diff options
Diffstat (limited to 'modules/swinir.py')
| -rw-r--r-- | modules/swinir.py | 123 |
1 files changed, 0 insertions, 123 deletions
diff --git a/modules/swinir.py b/modules/swinir.py deleted file mode 100644 index 8c534495..00000000 --- a/modules/swinir.py +++ /dev/null @@ -1,123 +0,0 @@ -import sys
-import traceback
-import cv2
-import os
-import contextlib
-import numpy as np
-from PIL import Image
-import torch
-import modules.images
-from modules.shared import cmd_opts, opts, device
-from modules.swinir_arch import SwinIR as net
-
-precision_scope = (
- torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-)
-
-
-def load_model(filename, scale=4):
- model = net(
- 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",
- )
-
- pretrained_model = torch.load(filename)
- model.load_state_dict(pretrained_model["params_ema"], strict=True)
- if not cmd_opts.no_half:
- model = model.half()
- return model
-
-
-def load_models(dirname):
- for file in os.listdir(dirname):
- path = os.path.join(dirname, file)
- model_name, extension = os.path.splitext(file)
-
- if extension != ".pt" and extension != ".pth":
- continue
-
- try:
- modules.shared.sd_upscalers.append(UpscalerSwin(path, model_name))
- except Exception:
- print(f"Error loading SwinIR model: {path}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
-
-def upscale(
- img,
- model,
- tile=opts.SWIN_tile,
- tile_overlap=opts.SWIN_tile_overlap,
- window_size=8,
- scale=4,
-):
- 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)
- with torch.no_grad(), precision_scope("cuda"):
- _, _, 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 = output[..., : h_old * scale, : w_old * scale]
- output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
- if output.ndim == 3:
- output = np.transpose(
- output[[2, 1, 0], :, :], (1, 2, 0)
- ) # CHW-RGB to HCW-BGR
- output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
- 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=torch.half, device=device).type_as(img)
- W = torch.zeros_like(E, dtype=torch.half, device=device)
-
- for h_idx in h_idx_list:
- for w_idx in w_idx_list:
- 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)
- output = E.div_(W)
-
- return output
-
-
-class UpscalerSwin(modules.images.Upscaler):
- def __init__(self, filename, title):
- self.name = title
- self.model = load_model(filename)
-
- def do_upscale(self, img):
- model = self.model.to(device)
- img = upscale(img, model)
- return img
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