fix crash in txt2img and img2img w/ inpainting models and perlin > 0

- get_perlin_noise() was returning 9 channels; fixed code to return
  noise for just the 4 image channels and not the mask ones.

- Closes Issue #2541

ldm/invoke/generator/base.py

@@ -240,7 +240,12 @@ class Generator:
 
     def get_perlin_noise(self,width,height):
         fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
-        noise = torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
+        # limit noise to only the diffusion image channels, not the mask channels
+        input_channels = min(self.latent_channels, 4)
+        noise = torch.stack([
+            rand_perlin_2d((height, width),
+                           (8, 8),
+                           device = self.model.device).to(fixdevice) for _ in range(input_channels)], dim=0).to(self.model.device)
         return noise
 
     def new_seed(self):
@@ -341,3 +346,27 @@ class Generator:
 
     def torch_dtype(self)->torch.dtype:
         return torch.float16 if self.precision == 'float16' else torch.float32
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height):
+        device         = self.model.device
+        # limit noise to only the diffusion image channels, not the mask channels
+        input_channels = min(self.latent_channels, 4)
+        if self.use_mps_noise or device.type == 'mps':
+            x = torch.randn([1,
+                             input_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device='cpu').to(device)
+        else:
+            x = torch.randn([1,
+                             input_channels,
+                             height // self.downsampling_factor,
+                             width  // self.downsampling_factor],
+                            dtype=self.torch_dtype(),
+                            device=device)
+        if self.perlin > 0.0:
+            perlin_noise = self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
+            x = (1-self.perlin)*x + self.perlin*perlin_noise
+        return x

ldm/invoke/generator/img2img.py

@@ -63,22 +63,3 @@ class Img2Img(Generator):
             shape = like.shape
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
         return x
-
-    def get_noise(self,width,height):
-        # copy of the Txt2Img.get_noise
-        device         = self.model.device
-        if self.use_mps_noise or device.type == 'mps':
-            x = torch.randn([1,
-                                self.latent_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device='cpu').to(device)
-        else:
-            x = torch.randn([1,
-                                self.latent_channels,
-                                height // self.downsampling_factor,
-                                width  // self.downsampling_factor],
-                               device=device)
-        if self.perlin > 0.0:
-            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
-        return x

ldm/invoke/generator/txt2img.py

@@ -51,26 +51,4 @@ class Txt2Img(Generator):
         return make_image
 
 
-    # returns a tensor filled with random numbers from a normal distribution
-    def get_noise(self,width,height):
-        device         = self.model.device
-        # limit noise to only the diffusion image channels, not the mask channels
-        input_channels = min(self.latent_channels, 4)
-        if self.use_mps_noise or device.type == 'mps':
-            x = torch.randn([1,
-                             input_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device='cpu').to(device)
-        else:
-            x = torch.randn([1,
-                             input_channels,
-                             height // self.downsampling_factor,
-                             width  // self.downsampling_factor],
-                            dtype=self.torch_dtype(),
-                            device=device)
-        if self.perlin > 0.0:
-            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
-        return x