v0.8, greenscreen, slash for combine text..

README.md

@@ -1,4 +1,4 @@
-# 🔗 Comfyui : Bjornulf_custom_nodes v0.7 🔗
+# 🔗 Comfyui : Bjornulf_custom_nodes v0.8 🔗
 
 # Dependencies
 
@@ -14,6 +14,9 @@
 - **v0.5**: New node : Image to grayscale (black & white) - Convert an image to grayscale.
 - **v0.6**: New node : Combine images (Background + Overlay) - Combine two images into a single image.
 - **v0.7**: Replace Save API node with Save Bjornulf Lobechat node. (For my custom lobe-chat)
+- **v0.8**: Combine images : add an option to put image top, bottom or center.
+- **v0.8**: Combine texts : add option for slashes /
+- **v0.8**: Add basic node to transform greenscreen in to transparency.
 
 # 📝 Nodes descriptions
 
@@ -167,4 +170,12 @@ But you can sometimes also want a black and white image...
 
 **Description:**  
 Combine two images into a single image : a background and one (or several) transparent overlay. (allow for video frames.)  
-❗ Warning : For now, `background` is a static image. (I will allow video there later too.)  
+Update 0.8 : Also have a option to put image top, bottom or center.
+❗ Warning : For now, `background` is a static image. (I will allow video there later too.)  
+
+## 25 - 🟩➜▢ Green Screen to Transparency
+![Combine Images](screenshots/greeenscreen_to_transparency.png)
+
+**Description:**  
+Transform greenscreen into transparency.  
+Need clean greenscreen ofc. (Can adjust threshold but very basic node.)

__init__.py

@@ -29,6 +29,7 @@ from .remove_transparency import RemoveTransparency
 from .image_to_grayscale import GrayscaleTransform
 from .combine_background_overlay import CombineBackgroundOverlay
 from .save_bjornulf_lobechat import SaveBjornulfLobeChat
+from .green_to_transparency import GreenScreenToTransparency
 # from .check_black_image import CheckBlackImage
 # from .clear_vram import ClearVRAM
 
@@ -37,6 +38,7 @@ from .save_bjornulf_lobechat import SaveBjornulfLobeChat
 NODE_CLASS_MAPPINGS = {
     # "Bjornulf_CustomStringType": CustomStringType,
     "Bjornulf_ollamaLoader": ollamaLoader,
+    "Bjornulf_GreenScreenToTransparency": GreenScreenToTransparency,
     # "Bjornulf_CheckBlackImage": CheckBlackImage,
     # "Bjornulf_ClearVRAM": ClearVRAM,
     "Bjornulf_SaveBjornulfLobeChat": SaveBjornulfLobeChat,
@@ -74,6 +76,7 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     # "Bjornulf_CustomStringType": "!!! CUSTOM STRING TYPE !!!",
     "Bjornulf_ollamaLoader": "🦙 Ollama (Description)",
+    "Bjornulf_GreenScreenToTransparency": "🟩➜▢ Green Screen to Transparency",
     # "Bjornulf_CheckBlackImage": "🔲 Check Black Image (Empty mask)",
     "Bjornulf_SaveBjornulfLobeChat": "🖼💬 Save image for Bjornulf LobeChat",
     # "Bjornulf_ClearVRAM": "🧹 Clear VRAM",
@@ -81,8 +84,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "Bjornulf_ShowInt": "👁 Show (Int)",
     "Bjornulf_ShowFloat": "👁 Show (Float)",
     "Bjornulf_CombineBackgroundOverlay": "🖼+🖼 Combine images (Background+Overlay alpha)",
-    "Bjornulf_GrayscaleTransform": "🔲 Image to grayscale (black & white)",
+    "Bjornulf_GrayscaleTransform": "🖼➜🔲 Image to grayscale (black & white)",
-    "Bjornulf_RemoveTransparency": "🔲 Remove image Transparency (alpha)",
+    "Bjornulf_RemoveTransparency": "▢➜⬛ Remove image Transparency (alpha)",
+    # "🔲➜⬛ Transparency to color",
     "Bjornulf_ResizeImage": "📏 Resize Image",
     "Bjornulf_SaveImagePath": "🖼 Save Image (exact path, exact name) ⚠️💣",
     "Bjornulf_SaveImageToFolder": "🖼📁 Save Image to a folder",

combine_background_overlay.py

@@ -9,6 +9,7 @@ class CombineBackgroundOverlay:
             "required": {
                 "background": ("IMAGE",),
                 "overlay_alpha": ("IMAGE",),
+                "position": (["middle", "top", "bottom"],),
             },
         }
 
@@ -16,7 +17,7 @@ class CombineBackgroundOverlay:
     FUNCTION = "combine_background_overlay"
     CATEGORY = "Bjornulf"
 
-    def combine_background_overlay(self, background, overlay_alpha):
+    def combine_background_overlay(self, background, overlay_alpha, position):
         # Convert background from torch tensor to numpy array
         bg = background[0].numpy()
         bg = (bg * 255).astype(np.uint8)
@@ -36,9 +37,14 @@ class CombineBackgroundOverlay:
                 ov_img = Image.fromarray(ov, 'RGB')
                 ov_img = ov_img.convert('RGBA')
 
-            # Calculate position to center the overlay
+            # Calculate position based on user selection
             x = (bg_img.width - ov_img.width) // 2
-            y = (bg_img.height - ov_img.height) // 2
+            if position == "middle":
+                y = (bg_img.height - ov_img.height) // 2
+            elif position == "top":
+                y = 0
+            else:  # bottom
+                y = bg_img.height - ov_img.height
 
             # Create a new image for this overlay
             result = Image.new('RGBA', bg_img.size, (0, 0, 0, 0))
@@ -46,7 +52,7 @@ class CombineBackgroundOverlay:
             # Paste the background
             result.paste(bg_img, (0, 0))
 
-            # Paste the overlay in the center
+            # Paste the overlay in the selected position
             result.paste(ov_img, (x, y), ov_img)
 
             # Convert back to numpy array and then to torch tensor

combine_texts.py

@@ -4,7 +4,7 @@ class CombineTexts:
         return {
             "required": {
                 "number_of_inputs": ("INT", {"default": 2, "min": 2, "max": 10, "step": 1}),
-                "delimiter": (["newline", "comma", "space"], {"default": "newline"}),
+                "delimiter": (["newline", "comma", "space", "slash"], {"default": "newline"}),
                 "text_1": ("STRING", {"forceInput": True}),
                 "text_2": ("STRING", {"forceInput": True}),
             },
@@ -42,5 +42,7 @@ class CombineTexts:
             return ","
         elif delimiter == "space":
             return " "
+        elif delimiter == "slash":
+            return "/"
         else:
             return "\n"

green_to_transparency.py

@@ -0,0 +1,76 @@
+import numpy as np
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+
+class GreenScreenToTransparency:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE", {}),
+                "threshold": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    FUNCTION = "remove_green_screen"
+    RETURN_TYPES = ("IMAGE",)
+    OUTPUT_NODE = True
+    CATEGORY = "Bjornulf"
+
+    def remove_green_screen(self, image, threshold=0.1, prompt=None, extra_pnginfo=None):
+        # Ensure the input image is on CPU and convert to numpy array
+        image_np = image.cpu().numpy()
+        
+        # Check if the image is in the format [batch, height, width, channel]
+        if image_np.ndim == 4:
+            # If so, we'll process each image in the batch
+            processed_images = []
+            for img in image_np:
+                processed_img = self._process_single_image(img, threshold)
+                processed_images.append(processed_img)
+            
+            # Stack the processed images back into a batch
+            processed_batch = np.stack(processed_images)
+            # Convert to torch tensor
+            processed_tensor = torch.from_numpy(processed_batch)
+        else:
+            # If it's a single image, process it directly
+            processed_np = self._process_single_image(image_np, threshold)
+            # Add batch dimension if it was originally present
+            if image.dim() == 4:
+                processed_np = np.expand_dims(processed_np, axis=0)
+            # Convert to torch tensor
+            processed_tensor = torch.from_numpy(processed_np)
+
+        # Update metadata if needed
+        if extra_pnginfo is not None:
+            extra_pnginfo["green_screen_removed"] = True
+
+        return (processed_tensor, prompt, extra_pnginfo)
+
+    def _process_single_image(self, img, threshold):
+        # Convert to PIL Image
+        pil_img = Image.fromarray((img * 255).astype(np.uint8))
+        
+        # Convert the image to RGBA mode
+        pil_img = pil_img.convert("RGBA")
+        
+        # Get image data as numpy array
+        data = np.array(pil_img)
+        
+        # Create a mask for green pixels
+        r, g, b, a = data[:,:,0], data[:,:,1], data[:,:,2], data[:,:,3]
+        mask = (g > r + threshold * 255) & (g > b + threshold * 255)
+        
+        # Set alpha channel to 0 for green pixels
+        data[:,:,3] = np.where(mask, 0, a)
+        
+        # Create a new image with the updated data
+        result = Image.fromarray(data)
+        
+        # Convert back to numpy and normalize
+        processed_np = np.array(result).astype(np.float32) / 255.0
+        
+        return processed_np

resize_image.py

@@ -15,7 +15,7 @@ class ResizeImage:
         }
 
     FUNCTION = "resize_image"
-    RETURN_TYPES = ("IMAGE", "PROMPT", "EXTRA_PNGINFO")
+    RETURN_TYPES = ("IMAGE",)
     OUTPUT_NODE = True
     CATEGORY = "Bjornulf"
 

workflows/FLUX_troll_test.json

@@ -251,9 +251,9 @@
         "t5xxl_fp16.safetensors",
         "clip_l.safetensors",
         "flux",
-        "flux1-schnell.sft",
+        "flux1-schnell.safetensors",
         "fp8_e5m2",
-        "ae.sft"
+        "ae.safetensors"
       ]
     }
   ],
@@ -733,4 +733,4 @@
     }
   },
   "version": 0.4
-}
+}