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comfyanonymouscomfyanonymous
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Move nodes from previous PR into their own file. (#12066)
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2 files changed

+80
-67
lines changed

2 files changed

+80
-67
lines changed

‎comfy_extras/nodes_lora_debug.py‎

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

‎nodes.py‎

Lines changed: 1 addition & 67 deletions
Original file line numberDiff line numberDiff line change
@@ -722,69 +722,6 @@ def INPUT_TYPES(s):
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def load_lora_model_only(self, model, lora_name, strength_model):
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return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
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class LoraLoaderBypass:
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"""
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Apply LoRA in bypass mode without modifying base model weights.
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Bypass mode computes: output = base_forward(x) + lora_path(x)
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This is useful for training and when model weights are offloaded.
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"""
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def __init__(self):
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self.loaded_lora = None
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
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"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
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"lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
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"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."}),
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"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."}),
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}
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}
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RETURN_TYPES = ("MODEL", "CLIP")
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OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.")
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FUNCTION = "load_lora"
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CATEGORY = "loaders"
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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."
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def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
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if strength_model == 0 and strength_clip == 0:
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return (model, clip)
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lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
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lora = None
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if self.loaded_lora is not None:
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if self.loaded_lora[0] == lora_path:
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lora = self.loaded_lora[1]
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else:
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self.loaded_lora = None
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if lora is None:
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lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
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self.loaded_lora = (lora_path, lora)
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model_lora, clip_lora = comfy.sd.load_bypass_lora_for_models(model, clip, lora, strength_model, strength_clip)
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return (model_lora, clip_lora)
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class LoraLoaderBypassModelOnly(LoraLoaderBypass):
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"lora_name": (folder_paths.get_filename_list("loras"), ),
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"strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "load_lora_model_only"
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def load_lora_model_only(self, model, lora_name, strength_model):
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return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
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class VAELoader:
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video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
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image_taes = ["taesd", "taesdxl", "taesd3", "taef1"]
@@ -2130,8 +2067,6 @@ def expand_image(self, image, left, top, right, bottom, feathering):
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"LatentFlip": LatentFlip,
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"LatentCrop": LatentCrop,
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"LoraLoader": LoraLoader,
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"LoraLoaderBypass": LoraLoaderBypass,
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"LoraLoaderBypassModelOnly": LoraLoaderBypassModelOnly,
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"CLIPLoader": CLIPLoader,
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"UNETLoader": UNETLoader,
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"DualCLIPLoader": DualCLIPLoader,
@@ -2171,8 +2106,6 @@ def expand_image(self, image, left, top, right, bottom, feathering):
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"CheckpointLoaderSimple": "Load Checkpoint",
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"VAELoader": "Load VAE",
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"LoraLoader": "Load LoRA",
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"LoraLoaderBypass": "Load LoRA (Bypass)",
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"LoraLoaderBypassModelOnly": "Load LoRA (Bypass, Model Only)",
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"CLIPLoader": "Load CLIP",
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"ControlNetLoader": "Load ControlNet Model",
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"DiffControlNetLoader": "Load ControlNet Model (diff)",
@@ -2498,6 +2431,7 @@ async def init_builtin_extra_nodes():
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"nodes_wanmove.py",
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"nodes_image_compare.py",
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"nodes_zimage.py",
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"nodes_lora_debug.py"
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]
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import_failed = []

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