## Summary
LTX-2 (`ltx-core`, `ltx-pipelines`, `ltx-trainer`) is a third-party
dependency developed and provided by Lightricks. It is governed by the
[LTX Community License
Agreement](https://github.com/Lightricks/LTX-2/blob/main/LICENSE),
**not** the Apache 2.0 license that covers NVIDIA Model Optimizer. Per
legal guidance, all integration points must clearly surface this to
users.
- Add `[!WARNING]` license notice blocks at the top of all LTX-2-related
READMEs (`examples/diffusers`, `examples/diffusers/distillation`,
`examples/windows/diffusers/qad_example`)
- Add `warnings.warn(UserWarning)` at every LTX package import site in
Python files, covering both top-level and lazy imports:
- `examples/diffusers/distillation/distillation_trainer.py`
- `examples/diffusers/quantization/calibration.py`
- `examples/diffusers/quantization/pipeline_manager.py`
-
`examples/windows/diffusers/qad_example/sample_example_qad_diffusers.py`
- `modelopt/torch/export/diffusers_utils.py`
- `modelopt/torch/quantization/plugins/diffusion/ltx2.py`
- Add license notice comment to `requirements.txt` files that list LTX
packages, so the obligation is visible at install time
- Update `.github/CODEOWNERS` so all `requirements*.txt` files (covering
variants like `requirements-dev.txt`) are owned by
`@NVIDIA/modelopt-setup-codeowners` regardless of location, via a
last-match-wins rule
**Design notes:**
- For library files (`diffusers_utils.py`, `ltx2.py`), the warning is
placed at the lazy import site inside functions — it fires only when
LTX-2 code paths are actually invoked, not at module import time, to
avoid polluting non-LTX users
- For example entry-point scripts that are LTX-2-only, the warning fires
at module load time (after all imports, to satisfy ruff E402)
## Test plan
- [ ] Confirm `pre-commit run --all-files` passes (ruff, mypy,
markdownlint, bandit all clean)
- [ ] Verify warning appears at runtime when running an LTX-2
quantization or distillation example
- [ ] Confirm non-LTX code paths (FLUX, SDXL, SD3) do not emit the
warning
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Added third-party license notices across documentation and
requirements files clarifying LTX-2 packages are governed by the LTX
Community License Agreement rather than NVIDIA Model Optimizer's Apache
2.0 license.
* **Chores**
* Updated code ownership configuration for requirements files.
* Added runtime warnings to notify when LTX-2 dependencies are accessed.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
### What does this PR do?
Type of change: ? New benchmark addition
<!-- Details about the change. -->
This PR adds the following to examples/windows/:
1) FVD (Fréchet Video Distance) evaluation tool — a standalone script
for computing FVD between two sets of videos using a pre-trained I3D
model (Kinetics-400, 1024-dim pooled features).
2) Directory reorganization — moved torch_onnx/ and qad_example/ under a
new diffusers/ folder for clearer grouping of diffusion model examples.
3) Minor fixes: Updated WikiText-2 dataset link in
perplexity_metrics/README.md (old wikitext → Salesforce/wikitext)
### Usage
```python
python compute_fvd.py \
--ref-dir /path/to/reference/videos \
--gen-dir /path/to/generated/videos \
--output results.json \
--device cuda
```
### Testing
Evaluated FVD tool on LTX-2.3 outputs comparing QAD vs PTQ checkpoints
(both NVFp4) against a BF16 baseline across 11 VBench dimensions.
### Before your PR is "*Ready for review*"
Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
and your commits are signed (`git commit -s -S`).
Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).
- Is this change backward compatible?: ✅
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A <!---
Mandatory -->
- Did you write any new necessary tests?: N/A <!--- Mandatory for new
features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->
### Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added FVD (Fréchet Video Distance) evaluation: command‑line tool,
I3D‑based feature extraction, PCA option, and quantized comparisons (PTQ
vs QAD) with per‑category and overall results showing QAD's lower
average FVD.
* **Documentation**
* Expanded benchmarks TOC with “Additional Metrics”, added FVD
evaluation guide, usage examples, troubleshooting, and updated
perplexity dataset reference.
* **Chores**
* Added Python requirements for the FVD benchmark.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: ynankani <ynankani@nvidia.com>
Signed-off-by: Yash Nankani <ynankani@nvidia.com>
