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lora_stacks.py

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+import os
+import random
+from folder_paths import get_filename_list, get_full_path
+import comfy.sd
+import comfy.utils
+
+class AllLoraSelector:
+    @classmethod
+    def INPUT_TYPES(cls):
+        lora_list = get_filename_list("loras")
+        optional_inputs = {}
+        
+        # Add a default value if lora_list is empty
+        if not lora_list:
+            lora_list = ["none"]
+            
+        for i in range(1, 21):
+            optional_inputs[f"lora_{i}"] = (lora_list, {"default": lora_list[0]})
+            optional_inputs[f"strength_model_{i}"] = ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01})
+            optional_inputs[f"strength_clip_{i}"] = ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01})
+
+        return {
+            "required": {
+                "number_of_loras": ("INT", {"default": 3, "min": 1, "max": 20, "step": 1}),
+                "model": ("MODEL",),
+                "clip": ("CLIP",),
+            },
+            "optional": optional_inputs
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("model", "clip", "lora_paths", "lora_names", "lora_folders")
+    FUNCTION = "apply_all_loras"
+    CATEGORY = "Bjornulf"
+
+    def apply_all_loras(self, number_of_loras, model, clip, **kwargs):
+        available_loras = []
+        strengths_model = []
+        strengths_clip = []
+        
+        # Collect LoRAs and their strengths
+        for i in range(1, number_of_loras + 1):
+            lora_key = f"lora_{i}"
+            strength_model_key = f"strength_model_{i}"
+            strength_clip_key = f"strength_clip_{i}"
+            
+            if lora_key in kwargs and kwargs[lora_key] and kwargs[lora_key] != "none":
+                available_loras.append(kwargs[lora_key])
+                strengths_model.append(kwargs.get(strength_model_key, 1.0))
+                strengths_clip.append(kwargs.get(strength_clip_key, 1.0))
+        
+        if not available_loras:
+            return (model, clip, "", "", "")
+
+        # Initialize lists for collecting metadata
+        lora_paths = []
+        lora_names = []
+        lora_folders = []
+        
+        # Create a copy of the initial model and clip
+        result_model = model.clone()
+        result_clip = clip.clone()
+        
+        # Apply each LoRA sequentially
+        for selected_lora, strength_model, strength_clip in zip(available_loras, strengths_model, strengths_clip):
+            # Get LoRA metadata
+            lora_name = os.path.splitext(os.path.basename(selected_lora))[0]
+            lora_path = get_full_path("loras", selected_lora)
+            lora_folder = os.path.basename(os.path.dirname(lora_path))
+            
+            # Load and apply LoRA
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            model_lora, clip_lora = comfy.sd.load_lora_for_models(
+                result_model, result_clip, lora, strength_model, strength_clip
+            )
+            
+            # Update results
+            result_model = model_lora
+            if clip_lora is not None:
+                result_clip = clip_lora
+            
+            # Collect metadata
+            lora_paths.append(lora_path)
+            lora_names.append(lora_name)
+            lora_folders.append(lora_folder)
+        
+        return (
+            result_model, 
+            result_clip, 
+            ",".join(lora_paths), 
+            ",".join(lora_names), 
+            ",".join(lora_folders)
+        )