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scale v-pred loss like noise pred

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Kohya S
2023-06-03 10:52:22 +09:00
parent 0f0158ddaa
commit ec2efe52e4
7 changed files with 79 additions and 17 deletions
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11
train_textual_inversion_XTI.py
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@@ -20,7 +20,7 @@ from library.config_util import (
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight, pyramid_noise_like, apply_noise_offset
from library.custom_train_functions import apply_snr_weight, prepare_scheduler_for_custom_training, pyramid_noise_like, apply_noise_offset, scale_v_prediction_loss_like_noise_prediction
from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
imagenet_templates_small = [
@@ -372,6 +372,7 @@ def train(args):
noise_scheduler = DDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
)
prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion" if args.log_tracker_name is None else args.log_tracker_name)
@@ -451,11 +452,13 @@ def train(args):
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
loss = loss.mean([1, 2, 3])
loss_weights = batch["loss_weights"] # 各sampleごとのweight
loss = loss * loss_weights
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
loss_weights = batch["loss_weights"] # 各sampleごとのweight
loss = loss * loss_weights
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし