### What does this PR do?
Type of change: Bug fix <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->
<!-- Details about the change. -->
- Added a flag merged_base_safetensor_path to the example code so that
user can export the ComfyUI style ckpt.
### Usage
```bash
python quantize.py \
--model ltx-2 --format fp4 --batch-size 1 --calib-size 32 --n-steps 40 \
--extra-param checkpoint_path=./ltx-2-19b-dev-fp8.safetensors \
--extra-param distilled_lora_path=./ltx-2-19b-distilled-lora-384.safetensors \
--extra-param spatial_upsampler_path=./ltx-2-spatial-upscaler-x2-1.0.safetensors \
--extra-param gemma_root=./gemma-3-12b-it-qat-q4_0-unquantized \
--extra-param fp8transformer=true \
--quantized-torch-ckpt-save-path ./ltx-2-transformer.pt \
--hf-ckpt-dir ./LTX2-NVFP4/ \
--extra-param merged_base_safetensor_path=./ltx-2-19b-dev-fp8.safetensors
```
### Testing
<!-- Mention how have you tested your change if applicable. -->
### 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`, using
`torch.load(..., weights_only=True)`, avoiding `pickle`, etc.).
- Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain
why. -->
- If you copied code from any other source, did you follow IP policy in
[CONTRIBUTING.md](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md#-copying-code-from-other-sources)?:
✅ / ❌ / 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
* **Documentation**
* Added new command-line parameters documentation for LTX-2 FP4
quantization examples (--hf-ckpt-dir and merged_base_safetensor_path
configuration options)
* **Improvements**
* Enhanced quantization pipeline to support conditional export behavior
based on model type
* Expanded LTX-Video model filtering patterns for more comprehensive
block detection
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
## What does this PR do?
**Type of change:** documentation <!-- Use one of the following: Bug
fix, new feature, new example, new tests, documentation. -->
**Overview:**
1. Added multi‑backbone support for quantization: --backbone now accepts
space- or comma-separated lists and resolves to a list of backbone
modules.
2. Introduced PipelineManager.iter_backbones() to iterate named backbone
modules and updated get_backbone() to return a single module or a
ModuleList for multi‑backbone.
3. Updated ExportManager to save/restore per‑backbone checkpoints when a
directory is provided, with {backbone_name}.pt files, and to create
target directories when missing.
4. Simplified save_checkpoint() calls to rely on the registered
pipeline_manager by default.
**Usage: **
```bash
python quantize.py --model wan2.2-t2v-14b --format fp4 --batch-size 1 --calib-size 32 \
--n-steps 30 --backbone transformer transformer_2 --model-dtype BFloat16 \
--quantized-torch-ckpt-save-path ./wan22_mo_ckpts \
--hf-ckpt-dir ./wan2.2-t2v-14b
```
Plans
- [x] [1/4] Add the basic functionalities to support limited image
models with NVFP4 + FP8, with some refactoring on the previous LLM code
and the diffusers example. PIC: @jingyu-ml
- [x] [2/4] Add support to more video gen models. PIC: @jingyu-ml
- [x] [3/4] Add test cases, refactor on the doc, and all related README.
PIC: @jingyu-ml
- [ ] [4/4] Add the final support to ComfyUI. PIC @jingyu-ml
## Testing
<!-- Mention how have you tested your change if applicable. -->
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: No <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**:No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Unified Hugging Face export support for diffusers pipelines and
components
* LTX-2 and Wan2.2 (T2V) support in diffusers quantization workflow
* Comprehensive ONNX export and TensorRT engine build documentation for
diffusion models
* **Documentation**
* Updated to clarify support for both transformers and diffusers models
in unified export API
* Expanded diffusers examples with LoRA fusion guidance and additional
model options (Flux, SD3, SDXL variants)
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
## What does this PR do?
**Type of change:** ? Refactor and stabilization
**Overview:**
* Enforce use of FSDP-2 on KD and QAD trainers in HF plugins/examples so
that we can remove multiple restrictions
## Usage
<!-- You can potentially add a usage example below. -->
```python
# Add a code snippet demonstrating how to use this
```
## Testing
<!-- Mention how have you tested your change if applicable. -->
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
---------
Signed-off-by: Asha Anoosheh <aanoosheh@nvidia.com>
- [x] Product Rename: TensorRT Model Optimizer to Model Optimizer
(OMNIML-3033)
- [x] Mention in Latest News section with date on the date of merging
this PR (12/08)
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:**
Bug fix
**Overview:**
1. The diffusion_trt.py needs the dynamic_shapes when running trtexec
for engine building. A previous change altered the format of
dynamic_shapes, fix it here.
2. the dynamic_shapes logic gets cleaned up. The existing logic is very
confusing
3. recover min-batch_size config for some pipelines. Previously some
pipelines set the min batch_size to be > 1, which was odd, so a previous
change sets them to be 1, but it turns out the oddity has a reason, the
trt engine building fails with the altered batch_size min/opt, thus
recover them.
## Testing
pytest tests/examples/diffusers
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:**
Minor code change
**Overview:**
- Select the high precision dtype directly based on model type - FP16
for Stable Diffusion models, BF16 for Flux
## Testing
```python
python diffusion_trt.py --model flux-dev --benchmark
```
## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->
- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: No (No option to specify
dtype while loading pipeline)
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes
---------
Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
jingyu-ml