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Implement pseudo Huber loss for Flux and SD3

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recris
2024-11-27 18:11:51 +00:00
parent 2a61fc0784
commit 420a180d93
15 changed files with 76 additions and 61 deletions
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4
train_textual_inversion.py
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@@ -585,7 +585,7 @@ class TextualInversionTrainer:
# 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, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
noise, noisy_latents, timesteps = train_util.get_noise_noisy_latents_and_timesteps(
args, noise_scheduler, latents
)
@@ -602,7 +602,7 @@ class TextualInversionTrainer:
target = noise
loss = train_util.conditional_loss(
noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
args, noise_pred.float(), target.float(), timesteps, "none", noise_scheduler
)
if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None):
loss = apply_masked_loss(loss, batch)