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https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-24 14:42:11 -03:00
fix: isolate extra unet paths from checkpoints to prevent type misclassification
Refactor _prepare_checkpoint_paths() to return a tuple instead of having side effects on instance variables. This prevents extra unet paths from being incorrectly classified as checkpoints when processing extra paths. - Changed return type from List[str] to Tuple[List[str], List[str], List[str]] (all_paths, checkpoint_roots, unet_roots) - Updated _init_checkpoint_paths() and _apply_library_paths() callers - Fixed extra paths processing to properly isolate main and extra roots - Updated test_checkpoint_path_overlap.py tests for new API This ensures models in extra unet paths are correctly identified as diffusion_model type and don't appear in checkpoints list.
This commit is contained in:
Will Miao
205
py/nodes/unet_loader.py
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205
py/nodes/unet_loader.py
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import logging
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import os
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from typing import List, Tuple
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import torch
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import comfy.sd
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from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
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logger = logging.getLogger(__name__)
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class UNETLoaderLM:
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"""UNET Loader with support for extra folder paths
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Loads diffusion models/UNets from both standard ComfyUI folders and LoRA Manager's
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extra folder paths, providing a unified interface for UNET loading.
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Supports both regular diffusion models and GGUF format models.
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"""
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NAME = "UNETLoaderLM"
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CATEGORY = "Lora Manager/loaders"
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@classmethod
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def INPUT_TYPES(s):
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# Get list of unet names from scanner (includes extra folder paths)
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unet_names = s._get_unet_names()
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return {
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"required": {
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"unet_name": (
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unet_names,
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{"tooltip": "The name of the diffusion model to load."},
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),
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"weight_dtype": (
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["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],
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{"tooltip": "The dtype to use for the model weights."},
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),
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}
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}
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RETURN_TYPES = ("MODEL",)
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RETURN_NAMES = ("MODEL",)
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OUTPUT_TOOLTIPS = ("The model used for denoising latents.",)
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FUNCTION = "load_unet"
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@classmethod
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def _get_unet_names(cls) -> List[str]:
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"""Get list of diffusion model names from scanner cache in ComfyUI format (relative path with extension)"""
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try:
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from ..services.service_registry import ServiceRegistry
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import asyncio
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async def _get_names():
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scanner = await ServiceRegistry.get_checkpoint_scanner()
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cache = await scanner.get_cached_data()
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# Get all model roots for calculating relative paths
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model_roots = scanner.get_model_roots()
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# Filter only diffusion_model type and format names
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names = []
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for item in cache.raw_data:
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if item.get("sub_type") == "diffusion_model":
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file_path = item.get("file_path", "")
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if file_path:
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# Format as ComfyUI-style: "folder/model_name.ext"
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formatted_name = _format_model_name_for_comfyui(
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file_path, model_roots
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)
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if formatted_name:
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names.append(formatted_name)
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return sorted(names)
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try:
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loop = asyncio.get_running_loop()
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import concurrent.futures
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def run_in_thread():
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new_loop = asyncio.new_event_loop()
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asyncio.set_event_loop(new_loop)
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try:
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return new_loop.run_until_complete(_get_names())
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finally:
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new_loop.close()
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future = executor.submit(run_in_thread)
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return future.result()
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except RuntimeError:
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return asyncio.run(_get_names())
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except Exception as e:
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logger.error(f"Error getting unet names: {e}")
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return []
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def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple:
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"""Load a diffusion model by name, supporting extra folder paths
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Args:
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unet_name: The name of the diffusion model to load (format: "folder/model_name.ext")
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weight_dtype: The dtype to use for model weights
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Returns:
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Tuple of (MODEL,)
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"""
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# Get absolute path from cache using ComfyUI-style name
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unet_path, metadata = get_checkpoint_info_absolute(unet_name)
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if metadata is None:
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raise FileNotFoundError(
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f"Diffusion model '{unet_name}' not found in LoRA Manager cache. "
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"Make sure the model is indexed and try again."
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)
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# Check if it's a GGUF model
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if unet_path.endswith(".gguf"):
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return self._load_gguf_unet(unet_path, unet_name, weight_dtype)
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# Load regular diffusion model using ComfyUI's API
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logger.info(f"Loading diffusion model from: {unet_path}")
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# Build model options based on weight_dtype
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model_options = {}
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if weight_dtype == "fp8_e4m3fn":
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model_options["dtype"] = torch.float8_e4m3fn
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elif weight_dtype == "fp8_e4m3fn_fast":
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model_options["dtype"] = torch.float8_e4m3fn
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model_options["fp8_optimizations"] = True
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elif weight_dtype == "fp8_e5m2":
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model_options["dtype"] = torch.float8_e5m2
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model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
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return (model,)
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def _load_gguf_unet(
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self, unet_path: str, unet_name: str, weight_dtype: str
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) -> Tuple:
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"""Load a GGUF format diffusion model
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Args:
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unet_path: Absolute path to the GGUF file
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unet_name: Name of the model for error messages
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weight_dtype: The dtype to use for model weights
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Returns:
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Tuple of (MODEL,)
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"""
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try:
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# Try to import ComfyUI-GGUF modules
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from custom_nodes.ComfyUI_GGUF.loader import gguf_sd_loader
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from custom_nodes.ComfyUI_GGUF.ops import GGMLOps
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from custom_nodes.ComfyUI_GGUF.nodes import GGUFModelPatcher
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except ImportError:
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raise RuntimeError(
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f"Cannot load GGUF model '{unet_name}'. "
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"ComfyUI-GGUF is not installed. "
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"Please install ComfyUI-GGUF from https://github.com/city96/ComfyUI-GGUF "
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"to load GGUF format models."
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)
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logger.info(f"Loading GGUF diffusion model from: {unet_path}")
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try:
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# Load GGUF state dict
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sd, extra = gguf_sd_loader(unet_path)
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# Prepare kwargs for metadata if supported
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kwargs = {}
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import inspect
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valid_params = inspect.signature(
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comfy.sd.load_diffusion_model_state_dict
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).parameters
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if "metadata" in valid_params:
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kwargs["metadata"] = extra.get("metadata", {})
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# Setup custom operations with GGUF support
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ops = GGMLOps()
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# Handle weight_dtype for GGUF models
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if weight_dtype in ("default", None):
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ops.Linear.dequant_dtype = None
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elif weight_dtype in ["target"]:
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ops.Linear.dequant_dtype = weight_dtype
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else:
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ops.Linear.dequant_dtype = getattr(torch, weight_dtype, None)
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# Load the model
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model = comfy.sd.load_diffusion_model_state_dict(
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sd, model_options={"custom_operations": ops}, **kwargs
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)
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if model is None:
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raise RuntimeError(
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f"Could not detect model type for GGUF diffusion model: {unet_path}"
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)
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# Wrap with GGUFModelPatcher
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model = GGUFModelPatcher.clone(model)
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return (model,)
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except Exception as e:
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logger.error(f"Error loading GGUF diffusion model '{unet_name}': {e}")
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raise RuntimeError(
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f"Failed to load GGUF diffusion model '{unet_name}': {str(e)}"
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)
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