mirror of
https://github.com/vladmandic/automatic
synced 2026-08-31 09:31:00 +02:00
Vladimir Mandic
104 lines
5.2 KiB
Python
104 lines
5.2 KiB
Python
import os
|
|
import time
|
|
import torch
|
|
import numpy as np
|
|
from PIL import Image
|
|
from modules.upscaler import Upscaler, UpscalerData
|
|
from modules import devices, paths, errors
|
|
from modules.logger import log
|
|
|
|
|
|
MODELS = {
|
|
"Spandrel 4x RealPLKSR NomosWebPhoto": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/4xNomosWebPhoto_RealPLKSR.safetensors",
|
|
"Spandrel 2x RealPLKSR AnimeSharpV2": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/2x-AnimeSharpV2_RPLKSR_Sharp.pth",
|
|
"Spandrel 2x RealESRGAN Compact": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/RealESRGAN-2x-Compact.pth",
|
|
"Spandrel 2x RealESRGAN UltraCompact": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/RealESRGAN-2x-UltraCompact.pth",
|
|
"Spandrel 4x RealSAFMN++": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/Real-SAFMN++.pth",
|
|
"Spandrel 2x RealSAFMN": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/Real-SAFMN-x2.pth",
|
|
"Spandrel 4x RealSAFMN": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/Real-SAFMN-x4-v2.pth",
|
|
"Spandrel 2x SAFMN PureScale": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/2x_SAFMN_PureScale.pth",
|
|
"Spandrel 2x SAFMN PureScale Sharper": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/2x_SAFMN_PureScale_sharper.pth",
|
|
"Spandrel 4x SAFMN PureScale": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/4x_SAFMN_PureScale.pth",
|
|
}
|
|
|
|
class UpscalerSpandrel(Upscaler):
|
|
def __init__(self, dirname=None): # pylint: disable=unused-argument
|
|
super().__init__(False)
|
|
self.name = "Spandrel"
|
|
self.model_path = os.path.join(paths.models_path, 'Spandrel')
|
|
self.user_path = os.path.join(paths.models_path, 'Spandrel')
|
|
self.selected = None
|
|
self.model = None
|
|
self.scalers = self.find_scalers()
|
|
found = [os.path.basename(s.data_path) for s in self.scalers]
|
|
for k, v in MODELS.items():
|
|
fn = os.path.basename(v)
|
|
if fn not in found:
|
|
scaler = UpscalerData(name=k, path=v, upscaler=self)
|
|
self.scalers.append(scaler)
|
|
else:
|
|
for i, _s in enumerate(self.scalers): # update name of existing scaler if it was found
|
|
if os.path.basename(self.scalers[i].data_path) == fn:
|
|
self.scalers[i].name = k
|
|
break
|
|
|
|
def process(self, img: Image.Image, output_type='pil', quiet=False):
|
|
if isinstance(img, Image.Image):
|
|
from modules.image import convert
|
|
img = img.convert('RGB')
|
|
tensor = convert.to_tensor(img).unsqueeze(0).to(devices.device)
|
|
elif isinstance(img, np.ndarray):
|
|
from modules.image import convert
|
|
tensor = convert.to_tensor(img).unsqueeze(0).to(devices.device)
|
|
elif isinstance(img, torch.Tensor):
|
|
tensor = img.to(devices.device).float()
|
|
else:
|
|
log.error(f'Spandrel: unsupported input type={type(img)}')
|
|
return img
|
|
t0 = time.time()
|
|
with devices.inference_context():
|
|
if tensor.max() > 1.0: # tensor is in [0,255] range, convert to [0,1]
|
|
tensor = tensor.div_(255.0)
|
|
if tensor.min() < 0: # tensor is in [-1,1] range, convert to [0,1]
|
|
tensor = (tensor + 1.0) / 2.0
|
|
tensor = self.model(tensor)
|
|
tensor = tensor.clamp(0, 1).squeeze(0).cpu()
|
|
t1 = time.time()
|
|
if output_type == 'pil':
|
|
upscaled = convert.to_pil(tensor)
|
|
if not quiet:
|
|
log.debug(f'Upscale: name="{self.selected}" input={img.size} type={output_type} output={upscaled.size} time={t1 - t0:.3f}')
|
|
elif output_type == 'nd':
|
|
upscaled = (255.0 * tensor).float().numpy().astype(np.uint8)
|
|
if not quiet:
|
|
log.debug(f'Upscale: name="{self.selected}" input={img.shape} type={output_type} output={upscaled.shape} time={t1 - t0:.3f}')
|
|
elif output_type == 'tensor':
|
|
upscaled = tensor
|
|
if not quiet:
|
|
log.debug(f'Upscale: name="{self.selected}" input={img.shape} type={output_type} output={list(upscaled.shape)} time={t1 - t0:.3f}')
|
|
else:
|
|
upscaled = img
|
|
log.error(f'Upscale: type={output_type} unsupported')
|
|
return upscaled
|
|
|
|
def load_model(self, path: str):
|
|
from installer import install
|
|
if path is None:
|
|
return
|
|
install('spandrel')
|
|
import spandrel
|
|
self.selected = path
|
|
model = self.find_model(path)
|
|
self.model = spandrel.ModelLoader().load_from_file(model.local_data_path)
|
|
self.model.to(devices.device).eval()
|
|
|
|
def do_upscale(self, img: Image.Image | torch.Tensor | np.ndarray, selected_model: str | None = None, output_type='pil', quiet=False):
|
|
try:
|
|
if (self.model is None) or (self.selected != selected_model):
|
|
self.load_model(selected_model)
|
|
return self.process(img, output_type=output_type, quiet=quiet)
|
|
except Exception as e:
|
|
log.error(f'Spandrel: {e}')
|
|
errors.display(e, "Spandrel")
|
|
return img
|