v1.1.7

README.md

@@ -1,6 +1,6 @@
-# 🔗 Comfyui : Bjornulf_custom_nodes v1.1.6 🔗
+# 🔗 Comfyui : Bjornulf_custom_nodes v1.1.7 🔗
 
-A list of 168 custom nodes for Comfyui : Display, manipulate, create and edit text, images, videos, loras, generate characters and more.  
+A list of 170 custom nodes for Comfyui : Display, manipulate, create and edit text, images, videos, loras, generate characters and more.  
 You can manage looping operations, generate randomized content, trigger logical conditions, pause and manually control your workflows and even work with external AI tools, like Ollama or Text To Speech.  
 
 ⚠️ Warning : Very active development. Work in progress. 🏗  

__init__.py

@@ -141,9 +141,13 @@ from .load_image_from_path import LoadImageWithTransparencyFromPath
 # from .kofi_nodes import CivitAILoraSelectorWanVideo, CivitAILoraSelectorHunyuan
 # from .json_prompt_extractor import JSONImagePromptExtractor
 
+from .upscaler_transparency import ImageUpscaleWithModelTransparency
+from .load_base64_transparency import loadImageBase64Transparency
 #RemoteTextEncodingWithCLIPs
 
 NODE_CLASS_MAPPINGS = {
+    "Bjornulf_ImageUpscaleWithModelTransparency": ImageUpscaleWithModelTransparency,
+    "Bjornulf_loadImageBase64Transparency": loadImageBase64Transparency,
     # "Bjornulf_LoraSelectorHunyuan": CivitAILoraSelectorHunyuan,
     # "Bjornulf_LoraSelectorWanVideo": CivitAILoraSelectorWanVideo,
     # "Bjornulf_JSONImagePromptExtractor": JSONImagePromptExtractor,
@@ -337,6 +341,8 @@ NODE_CLASS_MAPPINGS = {
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
+    "Bjornulf_loadImageBase64Transparency": "📥🖼 Load Image Base64 (Transparency)",
+    "Bjornulf_ImageUpscaleWithModelTransparency": "🖼 Upscale Image with Transparency (with model)",
     #"Bjornulf_LoraSelectorHunyuan": "☕ Lora Selector Hunyuan",
     #"Bjornulf_LoraSelectorWanVideo": "☕ Lora Selector WanVideo",
     #"Bjornulf_JSONImagePromptExtractor": "JSONImagePromptExtractor", 

load_base64_transparency.py

@@ -0,0 +1,67 @@
+import base64
+import cv2
+import numpy as np
+import torch
+
+class loadImageBase64Transparency:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "base64_data": ("STRING", {"default": ""}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    OUTPUT_NODE = True
+    FUNCTION = "load_image"
+    CATEGORY = "EasyUse/Image/LoadImage"
+
+    def convert_color(self, image):
+        """Convert image color space while preserving alpha channel"""
+        if len(image.shape) > 2 and image.shape[2] >= 4:
+            # Handle BGRA to RGBA conversion
+            return cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+        elif len(image.shape) > 2 and image.shape[2] == 3:
+            # Handle BGR to RGB conversion
+            return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        else:
+            # Grayscale image
+            return image
+
+    def load_image(self, base64_data, prompt=None, extra_pnginfo=None):
+        # Decode base64 data to image
+        nparr = np.frombuffer(base64.b64decode(base64_data), np.uint8)
+        result = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
+        
+        # Split channels to check for alpha
+        channels = cv2.split(result)
+        has_alpha = len(channels) > 3
+        
+        if has_alpha:
+            # Extract alpha channel as mask
+            mask = channels[3].astype(np.float32) / 255.0
+            mask = torch.from_numpy(mask)
+        else:
+            # Create full opacity mask if no alpha channel
+            mask = torch.ones(channels[0].shape, dtype=torch.float32, device="cpu")
+
+        # Convert color space while preserving channels
+        result = self.convert_color(result)
+        
+        # Normalize to 0-1 range
+        result = result.astype(np.float32) / 255.0
+        
+        # Convert to tensor and add batch dimension
+        if has_alpha:
+            # Keep all 4 channels (RGBA)
+            new_images = torch.from_numpy(result)[None,]
+        else:
+            # RGB only
+            new_images = torch.from_numpy(result)[None,]
+
+        # Ensure mask has batch dimension
+        mask = mask.unsqueeze(0)
+
+        return (new_images, mask)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "bjornulf_custom_nodes"
-description = "169 ComfyUI nodes : Display, manipulate, and edit text, images, videos, loras, generate characters and more. Manage looping operations, generate randomized content, use logical conditions and work with external AI tools, like Ollama or Text To Speech, etc..."
+description = "170 ComfyUI nodes : Display, manipulate, and edit text, images, videos, loras, generate characters and more. Manage looping operations, generate randomized content, use logical conditions and work with external AI tools, like Ollama or Text To Speech, etc..."
-version = "1.1.6"
+version = "1.1.7"
 license = {file = "LICENSE"}
 
