Details on direct distillation

Hi, thanks for your great contributions to the community! I’ve been using DiffSynth for a long time. When I look up the code of loss.py, I'm confuse about the algorithm of the direct distillation.

Code:

def DirectDistillLoss(pipe: BasePipeline, **inputs):
    pipe.scheduler.set_timesteps(inputs["num_inference_steps"])
    pipe.scheduler.training = True
    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
    for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
        timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
        noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep, progress_id=progress_id)
        inputs["latents"] = pipe.step(pipe.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs)
    loss = torch.nn.functional.mse_loss(inputs["latents"].float(), inputs["input_latents"].float())
    return loss

The loss function and pseudo code:

$$ \mathcal{L}{\text{direct}} = \text{MSE}(\boldsymbol{z}{\text{student}}, \boldsymbol{z}_{\text{teacher}}) $$

seed = xxx
with torch.no_grad():
    image_1 = pipe(prompt, steps=50, seed=seed, cfg=4)
image_2 = pipe(prompt, steps=4, seed=seed, cfg=1)
loss = torch.nn.functional.mse_loss(image_1, image_2)

So I got noise as inputs["latents"] and image latents as inputs["input_latents"] after QwenImageUnit_InputImageEmbedder stage. Why it could be considered as Student-Teacher model ? Why do you consider random noise as student model instead of few step inference result How can we set NFE (few steps like 4 or 8) with training? Thank you for your time, and thanks again for all your work on this project!

Read diffsynth/diffusion/loss.py and the DirectDistillLoss entry point, then trace the scheduler, model_fn, step, and QwenImageUnit_InputImageEmbedder context named in the issue. A useful resolution should explain the student and teacher roles and how NFE is selected during training, with the relevant code locations identified.