nodes/ComplexRobot-ComfyUI-Simple-VFI
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Refactor and optimize algorithm.

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ComplexRobot
2025-03-16 20:57:44 -07:00
parent 8f9a770cb9
commit 8d991977c6
3 changed files with 52 additions and 31 deletions
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README.MD
+10 -10
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@@ -16,13 +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. |
| 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. |
| Input | Description |
|----------------|--------------------------------------------------------------------------------------------------------|
| images | Image frames to interpolate. |
| scale_method | Method used to interpolate the frames. Lanczos and bislerp may be slow compared to other methods. |
| 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/sharpen to neighboring frames. A larger radius uses more VRAM. |
| 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
+41 -20
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@@ -1,9 +1,9 @@
import torch
import torch.nn.functional as F
import torch.nn.functional as functional
from nodes import ImageScale
import comfy.model_management as model_management
from comfy_extras.nodes_post_processing import Blur, Sharpen
import comfy.model_management as mm
class Simple_Frame_Interpolation:
@@ -30,38 +30,59 @@ class Simple_Frame_Interpolation:
@torch.no_grad()
def process(self, images, scale_method, multiplier, batch_size, gaussian_blur, blur_radius, blur_sigma, sharpen_alpha):
device = mm.get_torch_device()
device = model_management.get_torch_device()
blur_enabled = gaussian_blur and blur_radius != 0 and blur_sigma != 0
scale_batch_size = batch_size * (2 * blur_radius + 1 if blur_enabled else 1)
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")
# convert batch into one large image with horizontal dimension = flattened image, vertical dimension = time
images = images.reshape(1, B, H * W, C)
# transpose for correct memory layout
images = images.permute(0, 2, 1, 3)
# reshape into batches where batch = single image pixel, vertical = time, horizontal = nothing (interpolate time only)
images = images.reshape(H * W, B, 1, C)
if gaussian_blur and blur_radius != 0 and blur_sigma != 0:
# scale images on the time axis (increase or reduce frame count)
scaled_list = []
for current_image in images.split(scale_batch_size):
current_image = current_image.contiguous().to(device)
# for lanczos and bislerp, it is faster to pass as a single unbatched image
if scale_method == "lanczos" or scale_method == "bislerp":
current_image = current_image.reshape(1, current_image.size(0), B, C)
current_image = current_image.permute(0, 2, 1, 3)
current_image, = ImageScale.upscale(self, current_image, scale_method, current_image.size(-2), new_frame_count, crop="disabled")
current_image = current_image.permute(0, 2, 1, 3)
current_image = current_image.reshape(current_image.size(1), new_frame_count, 1, C)
else:
current_image, = ImageScale.upscale(self, current_image, scale_method, 1, new_frame_count, crop="disabled")
scaled_list += current_image.cpu().split(1)
model_management.throw_exception_if_processing_interrupted()
images = torch.stack(scaled_list).squeeze(1).cpu()
if blur_enabled:
B2, H2, W2, C2 = images.shape
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, :, :, :]
blurred_list = []
for current_image in images.split(batch_size):
# add padding so the reflect padding added by blur/sharpen will succeed (inefficient!)
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 = functional.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 :
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()
blurred_list += current_image[:, :, blur_radius:-blur_radius, :].cpu().split(1)
model_management.throw_exception_if_processing_interrupted()
images = torch.stack(blurred_list).squeeze(1).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))
# invert initial operations
images = images.reshape(1, H * W, new_frame_count, C)
images = images.permute(0, 2, 1, 3)
images = images.reshape(new_frame_count, H, W, C)
return (images,)
pyproject.toml
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@@ -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 = "1.0.1"
version = "1.0.2"
license = "LICENSE"
dependencies = ["open-clip-torch"]