 [project.urls]

upscaler_transparency.py

@@ -0,0 +1,83 @@
+import comfy
+from comfy import model_management
+import torch
+import comfy.utils
+
+class ImageUpscaleWithModelTransparency:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "upscale_model": ("UPSCALE_MODEL",),
+                              "image": ("IMAGE",),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "image/upscaling"
+
+    def upscale(self, upscale_model, image):
+        device = model_management.get_torch_device()
+
+        # Check if image has alpha channel (4 channels)
+        has_alpha = image.shape[-1] == 4
+        
+        if has_alpha:
+            # Split RGB and alpha channels
+            rgb_image = image[..., :3]
+            alpha_channel = image[..., 3:4]
+        else:
+            rgb_image = image
+            alpha_channel = None
+
+        # Calculate memory requirements (based on RGB channels)
+        memory_required = model_management.module_size(upscale_model.model)
+        memory_required += (512 * 512 * 3) * rgb_image.element_size() * max(upscale_model.scale, 1.0) * 384.0
+        memory_required += rgb_image.nelement() * rgb_image.element_size()
+        
+        # Add memory for alpha channel processing if present
+        if has_alpha:
+            memory_required += alpha_channel.nelement() * alpha_channel.element_size() * max(upscale_model.scale, 1.0) ** 2
+        
+        model_management.free_memory(memory_required, device)
+
+        upscale_model.to(device)
+        
+        # Upscale RGB channels
+        in_img = rgb_image.movedim(-1,-3).to(device)
+
+        tile = 512
+        overlap = 32
+
+        oom = True
+        while oom:
+            try:
+                steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
+                pbar = comfy.utils.ProgressBar(steps)
+                upscaled_rgb = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
+                oom = False
+            except model_management.OOM_EXCEPTION as e:
+                tile //= 2
+                if tile < 128:
+                    raise e
+
+        upscale_model.to("cpu")
+        upscaled_rgb = torch.clamp(upscaled_rgb.movedim(-3,-1), min=0, max=1.0)
+        
+        # Handle alpha channel if present
+        if has_alpha:
+            # Upscale alpha channel using simple interpolation
+            alpha_upscaled = torch.nn.functional.interpolate(
+                alpha_channel.movedim(-1,-3).to(device), 
+                size=(upscaled_rgb.shape[1], upscaled_rgb.shape[2]), 
+                mode='bilinear', 
+                align_corners=False
+            ).movedim(-3,-1).to(upscaled_rgb.device)
+            
+            # Clamp alpha values to [0, 1]
+            alpha_upscaled = torch.clamp(alpha_upscaled, min=0, max=1.0)
+            
+            # Combine RGB and alpha channels
+            result = torch.cat([upscaled_rgb, alpha_upscaled], dim=-1)
+        else:
+            result = upscaled_rgb
+
+        return (result,)