color match
This commit is contained in:
JJ
+366
-339
@@ -1,8 +1,8 @@
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|
||||
],
|
||||
[
|
||||
349,
|
||||
161,
|
||||
0,
|
||||
123,
|
||||
2,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
350,
|
||||
161,
|
||||
1,
|
||||
158,
|
||||
0,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
351,
|
||||
161,
|
||||
1,
|
||||
123,
|
||||
3,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
352,
|
||||
146,
|
||||
0,
|
||||
161,
|
||||
2,
|
||||
5,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
@@ -3926,6 +3881,78 @@
|
||||
104,
|
||||
2,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
364,
|
||||
118,
|
||||
0,
|
||||
168,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
365,
|
||||
117,
|
||||
0,
|
||||
168,
|
||||
1,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
366,
|
||||
146,
|
||||
0,
|
||||
168,
|
||||
2,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
367,
|
||||
168,
|
||||
0,
|
||||
155,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
368,
|
||||
168,
|
||||
0,
|
||||
137,
|
||||
1,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
369,
|
||||
168,
|
||||
0,
|
||||
123,
|
||||
2,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
370,
|
||||
168,
|
||||
1,
|
||||
158,
|
||||
0,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
371,
|
||||
168,
|
||||
1,
|
||||
123,
|
||||
3,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
372,
|
||||
124,
|
||||
0,
|
||||
168,
|
||||
4,
|
||||
"IMAGE"
|
||||
]
|
||||
],
|
||||
"groups": [
|
||||
@@ -3946,10 +3973,10 @@
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 0.5559917313492839,
|
||||
"scale": 0.6303940863128492,
|
||||
"offset": [
|
||||
895.7180320253976,
|
||||
656.250667639637
|
||||
1548.6123884557073,
|
||||
209.80359321404143
|
||||
]
|
||||
},
|
||||
"frontendVersion": "1.33.5",
|
||||
|
||||
+94
-2
@@ -4,6 +4,7 @@ import folder_paths
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import os
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
class ImageBlend_GPU:
|
||||
# Moved blend modes inside the class
|
||||
@@ -225,7 +226,21 @@ class CreateImageList:
|
||||
"export_alpha": ("BOOLEAN", {"default": True})
|
||||
},
|
||||
"optional": {
|
||||
"image1": ("IMAGE",)
|
||||
"image1": ("IMAGE",),
|
||||
"image_ref": ("IMAGE",),
|
||||
"method": (
|
||||
[
|
||||
'mkl',
|
||||
'hm',
|
||||
'reinhard',
|
||||
'mvgd',
|
||||
'hm-mvgd-hm',
|
||||
'hm-mkl-hm',
|
||||
], {
|
||||
"default": 'mkl'
|
||||
}),
|
||||
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
|
||||
"multithread": ("BOOLEAN", {"default": True}),
|
||||
},
|
||||
"hidden": {
|
||||
"unique_id": "UNIQUE_ID",
|
||||
@@ -237,6 +252,16 @@ class CreateImageList:
|
||||
RETURN_NAMES = ("images", "masks")
|
||||
FUNCTION = "batch"
|
||||
CATEGORY = "WanVideoWrapper_QQ/image"
|
||||
DESCRIPTION = """
|
||||
Creates a batch of images from multiple dynamic image inputs.
|
||||
Optionally applies color matching to all images if image_ref is provided.
|
||||
|
||||
Color matching uses the color-matcher library to transfer color characteristics
|
||||
from a reference image to all batch images. This is useful for maintaining
|
||||
consistent color grading across multiple images.
|
||||
|
||||
Supports alpha channel preservation with export_alpha option.
