首尾帧训练时的loss问题

FlowMatchSFTLoss中只有对首帧输入的处理，没有隔离尾帧的latent，这样在首尾帧训练时会不会有影响？

def FlowMatchSFTLoss(pipe: BasePipeline, **inputs):
    if "lora" in inputs:
        # Image-to-LoRA models need to load lora here.
        pipe.clear_lora(verbose=0)
        pipe.load_lora(pipe.dit, state_dict=inputs["lora"], hotload=True, verbose=0)

    max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * len(pipe.scheduler.timesteps))
    min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * len(pipe.scheduler.timesteps))

    timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,))
    timestep = pipe.scheduler.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
    
    noise = torch.randn_like(inputs["input_latents"])
    inputs["latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
    training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
    
    if "first_frame_latents" in inputs:
        inputs["latents"][:, :, 0:1] = inputs["first_frame_latents"]
    
    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
    noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep)
    
    if "first_frame_latents" in inputs:
        noise_pred = noise_pred[:, :, 1:]
        training_target = training_target[:, :, 1:]
    
    loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
    loss = loss * pipe.scheduler.training_weight(timestep)
    return loss

Start by locating FlowMatchSFTLoss and trace how input_latents, first_frame_latents, and the model output are handled during first-and-last-frame training. Determine whether the tail-frame latent should be isolated and whether the loss currently includes an unintended frame; done means the expected frame masking behavior is established and covered by the relevant training check.