Bring back Perlin offset noise

1 files changed, 14 insertions, 1 deletions

diff --git a/training/functional.py b/training/functional.py
index 015fe5e..a5b339d 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -278,10 +278,11 @@ def loss_step(
     with_prior_preservation: bool,
     prior_loss_weight: float,
     seed: int,
+    perlin_strength: float,
     step: int,
     batch: dict[str, Any],
     eval: bool = False,
-    min_snr_gamma: int = 5
+    min_snr_gamma: int = 5,
 ):
     # Convert images to latent space
     latents = vae.encode(batch["pixel_values"]).latent_dist.sample()
@@ -300,6 +301,16 @@ def loss_step(
         generator=generator
     )
 
+    if perlin_strength != 0:
+        noise += perlin_strength * perlin_noise(
+            latents.shape,
+            res=1,
+            octaves=4,
+            dtype=latents.dtype,
+            device=latents.device,
+            generator=generator
+        )
+
     # Sample a random timestep for each image
     timesteps = torch.randint(
         0,
@@ -600,6 +611,7 @@ 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(
@@ -635,6 +647,7 @@ def train(
         with_prior_preservation,
         prior_loss_weight,
         seed,
+        perlin_strength,
     )
 
     if accelerator.is_main_process: