_post_norm = len(self.encoder.layers) == num_hidden_layers
+
+        if require_post_norm:
+            self.post_layernorm = nn.LayerNorm(embed_dim, eps=arch_config.layer_norm_eps)
+        else:
+            self.post_layernorm = None
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        feature_sample_layers: list[int] | None = None,
+    ) -> torch.Tensor:
+        hidden_states = self.embeddings(pixel_values)
+
+        return_all_hidden_states = feature_sample_layers is not None
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            return_all_hidden_states=return_all_hidden_states,
+        )
+
+        if not return_all_hidden_states:
+            encoder_outputs = encoder_outputs[0]
+
+        # Apply post-layernorm
+        if self.post_layernorm is not None:
+            if isinstance(encoder_outputs, list):
+                encoder_outputs[-1] = self.post_layernorm(encoder_outputs[-1])
+            else:
+                encoder_outputs = self.post_layernorm(encoder_outputs)
+
+        return encoder_outputs
+
+
+class SiglipVisionModel(ImageEncoder):
+    """SigLIP Vision Model for FastVideo."""
+    
+    config_class = SiglipVisionConfig
+    main_input_name = "pixel_values"
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
+
+    def __init__(self, config: SiglipVisionConfig) -> None:
+        super().__init__(config)
+        self.config = config
+        self.vision_model = SiglipVisionTransformer(
+            config=config,
+            quant_config=config.quant_config,
+            num_hidden_layers_override=config.num_hidden_layers_override,
+            require_post_norm=config.require_post_norm,
+            prefix=f"{config.prefix}.vision_model",
+        )
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        feature_sample_layers: list[int] | None = None,
+        **kwargs,
+    ) -> BaseEncoderOutput:
+        last_hidden_state = self.vision_model(pixel_values, feature_sample_layers)
+        return BaseEncoderOutput(last_hidden_state=last_hidden_state)
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        layer_count = len(self.vision_model.encoder.layers)
+
+        for name, loaded_weight in weights:
+            # Skip projection layers if any
+            if name.startswith("visual_projection"):
+                continue
+                
+            # Skip head if any
+            if "head" in name:
+                continue
+
+            # Post layernorm handling
+            if (name.startswith("vision_model.post_layernorm")
+                    and self.vision_model.post_layernorm is None):
+                continue
+
+            # Omit layers when num_hidden_layers_override is set
+            if name.startswith("vision_model.encoder.layers"):
+                layer_idx = int(name.split(".")[3])
+                if layer_idx >= layer_count:
+                    continue
+
+            # Handle QKV projection weight mapping
+            for (param_name, weight_name, shard_id) in self.config.arch_config.stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                if name in params_dict:
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name in params_dict:
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+            
+        return loaded_params

fastvideo/models/loader/component_loader.py

@@ -80,11 +80,15 @@ class ComponentLoader(ABC):
             "transformer": (TransformerLoader, "diffusers"),
             "transformer_2": (TransformerLoader, "diffusers"),
             "vae": (VAELoader, "diffusers"),
+            "audio_vae": (AudioDecoderLoader, "diffusers"),
+            "audio_decoder": (AudioDecoderLoader, "diffusers"),
+            "vocoder": (VocoderLoader, "diffusers"),
             "text_encoder": (TextEncoderLoader, "transformers"),
             "text_encoder_2": (TextEncoderLoader, "transformers"),
             "tokenizer": (TokenizerLoader, "transformers"),
             "tokenizer_2": (TokenizerLoader, "transformers"),
             "image_processor": (ImageProcessorLoader, "transformers"),
+            "feature_extractor": (ImageProcessorLoader, "transformers"),
             "image_encoder": (ImageEncoderLoader, "transformers"),
         }
 
