You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
114 lines
4.4 KiB
Python
114 lines
4.4 KiB
Python
import logging
|
|
from typing import TYPE_CHECKING, Union
|
|
|
|
from imaginairy.config import DEFAULT_UPSCALE_MODEL
|
|
from imaginairy.utils import get_device
|
|
|
|
if TYPE_CHECKING:
|
|
from PIL import Image
|
|
|
|
from imaginairy.schema import LazyLoadingImage
|
|
|
|
|
|
upscale_model_lookup = {
|
|
# RealESRGAN
|
|
"ultrasharp": "https://huggingface.co/lokCX/4x-Ultrasharp/resolve/1856559b50de25116a7c07261177dd128f1f5664/4x-UltraSharp.pth",
|
|
"realesrgan-x4-plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
|
|
"realesrgan-x2-plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
|
|
# ESRGAN
|
|
"esrgan-x4": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
|
|
# HAT
|
|
"real-hat": "https://huggingface.co/imaginairy/model-weights/resolve/main/weights/super-resolution/hat/Real_HAT_GAN_SRx4.safetensors",
|
|
"real-hat-sharper": "https://huggingface.co/imaginairy/model-weights/resolve/main/weights/super-resolution/hat/Real_HAT_GAN_sharper.safetensors",
|
|
"4xNomos8kHAT-L": "https://huggingface.co/imaginairy/model-weights/resolve/main/weights/super-resolution/hat/4xNomos8kHAT-L_otf.safetensors",
|
|
}
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def upscale_image(
|
|
img: "Union[LazyLoadingImage, Image.Image]",
|
|
upscaler_model: str = DEFAULT_UPSCALE_MODEL,
|
|
tile_size: int = 512,
|
|
tile_pad: int = 50,
|
|
repetition: int = 1,
|
|
device=None,
|
|
) -> "Image.Image":
|
|
"""
|
|
Upscales an image using a specified super-resolution model.
|
|
|
|
Supports various upscaling models defined in the `upscale_model_lookup` dictionary, as well as direct URLs to models.
|
|
It can process the image in tiles (to manage memory usage on large images) and supports multiple passes for upscaling.
|
|
|
|
Args:
|
|
img (LazyLoadingImage | Image.Image): The input image to be upscaled.
|
|
upscaler_model (str, optional): Key for the upscaling model to use. Defaults to DEFAULT_UPSCALE_MODEL.
|
|
tile_size (int, optional): Size of the tiles used for processing the image. Defaults to 512.
|
|
tile_pad (int, optional): Padding size for each tile. Defaults to 50.
|
|
repetition (int, optional): Number of times the upscaling is repeated. Defaults to 1.
|
|
device: The device (CPU/GPU) to be used for computation. If None, the best available device is used.
|
|
|
|
Returns:
|
|
Image.Image: The upscaled image as a PIL Image object.
|
|
"""
|
|
import torch
|
|
import torchvision.transforms.functional as F
|
|
from spandrel import ImageModelDescriptor, ModelLoader
|
|
|
|
from imaginairy.utils.downloads import get_cached_url_path
|
|
from imaginairy.utils.tile_up import tile_process
|
|
|
|
device = device or get_device()
|
|
|
|
if upscaler_model in upscale_model_lookup:
|
|
model_url = upscale_model_lookup[upscaler_model]
|
|
model_path = get_cached_url_path(model_url)
|
|
elif upscaler_model.startswith(("https://", "http://")):
|
|
model_url = upscaler_model
|
|
model_path = get_cached_url_path(model_url)
|
|
else:
|
|
model_path = upscaler_model
|
|
|
|
model = ModelLoader().load_from_file(model_path)
|
|
logger.debug(f"Upscaling image with model {model.architecture}@{upscaler_model}")
|
|
|
|
assert isinstance(model, ImageModelDescriptor)
|
|
|
|
model.to(torch.device(device)).eval()
|
|
|
|
image_tensor = load_image(img).to(device)
|
|
with torch.no_grad():
|
|
for _ in range(repetition):
|
|
if tile_size > 0:
|
|
image_tensor = tile_process(
|
|
image_tensor,
|
|
scale=model.scale,
|
|
model=model,
|
|
tile_size=tile_size,
|
|
tile_pad=tile_pad,
|
|
)
|
|
else:
|
|
image_tensor = model(image_tensor)
|
|
|
|
image_tensor = image_tensor.squeeze(0)
|
|
image = F.to_pil_image(image_tensor)
|
|
image = image.resize((img.width * model.scale, img.height * model.scale))
|
|
|
|
return image
|
|
|
|
|
|
def load_image(img: "Union[LazyLoadingImage, Image.Image]"):
|
|
"""
|
|
Converts a LazyLoadingImage or PIL Image into a PyTorch tensor.
|
|
"""
|
|
from torchvision import transforms
|
|
|
|
from imaginairy.schema import LazyLoadingImage
|
|
|
|
if isinstance(img, LazyLoadingImage):
|
|
img = img.as_pillow()
|
|
transform = transforms.ToTensor()
|
|
image_tensor = transform(img)
|
|
|
|
image_tensor = image_tensor.unsqueeze(0)
|
|
return image_tensor.to(get_device())
|