| | import gc |
| | import os |
| | import time |
| | from typing import Dict, List, Callable, Union |
| | from copy import deepcopy |
| | from collections import OrderedDict |
| | import re |
| | import importlib |
| |
|
| | from utils.logger import logger as LOGGER |
| | from utils import shared |
| |
|
| |
|
| | GPUINTENSIVE_SET = {'cuda', 'mps', 'xpu', 'privateuseone'} |
| |
|
| | def register_hooks(hooks_registered: OrderedDict, callbacks: Union[List, Callable, Dict]): |
| | if callbacks is None: |
| | return |
| | if isinstance(callbacks, (Dict, OrderedDict)): |
| | for k, v in callbacks.items(): |
| | hooks_registered[k] = v |
| | else: |
| | nhooks = len(hooks_registered) |
| |
|
| | if isinstance(callbacks, Callable): |
| | callbacks = [callbacks] |
| | for callback in callbacks: |
| | hk = 'hook_' + str(nhooks).zfill(2) |
| | while True: |
| | if hk not in hooks_registered: |
| | break |
| | hk = hk + '_' + str(time.time_ns()) |
| | hooks_registered[hk] = callback |
| | nhooks += 1 |
| |
|
| |
|
| | def patch_module_params(cfg_param, module_params, module_name: str = ''): |
| | |
| | cfg_key_set = set(cfg_param.keys()) |
| | module_key_set = set(module_params.keys()) |
| | for ck in cfg_key_set: |
| | if ck not in module_key_set: |
| | LOGGER.warning(f'Found invalid {module_name} config: {ck}') |
| | cfg_param.pop(ck) |
| |
|
| | for mk in module_key_set: |
| | if mk not in cfg_key_set: |
| | if not mk.startswith('__') and mk != 'description': |
| | LOGGER.info(f'Found new {module_name} config: {mk}') |
| | cfg_param[mk] = module_params[mk] |
| | else: |
| | mparam = module_params[mk] |
| | cparam = cfg_param[mk] |
| | if isinstance(mparam, dict): |
| | tgt_type = mparam.get('data_type', type(mparam['value'])) |
| | if isinstance(cparam, dict): |
| | if 'value' in cparam: |
| | v = cparam['value'] |
| | elif isinstance(mparam['value'], dict): |
| | for k in mparam['value']: |
| | if k in cparam: |
| | mparam['value'][k] = cparam[k] |
| | v = mparam['value'] |
| | else: |
| | v = mparam['value'] |
| | else: |
| | v = cparam |
| | valid = True |
| | if tgt_type != type(v): |
| | try: |
| | v = tgt_type(v) |
| | except: |
| | valid = False |
| | LOGGER.warning(f'Invalid param value {v} for defined dtype: {tgt_type}, it will be set to default value: {mparam}') |
| | if valid: |
| | mparam['value'] = v |
| | cfg_param[mk] = mparam |
| | else: |
| | if type(cparam) != type(mparam): |
| | if not isinstance(mparam, dict) and isinstance(cparam, dict): |
| | cparam = cparam['value'] |
| | try: |
| | cfg_param[mk] = type(mparam)(cparam) |
| | except ValueError: |
| | LOGGER.warning(f'Invalid param value {cparam} for defined dtype: {type(mparam)}, it will be set to default value: {mparam}') |
| | cfg_param[mk] = mparam |
| | |
| | cfg_key_list = list(cfg_param.keys()) |
| | module_key_list = list(module_params.keys()) |
| | if cfg_key_list != module_key_list: |
| | new_params = {key: cfg_param[key] for key in module_key_list} |
| | cfg_param.clear() |
| | cfg_param.update(new_params) |
| | module_key_set = set(module_params.keys()) |
| | cfg_param['__param_patched'] = True |
| | return cfg_param |
| |
|
| |
|
| | def merge_config_module_params(config_params: Dict, module_keys: List, get_module: Callable) -> Dict: |
| | for module_key in module_keys: |
| | module_params = get_module(module_key).params |
| | if module_key not in config_params or config_params[module_key] is None: |
| | config_params[module_key] = module_params |
| | else: |
| | patch_module_params(config_params[module_key], module_params, module_key) |
| | return config_params |
| |
|
| |
|
| | def standardize_module_params(params): |
| | if params is None: |
| | return |
| | for k, v in params.items(): |
| | if not isinstance(v, dict): |
| | v = {'value': v} |
| | if 'data_type' not in v: |
| | v['data_type'] = type(v['value']) |
| | params[k] = v |
| |
|
| |
|
| | class BaseModule: |
| |
|
| | params: Dict = None |
