Update

3 files changed, 13 insertions, 25 deletions

diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index f27be78..f426de1 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -307,10 +307,14 @@ class VlpnStableDiffusion(DiffusionPipeline):
         return timesteps, num_inference_steps - t_start
 
     def prepare_image(self, batch_size, width, height, dtype, device, generator=None):
-        noise = perlin_noise(
-            batch_size, 1, width, height, res=1, octaves=4, generator=generator, dtype=dtype, device=device
-        ).expand(batch_size, 3, width, height)
-        return (1.4 * noise).clamp(-1, 1)
+        return (1.4 * perlin_noise(
+            (batch_size, 1, width, height),
+            res=1,
+            octaves=4,
+            generator=generator,
+            dtype=dtype,
+            device=device
+        )).clamp(-1, 1).expand(batch_size, 3, width, height)
 
     def prepare_latents_from_image(self, init_image, timestep, batch_size, dtype, device, generator=None):
         init_image = init_image.to(device=device, dtype=dtype)
@@ -390,7 +394,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
         sag_scale: float = 0.75,
         eta: float = 0.0,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        image: Optional[Union[torch.FloatTensor, PIL.Image.Image, Literal["noise"]]] = None,
+        image: Optional[Union[torch.FloatTensor, PIL.Image.Image, Literal["noise"]]] = "noise",
         output_type: str = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
diff --git a/training/functional.py b/training/functional.py
index db46766..27a43c2 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -254,7 +254,6 @@ 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],
@@ -277,19 +276,6 @@ 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,
@@ -574,7 +560,6 @@ 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(
@@ -609,7 +594,6 @@ def train(
         text_encoder,
         with_prior_preservation,
         prior_loss_weight,
-        perlin_strength,
         seed,
     )
 
diff --git a/util/noise.py b/util/noise.py
index 3c4f82d..e3ebdb2 100644
--- a/util/noise.py
+++ b/util/noise.py
@@ -48,13 +48,13 @@ def rand_perlin_2d_octaves(shape, res, octaves=1, persistence=0.5, dtype=None, d
     return noise
 
 
-def perlin_noise(batch_size: int, channels: int, width: int, height: int, res=8, octaves=1, dtype=None, device=None, generator=None):
+def perlin_noise(shape: tuple[int, int, int, int], res=8, octaves=1, dtype=None, device=None, generator=None):
     return torch.stack([
         torch.stack([
             rand_perlin_2d_octaves(
-                (width, height), (res, res), octaves, dtype=dtype, device=device, generator=generator
+                (shape[2], shape[3]), (res, res), octaves, dtype=dtype, device=device, generator=generator
             )
-            for _ in range(channels)
+            for _ in range(shape[1])
         ])
-        for _ in range(batch_size)
+        for _ in range(shape[0])
     ])