@@ -242,6 +246,47 @@ class TextEncoderLoader(ComponentLoader):
         model_config.pop("model_type", None)
         model_config.pop("tokenizer_class", None)
         model_config.pop("torch_dtype", None)
+        repo_root = os.path.dirname(model_path)
+        index_path = os.path.join(repo_root, "model_index.json")
+        gemma_path = ""
+        gemma_path_from_candidate = False
+        if os.path.isfile(index_path):
+            try:
+                with open(index_path, encoding="utf-8") as f:
+                    model_index = json.load(f)
+                gemma_path = model_index.get("gemma_model_path", "")
+            except json.JSONDecodeError:
+                gemma_path = ""
+        if not gemma_path:
+            candidate = os.path.normpath(os.path.join(model_path, "gemma"))
+            if os.path.isdir(candidate):
+                gemma_path = candidate
+                gemma_path_from_candidate = True
+                model_config["gemma_model_path"] = gemma_path
+        if gemma_path and not gemma_path_from_candidate:
+            if not os.path.isabs(gemma_path):
+                model_config["gemma_model_path"] = os.path.normpath(
+                    os.path.join(repo_root, gemma_path)
+                )
+        transformer_config_path = os.path.join(
+            repo_root, "transformer", "config.json"
+        )
+        if os.path.isfile(transformer_config_path):
+            try:
+                with open(transformer_config_path, encoding="utf-8") as f:
+                    transformer_config = json.load(f)
+                if (
+                    "connector_double_precision_rope" not in model_config
+                    or not model_config["connector_double_precision_rope"]
+                ):
+                    if transformer_config.get("double_precision_rope") is True:
+                        model_config["connector_double_precision_rope"] = True
+                if "connector_rope_type" not in model_config:
+                    rope_type = transformer_config.get("rope_type")
+                    if rope_type is not None:
+                        model_config["connector_rope_type"] = rope_type
+            except json.JSONDecodeError:
+                pass
         logger.info("HF Model config: %s", model_config)
 
         # @TODO(Wei): Better way to handle this?
@@ -346,6 +391,8 @@ class TextEncoderLoader(ComponentLoader):
 
             from fastvideo.platforms import current_platform
 
+            logger.info("Loading text encoder with cpu_offload: %s", use_cpu_offload)
+
             if use_cpu_offload:
                 pin_cpu_memory = fastvideo_args.pin_cpu_memory and is_pin_memory_available()
                 # Disable FSDP for MPS as it's not compatible
@@ -489,8 +536,20 @@ class TokenizerLoader(ComponentLoader):
             # in v0, this was same string as encoder_name "ClipTextModel"
             # TODO(will): pass these tokenizer kwargs from inference args? Maybe
             # other method of config?
-            padding_size="right",
         )
+        padding_side = None
+        if hasattr(fastvideo_args.pipeline_config, "text_encoder_configs"):
+            try:
+                arch_config = fastvideo_args.pipeline_config.text_encoder_configs[
+                    0
+                ].arch_config
+                padding_side = getattr(arch_config, "padding_side", None)
+            except Exception:
+                padding_side = None
+        if padding_side:
+            tokenizer.padding_side = padding_side
+        if tokenizer.pad_token is None and tokenizer.eos_token is not None:
+            tokenizer.pad_token = tokenizer.eos_token
         logger.info("Loaded tokenizer: %s", tokenizer.__class__.__name__)
         return tokenizer
 
@@ -501,15 +560,11 @@ class VAELoader(ComponentLoader):
     def load(self, model_path: str, fastvideo_args: FastVideoArgs):
         """Load the VAE based on the model path, and inference args."""
         config = get_diffusers_config(model=model_path)
+        config.pop("_name_or_path", None)
         class_name = config.pop("_class_name")
-        assert class_name is not None, (
-            "Model config does not contain a _class_name attribute. Only diffusers format is supported."
-        )
+        assert class_name is not None, "Model config does not contain a _class_name attribute. Only diffusers format is supported."
         fastvideo_args.model_paths["vae"] = model_path
 
-        vae_config = fastvideo_args.pipeline_config.vae_config
-        vae_config.update_model_arch(config)
-
         from fastvideo.platforms import current_platform
 
         if fastvideo_args.vae_cpu_offload:
@@ -543,8 +598,29 @@ class VAELoader(ComponentLoader):
                 vae.load_state_dict(sd, strict=False)
                 return vae.eval()
 
-            vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
-            vae = vae_cls(vae_config).to(target_device)
+            # LTX-2 uses CausalVideoAutoencoder with nested "vae" config
+            if class_name == "CausalVideoAutoencoder" and "vae" in config:
+                vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
+                vae = vae_cls(config).to(target_device)
+                if hasattr(vae, "set_tiling_config"):
+                    vae_config = fastvideo_args.pipeline_config.vae_config
+                    vae.set_tiling_config(
+                        spatial_tile_size_in_pixels=getattr(
+                            vae_config, "ltx2_spatial_tile_size_in_pixels", 512),
+                        spatial_tile_overlap_in_pixels=getattr(
+                            vae_config, "ltx2_spatial_tile_overlap_in_pixels", 64),
+                        temporal_tile_size_in_frames=getattr(
+                            vae_config, "ltx2_temporal_tile_size_in_frames", 64),
+                        temporal_tile_overlap_in_frames=getattr(
+                            vae_config,
+                            "ltx2_temporal_tile_overlap_in_frames", 24),
+                    )
+            else:
+                config.pop("_class_name", None)
+                vae_config = fastvideo_args.pipeline_config.vae_config
+                vae_config.update_model_arch(config)
+                vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
+                vae = vae_cls(vae_config).to(target_device)
 
         # Find all safetensors files
         safetensors_list = glob.glob(
@@ -553,23 +629,108 @@ class VAELoader(ComponentLoader):
             raise ValueError(f"No safetensors files found in {model_path}")
         # Common case: a single `.safetensors` checkpoint file.
         # Some models may be sharded into multiple files; in that case we merge.
-        if len(safetensors_list) == 1:
-            loaded = safetensors_load_file(safetensors_list[0])
-        else:
-            loaded = {}
-            for sf_file in safetensors_list:
-                loaded.update(safetensors_load_file(sf_file))
+        loaded = {}
+        for sf_file in safetensors_list:
+            loaded.update(safetensors_load_file(sf_file))
+
+        # LTX-2 CausalVideoAutoencoder needs per_channel_statistics remapping
+        if class_name == "CausalVideoAutoencoder" and "vae" in config:
+            per_channel_prefixes = (
+                "per_channel_statistics.",
+                "vae.per_channel_statistics.",
+            )
+            remapped = {}
+            for key, tensor in loaded.items():
+                remapped[key] = tensor
+                for prefix in per_channel_prefixes:
+                    if key.startswith(prefix):
+                        suffix = key[len(prefix):]
+                        remapped.setdefault(
+                            f"encoder.per_channel_statistics.{suffix}",
+                            tensor,
+                        )
+                        remapped.setdefault(
+                            f"decoder.per_channel_statistics.{suffix}",
+                            tensor,
+                        )
+                        break
+            loaded = remapped
+
         vae.load_state_dict(loaded, strict=False)
 
         return vae.eval()
 
 
+class AudioDecoderLoader(ComponentLoader):
+    """Loader for LTX-2 audio decoder (audio_vae component)."""
+
+    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
+        config = get_diffusers_config(model=model_path)
+        class_name = config.pop("_class_name", None) or "LTX2AudioDecoder"
+
+        model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
+        target_device = get_local_torch_device()
+
+        precision = getattr(
+            fastvideo_args.pipeline_config, "audio_decoder_precision", "bf16"
+        )
+        with set_default_torch_dtype(PRECISION_TO_TYPE[precision]):
+            audio_decoder = model_cls(config).to(target_device)
+
+        safetensors_list = glob.glob(
+            os.path.join(str(model_path), "*.safetensors")
+        )
+        loaded: dict[str, torch.Tensor] = {}
+        for sf_file in safetensors_list:
+            loaded.update(safetensors_load_file(sf_file))
+
+        decoder_state = {}
+        for name, tensor in loaded.items():
+            if name.startswith("decoder."):
+                decoder_state[name.replace("decoder.", "")] = tensor
+            elif name.startswith("per_channel_statistics."):
+                decoder_state[name] = tensor
+
+        target_module = getattr(audio_decoder, "model", audio_decoder)
+        target_module.load_state_dict(decoder_state, strict=False)
+        return audio_decoder.eval()
+
+
+class VocoderLoader(ComponentLoader):
+    """Loader for LTX-2 vocoder."""
+
+    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
+        config = get_diffusers_config(model=model_path)
+        class_name = config.pop("_class_name", None) or "LTX2Vocoder"
+
+        model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
+        target_device = get_local_torch_device()
+
+        precision = getattr(
+            fastvideo_args.pipeline_config, "vocoder_precision", "bf16"
+        )
+        with set_default_torch_dtype(PRECISION_TO_TYPE[precision]):
+            vocoder = model_cls(config).to(target_device)
+
+        safetensors_list = glob.glob(
+            os.path.join(str(model_path), "*.safetensors")
+        )
+        loaded: dict[str, torch.Tensor] = {}
+        for sf_file in safetensors_list:
+            loaded.update(safetensors_load_file(sf_file))
+
+        target_module = getattr(vocoder, "model", vocoder)
+        target_module.load_state_dict(loaded, strict=False)
+        return vocoder.eval()
+
+
 class TransformerLoader(ComponentLoader):
     """Loader for transformer."""
 
