Refactor transform functions and update node mappers

- Moved and redefined transform functions for KSampler, EmptyLatentImage, CLIPTextEncode, and FluxGuidance to improve organization and maintainability.
- Updated NODE_MAPPERS to include new input tracking for clip_skip in KSampler and added new transform functions for LatentUpscale and CLIPSetLastLayer.
- Enhanced the transform_sampler_custom_advanced function to handle clip_skip extraction from model inputs.

py/workflow/ext/comfyui_core.py

@@ -94,6 +94,7 @@ def transform_sampler_custom_advanced(inputs: Dict) -> Dict:
         if "model" in guider and isinstance(guider["model"], dict):
             result["checkpoint"] = guider["model"].get("checkpoint", "")
             result["loras"] = guider["model"].get("loras", "")
+            result["clip_skip"] = str(int(guider["model"].get("clip_skip", "-1")) * -1)
     
     # Extract dimensions from latent_image
     if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
@@ -107,6 +108,73 @@ def transform_sampler_custom_advanced(inputs: Dict) -> Dict:
     
     return result
 
+def transform_ksampler(inputs: Dict) -> Dict:
+    """Transform function for KSampler nodes"""
+    result = {
+        "seed": str(inputs.get("seed", "")),
+        "steps": str(inputs.get("steps", "")),
+        "cfg": str(inputs.get("cfg", "")),
+        "sampler": inputs.get("sampler_name", ""),
+        "scheduler": inputs.get("scheduler", ""),
+    }
+    
+    # Process positive prompt
+    if "positive" in inputs:
+        result["prompt"] = inputs["positive"]
+    
+    # Process negative prompt
+    if "negative" in inputs:
+        result["negative_prompt"] = inputs["negative"]
+    
+    # Get dimensions from latent image
+    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
+        width = inputs["latent_image"].get("width", 0)
+        height = inputs["latent_image"].get("height", 0)
+        if width and height:
+            result["size"] = f"{width}x{height}"
+    
+    # Add clip_skip if present
+    if "clip_skip" in inputs:
+        result["clip_skip"] = str(inputs.get("clip_skip", ""))
+
+    # Add guidance if present
+    if "guidance" in inputs:
+        result["guidance"] = str(inputs.get("guidance", ""))
+
+    # Add model if present
+    if "model" in inputs:
+        result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
+        result["loras"] = inputs.get("model", {}).get("loras", "")
+        result["clip_skip"] = str(inputs.get("model", {}).get("clip_skip", -1) * -1)
+        
+    return result
+
+def transform_empty_latent(inputs: Dict) -> Dict:
+    """Transform function for EmptyLatentImage nodes"""
+    width = inputs.get("width", 0)
+    height = inputs.get("height", 0)
+    return {"width": width, "height": height, "size": f"{width}x{height}"}
+
+def transform_clip_text(inputs: Dict) -> Any:
+    """Transform function for CLIPTextEncode nodes"""
+    return inputs.get("text", "")
+
+def transform_flux_guidance(inputs: Dict) -> Dict:
+    """Transform function for FluxGuidance nodes"""
+    result = {}
+    
+    if "guidance" in inputs:
+        result["guidance"] = inputs["guidance"]
+    
+    if "conditioning" in inputs:
+        conditioning = inputs["conditioning"]
+        if isinstance(conditioning, str):
+            result["prompt"] = conditioning
+        else:
+            result["prompt"] = "Unknown prompt"
+    
+    return result
+
 def transform_unet_loader(inputs: Dict) -> Dict:
     """Transform function for UNETLoader node"""
     unet_name = inputs.get("unet_name", "")
@@ -117,6 +185,27 @@ def transform_checkpoint_loader(inputs: Dict) -> Dict:
     ckpt_name = inputs.get("ckpt_name", "")
     return {"checkpoint": ckpt_name} if ckpt_name else {}
 
