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from typing import List, Dict |
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import torch |
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from loguru import logger |
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import numpy as np |
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from iopaint.download import scan_models |
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from iopaint.helper import switch_mps_device |
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from iopaint.model import models, ControlNet, SD, SDXL |
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from iopaint.model.utils import torch_gc, is_local_files_only |
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from iopaint.schema import InpaintRequest, ModelInfo, ModelType |
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class ModelManager: |
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def __init__(self, name: str, device: torch.device, **kwargs): |
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self.name = name |
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self.device = device |
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self.kwargs = kwargs |
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self.available_models: Dict[str, ModelInfo] = {} |
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self.scan_models() |
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self.enable_controlnet = kwargs.get("enable_controlnet", False) |
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controlnet_method = kwargs.get("controlnet_method", None) |
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if ( |
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controlnet_method is None |
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and name in self.available_models |
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and self.available_models[name].support_controlnet |
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): |
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controlnet_method = self.available_models[name].controlnets[0] |
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self.controlnet_method = controlnet_method |
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self.model = self.init_model(name, device, **kwargs) |
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@property |
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def current_model(self) -> ModelInfo: |
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return self.available_models[self.name] |
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def init_model(self, name: str, device, **kwargs): |
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logger.info(f"Loading model: {name}") |
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if name not in self.available_models: |
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raise NotImplementedError( |
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f"Unsupported model: {name}. Available models: {list(self.available_models.keys())}" |
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) |
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model_info = self.available_models[name] |
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kwargs = { |
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**kwargs, |
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"model_info": model_info, |
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"enable_controlnet": self.enable_controlnet, |
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"controlnet_method": self.controlnet_method, |
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} |
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if model_info.support_controlnet and self.enable_controlnet: |
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return ControlNet(device, **kwargs) |
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elif model_info.name in models: |
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return models[name](device, **kwargs) |
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else: |
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if model_info.model_type in [ |
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ModelType.DIFFUSERS_SD_INPAINT, |
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ModelType.DIFFUSERS_SD, |
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]: |
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return SD(device, **kwargs) |
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if model_info.model_type in [ |
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ModelType.DIFFUSERS_SDXL_INPAINT, |
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ModelType.DIFFUSERS_SDXL, |
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]: |
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return SDXL(device, **kwargs) |
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raise NotImplementedError(f"Unsupported model: {name}") |
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@torch.inference_mode() |
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def __call__(self, image, mask, config: InpaintRequest): |
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""" |
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Args: |
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image: [H, W, C] RGB |
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mask: [H, W, 1] 255 means area to repaint |
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config: |
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Returns: |
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BGR image |
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""" |
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self.switch_controlnet_method(config) |
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self.enable_disable_freeu(config) |
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self.enable_disable_lcm_lora(config) |
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return self.model(image, mask, config).astype(np.uint8) |
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def scan_models(self) -> List[ModelInfo]: |
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available_models = scan_models() |
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self.available_models = {it.name: it for it in available_models} |
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return available_models |
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def switch(self, new_name: str): |
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if new_name == self.name: |
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return |
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old_name = self.name |
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old_controlnet_method = self.controlnet_method |
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self.name = new_name |
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if ( |
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self.available_models[new_name].support_controlnet |
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and self.controlnet_method |
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not in self.available_models[new_name].controlnets |
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): |
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self.controlnet_method = self.available_models[new_name].controlnets[0] |
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try: |
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del self.model |
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torch_gc() |
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self.model = self.init_model( |
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new_name, switch_mps_device(new_name, self.device), **self.kwargs |
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) |
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except Exception as e: |
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self.name = old_name |
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self.controlnet_method = old_controlnet_method |
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logger.info(f"Switch model from {old_name} to {new_name} failed, rollback") |
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self.model = self.init_model( |
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old_name, switch_mps_device(old_name, self.device), **self.kwargs |
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) |
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raise e |
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def switch_controlnet_method(self, config): |
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if not self.available_models[self.name].support_controlnet: |
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return |
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if ( |
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self.enable_controlnet |
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and config.controlnet_method |
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and self.controlnet_method != config.controlnet_method |
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): |
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old_controlnet_method = self.controlnet_method |
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self.controlnet_method = config.controlnet_method |
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self.model.switch_controlnet_method(config.controlnet_method) |
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logger.info( |
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f"Switch Controlnet method from {old_controlnet_method} to {config.controlnet_method}" |
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) |
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elif self.enable_controlnet != config.enable_controlnet: |
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self.enable_controlnet = config.enable_controlnet |
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self.controlnet_method = config.controlnet_method |
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pipe_components = { |
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"vae": self.model.model.vae, |
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"text_encoder": self.model.model.text_encoder, |
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"unet": self.model.model.unet, |
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} |
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if hasattr(self.model.model, "text_encoder_2"): |
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pipe_components["text_encoder_2"] = self.model.model.text_encoder_2 |
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self.model = self.init_model( |
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self.name, |
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switch_mps_device(self.name, self.device), |
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pipe_components=pipe_components, |
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**self.kwargs, |
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) |
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if not config.enable_controlnet: |
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logger.info(f"Disable controlnet") |
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else: |
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logger.info(f"Enable controlnet: {config.controlnet_method}") |
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def enable_disable_freeu(self, config: InpaintRequest): |
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if str(self.model.device) == "mps": |
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return |
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if self.available_models[self.name].support_freeu: |
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if config.sd_freeu: |
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freeu_config = config.sd_freeu_config |
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self.model.model.enable_freeu( |
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s1=freeu_config.s1, |
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s2=freeu_config.s2, |
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b1=freeu_config.b1, |
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b2=freeu_config.b2, |
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) |
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else: |
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self.model.model.disable_freeu() |
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def enable_disable_lcm_lora(self, config: InpaintRequest): |
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if self.available_models[self.name].support_lcm_lora: |
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lcm_lora_loaded = bool(self.model.model.get_list_adapters()) |
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if config.sd_lcm_lora: |
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if not lcm_lora_loaded: |
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self.model.model.load_lora_weights( |
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self.model.lcm_lora_id, |
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weight_name="pytorch_lora_weights.safetensors", |
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local_files_only=is_local_files_only(), |
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) |
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else: |
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if lcm_lora_loaded: |
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self.model.model.disable_lora() |
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