mirror of
https://github.com/NVIDIA/Model-Optimizer.git
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Zhehao-Hu
0b67ea93d3
### What does this PR do?
Type of change: Documentation, deprecation.
Deprecates the Diffusers ONNX export and TensorRT engine deployment
workflow in ModelOpt 0.48.0, with removal no earlier than 0.49.0 after
the one-release migration period. The TensorRT team consultation
confirmed the direction toward TensorRT-LLM VisualGen and unified HF
checkpoints.
Adds visible runtime warnings and deprecated CLI help while preserving
legacy functionality. Documents HF checkpoint migration and links to
vLLM-Omni, SGLang Diffusion, and TensorRT-LLM VisualGen deployment
guidance.
Moves the six legacy ONNX/TRT integration cases into a separate file
excluded from automatic example CI. They remain runnable manually;
supported HF export, Wan quantization, and PyTorch inference coverage
remain in CI.
### Usage
From `examples/diffusers/quantization`, export an HF checkpoint instead
of ONNX:
```bash
python quantize.py \
--model flux-schnell --model-dtype BFloat16 \
--format fp8 --collect-method default \
--batch-size 1 --calib-size 128 --n-steps 4 \
--hf-ckpt-dir ./flux-schnell-fp8
```
Choose a diffusion backend that supports the model, precision, hardware,
and checkpoint layout. The README includes migration guidance and
backend documentation links.
### Testing
Local validation passed:
- Runnable warning regressions: **9 passed** in the CPU-only virtual
environment with Hugging Face and dataset downloads disabled:
```bash
HF_HUB_OFFLINE=1 HF_DATASETS_OFFLINE=1 PYTHONDONTWRITEBYTECODE=1 \
.venv/bin/python -m pytest
tests/examples/diffusers/test_onnx_trt_deprecation.py -q -p
no:cacheprovider
```
These invoke the real CLI entry points and stop before model setup. They
cover ONNX quantization/export and restore/export, default TensorRT
execution, ONNX and existing-engine loading, and absence of the specific
deprecation warning for quantization-only, HF-only, `--torch`, and
`--torch --torch-compile` modes. Each case asserts that execution
reached the deliberate stopping point.
- Pytest collection with GPU capability metadata mocked: **6 legacy
cases** when collected alone; **25 unique cases** across the supported
and legacy test modules, without duplicate collection. A CI-style
directory-discovery check retained the 19 supported cases (6
FLUX/SD3/SDXL round-trips, 7 Wan cases, and 6 PyTorch inference cases)
while excluding the legacy file.
- Parameter-preservation checks confirmed the model arguments, IDs, and
hardware gates survived the test reorganization.
- Applicable pre-commit hooks and whitespace checks.
- Workflow YAML parsing, dedicated Diffusers job/required-check wiring,
and shell checks for Diffusers-only, ONNX-only, shared-file,
nightly/on-demand, and no-change gating scenarios.
- Shell-wrapper syntax/startup-warning checks, added local documentation
links, and migration-command shell syntax.
### 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?: ✅ Legacy commands remain
available with warnings.
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
- Did you write any new necessary tests?: N/A Existing integration tests
were preserved and reorganized.
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅
- Did you get Claude approval on this PR?: N/A
### Additional Information
Actual removal will be handled separately after the migration period.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Documentation
* Added guidance for exporting Diffusers models as unified Hugging Face
checkpoints and deploying them with compatible backends.
* Clarified that backend support varies by model, quantization format,
and hardware, and that Hugging Face checkpoints cannot be used with the
legacy TensorRT engine workflow.
## Deprecations
* The Diffusers ONNX export and TensorRT deployment workflow is
deprecated in ModelOpt 0.48.0 and may be removed no earlier than 0.49.0.
It remains available during the migration period and displays warnings;
PyTorch inference remains supported.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Zhehao Hu <zhehaoh@nvidia.com>