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import contextlib |
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import os |
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import numpy as np |
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import torch |
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from PIL import Image |
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from basicsr.utils.download_util import load_file_from_url |
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from tqdm import tqdm |
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from modules import modelloader, devices, script_callbacks, shared |
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from modules.shared import cmd_opts, opts, state |
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from swinir_model_arch import SwinIR as net |
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from swinir_model_arch_v2 import Swin2SR as net2 |
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from modules.upscaler import Upscaler, UpscalerData |
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device_swinir = devices.get_device_for('swinir') |
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class UpscalerSwinIR(Upscaler): |
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def __init__(self, dirname): |
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self.name = "SwinIR" |
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self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \ |
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"/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ |
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"-L_x4_GAN.pth " |
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self.model_name = "SwinIR 4x" |
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self.user_path = dirname |
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super().__init__() |
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scalers = [] |
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model_files = self.find_models(ext_filter=[".pt", ".pth"]) |
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for model in model_files: |
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if "http" in model: |
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name = self.model_name |
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else: |
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name = modelloader.friendly_name(model) |
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model_data = UpscalerData(name, model, self) |
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scalers.append(model_data) |
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self.scalers = scalers |
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def do_upscale(self, img, model_file): |
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model = self.load_model(model_file) |
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if model is None: |
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return img |
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model = model.to(device_swinir, dtype=devices.dtype) |
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img = upscale(img, model) |
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try: |
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torch.cuda.empty_cache() |
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except: |
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pass |
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return img |
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def load_model(self, path, scale=4): |
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if "http" in path: |
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dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth") |
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filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True) |
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else: |
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filename = path |
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if filename is None or not os.path.exists(filename): |
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return None |
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if filename.endswith(".v2.pth"): |
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model = net2( |
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upscale=scale, |
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in_chans=3, |
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img_size=64, |
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window_size=8, |
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img_range=1.0, |
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depths=[6, 6, 6, 6, 6, 6], |
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embed_dim=180, |
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num_heads=[6, 6, 6, 6, 6, 6], |
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mlp_ratio=2, |
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upsampler="nearest+conv", |
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resi_connection="1conv", |
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) |
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params = None |
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else: |
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model = net( |
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upscale=scale, |
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in_chans=3, |
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img_size=64, |
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window_size=8, |
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img_range=1.0, |
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depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], |
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embed_dim=240, |
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num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], |
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mlp_ratio=2, |
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upsampler="nearest+conv", |
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resi_connection="3conv", |
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) |
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params = "params_ema" |
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pretrained_model = torch.load(filename) |
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if params is not None: |
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model.load_state_dict(pretrained_model[params], strict=True) |
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else: |
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model.load_state_dict(pretrained_model, strict=True) |
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return model |
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def upscale( |
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img, |
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model, |
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tile=None, |
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tile_overlap=None, |
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window_size=8, |
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scale=4, |
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): |
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tile = tile or opts.SWIN_tile |
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tile_overlap = tile_overlap or opts.SWIN_tile_overlap |
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img = np.array(img) |
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img = img[:, :, ::-1] |
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img = np.moveaxis(img, 2, 0) / 255 |
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img = torch.from_numpy(img).float() |
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img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype) |
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with torch.no_grad(), devices.autocast(): |
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_, _, h_old, w_old = img.size() |
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h_pad = (h_old // window_size + 1) * window_size - h_old |
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w_pad = (w_old // window_size + 1) * window_size - w_old |
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img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :] |
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img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad] |
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output = inference(img, model, tile, tile_overlap, window_size, scale) |
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output = output[..., : h_old * scale, : w_old * scale] |
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output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() |
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if output.ndim == 3: |
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output = np.transpose( |
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output[[2, 1, 0], :, :], (1, 2, 0) |
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) |
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output = (output * 255.0).round().astype(np.uint8) |
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return Image.fromarray(output, "RGB") |
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def inference(img, model, tile, tile_overlap, window_size, scale): |
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b, c, h, w = img.size() |
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tile = min(tile, h, w) |
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assert tile % window_size == 0, "tile size should be a multiple of window_size" |
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sf = scale |
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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(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img) |
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W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir) |
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with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: |
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for h_idx in h_idx_list: |
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if state.interrupted or state.skipped: |
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break |
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for w_idx in w_idx_list: |
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if state.interrupted or state.skipped: |
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break |
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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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pbar.update(1) |
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output = E.div_(W) |
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return output |
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def on_ui_settings(): |
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import gradio as gr |
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shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling"))) |
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shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling"))) |
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script_callbacks.on_ui_settings(on_ui_settings) |
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