nodes/tritant-ComfyUI_Layers_Utility
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update layers system

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tritant
2025-07-20 22:57:46 +02:00
committed by GitHub
parent 1b8e92c9c6
commit 9e61aa2b50
1 changed files with 37 additions and 14 deletions
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layer_system_final.py
+37 -14
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@@ -4,8 +4,8 @@ import json
# +++ FONCTION HELPER (INCHANGÉE) +++
def prepare_layer(top_image, base_image, resize_mode, scale, offset_x, offset_y):
"""Prépare un calque (image ou masque) en le redimensionnant et en le positionnant."""
B, base_H, base_W, C = base_image.shape
# Gérer les masques (1 canal) et les images (3+ canaux)
top_C = top_image.shape[3] if top_image.dim() > 3 else 1
_, top_H, top_W, _ = top_image.shape
@@ -16,7 +16,6 @@ def prepare_layer(top_image, base_image, resize_mode, scale, offset_x, offset_y)
top_image = F.interpolate(top_image.permute(0, 3, 1, 2), size=(new_H, new_W), mode='bilinear', align_corners=False).permute(0, 2, 3, 1)
top_H, top_W = new_H, new_W
# Créer un canevas avec le bon nombre de canaux (important pour les masques)
canvas = torch.zeros(B, base_H, base_W, top_C, device=base_image.device)
if resize_mode == 'stretch':
@@ -33,7 +32,6 @@ def prepare_layer(top_image, base_image, resize_mode, scale, offset_x, offset_y)
cover_H, cover_W = int(top_H * ratio), int(top_W * ratio)
resized_top = F.interpolate(top_image.permute(0, 3, 1, 2), size=(cover_H, cover_W), mode='bilinear', align_corners=False).permute(0, 2, 3, 1)
y_start, x_start = (cover_H - base_H) // 2, (cover_W - base_W) // 2
# S'assurer que le rognage ne dépasse pas les dimensions
src_y_end = min(y_start + base_H, cover_H)
src_x_end = min(x_start + base_W, cover_W)
canvas = resized_top[:, y_start:src_y_end, x_start:src_x_end, :]
@@ -69,19 +67,24 @@ class LayerSystem:
DESCRIPTION = "This custom node for ComfyUI provides a powerful and flexible dynamic layering system, similar to what you would find in image editing software like Photoshop."
def _blend(self, base, top, mode):
"""Applique un mode de fusion entre deux images."""
if mode == 'normal': return top
if mode == 'multiply': return base * top
if mode == 'screen': return 1.0 - (1.0 - base) * (1.0 - top)
if mode == 'overlay': return torch.where(base < 0.5, 2.0 * base * top, 1.0 - 2.0 * (1.0 - base) * (1.0 - top))
if mode == 'soft_light': return torch.where(top < 0.5, 2.0 * base * top + base.pow(2.0) * (1.0 - 2.0 * top), torch.sqrt(base) * (2.0 * top - 1.0) + 2.0 * base * (1.0 - top))
if mode == 'hard_light': return torch.where(top < 0.5, 2.0 * top * base, 1.0 - 2.0 * (1.0 - top) * (1.0 - base))
if mode == 'difference': return torch.abs(base - top)
if mode == 'color_dodge':
denominator = 1.0 - top
return torch.where(denominator < 1e-6, torch.ones_like(base), torch.clamp(base / (denominator + 1e-6), 0, 1))
if mode == 'color_burn':
return torch.where(top < 1e-6, torch.zeros_like(base), 1.0 - torch.clamp((1.0 - base) / (top + 1e-6), 0, 1))
return top
def composite_layers(self, base_image, _properties_json="{}", **kwargs):
final_image = base_image.clone()
# --- MODIFICATION AJOUTÉE ICI ---
# S'assurer que l'image de base est en RGB (3 canaux) en ignorant l'alpha
if final_image.shape[-1] == 4:
final_image = final_image[..., :3]
@@ -96,42 +99,62 @@ class LayerSystem:
layer_image = layers.get(layer_name)
if layer_image is None: continue
# --- MODIFICATION AJOUTÉE ICI ---
# Si l'image du calque a 4 canaux (RGBA), on ne garde que les 3 premiers (RGB)
if layer_image.shape[-1] == 4:
layer_image = layer_image[..., :3]
props = properties.get(layer_name, {})
if not props.get("enabled", True): continue
# On récupère les propriétés de transformation
resize_mode = props.get("resize_mode", "fit")
scale = props.get("scale", 1.0)
offset_x = props.get("offset_x", 0)
offset_y = props.get("offset_y", 0)
# On applique les transformations à l'image du calque
prepared_layer = prepare_layer(layer_image, final_image, resize_mode, scale, offset_x, offset_y)
# Appliquer les ajustements de couleur
brightness = props.get("brightness", 0.0)
if brightness != 0.0:
prepared_layer = torch.clamp(prepared_layer + brightness, 0.0, 1.0)
contrast = props.get("contrast", 0.0)
if contrast != 0.0:
contrast_factor = 1.0 + contrast
prepared_layer = torch.clamp((prepared_layer - 0.5) * contrast_factor + 0.5, 0.0, 1.0)
color_r = props.get("color_r", 1.0)
color_g = props.get("color_g", 1.0)
color_b = props.get("color_b", 1.0)
if color_r != 1.0 or color_g != 1.0 or color_b != 1.0:
prepared_layer[..., 0] = torch.clamp(prepared_layer[..., 0] * color_r, 0.0, 1.0)
prepared_layer[..., 1] = torch.clamp(prepared_layer[..., 1] * color_g, 0.0, 1.0)
prepared_layer[..., 2] = torch.clamp(prepared_layer[..., 2] * color_b, 0.0, 1.0)
# --- NOUVELLE FONCTIONNALITÉ : SATURATION ---
saturation = props.get("saturation", 1.0)
if saturation != 1.0:
grayscale = prepared_layer[..., 0] * 0.299 + prepared_layer[..., 1] * 0.587 + prepared_layer[..., 2] * 0.114
grayscale = grayscale.unsqueeze(-1) # Garder la dimension du canal
prepared_layer = torch.clamp(grayscale * (1.0 - saturation) + prepared_layer * saturation, 0.0, 1.0)
mode = props.get("blend_mode", "normal")
opacity = props.get("opacity", 1.0)
blended_image = self._blend(final_image, prepared_layer, mode)
composited_image = (1.0 - opacity) * final_image + opacity * blended_image
# --- LOGIQUE D'APPLICATION DU MASQUE CORRIGÉE ---
mask_name = layer_name.replace("layer_", "mask_")
mask = masks.get(mask_name)
if mask is not None:
# On s'assure que le masque est un tenseur 4D (B, H, W, C=1)
if mask.dim() == 3:
mask = mask.unsqueeze(-1)
# On applique les MÊMES transformations au masque pour qu'il suive l'image
prepared_mask = prepare_layer(mask, final_image, resize_mode, scale, offset_x, offset_y)
# Appliquer le masque
if props.get("invert_mask", False):
prepared_mask = 1.0 - prepared_mask
final_image = final_image * (1.0 - prepared_mask) + composited_image * prepared_mask
else:
final_image = composited_image
@@ -144,4 +167,4 @@ NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerSystem": "Layer System (Dynamic)"
}
}