Move nodes from previous PR into their own file.

comfy_extras/nodes_lora_debug.py

@@ -0,0 +1,79 @@
+import folder_paths
+import comfy.utils
+import comfy.sd
+
+
+class LoraLoaderBypass:
+    """
+    Apply LoRA in bypass mode without modifying base model weights.
+
+    Bypass mode computes: output = base_forward(x) + lora_path(x)
+    This is useful for training and when model weights are offloaded.
+    """
+
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
+                "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
+                "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
+    FUNCTION = "load_lora"
+
+    CATEGORY = "loaders"
+    DESCRIPTION = "Apply LoRA in bypass mode. Unlike regular LoRA, this doesn't modify model weights - instead it injects the LoRA computation during forward pass. Useful for training scenarios."
+    EXPERIMENTAL = True
+
+    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip)
+
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                self.loaded_lora = None
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        model_lora, clip_lora = comfy.sd.load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip)
+        return (model_lora, clip_lora)
+
+
+class LoraLoaderBypassModelOnly(LoraLoaderBypass):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "lora_name": (folder_paths.get_filename_list("loras"), ),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_lora_model_only"
+
+    def load_lora_model_only(self, model, lora_name, strength_model):
+        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LoraLoaderBypass": LoraLoaderBypass,
+    "LoraLoaderBypassModelOnly": LoraLoaderBypassModelOnly,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoraLoaderBypass": "Load LoRA (Bypass) (For debugging)",
+    "LoraLoaderBypassModelOnly": "Load LoRA (Bypass, Model Only) (for debugging)",
+}

nodes.py

@@ -722,69 +722,6 @@ def INPUT_TYPES(s):
     def load_lora_model_only(self, model, lora_name, strength_model):
         return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
 
-class LoraLoaderBypass:
-    """
-    Apply LoRA in bypass mode without modifying base model weights.
-
-    Bypass mode computes: output = base_forward(x) + lora_path(x)
-    This is useful for training and when model weights are offloaded.
-    """
-
-    def __init__(self):
-        self.loaded_lora = None
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
-                "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
-                "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
-                "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
-                "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
-            }
-        }
-
-    RETURN_TYPES = ("MODEL", "CLIP")
-    OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
-    FUNCTION = "load_lora"
-
-    CATEGORY = "loaders"
-    DESCRIPTION = "Apply LoRA in bypass mode. Unlike regular LoRA, this doesn't modify model weights - instead it injects the LoRA computation during forward pass. Useful for training scenarios."
-
-    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
-        if strength_model == 0 and strength_clip == 0:
-            return (model, clip)
-
-        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
-        lora = None
-        if self.loaded_lora is not None:
-            if self.loaded_lora[0] == lora_path:
-                lora = self.loaded_lora[1]
-            else:
-                self.loaded_lora = None
-
-        if lora is None:
-            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
-            self.loaded_lora = (lora_path, lora)
-
-        model_lora, clip_lora = comfy.sd.load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip)
-        return (model_lora, clip_lora)
-
-
-class LoraLoaderBypassModelOnly(LoraLoaderBypass):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "lora_name": (folder_paths.get_filename_list("loras"), ),
-                              "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "load_lora_model_only"
-
-    def load_lora_model_only(self, model, lora_name, strength_model):
-        return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
-
 class VAELoader:
     video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
     image_taes = ["taesd", "taesdxl", "taesd3", "taef1"]
@@ -2130,8 +2067,6 @@ def expand_image(self, image, left, top, right, bottom, feathering):
     "LatentFlip": LatentFlip,
     "LatentCrop": LatentCrop,
     "LoraLoader": LoraLoader,
-    "LoraLoaderBypass": LoraLoaderBypass,
-    "LoraLoaderBypassModelOnly": LoraLoaderBypassModelOnly,
     "CLIPLoader": CLIPLoader,
     "UNETLoader": UNETLoader,
     "DualCLIPLoader": DualCLIPLoader,
@@ -2171,8 +2106,6 @@ def expand_image(self, image, left, top, right, bottom, feathering):
     "CheckpointLoaderSimple": "Load Checkpoint",
     "VAELoader": "Load VAE",
     "LoraLoader": "Load LoRA",
-    "LoraLoaderBypass": "Load LoRA (Bypass)",
-    "LoraLoaderBypassModelOnly": "Load LoRA (Bypass, Model Only)",
     "CLIPLoader": "Load CLIP",
     "ControlNetLoader": "Load ControlNet Model",
     "DiffControlNetLoader": "Load ControlNet Model (diff)",
@@ -2498,6 +2431,7 @@ async def init_builtin_extra_nodes():
         "nodes_wanmove.py",
         "nodes_image_compare.py",
         "nodes_zimage.py",
+        "nodes_lora_debug.py"
     ]
 
     import_failed = []