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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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6
sdxl_train_control_net_lllite_old.py
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@@ -406,7 +406,7 @@ 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, huber_c = 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)
noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype
@@ -426,7 +426,9 @@ def train(args):
else:
target = noise
loss = train_util.conditional_loss(noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c)
loss = train_util.conditional_loss(
args, noise_pred.float(), target.float(), timesteps, "none", noise_scheduler
)
loss = loss.mean([1, 2, 3])
loss_weights = batch["loss_weights"] # 各sampleごとのweight