VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-08 14:34:23 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix: refactor huber-loss calculation in multiple training scripts

Browse Source
This commit is contained in:
Kohya S
2024-12-01 21:20:28 +09:00
parent 0fe6320f09
commit cc11989755
13 changed files with 52 additions and 70 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
sdxl_train_control_net_lllite.py
View File

@@ -463,9 +463,7 @@ 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)
noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype
@@ -484,9 +482,8 @@ 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)
loss = loss.mean([1, 2, 3])
loss_weights = batch["loss_weights"] # 各sampleごとのweight