more_timestep_sampling

library/train_util.py

@@ -3476,6 +3476,25 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         default=0.1,
         help="The huber loss parameter. Only used if one of the huber loss modes (huber or smooth l1) is selected with loss_type. default is 0.1 / Huber損失のパラメータ。loss_typeがhuberまたはsmooth l1の場合に有効。デフォルトは0.1",
     )
+    parser.add_argument(
+        "--timestep_sampling",
+        choices=["uniform", "sigmoid", "shift", "flux_shift"],
+        default="uniform",
+        help="Method to sample timesteps: uniform random, sigmoid of random normal, shift of sigmoid and FLUX.1 shifting."
+        " / タイムステップをサンプリングする方法：random uniform、random normalのsigmoid、sigmoidのシフト、FLUX.1のシフト。",
+    )
+    parser.add_argument(
+        "--sigmoid_scale",
+        type=float,
+        default=1.0,
+        help='Scale factor for sigmoid timestep sampling (only used when timestep-sampling is "sigmoid"). / sigmoidタイムステップサンプリングの倍率（timestep-samplingが"sigmoid"の場合のみ有効）。',
+    )
+    parser.add_argument(
+        "--discrete_flow_shift",
+        type=float,
+        default=1.0,
+        help="Discrete flow shift for the Euler Discrete Scheduler, default is 3.0. / Euler Discrete Schedulerの離散フローシフト、デフォルトは3.0。",
+    )
 
     parser.add_argument(
         "--lowram",
@@ -5198,9 +5217,32 @@ def save_sd_model_on_train_end_common(
         if args.huggingface_repo_id is not None:
             huggingface_util.upload(args, out_dir, "/" + model_name, force_sync_upload=True)
 
+def time_shift(mu: float, sigma: float, t: torch.Tensor):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
 
-def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, device):
-    timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu")
+def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return lambda x: m * x + b
+
+def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, latents, device):
+    # Sample a random timestep for each image
+    b_size, _, h, w = latents.shape
+
+    if args.timestep_sampling != "uniform":
+        shift = args.discrete_flow_shift
+        logits_norm = torch.randn(b_size,  device="cpu")
+        logits_norm = logits_norm * args.sigmoid_scale 
+        timesteps = logits_norm.sigmoid()
+        if args.timestep_sampling == "flux_shift":
+            mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))
+            timesteps = time_shift(mu, 1.0, timesteps)
+        else:
+            timesteps = (timesteps * shift) / (1 + (shift - 1) * timesteps)
+        t = timesteps.view(-1, 1, 1, 1)
+        timesteps = min_timestep + (timesteps * (max_timestep - min_timestep))
+    else:
+        timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device="cpu")
 
     if args.loss_type == "huber" or args.loss_type == "smooth_l1":
         if args.huber_schedule == "exponential":
@@ -5223,7 +5265,6 @@ def get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler,
     timesteps = timesteps.long().to(device)
     return timesteps, huber_c
 
-
 def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     # Sample noise that we'll add to the latents
     noise = torch.randn_like(latents, device=latents.device)
@@ -5238,12 +5279,10 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
             noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount
         )
 
-    # Sample a random timestep for each image
-    b_size = latents.shape[0]
     min_timestep = 0 if args.min_timestep is None else args.min_timestep
     max_timestep = noise_scheduler.config.num_train_timesteps if args.max_timestep is None else args.max_timestep
 
-    timesteps, huber_c = get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, b_size, latents.device)
+    timesteps, huber_c = get_timesteps_and_huber_c(args, min_timestep, max_timestep, noise_scheduler, latents, latents.device)
 
     # Add noise to the latents according to the noise magnitude at each timestep
     # (this is the forward diffusion process)