thu-ml/Causal-Forcing

I am working on an action-controlled Wan2.2 TI2V (Image-to-Video) model. When converting the model from bidirectional flow to an autoregressive teacher for distillation, I encountered the following issue: Training predictions: Look normal and reasonable. Inference results: Are significantly worse, with poor quality. I have attached the following for reference: Training prediction samples train_pred_step_001300.mp4 Inference output samples: ar_VID_20251229_164739_147_slice_295_back_180_step_001300.mp4 Training loss curves What could be causing this discrepancy between training and inference quality?

This is truly a remarkable piece of work! Many thanks to the author for open-sourcing it. I have a question about the critic loss computation. From my understanding, the student predicts $x_0$ using the generator's scheduler (denoted as scheduler_stu). Then scheduler_stu is used to add noise, and the resulting noisy sample is passed to the fake/real teachers. However, the teachers use a scheduler with a different shift value from the student's scheduler to predict $x_0$ from pred_flow. After that, the code converts $x_0$ back to pred_flow again using the generator's scheduler. I am wondering why the critic loss is not computed directly from the teacher's original output (pred_flow). Instead, pred_flow is first transformed to $x_0$ and then converted back using a different shift setting. Is this intentional, or could it be a bug? file path model/dmd.py _, pred_fake_image = self.fake_score( noisy_image_or_video=noisy_generated_image, # !!!!!!!!!!!!!!!!! conditional_dict=con