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Make timesteps work in the standard way when Huber loss is used #1628

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Sep 25, 2024
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Make timesteps work in the standard way when Huber loss is used #1628

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26 changes: 10 additions & 16 deletions library/train_util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5124,34 +5124,27 @@ def save_sd_model_on_train_end_common(


def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, device):

# TODO: if a huber loss is selected, it will use constant timesteps for each batch
# as. In the future there may be a smarter way
timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device='cpu')

if args.loss_type == "huber" or args.loss_type == "smooth_l1":
timesteps = torch.randint(min_timestep, max_timestep, (1,), device="cpu")
timestep = timesteps.item()

if args.huber_schedule == "exponential":
alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps
huber_c = math.exp(-alpha * timestep)
huber_c = torch.exp(-alpha * timesteps)
elif args.huber_schedule == "snr":
alphas_cumprod = noise_scheduler.alphas_cumprod[timestep]
alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps)
sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5
huber_c = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c
elif args.huber_schedule == "constant":
huber_c = args.huber_c
huber_c = torch.full((b_size,), args.huber_c)
else:
raise NotImplementedError(f"Unknown Huber loss schedule {args.huber_schedule}!")

timesteps = timesteps.repeat(b_size).to(device)
huber_c = huber_c.to(device)
elif args.loss_type == "l2":
timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=device)
huber_c = 1 # may be anything, as it's not used
huber_c = None # may be anything, as it's not used
else:
raise NotImplementedError(f"Unknown loss type {args.loss_type}")
timesteps = timesteps.long()

timesteps = timesteps.long().to(device)
return timesteps, huber_c


Expand Down Expand Up @@ -5190,20 +5183,21 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
return noise, noisy_latents, timesteps, huber_c


# NOTE: if you're using the scheduled version, huber_c has to depend on the timesteps already
def conditional_loss(
model_pred: torch.Tensor, target: torch.Tensor, reduction: str = "mean", loss_type: str = "l2", huber_c: float = 0.1
model_pred: torch.Tensor, target: torch.Tensor, reduction: str, loss_type: str, huber_c: Optional[torch.Tensor]
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Callers I could find are explicitly passing all arguments, so it should be safe to remove default values

):

if loss_type == "l2":
loss = torch.nn.functional.mse_loss(model_pred, target, reduction=reduction)
elif loss_type == "huber":
huber_c = huber_c.view(-1, 1, 1, 1)
loss = 2 * huber_c * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)
if reduction == "mean":
loss = torch.mean(loss)
elif reduction == "sum":
loss = torch.sum(loss)
elif loss_type == "smooth_l1":
huber_c = huber_c.view(-1, 1, 1, 1)
loss = 2 * (torch.sqrt((model_pred - target) ** 2 + huber_c**2) - huber_c)
if reduction == "mean":
loss = torch.mean(loss)
Expand Down