VictorMylle/Kohya-ss-sd-scripts
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Fix issues found in review

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recris
2024-11-28 20:38:32 +00:00
parent 420a180d93
commit 740ec1d526
2 changed files with 5 additions and 5 deletions
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2
fine_tune.py
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@@ -411,7 +411,7 @@ def train(args):
loss = loss.mean() # mean over batch dimension loss = loss.mean() # mean over batch dimension
else: else:
loss = train_util.conditional_loss( loss = train_util.conditional_loss(
args, noise_pred.float(), target.float(), timesteps, "none", noise_scheduler args, noise_pred.float(), target.float(), timesteps, "mean", noise_scheduler
) )
accelerator.backward(loss) accelerator.backward(loss)

8
library/train_util.py
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@@ -5829,8 +5829,8 @@ def save_sd_model_on_train_end_common(
def get_timesteps(min_timestep, max_timestep, b_size, device): def get_timesteps(min_timestep, max_timestep, b_size, device):
timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=device) timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu")
timesteps = timesteps.long() timesteps = timesteps.long().to(device)
return timesteps return timesteps
@@ -5875,8 +5875,8 @@ def get_huber_threshold(args, timesteps: torch.Tensor, noise_scheduler) -> torch
alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps
result = torch.exp(-alpha * timesteps) * args.huber_scale result = torch.exp(-alpha * timesteps) * args.huber_scale
elif args.huber_schedule == "snr": elif args.huber_schedule == "snr":
if not hasattr(noise_scheduler, 'alphas_cumprod'): if not hasattr(noise_scheduler, "alphas_cumprod"):
raise NotImplementedError(f"Huber schedule 'snr' is not supported with the current model.") raise NotImplementedError("Huber schedule 'snr' is not supported with the current model.")
alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps.cpu()) alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps.cpu())
sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5 sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5
result = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c result = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c