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Split val latents/batch and pick up val latents shape size which equal to training batch.

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Hina Chen
2024-12-28 11:42:15 +08:00
parent 62164e5792
commit 64bd5317dc
1 changed files with 27 additions and 18 deletions
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45
train_network.py
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@@ -1349,7 +1349,27 @@ class NetworkTrainer:
with torch.no_grad(): with torch.no_grad():
validation_steps = min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader) validation_steps = min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader)
for val_step in tqdm(range(validation_steps), desc="Validation Steps バリデーションテップ"): for val_step in tqdm(range(validation_steps), desc="Validation Steps バリデーションテップ"):
batch = next(cyclic_val_dataloader)
while True:
val_batch = next(cyclic_val_dataloader)
if "latents" in val_batch and val_batch["latents"] is not None:
val_latents = val_batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
else:
with torch.no_grad():
# latentに変換
val_latents = self.encode_images_to_latents(args, accelerator, vae, val_batch["images"].to(vae_dtype))
val_latents = val_latents.to(dtype=weight_dtype)
# NaNが含まれていれば警告を表示し0に置き換える
if torch.any(torch.isnan(val_latents)):
accelerator.print("NaN found in validation latents, replacing with zeros")
val_latents = torch.nan_to_num(val_latents, 0, out=val_latents)
val_latents = self.shift_scale_latents(args, val_latents)
if val_latents.shape == latents.shape:
break
timesteps_list = [10, 350, 500, 650, 990] timesteps_list = [10, 350, 500, 650, 990]
@@ -1357,13 +1377,13 @@ class NetworkTrainer:
for fixed_timesteps in timesteps_list: for fixed_timesteps in timesteps_list:
with torch.set_grad_enabled(False), accelerator.autocast(): with torch.set_grad_enabled(False), accelerator.autocast():
noise = torch.randn_like(latents, device=latents.device) noise = torch.randn_like(val_latents, device=val_latents.device)
b_size = latents.shape[0] b_size = val_latents.shape[0]
timesteps = torch.full((b_size,), fixed_timesteps, dtype=torch.long, device="cpu") timesteps = torch.full((b_size,), fixed_timesteps, dtype=torch.long, device="cpu")
timesteps = timesteps.long().to(latents.device) timesteps = timesteps.long().to(val_latents.device)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) noisy_latents = noise_scheduler.add_noise(val_latents, noise, timesteps)
with accelerator.autocast(): with accelerator.autocast():
noise_pred = self.call_unet( noise_pred = self.call_unet(
@@ -1373,27 +1393,16 @@ class NetworkTrainer:
noisy_latents.requires_grad_(False), noisy_latents.requires_grad_(False),
timesteps, timesteps,
text_encoder_conds, text_encoder_conds,
batch, val_batch,
weight_dtype, weight_dtype,
) )
if args.v_parameterization: if args.v_parameterization:
# v-parameterization training # v-parameterization training
target = noise_scheduler.get_velocity(latents, noise, timesteps) target = noise_scheduler.get_velocity(val_latents, noise, timesteps)
else: else:
target = noise target = noise
# huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
# loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
# if weighting is not None:
# loss = loss * weighting
# if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
# loss = apply_masked_loss(loss, batch)
# loss = loss.mean([1, 2, 3])
# min snr gamma, scale v pred loss like noise pred, v pred like loss, debiased estimation etc.
# loss = self.post_process_loss(loss, args, timesteps, noise_scheduler)
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none") loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
loss = loss.mean([1, 2, 3]) loss = loss.mean([1, 2, 3])
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization) loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)