Add a new node: Load image with transparency

README.md

@@ -1,4 +1,4 @@
-# 🔗 Comfyui : Bjornulf_custom_nodes v0.12 🔗
+# 🔗 Comfyui : Bjornulf_custom_nodes v0.13 🔗
 
 # Dependencies
 
@@ -21,6 +21,7 @@
 - **v0.10**: Add a new node : Loop (All Lines from input) - Iterate over all lines from an input text.
 - **v0.11**: Add a new node : Text with random Seed - Generate a random seed, along with text.
 - **v0.12**: Combine images : Add option to move vertically and horizontally. (from -50% to 150%)
+- **v0.13**: Add a new node: Load image with transparency (alpha) - Load an image with transparency.
 
 # 📝 Nodes descriptions
 
@@ -176,6 +177,7 @@ But you can sometimes also want a black and white image...
 Combine two images into a single image : a background and one (or several) transparent overlay. (allow to have a video there, just send all the frames and recombine them after.)  
 Update 0.11 : Add option to move vertically and horizontally. (from -50% to 150%)  
 ❗ Warning : For now, `background` is a static image. (I will allow video there later too.)  
+⚠️ Warning : If you want to directly load the image with transparency, use my node `🖼 Load Image with Transparency ▢` instead of the `Load Image` node.  
 
 ## 25 - 🟩➜▢ Green Screen to Transparency
 ![Greenscreen to Transparency](screenshots/greeenscreen_to_transparency.png)
@@ -224,4 +226,11 @@ Here is an example of the similarities that you want to avoid with SDXL with dif
 
 FLUX : Here is an example of 4 images without Random Seed node on the left, and on the right 4 images with Random Seed node :
 
-![Text with random Seed 5](screenshots/result_random_seed.png)
+![Text with random Seed 5](screenshots/result_random_seed.png)
+
+## 29 - 🖼 Load Image with Transparency ▢
+![Load image Alpha](screenshots/load_image_alpha.png)
+
+**Description:**  
+Load an image with transparency.  
+The default `Load Image` node will not load the transparency.  

__init__.py

@@ -33,6 +33,7 @@ from .green_to_transparency import GreenScreenToTransparency
 from .random_line_from_input import RandomLineFromInput
 from .loop_lines import LoopAllLines
 from .random_seed_with_text import TextToStringAndSeed
+from .load_image_alpha import LoadImageWithTransparency
 # from .check_black_image import CheckBlackImage
 # from .clear_vram import ClearVRAM
 
@@ -41,6 +42,7 @@ from .random_seed_with_text import TextToStringAndSeed
 NODE_CLASS_MAPPINGS = {
     # "Bjornulf_CustomStringType": CustomStringType,
     "Bjornulf_ollamaLoader": ollamaLoader,
+    "Bjornulf_LoadImageWithTransparency": LoadImageWithTransparency,
     "Bjornulf_LoopAllLines": LoopAllLines,
     "Bjornulf_TextToStringAndSeed": TextToStringAndSeed,
     "Bjornulf_GreenScreenToTransparency": GreenScreenToTransparency,
@@ -82,6 +84,7 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     # "Bjornulf_CustomStringType": "!!! CUSTOM STRING TYPE !!!",
     "Bjornulf_ollamaLoader": "🦙 Ollama (Description)",
+    "Bjornulf_LoadImageWithTransparency": "🖼 Load Image with Transparency ▢",
     "Bjornulf_GreenScreenToTransparency": "🟩➜▢ Green Screen to Transparency",
     # "Bjornulf_CheckBlackImage": "🔲 Check Black Image (Empty mask)",
     "Bjornulf_SaveBjornulfLobeChat": "🖼💬 Save image for Bjornulf LobeChat",

load_image_alpha.py

@@ -0,0 +1,80 @@
+import os
+import hashlib
+import numpy as np
+from PIL import Image, ImageOps, ImageSequence
+import torch
+import folder_paths
+import node_helpers
+
+class LoadImageWithTransparency:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":
+                    {"image": (sorted(files), {"image_upload": True})},
+                }
+
+    CATEGORY = "image"
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_image_alpha"
+
+    def load_image_alpha(self, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        
+        img = node_helpers.pillow(Image.open, image_path)
+        
+        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("RGBA")
+
+            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)
+            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:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return (output_image, output_mask)
+
+    @classmethod
+    def IS_CHANGED(s, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+
+        return True