| | logger = LOGGER |
| |
|
| | _preprocess_hooks: OrderedDict = None |
| | _postprocess_hooks: OrderedDict = None |
| |
|
| | download_file_list: List = None |
| | download_file_on_load = False |
| |
|
| | _load_model_keys: set = None |
| |
|
| | def __init__(self, **params) -> None: |
| | standardize_module_params(self.params) |
| | if self.params is not None and '__param_patched' not in params: |
| | params = patch_module_params(params, self.params, self) |
| | if params: |
| | if self.params is None: |
| | self.params = params |
| | else: |
| | self.params.update(params) |
| |
|
| | @classmethod |
| | def register_postprocess_hooks(cls, callbacks: Union[List, Callable]): |
| | """ |
| | these hooks would be shared among all objects inherited from the same super class |
| | """ |
| | assert cls._postprocess_hooks is not None |
| | register_hooks(cls._postprocess_hooks, callbacks) |
| |
|
| | @classmethod |
| | def register_preprocess_hooks(cls, callbacks: Union[List, Callable, Dict]): |
| | """ |
| | these hooks would be shared among all objects inherited from the same super class |
| | """ |
| | assert cls._preprocess_hooks is not None |
| | register_hooks(cls._preprocess_hooks, callbacks) |
| |
|
| | def get_param_value(self, param_key: str): |
| | assert self.params is not None and param_key in self.params |
| | p = self.params[param_key] |
| | if isinstance(p, dict): |
| | return p['value'] |
| | return p |
| | |
| | def set_param_value(self, param_key: str, param_value, convert_dtype=True): |
| | assert self.params is not None and param_key in self.params |
| | p = self.params[param_key] |
| | if isinstance(p, dict): |
| | if convert_dtype: |
| | try: |
| | val_type = p.get('data_type', type(p['value'])) |
| | param_value = val_type(param_value) |
| | except ValueError: |
| | dtype = type(p['value']) |
| | self.logger.warning(f'Invalid param value {param_value} for defined dtype: {dtype}') |
| | p['value'] = param_value |
| | else: |
| | if convert_dtype: |
| | try: |
| | param_value = type(p)(param_value) |
| | except ValueError: |
| | self.logger.warning(f'Invalid param value {param_value} for defined dtype: {type(p)}, revert to original value {p}') |
| | param_value = p |
| | self.params[param_key] = param_value |
| |
|
| | def updateParam(self, param_key: str, param_content): |
| | self.set_param_value(param_key, param_content) |
| |
|
| | @property |
| | def low_vram_mode(self): |
| | if 'low vram mode' in self.params: |
| | return self.get_param_value('low vram mode') |
| | return False |
| |
|
| | def is_cpu_intensive(self)->bool: |
| | if self.params is not None and 'device' in self.params: |
| | return self.params['device']['value'] == 'cpu' |
| | return False |
| |
|
| | def is_gpu_intensive(self) -> bool: |
| | if self.params is not None and 'device' in self.params: |
| | return self.params['device']['value'] in GPUINTENSIVE_SET |
| | return False |
| |
|
| | def is_computational_intensive(self) -> bool: |
| | if self.params is not None and 'device' in self.params: |
| | return True |
| | return False |
| | |
| | def unload_model(self, empty_cache=False): |
| | model_deleted = False |
| | if self._load_model_keys is not None: |
| | for k in self._load_model_keys: |
| | if hasattr(self, k): |
| | model = getattr(self, k) |
| | if model is not None: |
| | if hasattr(model, 'unload_model'): |
| | model.unload_model(empty_cache=False) |
| | del model |
| | setattr(self, k, None) |
| | model_deleted = True |
| | |
| | if empty_cache and model_deleted: |
| | soft_empty_cache() |
| |
|
| | return model_deleted |
| |
|
| | def load_model(self): |
| | |
| | self._load_model() |
| | return |
| |
|
| | def _load_model(self): |
| | return |
| |
|
| | def all_model_loaded(self): |
| | if self._load_model_keys is None: |
| | return True |
| | for k in self._load_model_keys: |
| | if not hasattr(self, k) or getattr(self, k) is None: |
| | return False |
| | return True |
| | |
| | def __del__(self): |
