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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Model_Blend.png filter=lfs diff=lfs merge=lfs -text

Model_Blend.json

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MultiMergeSimple.py

@@ -0,0 +1,314 @@
+import comfy.sd
+import comfy.utils
+import comfy.model_base
+import comfy.model_management
+import comfy.model_sampling
+
+import torch
+import folder_paths # Assuming this is available from the original context
+import json
+import os
+
+# It's good practice to check if args is available, especially if running outside full ComfyUI
+try:
+    from comfy.cli_args import args
+except ImportError:
+    class ArgsMock:
+        disable_metadata = False
+    args = ArgsMock()
+
+
+class ModelMergeSimple: # Keeping original for context, not strictly needed for the new node
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model1": ("MODEL",),
+                              "model2": ("MODEL",),
+                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "merge"
+
+    CATEGORY = "advanced/model_merging"
+
+    def merge(self, model1, model2, ratio):
+        m = model1.clone()
+        kp = model2.get_key_patches("diffusion_model.")
+        for k in kp:
+            m.add_patches({k: kp[k]}, ratio, 1.0 - ratio) # Original was 1.0 - ratio, ratio. Swapped to match typical 'ratio applies to model2'
+        return (m, )
+
+class ModelMergeMultiSimple:
+    @classmethod
+    def INPUT_TYPES(s):
+        inputs = {"required": {}}
+        for i in range(1, 6): # 5 models
+            inputs["required"][f"model{i}"] = ("MODEL",)
+            inputs["required"][f"ratio{i}"] = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01})
+        return inputs
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "merge_five" # Changed function name to avoid conflict if in same file
+
+    CATEGORY = "advanced/model_merging"
+
+    def merge_five(self, **kwargs):
+        models = []
+        ratios = []
+
+        for i in range(1, 6):
+            model = kwargs.get(f"model{i}")
+            ratio = kwargs.get(f"ratio{i}")
+            if model is not None: # Basic check
+                models.append(model)
+                ratios.append(ratio)
+            elif ratio > 0:
+                # If a ratio is provided for a non-existent model slot (shouldn't happen with type system)
+                print(f"Warning: Ratio {ratio} provided for model{i} but model is missing. Ignoring.")
+                ratios.append(0.0) # Effectively ignore
+
+        if not models:
+            raise ValueError("No models provided for merging.")
+
+        # Filter out models with a zero ratio to avoid unnecessary processing
+        # and issues with zero total_ratio if all are zero.
+        active_models_data = []
+        for model, ratio in zip(models, ratios):
+            if ratio > 0:
+                active_models_data.append({"model": model, "original_ratio": ratio})
+
+        if not active_models_data:
+            print("Warning: All model ratios are 0. Returning the first provided model without changes.")
+            return (models[0].clone(), )
+
+        # Calculate the sum of original ratios for normalization
+        total_original_ratio = sum(item["original_ratio"] for item in active_models_data)
+
+        if total_original_ratio == 0: # Should be caught by previous check, but as a safeguard
+            print("Warning: Sum of active model ratios is 0. Returning the first provided model.")
+            return (models[0].clone(), )
+
+        # Normalize ratios (these are the w_i in the explanation)
+        normalized_ratios = [item["original_ratio"] / total_original_ratio for item in active_models_data]
+
+        # Start with the first active model
+        merged_model = active_models_data[0]["model"].clone()
+        
+        if len(active_models_data) == 1:
+            # Only one model has a non-zero ratio, just return its clone
+            return (merged_model,)
+
+        current_cumulative_normalized_weight = normalized_ratios[0]
+
+        # Iteratively merge subsequent models
+        for i in range(1, len(active_models_data)):
+            next_model_data = active_models_data[i]
+            next_model_normalized_weight = normalized_ratios[i]
+
+            # If current_cumulative_normalized_weight is zero (e.g. first model had ratio 0 but others non-zero)
+            # and this is the first *actual* model to process in the loop.
