nodes/chflame163-ComfyUI_LayerStyle
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standardize log outputs

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chflame163
2024-02-16 15:26:45 +08:00
parent c98917e64c
commit f3cff7bb11
51 changed files with 56 additions and 53 deletions
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README.MD
+2
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@@ -1,6 +1,8 @@
# ComfyUI Layer Style
A set of nodes for ComfyUI that can composite layer and mask to achieve Photoshop like functionality.
It migrate some basic functions of PhotoShop to ComfyUI, aiming to centralize the workflow and reduce the frequency of software switching.
![image](image/title.png)
Nodes are divided into 5 groups according to their functions: LayerStyle, LayerColor, LayerMask, LayerUtility and LayerFilter.
README_CN.MD
+1 -1
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@@ -1,5 +1,5 @@
# ComfyUI Layer Style
一组为ComfyUI设计的节点，可以合成图层达到类似Photoshop的功能。
一组为ComfyUI设计的节点，可以合成图层达到类似Photoshop的功能。这些节点将PhotoShop的一部分基本功能迁移到ComfyUI，旨在集中工作流程，减少软件切换的频率。
![image](image/title.png)
节点按照功能分为5组：LayerStyle, LayerColor, LayerMask, LayerUtility和LayerFilter。
py/channel_shake.py
+1 -1
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@@ -53,7 +53,7 @@ class ChannelShake:
ret_image = Image.merge('RGB', [R, G, B])
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_adapter.py
+1 -1
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@@ -47,7 +47,7 @@ class ColorAdapter:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_correct_HSV.py
+1 -1
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@@ -44,7 +44,7 @@ class ColorCorrectHSV:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_correct_LAB.py
+1 -1
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@@ -44,7 +44,7 @@ class ColorCorrectLAB:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_correct_LUTapply.py
+1 -1
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@@ -34,7 +34,7 @@ class ColorCorrectLUTapply:
ret_image = lut_apply(_image, lut_file)
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_correct_RGB.py
+1 -1
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@@ -44,7 +44,7 @@ class ColorCorrectRGB:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_correct_YUV.py
+1 -1
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@@ -44,7 +44,7 @@ class ColorCorrectYUV:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_correct_brightness&contrast.py
+1 -1
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@@ -46,7 +46,7 @@ class ColorCorrectBrightnessAndContrast:
_image = color_image.enhance(factor=saturation)
ret_images.append(pil2tensor(_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_correct_exposure.py
+1 -1
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@@ -40,7 +40,7 @@ class ColorCorrectExposure:
t = np.clip((t - bp) * scale, 0.0, 1.0)
ret_images.append(torch.from_numpy(t))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
py/color_correct_gamma.py
+1 -1
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@@ -35,7 +35,7 @@ class ColorCorrectGamma:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_map.py
+1 -1
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@@ -44,7 +44,7 @@ class ColorMap:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/color_overlay.py
+1 -1
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@@ -77,7 +77,7 @@ class ColorOverlay:
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/crop_by_mask.py
+1 -1
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@@ -85,7 +85,7 @@ class CropByMask:
ret_images.append(pil2tensor(_canvas.crop(crop_box)))
ret_masks.append(image2mask(_mask.crop(crop_box)))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0), list(crop_box), pil2tensor(preview_image),)
py/drop_shadow.py
+1 -1
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@@ -93,7 +93,7 @@ class DropShadow:
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
py/extand_canvas.py
+1 -1
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@@ -76,7 +76,7 @@ class ExtendCanvas:
ret_images.append(pil2tensor(_canvas))
ret_masks.append(image2mask(_mask_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
py/gaussian_blur.py
+1 -1
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@@ -34,7 +34,7 @@ class GaussianBlur:
ret_images.append(pil2tensor(gaussian_blur(_canvas, blur)))
log(f'GaussianBlur Processed {len(ret_images)} image(s).')
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/gradient_overlay.py
+1 -1
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@@ -89,7 +89,7 @@ class GradientOverlay:
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/image_blend.py
+1 -1
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@@ -73,7 +73,7 @@ class ImageBlend:
_canvas.paste(_comp, mask=_mask)
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/image_blend_advance.py
+1 -1
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@@ -121,7 +121,7 @@ class ImageBlendAdvance:
ret_images.append(pil2tensor(_canvas))
ret_masks.append(image2mask(_compmask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/image_channel_merge.py
+1 -1
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@@ -59,7 +59,7 @@ class ImageChannelMerge:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/image_channel_split.py
+1 -1
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@@ -41,7 +41,7 @@ class ImageChannelSplit:
c3_images.append(pil2tensor(channel3))
c4_images.append(pil2tensor(channel4))
log(f"{NODE_NAME} Processed {len(c1_images)} image(s).")
log(f"{NODE_NAME} Processed {len(c1_images)} image(s).", message_type='finish')
return (torch.cat(c1_images, dim=0), torch.cat(c2_images, dim=0), torch.cat(c3_images, dim=0), torch.cat(c4_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/image_mask_scale_as.py
+3 -3
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@@ -77,13 +77,13 @@ class ImageMaskScaleAs:
_mask = fit_resize_image(_mask, target_width, target_height, fit, resize_sampler).convert('L')
ret_masks.append(image2mask(_mask))
if len(ret_images) > 0 and len(ret_masks) >0:
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0), [orig_width, orig_height],)
elif len(ret_images) > 0 and len(ret_masks) == 0:
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), None,)
elif len(ret_images) == 0 and len(ret_masks) > 0:
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
return (None, torch.cat(ret_masks, dim=0), [orig_width, orig_height],)
else:
log(f"Error: {NODE_NAME} skipped, because the available image or mask is not found.", message_type='error')
py/image_opacity.py
+1 -1
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@@ -74,7 +74,7 @@ class ImageOpacity:
ret_images.append(pil2tensor(ret_image))
ret_masks.append(image2mask(ret_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/image_scale_restore.py
+1 -1
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@@ -98,7 +98,7 @@ class ImageScaleRestore:
ret_images.append(pil2tensor(ret_image))
ret_masks.append(image2mask(ret_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0), [orig_width, orig_height],)
py/image_shift.py
+1 -1
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@@ -78,7 +78,7 @@ class ImageShift:
ret_masks.append(image2mask(_mask))
ret_border_masks.append(image2mask(_border))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0), torch.cat(ret_border_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/imagefunc.py
+4 -2
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@@ -26,9 +26,11 @@ def log(message:str, message_type:str='info'):
name = 'LayerStyle'
if message_type == 'error':
message = '\033[1;31m' + message + '\033[m'
message = '\033[1;41m' + message + '\033[m'
elif message_type == 'warning':
message = '\033[1;35m' + message + '\033[m'
message = '\033[1;31m' + message + '\033[m'
elif message_type == 'finish':
message = '\033[1;32m' + message + '\033[m'
else:
message = '\033[1;33m' + message + '\033[m'
print(f"# 😺dzNodes: {name} -> {message}")
py/inner_glow.py
+1 -1
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@@ -94,7 +94,7 @@ class InnerGlow:
_layer.paste(_canvas, mask=ImageChops.invert(_mask))
ret_images.append(pil2tensor(_layer))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
py/inner_shadow.py
+1 -1
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@@ -90,7 +90,7 @@ class InnerShadow:
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/mask_box_detect.py
+1 -1
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@@ -62,7 +62,7 @@ class MaskBoxDetect:
preview_image = tensor2pil(mask).convert('RGB')
preview_image = draw_rect(preview_image, x - x_adjust, y - y_adjust, width, height, line_color="#F00000", line_width=int(preview_image.height / 60))
preview_image = draw_rect(preview_image, x, y, width, height, line_color="#00F000", line_width=int(preview_image.height / 40))
log(f"{NODE_NAME} Processed.")
