ig.prediction_type,
    alpha_schedule,
    sigma_schedule,
)

model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0 It gets the teacher model predictions and the LCM predictions next, calculates the loss, and then backpropagates it to the LCM.   Copied if args.loss_type == "l2":
    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
elif args.loss_type == "huber":
    loss = torch.mean(
        torch.sqrt(