Implement KJNodes extension with new mappers and transform functions

- Added KJNodes mappers for JoinStrings, StringConstantMultiline, and EmptyLatentImagePresets.
- Introduced transform functions to handle string joining, string constants, and dimension extraction with optional inversion.
- Registered new mappers and logged successful registration for better traceability.

py/workflow/ext/kjnodes.py

@@ -0,0 +1,81 @@
+"""
+KJNodes mappers extension for ComfyUI workflow parsing
+"""
+import logging
+import re
+from typing import Dict, Any
+
+logger = logging.getLogger(__name__)
+
+# Import the mapper registration functions from the parent module
+from workflow.mappers import create_mapper, register_mapper
+
+# =============================================================================
+# Transform Functions
+# =============================================================================
+
+def transform_join_strings(inputs: Dict) -> str:
+    """Transform function for JoinStrings nodes"""
+    string1 = inputs.get("string1", "")
+    string2 = inputs.get("string2", "")
+    delimiter = inputs.get("delimiter", "")
+    return f"{string1}{delimiter}{string2}"
+
+def transform_string_constant(inputs: Dict) -> str:
+    """Transform function for StringConstant nodes"""
+    return inputs.get("string", "")
+
+def transform_empty_latent_presets(inputs: Dict) -> Dict:
+    """Transform function for EmptyLatentImagePresets nodes"""
+    dimensions = inputs.get("dimensions", "")
+    invert = inputs.get("invert", False)
+    
+    # Extract width and height from dimensions string
+    # Expected format: "width x height (ratio)" or similar
+    width = 0
+    height = 0
+    
+    if dimensions:
+        # Try to extract dimensions using regex
+        match = re.search(r'(\d+)\s*x\s*(\d+)', dimensions)
+        if match:
+            width = int(match.group(1))
+            height = int(match.group(2))
+    
+    # If invert is True, swap width and height
+    if invert and width and height:
+        width, height = height, width
+    
+    return {"width": width, "height": height, "size": f"{width}x{height}"}
+
+# =============================================================================
+# Register Mappers
+# =============================================================================
+
+# Define the mappers for KJNodes
+KJNODES_MAPPERS = {
+    "JoinStrings": {
+        "inputs_to_track": ["string1", "string2", "delimiter"],
+        "transform_func": transform_join_strings
+    },
+    "StringConstantMultiline": {
+        "inputs_to_track": ["string"],
+        "transform_func": transform_string_constant
+    },
+    "EmptyLatentImagePresets": {
+        "inputs_to_track": ["dimensions", "invert", "batch_size"],
+        "transform_func": transform_empty_latent_presets
+    }
+}
+
+# Register all KJNodes mappers
+for node_type, config in KJNODES_MAPPERS.items():
+    mapper = create_mapper(
+        node_type=node_type,
+        inputs_to_track=config["inputs_to_track"],
+        transform_func=config["transform_func"]
+    )
+    register_mapper(mapper)
+    logger.info(f"Registered KJNodes mapper for node type: {node_type}")
+
+logger.info(f"Loaded KJNodes extension with {len(KJNODES_MAPPERS)} mappers") 

py/workflow/mappers.py

@@ -52,6 +52,7 @@ def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'Workflo
     mapper = get_mapper(node_type)
     
     if not mapper:
+        logger.warning(f"No mapper found for node type: {node_type}")
         return None
         
     result = {}
@@ -93,14 +94,11 @@ def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'Workflo
         return result
 
 # =============================================================================
-# Default Mapper Definitions
+# Transform Functions
 # =============================================================================
 
-def register_default_mappers() -> None:
+def transform_ksampler(inputs: Dict) -> Dict:
-    """Register all default mappers"""
+    """Transform function for KSampler nodes"""
-    
-    # KSampler mapper
-    def transform_ksampler(inputs: Dict) -> Dict:
     result = {
         "seed": str(inputs.get("seed", "")),
         "steps": str(inputs.get("steps", "")),
@@ -130,45 +128,18 @@ def register_default_mappers() -> None:
         
     return result
 
-    register_mapper(create_mapper(
+def transform_empty_latent(inputs: Dict) -> Dict:
-        node_type="KSampler",
+    """Transform function for EmptyLatentImage nodes"""
-        inputs_to_track=["seed", "steps", "cfg", "sampler_name", "scheduler", 
-                        "denoise", "positive", "negative", "latent_image",
-                        "model", "clip_skip"],
-        transform_func=transform_ksampler
-    ))
-    
-    # EmptyLatentImage mapper
-    def transform_empty_latent(inputs: Dict) -> Dict:
     width = inputs.get("width", 0)
     height = inputs.get("height", 0)
     return {"width": width, "height": height, "size": f"{width}x{height}"}
 
-    register_mapper(create_mapper(
+def transform_clip_text(inputs: Dict) -> Any:
-        node_type="EmptyLatentImage",
+    """Transform function for CLIPTextEncode nodes"""
-        inputs_to_track=["width", "height", "batch_size"],
-        transform_func=transform_empty_latent
-    ))
-    
-    # SD3LatentImage mapper - reuses same transform function as EmptyLatentImage
-    register_mapper(create_mapper(
-        node_type="EmptySD3LatentImage",
-        inputs_to_track=["width", "height", "batch_size"],
-        transform_func=transform_empty_latent
-    ))
-    
-    # CLIPTextEncode mapper
-    def transform_clip_text(inputs: Dict) -> Any:
     return inputs.get("text", "")
 
