Added Perlin noise to training

1 files changed, 17 insertions, 0 deletions

diff --git a/training/functional.py b/training/functional.py
index 1c38635..db46766 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -23,6 +23,7 @@ from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embe
 from models.clip.util import get_extended_embeddings
 from models.clip.tokenizer import MultiCLIPTokenizer
 from training.util import AverageMeter
+from util.noise import perlin_noise
 
 
 def const(result=None):
@@ -253,6 +254,7 @@ def loss_step(
     text_encoder: CLIPTextModel,
     with_prior_preservation: bool,
     prior_loss_weight: float,
+    perlin_strength: float,
     seed: int,
     step: int,
     batch: dict[str, Any],
@@ -275,6 +277,19 @@ def loss_step(
         generator=generator
     )
 
+    if perlin_strength != 0:
+        noise += perlin_strength * perlin_noise(
+            latents.shape[0],
+            latents.shape[1],
+            latents.shape[2],
+            latents.shape[3],
+            res=1,
+            octaves=4,
+            dtype=latents.dtype,
+            device=latents.device,
+            generator=generator
+        )
+
     # Sample a random timestep for each image
     timesteps = torch.randint(
         0,
@@ -559,6 +574,7 @@ def train(
     global_step_offset: int = 0,
     with_prior_preservation: bool = False,
     prior_loss_weight: float = 1.0,
+    perlin_strength: float = 0.1,
     **kwargs,
 ):
     text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, extra = strategy.prepare(
@@ -593,6 +609,7 @@ def train(
         text_encoder,
         with_prior_preservation,
         prior_loss_weight,
+        perlin_strength,
         seed,
     )