     def load(self, model_path: str, fastvideo_args: FastVideoArgs):
         """Load the transformer based on the model path, and inference args."""
         config = get_diffusers_config(model=model_path)
+        config.pop("_name_or_path", None)
         hf_config = deepcopy(config)
         cls_name = config.pop("_class_name")
         if cls_name is None:
@@ -648,6 +809,11 @@ class TransformerLoader(ComponentLoader):
             or cls_name == "Cosmos25Transformer3DModel"
             or getattr(fastvideo_args.pipeline_config, "prefix", "") == "Cosmos25"
         )
+
+        add_cls_branch = False
+        if hasattr(fastvideo_args, "_adding_cls_branch") and fastvideo_args._adding_cls_branch:
+            strict_load = False
+            add_cls_branch = True
         model = maybe_load_fsdp_model(
             model_cls=model_cls,
             init_params={"config": dit_config, "hf_config": hf_config},
@@ -667,10 +833,11 @@ class TransformerLoader(ComponentLoader):
             training_mode=fastvideo_args.training_mode,
             enable_torch_compile=fastvideo_args.enable_torch_compile,
             torch_compile_kwargs=fastvideo_args.torch_compile_kwargs,
+            add_cls_branch=add_cls_branch,
         )
 
         total_params = sum(p.numel() for p in model.parameters())
-        logger.info("Loaded model with %.2fB parameters", total_params / 1e9)
+        logger.info("Loaded model with %.2fB parameters, with cpu_offload: %s", total_params / 1e9, fastvideo_args.dit_cpu_offload)
 
         assert next(model.parameters()).dtype == default_dtype, (
             "Model dtype does not match default dtype"
@@ -679,7 +846,18 @@ class TransformerLoader(ComponentLoader):
         model = model.eval()
 
         if fastvideo_args.inference_mode and fastvideo_args.dit_layerwise_offload:
-            enable_layerwise_offload(model)
+            # Check if model has nn.ModuleList for layerwise offload compatibility
+            has_module_list = any(
+                isinstance(m, nn.ModuleList) for m in model.children()
+            )
+            if has_module_list:
+                enable_layerwise_offload(model)
+            else:
+                logger.warning(
+                    "Layerwise offload requested but model %s does not have "
+                    "nn.ModuleList structure. Skipping layerwise offload.",
+                    cls_name
+                )
         return model
 
 
@@ -697,9 +875,11 @@ class SchedulerLoader(ComponentLoader):
 
         scheduler_cls, _ = ModelRegistry.resolve_model_cls(class_name)
 
+        if fastvideo_args.pipeline_config.flow_shift is not None and "shift" in config:
+            config["shift"] = fastvideo_args.pipeline_config.flow_shift
         scheduler = scheduler_cls(**config)
-        if fastvideo_args.pipeline_config.flow_shift is not None:
-            scheduler.set_shift(fastvideo_args.pipeline_config.flow_shift)
+        # if fastvideo_args.pipeline_config.flow_shift is not None:
+        #     scheduler.set_shift(fastvideo_args.pipeline_config.flow_shift)
         if fastvideo_args.pipeline_config.timesteps_scale is not None:
             scheduler.set_timesteps_scale(
                 fastvideo_args.pipeline_config.timesteps_scale
@@ -786,4 +966,4 @@ class PipelineComponentLoader:
         )
 