-    register_mapper(create_mapper(
+def transform_lora_loader(inputs: Dict) -> Dict:
-        node_type="CLIPTextEncode",
+    """Transform function for LoraLoader nodes"""
-        inputs_to_track=["text", "clip"],
-        transform_func=transform_clip_text
-    ))
-    
-    # LoraLoader mapper
-    def transform_lora_loader(inputs: Dict) -> Dict:
     loras_data = inputs.get("loras", [])
     lora_stack = inputs.get("lora_stack", {}).get("lora_stack", [])
     
@@ -199,14 +170,8 @@ def register_default_mappers() -> None:
     
     return {"loras": " ".join(lora_texts)}
 
-    register_mapper(create_mapper(
+def transform_lora_stacker(inputs: Dict) -> Dict:
-        node_type="Lora Loader (LoraManager)",
+    """Transform function for LoraStacker nodes"""
-        inputs_to_track=["loras", "lora_stack"],
-        transform_func=transform_lora_loader
-    ))
-    
-    # LoraStacker mapper
-    def transform_lora_stacker(inputs: Dict) -> Dict:
     loras_data = inputs.get("loras", [])
     result_stack = []
     
@@ -241,37 +206,8 @@ def register_default_mappers() -> None:
     
     return {"lora_stack": result_stack}
 
-    register_mapper(create_mapper(
+def transform_trigger_word_toggle(inputs: Dict) -> str:
-        node_type="Lora Stacker (LoraManager)",
+    """Transform function for TriggerWordToggle nodes"""
-        inputs_to_track=["loras", "lora_stack"],
-        transform_func=transform_lora_stacker
-    ))
-    
-    # JoinStrings mapper
-    def transform_join_strings(inputs: Dict) -> str:
-        string1 = inputs.get("string1", "")
-        string2 = inputs.get("string2", "")
-        delimiter = inputs.get("delimiter", "")
-        return f"{string1}{delimiter}{string2}"
-    
-    register_mapper(create_mapper(
-        node_type="JoinStrings",
-        inputs_to_track=["string1", "string2", "delimiter"],
-        transform_func=transform_join_strings
-    ))
-    
-    # StringConstant mapper
-    def transform_string_constant(inputs: Dict) -> str:
-        return inputs.get("string", "")
-    
-    register_mapper(create_mapper(
-        node_type="StringConstantMultiline",
-        inputs_to_track=["string"],
-        transform_func=transform_string_constant
-    ))
-    
-    # TriggerWordToggle mapper
-    def transform_trigger_word_toggle(inputs: Dict) -> str:
     toggle_data = inputs.get("toggle_trigger_words", [])
 
     if isinstance(toggle_data, dict) and "__value__" in toggle_data:
@@ -291,14 +227,8 @@ def register_default_mappers() -> None:
     
     return ", ".join(active_words)
 
-    register_mapper(create_mapper(
+def transform_flux_guidance(inputs: Dict) -> Dict:
-        node_type="TriggerWord Toggle (LoraManager)",
+    """Transform function for FluxGuidance nodes"""
-        inputs_to_track=["toggle_trigger_words"],
-        transform_func=transform_trigger_word_toggle
-    ))
-    
-    # FluxGuidance mapper
-    def transform_flux_guidance(inputs: Dict) -> Dict:
     result = {}
     
     if "guidance" in inputs:
@@ -313,11 +243,62 @@ def register_default_mappers() -> None:
     
     return result
 
-    register_mapper(create_mapper(
+# =============================================================================
-        node_type="FluxGuidance",
+# Node Mapper Definitions
-        inputs_to_track=["guidance", "conditioning"],
+# =============================================================================
-        transform_func=transform_flux_guidance
+
-    ))
+# Central definition of all supported node types and their configurations
+NODE_MAPPERS = {
+    # ComfyUI core nodes
+    "KSampler": {
+        "inputs_to_track": [
+            "seed", "steps", "cfg", "sampler_name", "scheduler", 
+            "denoise", "positive", "negative", "latent_image",
+            "model", "clip_skip"
+        ],
+        "transform_func": transform_ksampler
+    },
+    "EmptyLatentImage": {
+        "inputs_to_track": ["width", "height", "batch_size"],
+        "transform_func": transform_empty_latent
+    },
+    "EmptySD3LatentImage": {
+        "inputs_to_track": ["width", "height", "batch_size"],
+        "transform_func": transform_empty_latent
+    },
+    "CLIPTextEncode": {
+        "inputs_to_track": ["text", "clip"],
+        "transform_func": transform_clip_text
+    },
+    "FluxGuidance": {
+        "inputs_to_track": ["guidance", "conditioning"],
+        "transform_func": transform_flux_guidance
+    },
+    # LoraManager nodes
+    "Lora Loader (LoraManager)": {
+        "inputs_to_track": ["loras", "lora_stack"],
+        "transform_func": transform_lora_loader
+    },
+    "Lora Stacker (LoraManager)": {
+        "inputs_to_track": ["loras", "lora_stack"],
+        "transform_func": transform_lora_stacker
+    },
+    "TriggerWord Toggle (LoraManager)": {
+        "inputs_to_track": ["toggle_trigger_words"],
+        "transform_func": transform_trigger_word_toggle
+    }   
+}
+
+def register_default_mappers() -> None:
+    """Register all default mappers from the NODE_MAPPERS dictionary"""
+    for node_type, config in NODE_MAPPERS.items():
+        mapper = create_mapper(
+            node_type=node_type,
+            inputs_to_track=config["inputs_to_track"],
+            transform_func=config["transform_func"]
+        )
+        register_mapper(mapper)
+    logger.info(f"Registered {len(NODE_MAPPERS)} default node mappers")
 
 # =============================================================================
 # Extension Loading