Use fake_score features for DMD2 discriminator losses

[wlaud1001]

I really appreciate the amazing work of bringing together multiple distillation methods into a single unified framework.
While looking through the DMD2 implementation in FastGen, I noticed what seems to be a slight discrepancy from the original paper and reference implementation, so I wanted to submit this PR.

This PR updates DMD2 so that GAN losses use fake_score features instead of teacher features.

Changes:

• use fake_score features for generator GAN loss
• use fake_score features for discriminator real/fake inputs
• use fake_score features in the R1 perturbation path
• allow discriminator-side adversarial gradients to also update the fake_score backbone

teacher remains responsible for producing the x0 target used by VSD.

This change is motivated by the DMD2 paper and its reference implementations.
In Section 4.3 of Improved Distribution Matching Distillation for Fast Image Synthesis (NeurIPS 2024), the authors write:

"Our integration of a GAN classifier into DMD follows a minimalist design: we add a classification branch on top of the bottleneck of the fake diffusion denoiser (see Fig. 3)."

This is also consistent with existing implementations:

• Original DMD2 applies the classification head on fake_unet bottleneck features:
  https://github.com/tianweiy/DMD2/blob/8d8fa55633d47cfb81bbc7a892e7248f9518763f/main/edm/edm_guidance.py#L185
• NVIDIA Cosmos DMD2 similarly routes the adversarial path through fake_score intermediate features:
  https://github.com/nvidia-cosmos/cosmos-predict2.5/blob/7e5ffc83fefb2ae1c105c1185cdeb239efb1325c/cosmos_predict2/_src/predict2/distill/models/video2world_model_distill_dmd2.py#L244

If the current use of teacher features was intentional, I’d appreciate clarification on the intended design.