What does this PR do?

Type of change: New example + new modelopt.torch.fastgen library module.

Adds DMD2 (Distribution Matching Distillation) for Qwen-Image — distilling the base model into a few-step (1–4) generator. Includes the framework-agnostic modelopt.torch.fastgen loss library (DMD pipeline, EMA, optional GAN discriminator) and a NeMo AutoModel–based training example with a mock-data smoke config, a real-data config, and inference / export scripts.

Noted: the example script will be migrated to AutoModel repo

Usage

# Mock-data wiring smoke — runs end-to-end with no dataset to prepare
torchrun --nproc-per-node=8 \
    examples/diffusers/fastgen/dmd2_finetune.py \
    --config examples/diffusers/fastgen/configs/dmd2_qwen_image_smoke.yaml

See examples/diffusers/fastgen/README.md for real-data training and inference.

Testing

Unit tests under tests/unit/torch/fastgen/; pre-commit / code-quality clean.

Before your PR is "Ready for review"

• Backward compatible?: ✅ (new, additive module)
• Followed CONTRIBUTING.md for any copied code / new deps: ✅
• New tests added?: ✅
• Updated Changelog?: N/A

Summary by CodeRabbit

• New Features

  • Adds a FastGen-based distillation framework (DMD2) with student/fake-score training, EMA support, GAN discriminator branch, inference pipeline, and export utilities.
  • Qwen-Image integration with latent packing and feature-capture for plugin-enabled pipelines.

• Documentation

  • New README, example configs, and runnable example scripts for Qwen-Image distillation and inference.

• Tests

  • Comprehensive unit tests covering math parity, gradient routing, plugins, hooks, EMA, and recipe setup.