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