Simplify Timestep weighting

* Remove diffusers dependency in ts & sigma calc
* support Shift setting
* Add uniform distribution
* Default to Uniform distribution and shift 1

library/sd3_train_utils.py

@@ -253,12 +253,12 @@ def add_sd3_training_arguments(parser: argparse.ArgumentParser):
         " / 複数解像度学習時に解像度ごとに位置埋め込みをスケーリングする。SD3.5M以外では予期しない動作になります",
     )
 
-    # copy from Diffusers
+    # Dependencies of Diffusers noise sampler has been removed for clearity.
     parser.add_argument(
         "--weighting_scheme",
         type=str,
-        default="logit_normal",
+        default="uniform",
-        choices=["sigma_sqrt", "logit_normal", "mode", "cosmap"],
+        choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "uniform"],
         help="weighting scheme for timestep distribution and loss / タイムステップ分布と損失のための重み付けスキーム",
     )
     parser.add_argument(
@@ -279,8 +279,13 @@ def add_sd3_training_arguments(parser: argparse.ArgumentParser):
         default=1.29,
         help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`. / モード重み付けスキームのスケール。`'mode'`を`weighting_scheme`として使用する場合のみ有効",
     )
-
+    parser.add_argument(
-
+        "--training_shift",
+        type=float,
+        default=1.0,
+        help="Discrete flow shift for training timestep distribution adjustment, applied in addition to the weighting scheme, default is 1.0. /タイムステップ分布のための離散フローシフト、重み付けスキームの上に適用される、デフォルトは1.0。",
+    )
+    
 def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCaching: bool = True):
     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"
     if args.v_parameterization:
@@ -965,14 +970,20 @@ def get_noisy_model_input_and_timesteps(
         logit_std=args.logit_std,
         mode_scale=args.mode_scale,
     )
-    indices = (u * noise_scheduler.config.num_train_timesteps).long()
+    t_min = args.min_timestep if args.min_timestep is not None else 0
-    timesteps = noise_scheduler.timesteps[indices].to(device=device)
+    t_max = args.max_timestep if args.max_timestep is not None else 1000
+    shift = args.training_shift
 
-    # Add noise according to flow matching.
+    # weighting shift, value >1 will shift distribution to noisy side (focus more on overall structure), value <1 will shift towards less-noisy side (focus more on details)
-    sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
+    u = (u * shift) / (1 + (shift - 1) * u)
+
+    indices = (u * (t_max-t_min) + t_min).long()
+    timesteps = indices.to(device=device, dtype=dtype)
+
+    # sigmas according to dlowmatching
+    sigmas = timesteps / 1000
+    sigmas = sigmas.view(-1,1,1,1)
     noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents
 
     return noisy_model_input, timesteps, sigmas
 
-
-# endregion