         # Load the module
-        return loader.load(component_model_path, fastvideo_args)
+        return loader.load(component_model_path, fastvideo_args)

fastvideo/models/loader/fsdp_load.py

@@ -75,6 +75,7 @@ def maybe_load_fsdp_model(
     pin_cpu_memory: bool = True,
     enable_torch_compile: bool = False,
     torch_compile_kwargs: dict[str, Any] | None = None,
+    add_cls_branch: bool = False,
 ) -> torch.nn.Module:
     """
     Load the model with FSDP if is training, else load the model without FSDP.
@@ -96,6 +97,8 @@ def maybe_load_fsdp_model(
     logger.info("Loading model with default_dtype: %s", default_dtype)
     with set_default_dtype(default_dtype), torch.device("meta"):
         model = model_cls(**init_params)
+    if add_cls_branch:
+        model.adding_cls_branch(atten_dim=1536, num_class=1, time_embed_dim=0)
 
     # Check if we should use FSDP
     use_fsdp = training_mode or fsdp_inference
@@ -340,7 +343,7 @@ def load_model_from_full_model_state_dict(
                        unused_keys)
 
     # List of allowed parameter name patterns
-    ALLOWED_NEW_PARAM_PATTERNS = ["gate_compress", "proj_l"]  # Can be extended as needed
+    ALLOWED_NEW_PARAM_PATTERNS = ["gate_compress", "proj_l", "_cls_pred_branch", "_register_tokens", "_gan_ca_blocks"]  # Can be extended as needed
     for new_param_name in unused_keys:
         if not any(pattern in new_param_name
                    for pattern in ALLOWED_NEW_PARAM_PATTERNS):
@@ -353,14 +356,16 @@ def load_model_from_full_model_state_dict(
         meta_sharded_param = meta_sd.get(new_param_name)
         if not hasattr(meta_sharded_param, "device_mesh"):
             # Initialize with zeros
-            sharded_tensor = torch.zeros_like(meta_sharded_param,
-                                              device=device,
-                                              dtype=param_dtype)
+            # sharded_tensor = torch.zeros_like(meta_sharded_param,
+            #                                   device=device,
+            #                                   dtype=param_dtype)
+            sharded_tensor = meta_sharded_param.to(device=device, dtype=param_dtype)
         else:
             # Initialize with zeros and distribute
-            full_tensor = torch.zeros_like(meta_sharded_param,
-                                           device=device,
-                                           dtype=param_dtype)
+            # full_tensor = torch.zeros_like(meta_sharded_param,
+            #                                device=device,
+            #                                dtype=param_dtype)
+            full_tensor = meta_sharded_param.to(device=device, dtype=param_dtype)
             sharded_tensor = distribute_tensor(
                 full_tensor,
                 meta_sharded_param.device_mesh,

fastvideo/models/loader/utils.py

@@ -78,9 +78,10 @@ def hf_to_custom_state_dict(
     for source_param_name, full_tensor in hf_param_sd:  # type: ignore
         target_param_name, merge_index, num_params_to_merge = param_names_mapping(
             source_param_name)
-        reverse_param_names_mapping[target_param_name] = (source_param_name,
-                                                          merge_index,
-                                                          num_params_to_merge)
+        if merge_index is None:
+            reverse_param_names_mapping[target_param_name] = (source_param_name,
+                                                            merge_index,
+                                                            num_params_to_merge)
         if merge_index is not None:
             to_merge_params[target_param_name][merge_index] = full_tensor
             if len(to_merge_params[target_param_name]) == num_params_to_merge:

fastvideo/models/registry.py

@@ -26,6 +26,10 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
     ("dits", "hunyuanvideo", "HunyuanVideoTransformer3DModel"),
     "HunyuanVideo15Transformer3DModel":
     ("dits", "hunyuanvideo15", "HunyuanVideo15Transformer3DModel"),
+    "HYWorldTransformer3DModel":
+    ("dits", "hyworld", "HYWorldTransformer3DModel"),
+    "CausalHunyuanVideo15Transformer3DModel":
+    ("dits", "causal_hunyuanvideo15", "CausalHunyuanVideo15Transformer3DModel"),
     "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
     "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
     "StepVideoModel": ("dits", "stepvideo", "StepVideoModel"),
@@ -33,14 +37,15 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
     "Cosmos25Transformer3DModel": ("dits", "cosmos2_5", "Cosmos25Transformer3DModel"),
     "LongCatVideoTransformer3DModel": ("dits", "longcat_video_dit", "LongCatVideoTransformer3DModel"),  # Wrapper (Phase 1)
     "LongCatTransformer3DModel": ("dits", "longcat", "LongCatTransformer3DModel"),  # Native (Phase 2)
+    "LTX2Transformer3DModel": ("dits", "ltx2", "LTX2Transformer3DModel"),
 }
 
 _IMAGE_TO_VIDEO_DIT_MODELS = {
     # "HunyuanVideoTransformer3DModel": ("dits", "hunyuanvideo", "HunyuanVideoDiT"),
     "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
     "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
-    "MatrixGameWanModel": ("dits", "matrix_game", "MatrixGameWanModel"),
-    "CausalMatrixGameWanModel": ("dits", "matrix_game", "CausalMatrixGameWanModel"),
+    "MatrixGameWanModel": ("dits", "matrixgame", "MatrixGameWanModel"),
+    "CausalMatrixGameWanModel": ("dits", "matrixgame", "CausalMatrixGameWanModel"),
 }
 
 _TEXT_ENCODER_MODELS = {
@@ -54,20 +59,30 @@ _TEXT_ENCODER_MODELS = {
     "Reason1TextEncoder": ("encoders", "reason1", "Reason1TextEncoder"),
     "Qwen2_5_VLForConditionalGeneration":
     ("encoders", "reason1", "Reason1TextEncoder"),
+    "LTX2GemmaTextEncoderModel": ("encoders", "gemma", "LTX2GemmaTextEncoderModel"),
 }
 
 _IMAGE_ENCODER_MODELS: dict[str, tuple] = {
     # "HunyuanVideoTransformer3DModel": ("image_encoder", "hunyuanvideo", "HunyuanVideoImageEncoder"),
     "CLIPVisionModelWithProjection": ("encoders", "clip", "CLIPVisionModel"),
     "CLIPVisionModel": ("encoders", "clip", "CLIPVisionModel"),
+    "SiglipVisionModel": ("encoders", "siglip", "SiglipVisionModel"),
 }
 
 _VAE_MODELS = {
     "AutoencoderKLHunyuanVideo":
     ("vaes", "hunyuanvae", "AutoencoderKLHunyuanVideo"),
+    "AutoencoderKLHYWorld": ("vaes", "hyworldvae", "AutoencoderKLHYWorld"),
     "AutoencoderKLHunyuanVideo15": ("vaes", "hunyuan15vae", "AutoencoderKLHunyuanVideo15"),
     "AutoencoderKLWan": ("vaes", "wanvae", "AutoencoderKLWan"),
-    "AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo")
+    "AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo"),
+    "CausalVideoAutoencoder": ("vaes", "ltx2vae", "LTX2CausalVideoAutoencoder"),
+}
+
+_AUDIO_MODELS = {
+    "LTX2AudioEncoder": ("audio", "ltx2_audio_vae", "LTX2AudioEncoder"),
+    "LTX2AudioDecoder": ("audio", "ltx2_audio_vae", "LTX2AudioDecoder"),
+    "LTX2Vocoder": ("audio", "ltx2_audio_vae", "LTX2Vocoder"),
 }
 
 _SCHEDULERS = {
@@ -91,6 +106,7 @@ _FAST_VIDEO_MODELS = {
     **_TEXT_ENCODER_MODELS,
     **_IMAGE_ENCODER_MODELS,
     **_VAE_MODELS,
+    **_AUDIO_MODELS,
     **_SCHEDULERS,
 }