diff options
Diffstat (limited to 'extensions-builtin/ScuNET/scripts/scunet_model.py')
| -rw-r--r-- | extensions-builtin/ScuNET/scripts/scunet_model.py | 48 |
1 files changed, 10 insertions, 38 deletions
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index f799cb76..fe5e5a19 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -1,13 +1,9 @@ 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 +from modules import devices, errors, modelloader, script_callbacks, shared, upscaler_utils class UpscalerScuNET(modules.upscaler.Upscaler): @@ -40,46 +36,23 @@ class UpscalerScuNET(modules.upscaler.Upscaler): 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 + img = upscaler_utils.upscale_2( + img, + model, + tile_size=shared.opts.SCUNET_tile, + tile_overlap=shared.opts.SCUNET_tile_overlap, + scale=1, # ScuNET is a denoising model, not an upscaler + desc='ScuNET', + ) 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)) + return img def load_model(self, path: str): device = devices.get_device_for('scunet') @@ -93,7 +66,6 @@ class UpscalerScuNET(modules.upscaler.Upscaler): 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")) |