feat: Enhance KSamplerAdvancedExtractor to include additional sampling parameters and update metadata processing

py/metadata_collector/metadata_processor.py

@@ -11,15 +11,28 @@ class MetadataProcessor:
         primary_sampler = None
         primary_sampler_id = None
         
+        # First, check for KSamplerAdvanced with add_noise="enable"
         for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
             parameters = sampler_info.get("parameters", {})
-            denoise = parameters.get("denoise")
+            add_noise = parameters.get("add_noise")
             
-            # If denoise is 1.0, this is likely the primary sampler
-            if denoise == 1.0 or denoise == 1:
+            # If add_noise is "enable", this is likely the primary sampler for KSamplerAdvanced
+            if add_noise == "enable":
                 primary_sampler = sampler_info
                 primary_sampler_id = node_id
                 break
+        
+        # If no KSamplerAdvanced found, fall back to traditional KSampler with denoise=1
+        if primary_sampler is None:
+            for node_id, sampler_info in metadata.get(SAMPLING, {}).items():
+                parameters = sampler_info.get("parameters", {})
+                denoise = parameters.get("denoise")
+                
+                # If denoise is 1.0, this is likely the primary sampler
+                if denoise == 1.0 or denoise == 1:
+                    primary_sampler = sampler_info
+                    primary_sampler_id = node_id
+                    break
                 
         return primary_sampler_id, primary_sampler
     
@@ -109,6 +122,7 @@ class MetadataProcessor:
             "cfg_scale": None,
             "guidance": None,  # Add guidance parameter
             "sampler": None,
+            "scheduler": None,
             "checkpoint": None,
             "loras": "",
             "size": None,
@@ -129,10 +143,12 @@ class MetadataProcessor:
         if primary_sampler:
             # Extract sampling parameters
             sampling_params = primary_sampler.get("parameters", {})
-            params["seed"] = sampling_params.get("seed")
+            # Handle both seed and noise_seed
+            params["seed"] = sampling_params.get("seed") if sampling_params.get("seed") is not None else sampling_params.get("noise_seed")
             params["steps"] = sampling_params.get("steps")
             params["cfg_scale"] = sampling_params.get("cfg")
             params["sampler"] = sampling_params.get("sampler_name")
+            params["scheduler"] = sampling_params.get("scheduler")
             
             # Trace connections from the primary sampler
             if prompt and primary_sampler_id:

py/metadata_collector/node_extractors.py

@@ -85,6 +85,43 @@ class SamplerExtractor(NodeMetadataExtractor):
                         "node_id": node_id
                     }
 
+class KSamplerAdvancedExtractor(NodeMetadataExtractor):
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+            
+        sampling_params = {}
+        for key in ["noise_seed", "steps", "cfg", "sampler_name", "scheduler", "add_noise"]:
+            if key in inputs:
+                sampling_params[key] = inputs[key]
+                
+        metadata[SAMPLING][node_id] = {
+            "parameters": sampling_params,
+            "node_id": node_id
+        }
+        
+        # Extract latent image dimensions if available
+        if "latent_image" in inputs and inputs["latent_image"] is not None:
+            latent = inputs["latent_image"]
+            if isinstance(latent, dict) and "samples" in latent:
+                # Extract dimensions from latent tensor
+                samples = latent["samples"]
+                if hasattr(samples, "shape") and len(samples.shape) >= 3:
+                    # Correct shape interpretation: [batch_size, channels, height/8, width/8]
+                    # Multiply by 8 to get actual pixel dimensions
+                    height = int(samples.shape[2] * 8)
+                    width = int(samples.shape[3] * 8)
+                    
+                    if SIZE not in metadata:
+                        metadata[SIZE] = {}
+                        
+                    metadata[SIZE][node_id] = {
+                        "width": width,
+                        "height": height,
+                        "node_id": node_id
+                    }
+
 class LoraLoaderExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -201,14 +238,20 @@ class UNETLoaderExtractor(NodeMetadataExtractor):
         
 # Registry of node-specific extractors
 NODE_EXTRACTORS = {
-    "CheckpointLoaderSimple": CheckpointLoaderExtractor,
-    "CLIPTextEncode": CLIPTextEncodeExtractor,
+    # Sampling
     "KSampler": SamplerExtractor,
-    "LoraLoader": LoraLoaderExtractor,
-    "EmptyLatentImage": ImageSizeExtractor,
-    "LoraManagerLoader": LoraLoaderManagerExtractor,
+    "KSamplerAdvanced": KSamplerAdvancedExtractor,  # Add KSamplerAdvanced
     "SamplerCustomAdvanced": SamplerExtractor,  # Add SamplerCustomAdvanced
+    # Loaders
+    "CheckpointLoaderSimple": CheckpointLoaderExtractor,
     "UNETLoader": UNETLoaderExtractor,          # Updated to use dedicated extractor
+    "LoraLoader": LoraLoaderExtractor,
+    "LoraManagerLoader": LoraLoaderManagerExtractor,
+    # Conditioning
+    "CLIPTextEncode": CLIPTextEncodeExtractor,
+    # Latent
+    "EmptyLatentImage": ImageSizeExtractor,
+    # Flux
     "FluxGuidance": FluxGuidanceExtractor,      # Add FluxGuidance
     # Add other nodes as needed
 }