nodes/ComplexRobot-ComfyUI-Simple-VFI
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Add gaussian blur/sharpen capability.

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ComplexRobot
2025-03-16 05:00:12 -07:00
parent f6e19b146e
commit 06bb665393
2 changed files with 32 additions and 9 deletions
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README.MD
+12 -6
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@@ -12,10 +12,16 @@ Nodes for basic frame interpolation without the use of AI.
### Simple Frame Interpolation (VFI)
A node for doing very simple frame interpolation operations.
Uses basic operations (bicubic, lanczos, etc.) to blend the frames together.
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.
| | |
|--------------|--------------------------------------------------------------------------|
| 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. |
| 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. |
nodes.py
+20 -3
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@@ -1,6 +1,8 @@
import torch
import torch.nn.functional as F
from nodes import ImageScale
from comfy_extras.nodes_post_processing import Blur, Sharpen
class Simple_Frame_Interpolation:
scale_methods = ["nearest", "nearest-exact", "bilinear", "area", "bicubic", "lanczos", "bislerp"]
@@ -8,8 +10,12 @@ class Simple_Frame_Interpolation:
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE", ),
"scale_method": (s.scale_methods, {"default": "bislerp"}),
"multiplier": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 20.0, "step": 0.01}),
"scale_method": (s.scale_methods, {"default": "nearest-exact"}),
"multiplier": ("FLOAT", {"default": 0.5, "min": 0.01, "step": 0.01}),
"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}),
"sharpen_alpha": ("FLOAT", { "default": 0.1, "min": 0.0, "max": 5.0, "step": 0.01 }),
},
}
@@ -20,12 +26,23 @@ class Simple_Frame_Interpolation:
CATEGORY = "Simple-VFI"
@torch.no_grad()
def process(self, images, scale_method, multiplier):
def process(self, images, scale_method, multiplier, gaussian_blur, blur_radius, blur_sigma, sharpen_alpha):
B, H, W, C = images.shape
new_frame_count = max(round(B * multiplier), 1)
images = images.flatten(0, 2).unflatten(0, (1, B, H * W))
images, = ImageScale.upscale(self, images, scale_method, H * W, new_frame_count, crop="disabled")
if gaussian_blur and blur_radius != 0 and blur_sigma != 0:
B2, H2, W2, C2 = images.shape
images.flatten(1, 2).unsqueeze(2)
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(1, 2).unflatten(1, (H2, W2))
images = images.flatten(0, 2).unflatten(0, (new_frame_count, H, W))
return (images,)