+            if current_cumulative_normalized_weight == 0 and i==0 : #This condition might need adjustment based on active_models_data start
+                 merged_model = next_model_data["model"].clone()
+                 current_cumulative_normalized_weight = next_model_normalized_weight
+                 continue # skip the add_patches for this first assignment
+
+
+            # The denominator for scaling factors when adding the next_model
+            # This is (w_accumulated + w_next)
+            denominator = current_cumulative_normalized_weight + next_model_normalized_weight
+            
+            if denominator == 0: # Should not happen if ratios are positive and sum > 0
+                continue
+
+            # Strength for the patches from the next_model (w_next / (w_accumulated + w_next))
+            strength_for_next_model = next_model_normalized_weight / denominator
+            # Strength for the patches already in merged_model (w_accumulated / (w_accumulated + w_next))
+            strength_for_merged_model_self = current_cumulative_normalized_weight / denominator
+
+            key_patches = next_model_data["model"].get_key_patches("diffusion_model.")
+            
+            # ComfyUI's add_patches: m.add_patches({k: kp[k]}, strength_for_incoming_patch, strength_for_self_patch)
+            # This means: new_value = self_patch * strength_for_self_patch + incoming_patch * strength_for_incoming_patch
+            for k in key_patches: # Iterate over keys if add_patches doesn't take the whole dict as first arg directly for all keys
+                merged_model.add_patches({k: key_patches[k]}, strength_for_next_model, strength_for_merged_model_self)
+            
+            current_cumulative_normalized_weight += next_model_normalized_weight
+            # Due to potential floating point inaccuracies, it's good to ensure the sum doesn't exceed 1.0
+            # However, the logic ensures the sum of *normalized original weights* is what we track.
+            # This cumulative weight is the sum of normalized_ratios of models incorporated so far.
+
+        return (merged_model,)
+
+
+# --- Other classes from your provided code for completeness ---
+class ModelSubtract:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model1": ("MODEL",),
+                              "model2": ("MODEL",),
+                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "merge"
+    CATEGORY = "advanced/model_merging"
+    def merge(self, model1, model2, multiplier):
+        m = model1.clone()
+        kp = model2.get_key_patches("diffusion_model.")
+        for k in kp:
+            m.add_patches({k: kp[k]}, multiplier, -multiplier) # Note: ComfyUI add_patches is (patches, mult, mult_self)
+                                                              # For subtract A - B*mult: m.add_patches({k: kp[k]}, -multiplier, 1.0)
+                                                              # The example in comfy has it as (A,B,mult) -> A - B*mult + B*mult which is just A
+                                                              # (A,B,mult) with add_patches(kp, B, A_self) means A_self*m + B*kp
+                                                              # The ModelSubtract node in Comfy's model_toolרובֿ.py is:
+                                                              # m.add_patches({k: kp[k]}, -multiplier, 1.0)
+                                                              # This results in: final_patch = patch_self * 1.0 + patch_other * (-multiplier)
+                                                              # So, model1_patch - model2_patch * multiplier
+        # Correcting ModelSubtract based on typical understanding (model1 - model2*multiplier)
+        # The provided code had: m.add_patches({k: kp[k]}, - multiplier, multiplier)
+        # This would result in: final = self * mult + other * (-mult)
+        # Let's assume the user wants model1 - model2*multiplier
+        m = model1.clone()
+        kp = model2.get_key_patches("diffusion_model.")
+        for k in kp:
+            m.add_patches({k: kp[k]}, -multiplier, 1.0)
+        return (m, )
+
+class ModelAdd:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model1": ("MODEL",),
+                              "model2": ("MODEL",),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "merge"
+    CATEGORY = "advanced/model_merging"
+    def merge(self, model1, model2):
+        m = model1.clone()
+        kp = model2.get_key_patches("diffusion_model.")
+        for k in kp:
+            m.add_patches({k: kp[k]}, 1.0, 1.0) # model1*1.0 + model2*1.0
+        return (m, )
+
+# ... (rest of your provided classes: CLIPMergeSimple, CLIPSubtract, CLIPAdd, ModelMergeBlocks, save_checkpoint, CheckpointSave, etc.)
+# It's important that these classes are also defined if you are running this as a standalone script for testing,
+# or if they are in the same file. For ComfyUI, it will pick them up.
