feat: Add checkpoint hash retrieval and enhance metadata formatting in SaveImage class

py/nodes/save_image.py

@@ -5,6 +5,7 @@ import re
 import numpy as np
 import folder_paths # type: ignore
 from ..services.lora_scanner import LoraScanner
+from ..services.checkpoint_scanner import CheckpointScanner
 from ..metadata_collector.metadata_processor import MetadataProcessor
 from ..metadata_collector import get_metadata
 from PIL import Image, PngImagePlugin
@@ -60,6 +61,27 @@ class SaveImage:
                 return item.get('sha256')
         return None
 
+    async def get_checkpoint_hash(self, checkpoint_path):
+        """Get the checkpoint hash from cache"""
+        scanner = await CheckpointScanner.get_instance()
+        cache = await scanner.get_cached_data()
+        
+        if not checkpoint_path:
+            return None
+            
+        # Extract basename without extension
+        checkpoint_name = os.path.basename(checkpoint_path)
+        checkpoint_name = os.path.splitext(checkpoint_name)[0]
+        
+        # Normalize path separators for comparison
+        normalized_path = checkpoint_path.replace('\\', '/')
+        
+        for item in cache.raw_data:
+            if item.get('file_name') == checkpoint_name and item.get('file_path').endswith(normalized_path):
+                return item.get('sha256')
+                
+        return None
+
     async def format_metadata(self, metadata_dict):
         """Format metadata in the requested format similar to userComment example"""
         if not metadata_dict:
@@ -102,6 +124,10 @@ class SaveImage:
         if 'steps' in metadata_dict:
             params.append(f"Steps: {metadata_dict.get('steps')}")
         
+        # Combine sampler and scheduler information
+        sampler_name = None
+        scheduler_name = None
+        
         if 'sampler' in metadata_dict:
             sampler = metadata_dict.get('sampler')
             # Convert ComfyUI sampler names to user-friendly names
@@ -123,7 +149,6 @@ class SaveImage:
                 'ddim': 'DDIM'
             }
             sampler_name = sampler_mapping.get(sampler, sampler)
-            params.append(f"Sampler: {sampler_name}")
         
         if 'scheduler' in metadata_dict:
             scheduler = metadata_dict.get('scheduler')
@@ -135,10 +160,18 @@ class SaveImage:
                 'sgm_quadratic': 'SGM Quadratic'
             }
             scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-            params.append(f"Schedule type: {scheduler_name}")
         
-        # CFG scale (cfg_scale in metadata_dict)
-        if 'cfg_scale' in metadata_dict:
+        # Add combined sampler and scheduler information
+        if sampler_name:
+            if scheduler_name:
+                params.append(f"Sampler: {sampler_name} {scheduler_name}")
+            else:
+                params.append(f"Sampler: {sampler_name}")
+        
+        # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
+        if 'guidance' in metadata_dict:
+            params.append(f"CFG scale: {metadata_dict.get('guidance')}")
+        elif 'cfg_scale' in metadata_dict:
             params.append(f"CFG scale: {metadata_dict.get('cfg_scale')}")
         elif 'cfg' in metadata_dict:
             params.append(f"CFG scale: {metadata_dict.get('cfg')}")
@@ -156,17 +189,19 @@ class SaveImage:
             # Ensure checkpoint is a string before processing
             checkpoint = metadata_dict.get('checkpoint')
             if checkpoint is not None:
-                # Handle both string and other types safely
-                if isinstance(checkpoint, str):
-                    # Extract basename without path
-                    checkpoint = os.path.basename(checkpoint)
-                    # Remove extension if present
-                    checkpoint = os.path.splitext(checkpoint)[0]
-                else:
-                    # Convert non-string to string
-                    checkpoint = str(checkpoint)
+                # Get model hash
+                model_hash = await self.get_checkpoint_hash(checkpoint)
                 
-                params.append(f"Model: {checkpoint}")
+                # Extract basename without path
+                checkpoint_name = os.path.basename(checkpoint)
+                # Remove extension if present
+                checkpoint_name = os.path.splitext(checkpoint_name)[0]
+                
+                # Add model hash if available
+                if model_hash:
+                    params.append(f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}")
+                else:
+                    params.append(f"Model: {checkpoint_name}")
         
         # Add LoRA hashes if available
         if lora_hashes: