import os from abc import abstractmethod import PIL import numpy as np import torch from PIL import Image import modules.shared from modules import modelloader, shared LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST) class Upscaler: name = None model_path = None model_name = None model_url = None enable = True filter = None model = None user_path = None scalers: [] tile = True def __init__(self, create_dirs=False): self.mod_pad_h = None self.tile_size = modules.shared.opts.ESRGAN_tile self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap self.device = modules.shared.device self.img = None self.output = None self.scale = 1 self.half = not modules.shared.cmd_opts.no_half self.pre_pad = 0 self.mod_scale = None if self.model_path is None and self.name: self.model_path = os.path.join(shared.models_path, self.name) if self.model_path and create_dirs: os.makedirs(self.model_path, exist_ok=True) try: import cv2 self.can_tile = True except: pass @abstractmethod def do_upscale(self, img: PIL.Image, selected_model: str): return img def upscale(self, img: PIL.Image, scale, selected_model: str = None): self.scale = scale dest_w = int(img.width * scale) dest_h = int(img.height * scale) for i in range(3): shape = (img.width, img.height) img = self.do_upscale(img, selected_model) if shape == (img.width, img.height): break if img.width >= dest_w and img.height >= dest_h: break if img.width != dest_w or img.height != dest_h: img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS) return img @abstractmethod def load_model(self, path: str): pass def find_models(self, ext_filter=None) -> list: return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path) def update_status(self, prompt): print(f"\nextras: {prompt}", file=shared.progress_print_out) class UpscalerData: name = None data_path = None scale: int = 4 scaler: Upscaler = None model: None def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None): self.name = name self.data_path = path self.local_data_path = path self.scaler = upscaler self.scale = scale self.model = model class UpscalerNone(Upscaler): name = "None" scalers = [] def load_model(self, path): pass def do_upscale(self, img, selected_model=None): return img def __init__(self, dirname=None): super().__init__(False) self.scalers = [UpscalerData("None", None, self)] class UpscalerLanczos(Upscaler): scalers = [] def do_upscale(self, img, selected_model=None): return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=LANCZOS) def load_model(self, _): pass def __init__(self, dirname=None): super().__init__(False) self.name = "Lanczos" self.scalers = [UpscalerData("Lanczos", None, self)] class UpscalerNearest(Upscaler): scalers = [] def do_upscale(self, img, selected_model=None): return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST) def load_model(self, _): pass def __init__(self, dirname=None): super().__init__(False) self.name = "Nearest" self.scalers = [UpscalerData("Nearest", None, self)]