update grid_img display

node.py

@@ -97,8 +97,6 @@ class Eden_LoRa_trainer:
                     config, output_save_dir = e.value  # Capture the return value
                     break
 
-        validation_grid_img_path = os.path.join(output_save_dir, "validation_grid.jpg")
-
         attributes = {}
         attributes['grid_prompts'] = config.training_attributes["validation_prompts"]
         attributes['job_time_seconds'] = config.job_time
@@ -116,11 +114,22 @@ class Eden_LoRa_trainer:
             else:
                 lora_path = path
 
-        # Load the grid image:
-        grid_image = Image.open(validation_grid_img_path)
-        grid_image = np.array(grid_image).astype(np.float32) / 255.0
-        grid_image = torch.from_numpy(grid_image)[None,]
+        # Load the grid images:
+        grid_images = []
+        grid_dir = os.path.dirname(output_save_dir)
+        for f in os.listdir(grid_dir):
+            if "validation_grid" in f:
+                grid_image = Image.open(os.path.join(grid_dir, f))
+                grid_image = np.array(grid_image).astype(np.float32) / 255.0
+                grid_image = torch.from_numpy(grid_image)
+                grid_images.append(grid_image)
+        
+        grid_images = torch.stack(grid_images)
+        
+        # Make sure that grid_images always has 4 dimensions:
+        if len(grid_images.shape) == 3:
+            grid_images = grid_images.unsqueeze(0)
 
         final_msg = f"LoRa trained in {config.job_time/60:.1f} minutes. Files saved at {output_save_dir}"
 
-        return (grid_image, lora_path, embedding_path, final_msg)
+        return (grid_images, lora_path, embedding_path, final_msg)

train_configs/training_args_face_sdxl.json

@@ -1,5 +1,5 @@
 {
-    "name": "gene_sdxl",
+    "name": "xander_sdxl",
     "sd_model_version": "sdxl",
     "lora_training_urls": "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/xander.zip",
     "concept_mode": "face",