stable-diffusion-webui/modules/upscaler.py

146 lines
3.8 KiB
Python

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)]