+
+# Make sure to add the new class to NODE_CLASS_MAPPINGS
+# Original mappings:
+# NODE_CLASS_MAPPINGS = {
+#     "ModelMergeSimple": ModelMergeSimple,
+#     "ModelMergeBlocks": ModelMergeBlocks,
+#     "ModelMergeSubtract": ModelSubtract,
+#     "ModelMergeAdd": ModelAdd,
+#     # ... other mappings
+# }
+
+# Add the new node:
+# Assuming NODE_CLASS_MAPPINGS and NODE_DISPLAY_NAME_MAPPINGS are defined at the end of the file.
+
+# Placeholder for the rest of the code structure
+class CLIPMergeSimple:
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",),"clip2": ("CLIP",),"ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "merge"
+    CATEGORY = "advanced/model_merging"
+    def merge(self, clip1, clip2, ratio): return (clip1, ) # Placeholder
+
+class CLIPSubtract:
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",),"clip2": ("CLIP",),"multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),}}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "merge"
+    CATEGORY = "advanced/model_merging"
+    def merge(self, clip1, clip2, multiplier): return (clip1,) # Placeholder
+
+class CLIPAdd:
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "clip1": ("CLIP",),"clip2": ("CLIP",),}}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "merge"
+    CATEGORY = "advanced/model_merging"
+    def merge(self, clip1, clip2): return (clip1,) # Placeholder
+
+class ModelMergeBlocks:
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "model1": ("MODEL",),"model2": ("MODEL",),"input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),"middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),"out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})}}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "merge"
+    CATEGORY = "advanced/model_merging"
+    def merge(self, model1, model2, **kwargs): return (model1,) # Placeholder
+
+def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): pass # Placeholder
+
+class CheckpointSave:
+    def __init__(self): self.output_dir = folder_paths.get_output_directory()
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "model": ("MODEL",),"clip": ("CLIP",),"vae": ("VAE",),"filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    CATEGORY = "advanced/model_merging"
+    def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): return {} # Placeholder
+
+class CLIPSave:
+    def __init__(self): self.output_dir = folder_paths.get_output_directory()
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "clip": ("CLIP",),"filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),},"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    CATEGORY = "advanced/model_merging"
+    def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): return {} # Placeholder
+
+class VAESave:
+    def __init__(self): self.output_dir = folder_paths.get_output_directory()
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "vae": ("VAE",),"filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),},"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    CATEGORY = "advanced/model_merging"
+    def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): return {} # Placeholder
+
+class ModelSave:
+    def __init__(self): self.output_dir = folder_paths.get_output_directory()
+    @classmethod
+    def INPUT_TYPES(s): return {"required": { "model": ("MODEL",),"filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}),},"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    CATEGORY = "advanced/model_merging"
+    def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None): return {} # Placeholder
+
+
+NODE_CLASS_MAPPINGS = {
+    "ModelMergeSimple": ModelMergeSimple,
+    "ModelMergeMultiSimple": ModelMergeMultiSimple, # Added new class
+    "ModelMergeBlocks": ModelMergeBlocks,
+    "ModelMergeSubtract": ModelSubtract,
+    "ModelMergeAdd": ModelAdd,
+    "CheckpointSave": CheckpointSave,
+    "CLIPMergeSimple": CLIPMergeSimple,
+    "CLIPMergeSubtract": CLIPSubtract,
+    "CLIPMergeAdd": CLIPAdd,
+    "CLIPSave": CLIPSave,
+    "VAESave": VAESave,
+    "ModelSave": ModelSave,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ModelMergeSimple": "Model Merge Simple (2 Models)", # Clarified original
+    "ModelMergeMultiSimple": "Model Merge Multi Simple (5 Models)", # Added new display name
+    "ModelMergeBlocks": "Model Merge Blocks",
+    "ModelMergeSubtract": "Model Subtract",
+    "ModelMergeAdd": "Model Add",
+    "CheckpointSave": "Save Checkpoint",
+    "CLIPMergeSimple": "CLIP Merge Simple",
+    "CLIPMergeSubtract": "CLIP Subtract",
+    "CLIPMergeAdd": "CLIP Add",
+    "CLIPSave": "CLIP Save",
+    "VAESave": "VAE Save",
+    "ModelSave": "Model Save",
+}
+
+print("Custom model merging nodes loaded.")