167 lines
7.9 KiB
Python
167 lines
7.9 KiB
Python
# unet_loader.py
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import os
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import comfy.sd
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import comfy.utils
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import json
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from .base_loader import DiffusersLoaderBase
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from .utils import DiffusersUtils
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import torch
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class DiffusersUNETLoader(DiffusersLoaderBase):
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"sub_directory": (DiffusersUtils.get_model_directories(DiffusersUtils.get_base_path()),),
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"transformer_parts": (["all", "part_1", "part_2", "part_3"],),
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"weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],),
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}
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}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "load_unet/transformer"
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CATEGORY = "DiffusersLoader"
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@classmethod
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def load_unet(cls, sub_directory, transformer_parts="all", weight_dtype="default"):
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return (cls.load_model(sub_directory, transformer_parts, weight_dtype),)
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@classmethod
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def load_model(cls, sub_directory, transformer_parts="all", weight_dtype="default"):
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base_path = DiffusersUtils.get_base_path()
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sub_dir_path = os.path.join(base_path, sub_directory)
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model_type = cls.detect_model_type(sub_dir_path)
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print(f"DiffusersUNETLoader: Detected model type: {model_type}")
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unet_path = cls.get_unet_path(sub_dir_path, model_type, transformer_parts)
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DiffusersUtils.check_and_clear_cache('unet', unet_path)
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print(f"DiffusersUNETLoader: Attempting to load UNET model from: {unet_path}")
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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_e5m2":
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model_options["dtype"] = torch.float8_e5m2
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try:
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if model_type == "AuraFlow" and transformer_parts == "all" and unet_path.endswith('.json'):
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#Load model using index_file
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with open(unet_path, 'r') as f:
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index_data = json.load(f)
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weight_map = index_data['weight_map']
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state_dict = {}
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for key, file in weight_map.items():
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file_path = os.path.join(os.path.dirname(unet_path), file)
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state_dict.update(comfy.utils.load_torch_file(file_path))
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model = cls.load_diffusion_model_from_state_dict(state_dict, model_options=model_options)
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else:
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model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
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print("UNET/Transformer model loaded successfully")
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return model
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except RuntimeError as e:
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if "out of memory" in str(e):
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print("Out of memory error. Attempting to clear memory and retry...")
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DiffusersUtils.clear_memory()
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try:
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if model_type == "AuraFlow" and transformer_parts == "all" and unet_path.endswith('.json'):
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# Load model using the index file (retry)
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with open(unet_path, 'r') as f:
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index_data = json.load(f)
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weight_map = index_data['weight_map']
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state_dict = {}
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for key, file in weight_map.items():
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file_path = os.path.join(os.path.dirname(unet_path), file)
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state_dict.update(comfy.utils.load_torch_file(file_path))
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model = cls.load_diffusion_model_from_state_dict(state_dict, model_options=model_options)
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else:
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model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
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print("UNET/Transformer model loaded successfully after memory clear")
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return model
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except Exception as retry_e:
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print(f"Error loading model after memory clear: {retry_e}")
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raise
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else:
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print(f"DiffusersUNETLoader: Error loading UNET/Transformer model: {e}")
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raise
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@classmethod
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def load_diffusion_model_from_state_dict(cls, state_dict, model_options):
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model = comfy.sd.load_diffusion_model_state_dict(state_dict, model_options=model_options)
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return model
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@classmethod
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def get_unet_path(cls, sub_dir_path, model_type, transformer_parts):
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if model_type in ["AuraFlow", "Flux", "SD3"]:
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unet_folder = os.path.join(sub_dir_path, 'transformer')
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print(f"Using transformer folder for {model_type}: {unet_folder}")
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if model_type == "SD3":
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return cls.handle_sd3_transformer(unet_folder)
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if model_type == "AuraFlow":
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return cls.handle_auraflow_transformer(unet_folder, transformer_parts)
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elif model_type == "Flux":
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return cls.handle_flux_transformer(unet_folder, transformer_parts)
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else: # SD15, SDXL, etc.
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unet_folder = os.path.join(sub_dir_path, 'unet')
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if not os.path.exists(unet_folder):
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#Fallback to transformer folder if unet folder does not exist
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unet_folder = os.path.join(sub_dir_path, 'transformer')
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if os.path.exists(unet_folder):
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print(f"Unet folder not found, using transformer folder: {unet_folder}")
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return cls.handle_flux_transformer(unet_folder, transformer_parts)
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return DiffusersUtils.find_model_file(unet_folder)
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@classmethod
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def handle_transformer(cls, unet_folder, transformer_parts, num_parts):
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if transformer_parts == "all":
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combined_file_path = os.path.join(unet_folder, "combined_transformer.safetensors")
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if not os.path.exists(combined_file_path):
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print(f"Combined file not found, creating at: {combined_file_path}")
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try:
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combined_file_path = DiffusersUtils.combine_safetensor_files(unet_folder, os.path.dirname(unet_folder), num_parts=num_parts)
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except Exception as e:
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print(f"Error combining safetensor files: {e}")
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combined_file_path = DiffusersUtils.find_model_files(unet_folder, num_parts=1)[0]
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return combined_file_path
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else:
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part_num = int(transformer_parts.split('_')[1])
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return DiffusersUtils.find_model_files(unet_folder, num_parts=part_num)[-1]
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@classmethod
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def handle_auraflow_transformer(cls, unet_folder, transformer_parts):
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#Find file ending with 'index.json'
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index_files = [f for f in os.listdir(unet_folder) if f.endswith('index.json')]
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print(f"Index files: {index_files}")
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if index_files:
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index_file = os.path.join(unet_folder, index_files[0])
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print(f"Found index file: {index_file}")
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if transformer_parts == "all":
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return index_file
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else:
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with open(index_file, 'r') as f:
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index_data = json.load(f)
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part_num = int(transformer_parts.split('_')[1])
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part_files = [file for file in index_data['weight_map'].values() if f"-0000{part_num}-of-" in file]
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if part_files:
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return os.path.join(unet_folder, part_files[0])
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else:
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print(f"No file found for part {part_num}")
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return None
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else:
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print(f"No index file found in {unet_folder}. Checking for combined transformer step")
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return cls.handle_transformer(unet_folder, transformer_parts, num_parts=2)
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@classmethod
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def handle_sd3_transformer(cls, unet_folder):
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return DiffusersUtils.find_model_file(unet_folder)
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@classmethod
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def handle_flux_transformer(cls, unet_folder, transformer_parts):
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return cls.handle_transformer(unet_folder, transformer_parts, num_parts=3)
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