|
||||
"""
|
||||
|
||||
def load_image_with_alpha(self, image_path):
|
||||
"""Load image from path with alpha channel preserved"""
|
||||
@@ -317,7 +342,66 @@ class CreateImageList:
|
||||
print(f"Error in trace: {e}")
|
||||
return None
|
||||
|
||||
def batch(self, export_alpha, unique_id=None, prompt=None, **kwargs):
|
||||
def apply_color_match(self, images, image_ref, method, strength, multithread):
|
||||
"""Apply color matching to a batch of images using a reference image"""
|
||||
try:
|
||||
from color_matcher import ColorMatcher
|
||||
except:
|
||||
raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher")
|
||||
|
||||
images = images.cpu()
|
||||
image_ref = image_ref.cpu()
|
||||
batch_size = images.size(0)
|
||||
|
||||
# Handle alpha channel: extract RGB for color matching
|
||||
has_alpha = images.shape[-1] == 4
|
||||
if has_alpha:
|
||||
rgb_images = images[:, :, :, :3]
|
||||
alpha_channel = images[:, :, :, 3]
|
||||
else:
|
||||
rgb_images = images
|
||||
|
||||
# Extract RGB from reference if it has alpha
|
||||
if image_ref.shape[-1] == 4:
|
||||
image_ref_rgb = image_ref[:, :, :, :3]
|
||||
else:
|
||||
image_ref_rgb = image_ref
|
||||
|
||||
images_rgb = rgb_images.squeeze()
|
||||
images_ref = image_ref_rgb.squeeze()
|
||||
|
||||
image_ref_np = images_ref.numpy()
|
||||
images_rgb_np = images_rgb.numpy()
|
||||
|
||||
def process(i):
|
||||
cm = ColorMatcher()
|
||||
image_rgb_np_i = images_rgb_np if batch_size == 1 else images_rgb[i].numpy()
|
||||
image_ref_np_i = image_ref_np if image_ref_rgb.size(0) == 1 else images_ref[i].numpy()
|
||||
try:
|
||||
image_result = cm.transfer(src=image_rgb_np_i, ref=image_ref_np_i, method=method)
|
||||
image_result = image_rgb_np_i + strength * (image_result - image_rgb_np_i)
|
||||
return torch.from_numpy(image_result)
|
||||
except Exception as e:
|
||||
print(f"Color matching thread {i} error: {e}")
|
||||
return torch.from_numpy(image_rgb_np_i) # fallback
|
||||
|
||||
if multithread and batch_size > 1:
|
||||
max_threads = min(os.cpu_count() or 1, batch_size)
|
||||
with ThreadPoolExecutor(max_workers=max_threads) as executor:
|
||||
out = list(executor.map(process, range(batch_size)))
|
||||
else:
|
||||
out = [process(i) for i in range(batch_size)]
|
||||
|
||||
out = torch.stack(out, dim=0).to(torch.float32)
|
||||
out.clamp_(0, 1)
|
||||
|
||||
# Reattach alpha channel if it was present
|
||||
if has_alpha:
|
||||
out = torch.cat([out, alpha_channel.unsqueeze(-1)], dim=-1)
|
||||
|
||||
return out
|
||||
|
||||
def batch(self, export_alpha, unique_id=None, prompt=None, image_ref=None, method='mkl', strength=1.0, multithread=True, **kwargs):
|
||||
# Collect all image inputs from kwargs (dynamic inputs)
|
||||
images = []
|
||||
|
||||
@@ -350,6 +434,10 @@ class CreateImageList:
|
||||
if img.shape[-1] == 3:
|
||||
img = torch.nn.functional.pad(img, (0, 1), mode='constant', value=1.0)
|
||||
|
||||
# Apply color matching if image_ref is provided
|
||||
if image_ref is not None:
|
||||
img = self.apply_color_match(img, image_ref, method, strength, multithread)
|
||||
|
||||
# Extract masks
|
||||
masks = img[:, :, :, 3]
|
||||
|
||||
@@ -406,6 +494,10 @@ class CreateImageList:
|
||||
# Concatenate all images
|
||||
s = torch.cat(fitted_images, dim=0)
|
||||
|
||||
# Apply color matching if image_ref is provided
|
||||
if image_ref is not None:
|
||||
s = self.apply_color_match(s, image_ref, method, strength, multithread)
|
||||
|
||||
# Extract alpha channel as masks (MASK format is BHW, not BHWC)
|
||||
masks = s[:, :, :, 3] # Shape: [B, H, W]
|
||||
|
||||
|
||||
@@ -4,6 +4,16 @@ app.registerExtension({
|
||||
name: "WanVideoWrapper.CreateImageList",
|
||||
async beforeRegisterNodeDef(nodeType, nodeData, app) {
|
||||
if (nodeData.name === 'CreateImageList') {
|
||||
// Helper function to ensure image_ref is at the bottom
|
||||
const ensureImageRefAtBottom = function() {
|
||||
const imageRefIndex = this.inputs.findIndex(inp => inp.name === 'image_ref');
|
||||
if (imageRefIndex !== -1 && imageRefIndex < this.inputs.length - 1) {
|
||||
// Move image_ref to the end
|
||||
const imageRefInput = this.inputs.splice(imageRefIndex, 1)[0];
|
||||
this.inputs.push(imageRefInput);
|
||||
}
|
||||
};
|
||||
|
||||
nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
|
||||
const stackTrace = new Error().stack;
|
||||
|
||||
@@ -29,14 +39,14 @@ app.registerExtension({
|
||||
return;
|
||||
}
|
||||
|
||||
// Don't process the export_alpha input (it's a widget converted to input)
|
||||
// Don't process specific inputs (widgets converted to inputs or color matching params)
|
||||
const input = this.inputs[index];
|
||||
if (input && input.name === 'export_alpha') {
|
||||
if (input && (input.name === 'export_alpha' || input.name === 'image_ref' || input.name === 'method' || input.name === 'strength' || input.name === 'multithread')) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Count how many IMAGE type inputs we have (excluding export_alpha)
|
||||
const imageInputCount = this.inputs.filter(inp => inp.type === 'IMAGE').length;
|
||||
// Count how many IMAGE type inputs we have (excluding image_ref, which is for color matching)
|
||||
const imageInputCount = this.inputs.filter(inp => inp.type === 'IMAGE' && inp.name !== 'image_ref').length;
|
||||
|
||||
// When disconnecting, remove the input if there's more than one IMAGE input
|
||||
if (!connected && imageInputCount > 1) {
|
||||
@@ -49,17 +59,17 @@ app.registerExtension({
|
||||
}
|
||||
}
|
||||
|
||||
// Ensure at least one IMAGE input exists
|
||||
const remainingImageInputs = this.inputs.filter(inp => inp.type === 'IMAGE').length;
|
||||
// Ensure at least one IMAGE input exists (excluding image_ref)
|
||||
const remainingImageInputs = this.inputs.filter(inp => inp.type === 'IMAGE' && inp.name !== 'image_ref').length;
|
||||
if (remainingImageInputs === 0) {
|
||||
this.addInput("image1", "IMAGE");
|
||||
}
|
||||
|
||||
// Renumber all IMAGE inputs sequentially
|
||||
// Renumber all IMAGE inputs sequentially (excluding image_ref)
|
||||
let slot_i = 1;
|
||||
for (let i = 0; i < this.inputs.length; i++) {
|
||||
const inp = this.inputs[i];
|
||||
if (inp.type === 'IMAGE') {
|
||||
if (inp.type === 'IMAGE' && inp.name !== 'image_ref') {
|
||||
inp.name = `image${slot_i}`;
|
||||
slot_i++;
|
||||
}
|
||||
@@ -69,6 +79,9 @@ app.registerExtension({
|
||||
if (connected) {
|
||||
this.addInput(`image${slot_i}`, "IMAGE");
|
||||
}
|
||||
|
||||
// Ensure image_ref stays at the bottom
|
||||
ensureImageRefAtBottom.call(this);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user