log(f"{NODE_NAME} Processed.", message_type='finish')
return ( pil2tensor(preview_image), round(x_percent, 2), round(y_percent, 2), _width, _height, x, y,)
NODE_CLASS_MAPPINGS = {
py/mask_by_different.py
+1 -1
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@@ -66,7 +66,7 @@ class MaskByDifferent:
ret_masks.append(image2mask(_mask))
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
return (torch.cat(ret_masks, dim=0),)
py/mask_edge_shrink.py
+1 -1
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@@ -67,7 +67,7 @@ class MaskEdgeShrink:
ret_masks.append(image2mask(_layer))
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
return (torch.cat(ret_masks, dim=0),)
py/mask_edge_ultrl_detail.py
+1 -1
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@@ -65,7 +65,7 @@ class MaskEdgeUltraDetail:
ret_images.append(pil2tensor(ret_image))
ret_masks.append(image2mask(_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/mask_gradient.py
+1 -1
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@@ -136,7 +136,7 @@ class MaskGradient:
_canvas = chop_image(_mask, _canvas, 'normal', opacity)
ret_masks.append(image2mask(_canvas))
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
return (torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/mask_grow.py
+1 -1
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@@ -45,7 +45,7 @@ class MaskGrow:
_mask = l_masks[i]
ret_masks.append(expand_mask(image2mask(_mask), grow, blur) )
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
return (torch.cat(ret_masks, dim=0),)
py/mask_invert.py
-1
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@@ -37,7 +37,6 @@ class MaskInvert:
_mask = l_masks[i]
ret_masks.append(mask_invert(image2mask(_mask)))
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).")
return (torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/mask_motion_blur.py
+1 -1
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@@ -45,7 +45,7 @@ class MaskMotionBlur:
_blurimage = motion_blur(_mask, angle, blur)
ret_masks.append(image2mask(_blurimage))
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
return (torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/mask_stroke.py
+1 -1
View File
@@ -51,7 +51,7 @@ class MaskStroke:
stroke_mask = subtract_mask(outer_mask, inner_mask)
ret_masks.append(stroke_mask)
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} mask(s).", message_type='finish')
return (torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/motion_blur.py
+1 -1
View File
@@ -36,7 +36,7 @@ class MotionBlur:
ret_images.append(pil2tensor(motion_blur(_canvas, angle, blur)))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/outer_glow.py
+1 -1
View File
@@ -93,7 +93,7 @@ class OuterGlow:
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
py/pixel_spread.py
+1 -1
View File
@@ -71,7 +71,7 @@ class PixelSpread:
ret_images.append(torch.from_numpy(fg.astype(np.float32)))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/rembg_ultra.py
+1 -1
View File
@@ -40,7 +40,7 @@ class RemBgUltra:
ret_images.append(pil2tensor(ret_image))
ret_masks.append(image2mask(_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/restore_crop_box.py
+1 -1
View File
@@ -70,7 +70,7 @@ class RestoreCropBox:
ret_images.append(pil2tensor(_canvas))
ret_masks.append(image2mask(ret_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
py/segment_anything_ultra.py
+1 -1
View File
@@ -61,7 +61,7 @@ class SegmentAnythingUltra:
empty_mask = torch.zeros((1, height, width), dtype=torch.uint8, device="cpu")
return (empty_mask, empty_mask)
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
py/sharp&soft.py
+1 -1
View File
@@ -60,7 +60,7 @@ class SharpAndSoft:
details = (imgB / imgG - 1) * detail_mult + 1
dup[index] = np.clip(details * imgG - imgB + image, 0, 1)
log(f"{NODE_NAME} Processed {dup.shape[0]} image(s).")
log(f"{NODE_NAME} Processed {dup.shape[0]} image(s).", message_type='finish')
return (torch.from_numpy(dup),)
py/skin_beauty.py
+1 -1
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@@ -50,7 +50,7 @@ class SkinBeauty:
ret_images.append(pil2tensor(_canvas))
ret_masks.append(image2mask(light_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
py/soft_light.py
+1 -1
View File
@@ -49,7 +49,7 @@ class SoftLight:
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/stroke.py
+1 -1
View File
@@ -90,7 +90,7 @@ class Stroke:
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
py/text_image.py
+1 -1
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@@ -125,7 +125,7 @@ class TextImage:
_color = Image.new('RGB', size=(width, height), color=text_color)
_canvas.paste(_color, mask=_mask.convert('L'))
_canvas = RGB2RGBA(_canvas, _mask)
log(f"{NODE_NAME} Processed.")
log(f"{NODE_NAME} Processed.", message_type='finish')
return (pil2tensor(_canvas), image2mask(_mask),)
NODE_CLASS_MAPPINGS = {
py/water_color.py
+1 -1
View File
@@ -39,7 +39,7 @@ class WaterColor:
ret_images.append(pil2tensor(ret_image))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {