Update swinir.py

modules/swinir.py

@@ -1,61 +1,85 @@
 import sys
 import traceback
 import cv2
-from collections import OrderedDict
 import os
-import requests
+import contextlib
-from collections import namedtuple
 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(task = "realsr", large_model = True, model_path="C:/sd/ESRGANn/4x-large.pth", scale=4):
 
-    try:
+precision_scope = (
-        modules.shared.sd_upscalers.append(UpscalerSwin("McSwinnySwin"))
+    torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
-    except Exception:
+)
-        print(f"Error loading ESRGAN model", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
-    if not large_model:
-    # use 'nearest+conv' to avoid block artifacts
-        model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
-                    img_range=1., 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')
-    else:
-        # larger model size; use '3conv' to save parameters and memory; use ema for GAN training
-        model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
-                    img_range=1., 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(model_path)
+
+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
 
-    return model.half().to(device)
 
-def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, window_size = 8, scale = 4):
+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.GAN_tile,
+    tile_overlap=opts.GAN_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)
-    model = load_model()
     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, [2])], 2)[:, :, : h_old + h_pad, :]
-        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w_old + w_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[..., : 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 = 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')
+        return Image.fromarray(output, "RGB")
 
 
 def inference(img, model, tile, tile_overlap, window_size, scale):
@@ -66,27 +90,34 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     sf = scale
 
     stride = tile - tile_overlap
-    h_idx_list = list(range(0, h-tile, stride)) + [h-tile]
+    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
-    w_idx_list = list(range(0, w-tile, stride)) + [w-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)
+    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]
+            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)
+            E[
-            W[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch_mask)
+                ..., 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, title):
+    def __init__(self, filename, title):
         self.name = title
+        self.model = load_model(filename)
 
     def do_upscale(self, img):
-        img = upscale(img)
+        model = self.model.to(device)
+        img = upscale(img, model)
         return img