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Added multithread option to ColorMatch node

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This commit is contained in:
netjerikh 2025-07-18 11:54:19 +03:00
parent ad37ce656c
commit 0d2334de6d
1 changed files with 22 additions and 16 deletions
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38
nodes/image_nodes.py
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@ -28,6 +28,7 @@ try:
from server import PromptServer
except:
PromptServer = None
from concurrent.futures import ThreadPoolExecutor
script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@ -72,6 +73,7 @@ class ColorMatch:
},
"optional": {
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"multithread": ("BOOLEAN", {"default": True}),
}
}
@ -93,37 +95,41 @@ https://github.com/hahnec/color-matcher/
"""
def colormatch(self, image_ref, image_target, method, strength=1.0):
def colormatch(self, image_ref, image_target, method, strength=1.0, multithread=True):
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")
cm = ColorMatcher()
image_ref = image_ref.cpu()
image_target = image_target.cpu()
batch_size = image_target.size(0)
out = []
images_target = image_target.squeeze()
images_ref = image_ref.squeeze()
image_ref_np = images_ref.numpy()
images_target_np = images_target.numpy()
if image_ref.size(0) > 1 and image_ref.size(0) != batch_size:
raise ValueError("ColorMatch: Use either single reference image or a matching batch of reference images.")
for i in range(batch_size):
image_target_np = images_target_np if batch_size == 1 else images_target[i].numpy()
def process(i):
cm = ColorMatcher()
image_target_np_i = images_target_np if batch_size == 1 else images_target[i].numpy()
image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy()
try:
image_result = cm.transfer(src=image_target_np, ref=image_ref_np_i, method=method)
except BaseException as e:
print(f"Error occurred during transfer: {e}")
break
# Apply the strength multiplier
image_result = image_target_np + strength * (image_result - image_target_np)
out.append(torch.from_numpy(image_result))
image_result = cm.transfer(src=image_target_np_i, ref=image_ref_np_i, method=method)
image_result = image_target_np_i + strength * (image_result - image_target_np_i)
return torch.from_numpy(image_result)
except Exception as e:
print(f"Thread {i} error: {e}")
return torch.from_numpy(image_target_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)
return (out,)