+def transform_latent_upscale_by(inputs: Dict) -> Dict:
+    """Transform function for LatentUpscaleBy node"""
+    result = {}
+    
+    width = inputs["samples"].get("width", 0) * inputs["scale_by"]
+    height = inputs["samples"].get("height", 0) * inputs["scale_by"]
+    result["width"] = width
+    result["height"] = height
+    result["size"] = f"{width}x{height}"
+    
+    return result
+
+def transform_clip_set_last_layer(inputs: Dict) -> Dict:
+    """Transform function for CLIPSetLastLayer node"""
+    result = {}
+    
+    if "stop_at_clip_layer" in inputs:
+        result["clip_skip"] = inputs["stop_at_clip_layer"]
+    
+    return result
+
 # =============================================================================
 # Node Mapper Definitions
 # =============================================================================
@@ -128,6 +217,31 @@ NODE_MAPPERS_EXT = {
         "inputs_to_track": ["noise", "guider", "sampler", "sigmas", "latent_image"],
         "transform_func": transform_sampler_custom_advanced
     },
+    "KSampler": {
+        "inputs_to_track": [
+            "seed", "steps", "cfg", "sampler_name", "scheduler", 
+            "denoise", "positive", "negative", "latent_image",
+            "model", "clip_skip"
+        ],
+        "transform_func": transform_ksampler
+    },
+    # ComfyUI core nodes
+    "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
+    },
     "RandomNoise": {
         "inputs_to_track": ["noise_seed"],
         "transform_func": transform_random_noise
@@ -156,8 +270,16 @@ NODE_MAPPERS_EXT = {
         "inputs_to_track": ["ckpt_name"],
         "transform_func": transform_checkpoint_loader
     },
-    "CheckpointLoader": {
+    "LatentUpscale": {
-        "inputs_to_track": ["ckpt_name"],
+        "inputs_to_track": ["width", "height"],
-        "transform_func": transform_checkpoint_loader
+        "transform_func": transform_empty_latent
+    },
+    "LatentUpscaleBy": {
+        "inputs_to_track": ["samples", "scale_by"],
+        "transform_func": transform_latent_upscale_by
+    },
+    "CLIPSetLastLayer": {
+        "inputs_to_track": ["clip", "stop_at_clip_layer"],
+        "transform_func": transform_clip_set_last_layer
     }
 } 

py/workflow/mappers.py

@@ -97,55 +97,7 @@ def process_node(node_id: str, node_data: Dict, workflow: Dict, parser: 'Workflo
 # Transform Functions
 # =============================================================================
 
-def transform_ksampler(inputs: Dict) -> Dict:
-    """Transform function for KSampler nodes"""
-    result = {
-        "seed": str(inputs.get("seed", "")),
-        "steps": str(inputs.get("steps", "")),
-        "cfg": str(inputs.get("cfg", "")),
-        "sampler": inputs.get("sampler_name", ""),
-        "scheduler": inputs.get("scheduler", ""),
-    }
 
-    # Process positive prompt
-    if "positive" in inputs:
-        result["prompt"] = inputs["positive"]
-    
-    # Process negative prompt
-    if "negative" in inputs:
-        result["negative_prompt"] = inputs["negative"]
-    
-    # Get dimensions from latent image
-    if "latent_image" in inputs and isinstance(inputs["latent_image"], dict):
-        width = inputs["latent_image"].get("width", 0)
-        height = inputs["latent_image"].get("height", 0)
-        if width and height:
-            result["size"] = f"{width}x{height}"
-    
-    # Add clip_skip if present
-    if "clip_skip" in inputs:
-        result["clip_skip"] = str(inputs.get("clip_skip", ""))
-
-    # Add guidance if present
-    if "guidance" in inputs:
-        result["guidance"] = str(inputs.get("guidance", ""))
-
-    # Add model if present
-    if "model" in inputs:
-        result["checkpoint"] = inputs.get("model", {}).get("checkpoint", "")
-        result["loras"] = inputs.get("model", {}).get("loras", "")
-        
-    return result
-
-def transform_empty_latent(inputs: Dict) -> Dict:
-    """Transform function for EmptyLatentImage nodes"""
-    width = inputs.get("width", 0)
-    height = inputs.get("height", 0)
-    return {"width": width, "height": height, "size": f"{width}x{height}"}
-
-def transform_clip_text(inputs: Dict) -> Any:
-    """Transform function for CLIPTextEncode nodes"""
-    return inputs.get("text", "")
 
 def transform_lora_loader(inputs: Dict) -> Dict:
     """Transform function for LoraLoader nodes"""
@@ -182,6 +134,9 @@ def transform_lora_loader(inputs: Dict) -> Dict:
         "loras": " ".join(lora_texts)
     }
 
+    if "clip" in inputs:
+        result["clip_skip"] = inputs["clip"].get("clip_skip", "-1")
+    
     return result
 
 def transform_lora_stacker(inputs: Dict) -> Dict:
@@ -241,56 +196,16 @@ def transform_trigger_word_toggle(inputs: Dict) -> str:
     
     return ", ".join(active_words)
 
-def transform_flux_guidance(inputs: Dict) -> Dict:
-    """Transform function for FluxGuidance nodes"""
-    result = {}
-    
-    if "guidance" in inputs:
-        result["guidance"] = inputs["guidance"]
-    
-    if "conditioning" in inputs:
-        conditioning = inputs["conditioning"]
-        if isinstance(conditioning, str):
-            result["prompt"] = conditioning
-        else:
-            result["prompt"] = "Unknown prompt"
-    
-    return result
-
 # =============================================================================
 # Node Mapper Definitions
 # =============================================================================
 
 # 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": ["model", "loras", "lora_stack"],
+        "inputs_to_track": ["model", "clip", "loras", "lora_stack"],
         "transform_func": transform_lora_loader
     },
     "Lora Stacker (LoraManager)": {