add zero_terminal_snr option

fine_tune.py

@@ -23,8 +23,6 @@ from library.custom_train_functions import (
     apply_snr_weight,
     get_weighted_text_embeddings,
     prepare_scheduler_for_custom_training,
-    pyramid_noise_like,
-    apply_noise_offset,
     scale_v_prediction_loss_like_noise_prediction,
 )
 
@@ -273,6 +271,8 @@ def train(args):
         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 args.zero_terminal_snr:
+        custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
 
     if accelerator.is_main_process:
         accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name)

library/custom_train_functions.py

@@ -18,6 +18,42 @@ def prepare_scheduler_for_custom_training(noise_scheduler, device):
 
     noise_scheduler.all_snr = all_snr.to(device)
 
+def fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler):
+    # fix beta: zero terminal SNR
+    print(f"fix noise scheduler betas: https://arxiv.org/abs/2305.08891")
+
+    def enforce_zero_terminal_snr(betas):
+        # Convert betas to alphas_bar_sqrt
+        alphas = 1 - betas
+        alphas_bar = alphas.cumprod(0)
+        alphas_bar_sqrt = alphas_bar.sqrt()
+
+        # Store old values.
+        alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+        alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+        # Shift so last timestep is zero.
+        alphas_bar_sqrt -= alphas_bar_sqrt_T
+        # Scale so first timestep is back to old value.
+        alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+        # Convert alphas_bar_sqrt to betas
+        alphas_bar = alphas_bar_sqrt**2
+        alphas = alphas_bar[1:] / alphas_bar[:-1]
+        alphas = torch.cat([alphas_bar[0:1], alphas])
+        betas = 1 - alphas
+        return betas
+
+    betas = noise_scheduler.betas
+    betas = enforce_zero_terminal_snr(betas)
+    alphas = 1.0 - betas
+    alphas_cumprod = torch.cumprod(alphas, dim=0)
+
+    # print("original:", noise_scheduler.betas)
+    # print("fixed:", betas)
+
+    noise_scheduler.betas = betas
+    noise_scheduler.alphas = alphas
+    noise_scheduler.alphas_cumprod = alphas_cumprod
 
 def apply_snr_weight(loss, timesteps, noise_scheduler, gamma):
     snr = torch.stack([noise_scheduler.all_snr[t] for t in timesteps])

library/sdxl_train_util.py

@@ -343,9 +343,10 @@ def add_sdxl_training_arguments(parser: argparse.ArgumentParser):
 
 
 def verify_sdxl_training_args(args: argparse.Namespace):
-    assert (
+    assert not args.v2, "v2 cannot be enabled in SDXL training / SDXL学習ではv2を有効にすることはできません"
-        not args.v2 and not args.v_parameterization
+    if args.v_parameterization:
-    ), "v2 or v_parameterization cannot be enabled in SDXL training / SDXL学習ではv2とv_parameterizationを有効にすることはできません"
+        print("v_parameterization will be unexpected / SDXL学習ではv_parameterizationは想定外の動作になります")
+
     if args.clip_skip is not None:
         print("clip_skip will be unexpected / SDXL学習ではclip_skipは動作しません")
 

library/train_util.py

@@ -2750,6 +2750,11 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         default=None,
         help="add `latent mean absolute value * this value` to noise_offset (disabled if None, default) / latentの平均値の絶対値 * この値をnoise_offsetに加算する（Noneの場合は無効、デフォルト）",
     )
+    parser.add_argument(
+        "--zero_terminal_snr",
+        action="store_true",
+        help="fix noise scheduler betas to enforce zero terminal SNR / noise schedulerのbetasを修正して、zero terminal SNRを強制する",
+    )
     parser.add_argument(
         "--min_timestep",
         type=int,
@@ -2825,7 +2830,7 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
 
 def verify_training_args(args: argparse.Namespace):
     if args.v_parameterization and not args.v2:
-        print("v_parameterization should be with v2 / v1でv_parameterizationを使用することは想定されていません")
+        print("v_parameterization should be with v2 not v1 or sdxl / v1やsdxlでv_parameterizationを使用することは想定されていません")
     if args.v2 and args.clip_skip is not None:
         print("v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません")
 
@@ -2856,6 +2861,12 @@ def verify_training_args(args: argparse.Namespace):
             "scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます"
         )
 
+    if args.zero_terminal_snr and not args.v_parameterization:
+        print(
+            f"zero_terminal_snr is enabled, but v_parameterization is not enabled. training will be unexpected"
+            + " / zero_terminal_snrが有効ですが、v_parameterizationが有効ではありません。学習結果は想定外になる可能性があります"
+        )
+
 
 def add_dataset_arguments(
     parser: argparse.ArgumentParser, support_dreambooth: bool, support_caption: bool, support_caption_dropout: bool

sdxl_train.py

@@ -350,6 +350,8 @@ def train(args):
         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 args.zero_terminal_snr:
+        custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
 
     if accelerator.is_main_process:
         accelerator.init_trackers("finetuning" if args.log_tracker_name is None else args.log_tracker_name)

train_db.py

@@ -246,6 +246,8 @@ def train(args):
         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 args.zero_terminal_snr:
+        custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
 
     if accelerator.is_main_process:
         accelerator.init_trackers("dreambooth" if args.log_tracker_name is None else args.log_tracker_name)

train_network.py

@@ -487,6 +487,7 @@ class NetworkTrainer:
             "ss_multires_noise_iterations": args.multires_noise_iterations,
             "ss_multires_noise_discount": args.multires_noise_discount,
             "ss_adaptive_noise_scale": args.adaptive_noise_scale,
+            "ss_zero_terminal_snr": args.zero_terminal_snr,
             "ss_training_comment": args.training_comment,  # will not be updated after training
             "ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
             "ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
@@ -670,6 +671,8 @@ class NetworkTrainer:
             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 args.zero_terminal_snr:
+            custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
 
         if accelerator.is_main_process:
             accelerator.init_trackers("network_train" if args.log_tracker_name is None else args.log_tracker_name)

train_textual_inversion.py

@@ -487,6 +487,8 @@ class TextualInversionTrainer:
             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 args.zero_terminal_snr:
+            custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
 
         if accelerator.is_main_process:
             accelerator.init_trackers("textual_inversion" if args.log_tracker_name is None else args.log_tracker_name)

train_textual_inversion_XTI.py

@@ -384,6 +384,8 @@ def train(args):
         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 args.zero_terminal_snr:
+        custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
 
     if accelerator.is_main_process:
         accelerator.init_trackers("textual_inversion" if args.log_tracker_name is None else args.log_tracker_name)