update PoseNode, PainterNode

+ Return alpha channel mask input, if exists transparent canvas (template LoadImage node ComfyCore)
+ Update version

PainterNode/painter_node.py

@@ -6,11 +6,13 @@ from aiohttp import web
 import base64
 from io import BytesIO
 import time
-from PIL import Image, ImageOps
+from PIL import Image, ImageOps, ImageSequence
 import torch
 import numpy as np
 import glob
+
 import folder_paths
+import node_helpers
 
 # Directory node save settings
 CHUNK_SIZE = 1024
@@ -402,18 +404,50 @@ class PainterNode(object):
         # end - Piping image input
 
         image_path = folder_paths.get_annotated_filepath(image)
+        
+        img = node_helpers.pillow(Image.open, image_path)
 
-        i = Image.open(image_path)
-        i = ImageOps.exif_transpose(i)
-        image = i.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        if "A" in i.getbands():
-            mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
-            mask = 1.0 - torch.from_numpy(mask)
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+
+            if image.size[0] != w or image.size[1] != h:
+                continue
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            elif i.mode == 'P' and 'transparency' in i.info:
+                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
         else:
-            mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
-        return (image, mask.unsqueeze(0))
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return (output_image, output_mask)
 
     @classmethod
     def IS_CHANGED(self, image, unique_id, update_node=True, images=None):

PoseNode/pose_node.py

@@ -1,10 +1,11 @@
 import hashlib
 import os
-from PIL import Image, ImageOps
+from PIL import Image, ImageOps, ImageSequence
 import torch
 import numpy as np
-import folder_paths
 
+import folder_paths
+import node_helpers
 
 class PoseNode(object):
     @classmethod
@@ -32,21 +33,49 @@ class PoseNode(object):
     def output_pose(self, image):
         image_path = folder_paths.get_annotated_filepath(image)
 
-        i = Image.open(image_path)
-        i = ImageOps.exif_transpose(i)
+        img = node_helpers.pillow(Image.open, image_path)
 
-        if i.mode == 'RGBA':
-            mask = np.array(i.getchannel("A")).astype(np.float32) / 255.0
-            mask = 1.0 - torch.from_numpy(mask)
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
             image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+
+            if image.size[0] != w or image.size[1] != h:
+                continue
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            elif i.mode == 'P' and 'transparency' in i.info:
+                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
         else:
-            mask = torch.zeros((i.height, i.width), dtype=torch.float32, device="cpu")
-            image = i.convert("RGB")
+            output_image = output_images[0]
+            output_mask = output_masks[0]
 
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-
-        return (image, mask.unsqueeze(0))
+        return (output_image, output_mask)
 
     @classmethod
     def IS_CHANGED(self, image):

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_custom_nodes_alekpet"
 description = "Nodes: PoseNode, PainterNode, TranslateTextNode, TranslateCLIPTextEncodeNode, DeepTranslatorTextNode, DeepTranslatorCLIPTextEncodeNode, ArgosTranslateTextNode, ArgosTranslateCLIPTextEncodeNode, ChatGLM4TranslateCLIPTextEncodeNode, ChatGLM4TranslateTextNode, ChatGLM4InstructNode, ChatGLM4InstructMediaNode, PreviewTextNode, HexToHueNode, ColorsCorrectNode, IDENode."
-version = "1.0.83"
+version = "1.0.84"
 license = { file = "LICENSE" }
 
 [project.urls]