mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
import sys
|
|
|
|
import PIL.Image
|
|
import numpy as np
|
|
import torch
|
|
|
|
import modules.upscaler
|
|
from modules import devices, modelloader, script_callbacks, errors
|
|
from modules.shared import opts
|
|
from modules.upscaler_utils import tiled_upscale_2
|
|
|
|
|
|
class UpscalerScuNET(modules.upscaler.Upscaler):
|
|
def __init__(self, dirname):
|
|
self.name = "ScuNET"
|
|
self.model_name = "ScuNET GAN"
|
|
self.model_name2 = "ScuNET PSNR"
|
|
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
|
|
self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
|
|
self.user_path = dirname
|
|
super().__init__()
|
|
model_paths = self.find_models(ext_filter=[".pth"])
|
|
scalers = []
|
|
add_model2 = True
|
|
for file in model_paths:
|
|
if file.startswith("http"):
|
|
name = self.model_name
|
|
else:
|
|
name = modelloader.friendly_name(file)
|
|
if name == self.model_name2 or file == self.model_url2:
|
|
add_model2 = False
|
|
try:
|
|
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
|
|
scalers.append(scaler_data)
|
|
except Exception:
|
|
errors.report(f"Error loading ScuNET model: {file}", exc_info=True)
|
|
if add_model2:
|
|
scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
|
|
scalers.append(scaler_data2)
|
|
self.scalers = scalers
|
|
|
|
def do_upscale(self, img: PIL.Image.Image, selected_file):
|
|
|
|
devices.torch_gc()
|
|
|
|
try:
|
|
model = self.load_model(selected_file)
|
|
except Exception as e:
|
|
print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
|
|
return img
|
|
|
|
device = devices.get_device_for('scunet')
|
|
tile = opts.SCUNET_tile
|
|
h, w = img.height, img.width
|
|
np_img = np.array(img)
|
|
np_img = np_img[:, :, ::-1] # RGB to BGR
|
|
np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
|
|
torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
|
|
|
|
if tile > h or tile > w:
|
|
_img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
|
|
_img[:, :, :h, :w] = torch_img # pad image
|
|
torch_img = _img
|
|
|
|
with torch.no_grad():
|
|
torch_output = tiled_upscale_2(
|
|
torch_img,
|
|
model,
|
|
tile_size=opts.SCUNET_tile,
|
|
tile_overlap=opts.SCUNET_tile_overlap,
|
|
scale=1,
|
|
device=devices.get_device_for('scunet'),
|
|
desc="ScuNET tiles",
|
|
).squeeze(0)
|
|
torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
|
|
np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
|
|
del torch_img, torch_output
|
|
devices.torch_gc()
|
|
|
|
output = np_output.transpose((1, 2, 0)) # CHW to HWC
|
|
output = output[:, :, ::-1] # BGR to RGB
|
|
return PIL.Image.fromarray((output * 255).astype(np.uint8))
|
|
|
|
def load_model(self, path: str):
|
|
device = devices.get_device_for('scunet')
|
|
if path.startswith("http"):
|
|
# TODO: this doesn't use `path` at all?
|
|
filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
|
|
else:
|
|
filename = path
|
|
return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet')
|
|
|
|
|
|
def on_ui_settings():
|
|
import gradio as gr
|
|
from modules import shared
|
|
|
|
shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
|
|
shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
|
|
|
|
|
|
script_callbacks.on_ui_settings(on_ui_settings)
|