| | self.unload_model() |
| |
|
| | @property |
| | def debug_mode(self): |
| | return shared.DEBUG |
| | |
| | def flush(self, param_key: str): |
| | return None |
| |
|
| | os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' |
| | import torch |
| |
|
| | DEFAULT_DEVICE = 'cpu' |
| | AVAILABLE_DEVICES = ['cpu'] |
| | if hasattr(torch, 'cuda') and torch.cuda.is_available(): |
| | DEFAULT_DEVICE = 'cuda' |
| | AVAILABLE_DEVICES.append(DEFAULT_DEVICE) |
| | if hasattr(torch, 'xpu') and torch.xpu.is_available(): |
| | DEFAULT_DEVICE = 'xpu' if torch.xpu.is_available() else 'cpu' |
| | AVAILABLE_DEVICES.append(DEFAULT_DEVICE) |
| | if hasattr(torch, 'backends') and hasattr(torch.backends, 'mps') and torch.backends.mps.is_available(): |
| | DEFAULT_DEVICE = 'mps' |
| | AVAILABLE_DEVICES.append(DEFAULT_DEVICE) |
| |
|
| | try: |
| | import torch_directml |
| | if hasattr(torch, 'privateuseone') and torch_directml.device_count() > 0: |
| | torch.dml = torch_directml |
| | DEFAULT_DEVICE = f'privateuseone:{torch.dml.default_device()}' |
| | AVAILABLE_DEVICES += [f"privateuseone:{d}" for d in range(torch.dml.device_count())] |
| | except: |
| | |
| | pass |
| | BF16_SUPPORTED = DEFAULT_DEVICE == 'cuda' and torch.cuda.is_bf16_supported() or DEFAULT_DEVICE == 'xpu' and torch.xpu.is_bf16_supported() |
| |
|
| | def is_nvidia(): |
| | if DEFAULT_DEVICE == 'cuda': |
| | if torch.version.cuda: |
| | return True |
| | return False |
| |
|
| | def is_intel(): |
| | if DEFAULT_DEVICE == 'xpu': |
| | if torch.version.xpu: |
| | return True |
| | return False |
| |
|
| | def soft_empty_cache(): |
| | gc.collect() |
| | if DEFAULT_DEVICE == 'cuda': |
| | torch.cuda.empty_cache() |
| | torch.cuda.ipc_collect() |
| | elif DEFAULT_DEVICE == 'xpu': |
| | torch.xpu.empty_cache() |
| | |
| | elif DEFAULT_DEVICE == 'mps': |
| | torch.mps.empty_cache() |
| |
|
| |
|
| | def DEVICE_SELECTOR(not_supported:list[str]=[]): return deepcopy( |
| | { |
| | 'type': 'selector', |
| | 'options': [opt for opt in AVAILABLE_DEVICES if all(device not in opt for device in not_supported)], |
| | 'value': DEFAULT_DEVICE if not any(DEFAULT_DEVICE in device for device in not_supported) else 'cpu' |
| | } |
| | ) |
| |
|
| | TORCH_DTYPE_MAP = { |
| | 'fp32': torch.float32, |
| | 'fp16': torch.float16, |
| | 'bf16': torch.bfloat16, |
| | } |
| |
|
| | MODULE_SCRIPTS = { |
| | 'translator': {'module_dir': 'modules/translators', 'module_pattern': r'trans_(.*?).py'}, |
| | 'textdetector': {'module_dir': 'modules/textdetector', 'module_pattern': r'detector_(.*?).py'}, |
| | 'inpainter': {'module_dir': 'modules/inpaint', 'module_pattern': r'inpaint_(.*?).py'}, |
| | 'ocr': {'module_dir': 'modules/ocr', 'module_pattern': r'ocr_(.*?).py'}, |
| | } |
| | |
| | def init_module_registries(target_modules=None): |
| | def _load_module(module_dir: str, module_pattern: str): |
| | modules = os.listdir(module_dir) |
| | pattern = re.compile(module_pattern) |
| | module_path = module_dir.replace('/', '.') |
| | if not module_path.endswith('.'): |
| | module_path += '.' |
| | for module_name in modules: |
| | if pattern.match(module_name) is not None: |
| | try: |
| | module = module_path + module_name.replace('.py', '') |
| | importlib.import_module(module) |
| | except Exception as e: |
| | LOGGER.warning(f'Failed to import {module}: {e}') |
| |
|
| | if target_modules is None: |
| | target_modules = MODULE_SCRIPTS |
| | if isinstance(target_modules, str): |
| | target_modules = [target_modules] |
| |
|
| | for k in target_modules: |
| | _load_module(**MODULE_SCRIPTS[k]) |
| |
|
| |
|
| | def init_textdetector_registries(): |
| | init_module_registries('textdetector') |
| |
|
| |
|
| | def init_inpainter_registries(): |
| | init_module_registries('inpainter') |
| |
|
| |
|
| | def init_ocr_registries(): |
| | init_module_registries('ocr') |
| |
|
| |
|
| | def init_translator_registries(): |
| | init_module_registries('translator') |
| |
|
| |
|