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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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13
fine_tune.py
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@@ -380,9 +380,7 @@ 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():
@@ -394,11 +392,10 @@ def train(args):
else:
target = noise
huber_c = train_util.get_huber_threshold_if_needed(args, timesteps, noise_scheduler)
if args.min_snr_gamma or args.scale_v_pred_loss_like_noise_pred or args.debiased_estimation_loss:
# do not mean over batch dimension for snr weight or scale v-pred loss
loss = train_util.conditional_loss(
args, noise_pred.float(), target.float(), timesteps, "none", noise_scheduler
)
loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "none", huber_c)
loss = loss.mean([1, 2, 3])
if args.min_snr_gamma:
@@ -410,9 +407,7 @@ def train(args):
loss = loss.mean() # mean over batch dimension
else:
loss = train_util.conditional_loss(
args, noise_pred.float(), target.float(), timesteps, "mean", noise_scheduler
)
loss = train_util.conditional_loss(noise_pred.float(), target.float(), args.loss_type, "mean", huber_c)
accelerator.backward(loss)
if accelerator.sync_gradients and args.max_grad_norm != 0.0: