Update Crop and Pad in nodes_train.py

Added some code from KJNodes to Crop the center of the image and Pad does now really pad the image.
Also added ProgressBar in the GUI.

comfy_extras/nodes_train.py

@@ -6,7 +6,7 @@ import os
 import numpy as np
 import safetensors
 import torch
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image, ImageDraw, ImageFont, ImageStat
 from PIL.PngImagePlugin import PngInfo
 import torch.utils.checkpoint
 import tqdm
@@ -53,7 +53,9 @@ class TrainSampler(comfy.samplers.Sampler):
         cond = model_wrap.conds["positive"]
         dataset_size = sigmas.size(0)
         torch.cuda.empty_cache()
+        pbar_gui = comfy.utils.ProgressBar(self.total_steps)
         for i in (pbar:=tqdm.trange(self.total_steps, desc="Training LoRA", smoothing=0.01, disable=not comfy.utils.PROGRESS_BAR_ENABLED)):
+            pbar_gui.update(1)
             noisegen = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(self.seed + i * 1000)
             indicies = torch.randperm(dataset_size)[:self.batch_size].tolist()
 
@@ -115,8 +117,31 @@ class BiasDiff(torch.nn.Module):
         self.to(device=device)
         return self.passive_memory_usage()
 
+def get_edge_color(img):
+    """ code borrowed from https://github.com/kijai/ComfyUI-KJNodes """
+    """Sample edges and return dominant color"""
+    width, height = img.size
+    img = img.convert('RGB')
     
-def load_and_process_images(image_files, input_dir, resize_method="None", w=None, h=None):
+    # Create 1-pixel high/wide images from edges
+    top = img.crop((0, 0, width, 1))
+    bottom = img.crop((0, height-1, width, height))
+    left = img.crop((0, 0, 1, height))
+    right = img.crop((width-1, 0, width, height))
+    
+    # Combine edges into single image
+    edges = Image.new('RGB', (width*2 + height*2, 1))
+    edges.paste(top, (0, 0))
+    edges.paste(bottom, (width, 0))
+    edges.paste(left.resize((height, 1)), (width*2, 0))
+    edges.paste(right.resize((height, 1)), (width*2 + height, 0))
+    
+    # Get median color
+    stat = ImageStat.Stat(edges)
+    median = tuple(map(int, stat.median))
+    return median
+
+def load_and_process_images(image_files, input_dir, resize_method="None", width=None, height=None):
     """Utility function to load and process a list of images.
 
     Args:
@@ -140,23 +165,61 @@ def load_and_process_images(image_files, input_dir, resize_method="None", w=None
             img = img.point(lambda i: i * (1 / 255))
         img = img.convert("RGB")
 
-        if w is None and h is None:
+        if width is None and height is None:
-            w, h = img.size[0], img.size[1]
+            width, height = img.size[0], img.size[1]
 
         # Resize image to first image
-        if img.size[0] != w or img.size[1] != h:
+        if img.size[0] != width or img.size[1] != height:
+            """ code partially borrowed from https://github.com/kijai/ComfyUI-KJNodes """
             if resize_method == "Stretch":
-                img = img.resize((w, h), Image.Resampling.LANCZOS)
+                img = img.resize((width, height), Image.Resampling.LANCZOS)
-            elif resize_method == "Crop":
+                
-                img = img.crop((0, 0, w, h))
+            img_width, img_height = img.size
+            aspect_ratio = img_width / img_height
+            target_ratio = width / height
+
+            if resize_method == "Crop":
+                # Calculate dimensions for center crop
+                if aspect_ratio > target_ratio:
+                    # Image is wider - crop width
+                    new_width = int(height * aspect_ratio)
+                    img = img.resize((new_width, height), Image.Resampling.LANCZOS)
+                    left = (new_width - width) // 2
+                    img = img.crop((left, 0, left + width, height))
+                else:
+                    # Image is taller - crop height
+                    new_height = int(width / aspect_ratio)
+                    img = img.resize((width, new_height), Image.Resampling.LANCZOS)
+                    top = (new_height - height) // 2
+                    img = img.crop((0, top, width, top + height))
+
             elif resize_method == "Pad":
-                img = img.resize((w, h), Image.Resampling.LANCZOS)
+                pad_color = get_edge_color(img)
+                # Calculate dimensions for padding
+                if aspect_ratio > target_ratio:
+                    # Image is wider - pad height
+                    new_height = int(width / aspect_ratio)
+                    resized = img.resize((width, new_height), Image.Resampling.LANCZOS)
+                    padding = (height - new_height) // 2
+                    padded = Image.new('RGB', (width, height), pad_color)
+                    padded.paste(resized, (0, padding))
+                    img = padded
+                else:
+                    # Image is taller - pad width
+                    new_width = int(height * aspect_ratio)
+                    resized = img.resize((new_width, height), Image.Resampling.LANCZOS)
+                    padding = (width - new_width) // 2
+                    padded = Image.new('RGB', (width, height), pad_color)
+                    padded.paste(resized, (padding, 0))
+                    img = padded
+                    
             elif resize_method == "None":
                 raise ValueError(
                     "Your input image size does not match the first image in the dataset. Either select a valid resize method or use the same size for all images."
                 )
 
-        img_array = np.array(img).astype(np.float32) / 255.0
+        img_array = np.array(img).astype(np.float32)
+        img_array = img_array / np.float32(255.0)
         img_tensor = torch.from_numpy(img_array)[None,]
         output_images.append(img_tensor)