Perlin noise

library/custom_train_functions.py

@@ -410,9 +410,9 @@ def rand_perlin_2d_octaves(device, shape, res, octaves=1, persistence=0.5):
         amplitude *= persistence
     return noise
 
-def perlin_noise(noise, device):
+def perlin_noise(noise, device,octaves):
     b, c, w, h = noise.shape()
-    perlin = lambda : rand_perlin_2d_octaves(device,(w,h),(4,4),1)
+    perlin = lambda : rand_perlin_2d_octaves(device,(w,h),(4,4),octaves)
     noise_perlin_r = torch.rand(noise.shape, device=device) + perlin()
     noise_perlin_g = torch.rand(noise.shape, device=device) + perlin()
     noise_perlin_b = torch.rand(noise.shape, device=device) + perlin()
@@ -420,7 +420,7 @@ def perlin_noise(noise, device):
         (noise_perlin_r,
          noise_perlin_g,
          noise_perlin_b),
-        2)
+        1)
     return noise_perlin
 
 

library/train_util.py

@@ -2127,6 +2127,12 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         default=None,
         help="enable multires noise with this number of iterations (if enabled, around 6-10 is recommended) / Multires noiseを有効にしてこのイテレーション数を設定する（有効にする場合は6-10程度を推奨）",
     )
+    parser.add_argument(
+        "--perlin_noise",
+        type=int,
+        default=None,
+        help="enable perlin noise and set the octaves",
+    )
     parser.add_argument(
         "--multires_noise_discount",
         type=float,

train_db.py

@@ -23,7 +23,7 @@ from library.config_util import (
     BlueprintGenerator,
 )
 import library.custom_train_functions as custom_train_functions
-from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings, pyramid_noise_like, apply_noise_offset
+from library.custom_train_functions import apply_snr_weight, get_weighted_text_embeddings, pyramid_noise_like, apply_noise_offset,perlin_noise
 
 
 def train(args):
@@ -274,6 +274,8 @@ def train(args):
                     noise = apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
                 elif args.multires_noise_iterations:
                     noise = pyramid_noise_like(noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount)
+                elif args.perlin_noise:
+                    noise = perlin_noise(noise,latents.device,args.perlin_noise)
 
                 # Get the text embedding for conditioning
                 with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):