diff options
Diffstat (limited to 'extensions-builtin/SwinIR/scripts/swinir_model.py')
| -rw-r--r-- | extensions-builtin/SwinIR/scripts/swinir_model.py | 156 |
1 files changed, 52 insertions, 104 deletions
diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py index ae0d0e6a..8a555c79 100644 --- a/extensions-builtin/SwinIR/scripts/swinir_model.py +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -1,20 +1,18 @@ +import logging import sys -import platform import numpy as np import torch from PIL import Image -from tqdm import tqdm from modules import modelloader, devices, script_callbacks, shared -from modules.shared import opts, state -from swinir_model_arch import SwinIR -from swinir_model_arch_v2 import Swin2SR +from modules.shared import opts from modules.upscaler import Upscaler, UpscalerData +from modules.upscaler_utils import tiled_upscale_2 SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth" -device_swinir = devices.get_device_for('swinir') +logger = logging.getLogger(__name__) class UpscalerSwinIR(Upscaler): @@ -37,26 +35,29 @@ class UpscalerSwinIR(Upscaler): scalers.append(model_data) self.scalers = scalers - def do_upscale(self, img, model_file): - use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \ - and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows" + def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image: current_config = (model_file, opts.SWIN_tile) - if use_compile and self._cached_model_config == current_config: + device = self._get_device() + + if self._cached_model_config == current_config: model = self._cached_model else: - self._cached_model = None try: model = self.load_model(model_file) except Exception as e: print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr) return img - model = model.to(device_swinir, dtype=devices.dtype) - if use_compile: - model = torch.compile(model) - self._cached_model = model - self._cached_model_config = current_config - img = upscale(img, model) + self._cached_model = model + self._cached_model_config = current_config + + img = upscale( + img, + model, + tile=opts.SWIN_tile, + tile_overlap=opts.SWIN_tile_overlap, + device=device, + ) devices.torch_gc() return img @@ -69,69 +70,55 @@ class UpscalerSwinIR(Upscaler): ) else: filename = path - if filename.endswith(".v2.pth"): - model = Swin2SR( - upscale=scale, - in_chans=3, - img_size=64, - window_size=8, - img_range=1.0, - depths=[6, 6, 6, 6, 6, 6], - embed_dim=180, - num_heads=[6, 6, 6, 6, 6, 6], - mlp_ratio=2, - upsampler="nearest+conv", - resi_connection="1conv", - ) - params = None - else: - model = SwinIR( - upscale=scale, - in_chans=3, - img_size=64, - window_size=8, - img_range=1.0, - depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], - embed_dim=240, - num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], - mlp_ratio=2, - upsampler="nearest+conv", - resi_connection="3conv", - ) - params = "params_ema" - pretrained_model = torch.load(filename) - if params is not None: - model.load_state_dict(pretrained_model[params], strict=True) - else: - model.load_state_dict(pretrained_model, strict=True) - return model + model_descriptor = modelloader.load_spandrel_model( + filename, + device=self._get_device(), + dtype=devices.dtype, + expected_architecture="SwinIR", + ) + if getattr(opts, 'SWIN_torch_compile', False): + try: + model_descriptor.model.compile() + except Exception: + logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True) + return model_descriptor + + def _get_device(self): + return devices.get_device_for('swinir') def upscale( - img, - model, - tile=None, - tile_overlap=None, - window_size=8, - scale=4, + img, + model, + *, + tile: int, + tile_overlap: int, + window_size=8, + scale=4, + device, ): - tile = tile or opts.SWIN_tile - tile_overlap = tile_overlap or opts.SWIN_tile_overlap - img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype) + img = img.unsqueeze(0).to(device, dtype=devices.dtype) with torch.no_grad(), devices.autocast(): _, _, h_old, w_old = img.size() h_pad = (h_old // window_size + 1) * window_size - h_old w_pad = (w_old // window_size + 1) * window_size - w_old img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :] img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad] - output = inference(img, model, tile, tile_overlap, window_size, scale) + output = tiled_upscale_2( + img, + model, + tile_size=tile, + tile_overlap=tile_overlap, + scale=scale, + device=device, + desc="SwinIR tiles", + ) output = output[..., : h_old * scale, : w_old * scale] output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() if output.ndim == 3: @@ -142,51 +129,12 @@ def upscale( return Image.fromarray(output, "RGB") -def inference(img, model, tile, tile_overlap, window_size, scale): - # test the image tile by tile - b, c, h, w = img.size() - tile = min(tile, h, w) - assert tile % window_size == 0, "tile size should be a multiple of window_size" - sf = scale - - stride = tile - tile_overlap - h_idx_list = list(range(0, h - tile, stride)) + [h - tile] - w_idx_list = list(range(0, w - tile, stride)) + [w - tile] - E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img) - W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir) - - with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: - for h_idx in h_idx_list: - if state.interrupted or state.skipped: - break - - for w_idx in w_idx_list: - if state.interrupted or state.skipped: - break - - in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] - out_patch = model(in_patch) - out_patch_mask = torch.ones_like(out_patch) - - E[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch) - W[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch_mask) - pbar.update(1) - output = E.div_(W) - - return output - - def on_ui_settings(): import gradio as gr 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"))) 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"))) - if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows - shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) + shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) script_callbacks.on_ui_settings(on_ui_settings) |