Merge pull request #1177 from KohakuBlueleaf/random-strength-noise

Random strength for Noise Offset and input perturbation noise

Author: kohya-ss

library/train_util.py

@@ -3087,6 +3087,11 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         default=None,
         help="enable noise offset with this value (if enabled, around 0.1 is recommended) / Noise offsetを有効にしてこの値を設定する（有効にする場合は0.1程度を推奨）",
     )
+    parser.add_argument(
+        "--noise_offset_random_strength",
+        action="store_true",
+        help="use random strength between 0~noise_offset for noise offset. / noise offsetにおいて、0からnoise_offsetの間でランダムな強度を使用します。",
+    )
     parser.add_argument(
         "--multires_noise_iterations",
         type=int,
@@ -3100,6 +3105,12 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         help="enable input perturbation noise. used for regularization. recommended value: around 0.1 (from arxiv.org/abs/2301.11706) "
         + "/  input perturbation noiseを有効にする。正則化に使用される。推奨値: 0.1程度 (arxiv.org/abs/2301.11706 より)",
     )
+    parser.add_argument(
+        "--ip_noise_gamma_random_strength",
+        action="store_true",
+        help="Use random strength between 0~ip_noise_gamma for input perturbation noise."
+        + "/ input perturbation noiseにおいて、0からip_noise_gammaの間でランダムな強度を使用します。",
+    )
     # parser.add_argument(
     #     "--perlin_noise",
     #     type=int,
@@ -4656,7 +4667,11 @@ 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)
     if args.noise_offset:
-        noise = custom_train_functions.apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
+        if args.noise_offset_random_strength:
+            noise_offset = torch.rand(1, device=latents.device) * args.noise_offset
+        else:
+            noise_offset = args.noise_offset
+        noise = custom_train_functions.apply_noise_offset(latents, noise, noise_offset, args.adaptive_noise_scale)
     if args.multires_noise_iterations:
         noise = custom_train_functions.pyramid_noise_like(
             noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount
@@ -4673,7 +4688,11 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     # Add noise to the latents according to the noise magnitude at each timestep
     # (this is the forward diffusion process)
     if args.ip_noise_gamma:
-        noisy_latents = noise_scheduler.add_noise(latents, noise + args.ip_noise_gamma * torch.randn_like(latents), timesteps)
+        if args.ip_noise_gamma_random_strength:
+            strength = torch.rand(1, device=latents.device) * args.ip_noise_gamma
+        else:
+            strength = args.ip_noise_gamma
+        noisy_latents = noise_scheduler.add_noise(latents, noise + strength * torch.randn_like(latents), timesteps)
     else:
         noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)