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Fix issues found in review
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@@ -411,7 +411,7 @@ def train(args):
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loss = loss.mean() # mean over batch dimension
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else:
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loss = train_util.conditional_loss(
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args, noise_pred.float(), target.float(), timesteps, "none", noise_scheduler
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args, noise_pred.float(), target.float(), timesteps, "mean", noise_scheduler
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)
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accelerator.backward(loss)
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@@ -5829,8 +5829,8 @@ def save_sd_model_on_train_end_common(
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def get_timesteps(min_timestep, max_timestep, b_size, device):
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timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=device)
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timesteps = timesteps.long()
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timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu")
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timesteps = timesteps.long().to(device)
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return timesteps
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@@ -5875,8 +5875,8 @@ def get_huber_threshold(args, timesteps: torch.Tensor, noise_scheduler) -> torch
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alpha = -math.log(args.huber_c) / noise_scheduler.config.num_train_timesteps
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result = torch.exp(-alpha * timesteps) * args.huber_scale
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elif args.huber_schedule == "snr":
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if not hasattr(noise_scheduler, 'alphas_cumprod'):
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raise NotImplementedError(f"Huber schedule 'snr' is not supported with the current model.")
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if not hasattr(noise_scheduler, "alphas_cumprod"):
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raise NotImplementedError("Huber schedule 'snr' is not supported with the current model.")
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alphas_cumprod = torch.index_select(noise_scheduler.alphas_cumprod, 0, timesteps.cpu())
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sigmas = ((1.0 - alphas_cumprod) / alphas_cumprod) ** 0.5
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result = (1 - args.huber_c) / (1 + sigmas) ** 2 + args.huber_c
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