Correct blur/sharpen algorithm. Add batch_size.

README.MD

@@ -16,12 +16,13 @@ Uses basic operations (bicubic, lanczos, etc.) to blend the frames together.
 Also can apply a gaussian blur or sharpen effect to blend frames.
 Blur will apply a motion blur effect, while sharpen will reduce motion blur.
 
-| Input          | Description                                                               |
-|----------------|---------------------------------------------------------------------------|
-| images         | Image frames to interpolate.                                              |
-| scale_method   | Method used to interpolate the frames.                                    |
-| multiplier     | Value to multiply the number of frames by. Not limited to whole numbers.  |
-| gaussian_blur  | Enable gaussian blur/sharpen frame blending.                              |
-| blur_radius    | The spread of the blur to neighboring frames.                             |
-| blur_sigma     | Intensity of blur/sharpen. Positive values blur, negative values sharpen. |
-| sharpen_alpha  | When blur_sigma is negative, this increases the sharpen intensity.        |
+| Input          | Description                                                                    |
+|----------------|--------------------------------------------------------------------------------|
+| images         | Image frames to interpolate.                                                   |
+| scale_method   | Method used to interpolate the frames.                                         |
+| multiplier     | Value to multiply the number of frames by. Not limited to whole numbers.       |
+| batch_size     | Number of pixels to operate on at a time. Adjust for a speed/memory trade-off. |
+| gaussian_blur  | Enable gaussian blur/sharpen frame blending.                                   |
+| blur_radius    | The spread of the blur to neighboring frames.                                  |
+| blur_sigma     | Intensity of blur/sharpen. Positive values blur, negative values sharpen.      |
+| sharpen_alpha  | When blur_sigma is negative, this increases the sharpen intensity.             |

nodes.py

@@ -1,7 +1,10 @@
 import torch
+import torch.nn.functional as F
 
 from nodes import ImageScale
 from comfy_extras.nodes_post_processing import Blur, Sharpen
+import comfy.model_management as mm
+
 
 class Simple_Frame_Interpolation:
     scale_methods = ["nearest", "nearest-exact", "bilinear", "area", "bicubic", "lanczos", "bislerp"]
@@ -11,6 +14,7 @@ class Simple_Frame_Interpolation:
             "images": ("IMAGE", ),
             "scale_method": (s.scale_methods, {"default": "nearest-exact"}),
             "multiplier": ("FLOAT", {"default": 0.5, "min": 0.01, "step": 0.01}),
+            "batch_size": ("INT", { "default": 15, "min": 1, "step": 1 }),
             "gaussian_blur": ("BOOLEAN", {"default": True}),
             "blur_radius": ("INT", { "default": 2, "min": 1, "step": 1 }),
             "blur_sigma": ("FLOAT", { "default": 0.3, "min": -10.0, "max": 10.0, "step": 0.1}),
@@ -25,7 +29,8 @@ class Simple_Frame_Interpolation:
     CATEGORY = "Simple-VFI"
 
     @torch.no_grad()
-    def process(self, images, scale_method, multiplier, gaussian_blur, blur_radius, blur_sigma, sharpen_alpha):
+    def process(self, images, scale_method, multiplier, batch_size, gaussian_blur, blur_radius, blur_sigma, sharpen_alpha):
+        device = mm.get_torch_device()
         B, H, W, C = images.shape
         new_frame_count = max(round(B * multiplier), 1)
 
@@ -35,12 +40,26 @@ class Simple_Frame_Interpolation:
         if gaussian_blur and blur_radius != 0 and blur_sigma != 0:
             B2, H2, W2, C2 = images.shape
 
-            images.flatten(0, 2).unflatten(0, (W2, H2, 1))
-            if blur_sigma > 0:
-                images, = Blur.blur(self, images, blur_radius, blur_sigma)
-            elif blur_sigma < 0 :
-                images, = Sharpen.sharpen(self, images, blur_radius, -blur_sigma, sharpen_alpha)
-            images.flatten(0, 2).unflatten(0, (1, H2, W2))
+            images = images.permute(0, 2, 1, 3)
+            images = images.flatten(0, 2).unflatten(0, (W2, H2, 1))
+
+            for i in range(0, W2, batch_size):
+                end = min(i + batch_size, W2)
+                current_image = images[i:end, :, :, :]
+                current_image = current_image.permute(0, 3, 1, 2).contiguous().to(device)
+                current_image = F.interpolate(current_image, size=(H2, 2 * blur_radius + 1), mode='nearest-exact')
+                current_image = current_image.permute(0, 2, 3, 1)
+                print(f"{i} -> {current_image.shape}")
+
+                if blur_sigma > 0:
+                    current_image, = Blur.blur(self, current_image, blur_radius, blur_sigma)
+                elif blur_sigma < 0 :
+                    current_image, = Sharpen.sharpen(self, current_image, blur_radius, -blur_sigma, sharpen_alpha)
+
+                images[i:end] = current_image[:, :, blur_radius:-blur_radius, :].cpu()
+
+            images = images.flatten(0, 2).unflatten(0, (1, W2, H2))
+            images = images.permute(0, 2, 1, 3)
 
         images = images.flatten(0, 2).unflatten(0, (new_frame_count, H, W))
 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "ComfyUI-Simple-VFI"
 description = "Nodes for simple frame interpolation without the use of AI. Uses standard image operations to blend frames together."
-version = "1.0.0"
+version = "1.0.1"
 license = "LICENSE"
 dependencies = ["open-clip-torch"]