mirror of
https://github.com/vladmandic/sdnext.git
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99 lines
4.5 KiB
Python
99 lines
4.5 KiB
Python
from PIL import Image
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import numpy as np
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import torch
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from rich.progress import Progress, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn, TimeElapsedColumn
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from modules import devices
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from modules.postprocess.scunet_model_arch import SCUNet as net
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from modules.shared import opts, log, console
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from modules.upscaler import Upscaler, compile_upscaler
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class UpscalerSCUNet(Upscaler):
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def __init__(self, dirname):
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self.name = "SCUNet"
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self.user_path = dirname
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super().__init__()
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self.scalers = self.find_scalers()
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self.models = {}
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def load_model(self, path: str):
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info = self.find_model(path)
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if info is None:
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return
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if self.models.get(info.local_data_path, None) is not None:
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log.debug(f"Upscaler cached: type={self.name} model={info.local_data_path}")
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model=self.models[info.local_data_path]
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else:
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model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
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model.load_state_dict(torch.load(info.local_data_path), strict=True)
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model.eval()
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log.info(f"Upscaler loaded: type={self.name} model={info.local_data_path}")
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for _, v in model.named_parameters():
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v.requires_grad = False
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model = model.to(devices.device)
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model = compile_upscaler(model)
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self.models[info.local_data_path] = model
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return model
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@staticmethod
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@torch.no_grad()
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def tiled_inference(img, model):
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# test the image tile by tile
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h, w = img.shape[2:]
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tile = opts.upscaler_tile_size
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tile_overlap = opts.upscaler_tile_overlap
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if tile == 0:
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return model(img)
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assert tile % 8 == 0, "tile size should be a multiple of window_size"
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sf = 1
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stride = tile - tile_overlap
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h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
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w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
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E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=devices.device)
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W = torch.zeros_like(E, dtype=devices.dtype, device=devices.device)
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with Progress(TextColumn('[cyan]{task.description}'), BarColumn(), TaskProgressColumn(), TimeRemainingColumn(), TimeElapsedColumn(), console=console) as progress:
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task = progress.add_task(description="Upscaling", total=len(h_idx_list) * len(w_idx_list))
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for h_idx in h_idx_list:
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for w_idx in w_idx_list:
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in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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E[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch)
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W[
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..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
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].add_(out_patch_mask)
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progress.update(task, advance=1, description="Upscaling")
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output = E.div_(W)
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return output
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def do_upscale(self, img: Image.Image, selected_file):
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devices.torch_gc()
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model = self.load_model(selected_file)
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if model is None:
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return img
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tile = opts.upscaler_tile_size
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h, w = img.height, img.width
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np_img = np.array(img)
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np_img = np_img[:, :, ::-1] # RGB to BGR
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np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
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torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(devices.device) # type: ignore
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if tile > h or tile > w:
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_img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
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_img[:, :, :h, :w] = torch_img # pad image
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torch_img = _img
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torch_output = self.tiled_inference(torch_img, model).squeeze(0)
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torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
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np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
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del torch_img, torch_output
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devices.torch_gc()
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output = np_output.transpose((1, 2, 0)) # CHW to HWC
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output = output[:, :, ::-1] # BGR to RGB
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img = Image.fromarray((output * 255).astype(np.uint8))
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if opts.upscaler_unload and selected_file in self.models:
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del self.models[selected_file]
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log.debug(f"Upscaler unloaded: type={self.name} model={selected_file}")
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devices.torch_gc(force=True)
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return img
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