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fix: refactor huber-loss calculation in multiple training scripts

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Kohya S
2024-12-01 21:20:28 +09:00
parent 0fe6320f09
commit cc11989755
13 changed files with 52 additions and 70 deletions
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9
train_db.py
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@@ -370,9 +370,7 @@ def train(args):
# Sample noise, sample a random timestep for each image, and add noise to the latents,
# with noise offset and/or multires noise if specified
noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(
args, noise_scheduler, latents
)
noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents)
# Predict the noise residual
with accelerator.autocast():
@@ -384,9 +382,8 @@ def train(args):
else:
target = noise
loss = train_util.conditional_loss(
args, noise_pred.float(), target.float(), timesteps, "none", noise_scheduler
)
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 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])