From 6a9b33c848281cb02f38764e4f91ef767f5e3edd Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 7 Sep 2022 12:32:28 +0300
Subject: codeformer support

---
 modules/codeformer_model.py | 108 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 108 insertions(+)
 create mode 100644 modules/codeformer_model.py

(limited to 'modules/codeformer_model.py')

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
new file mode 100644
index 00000000..836417c2
--- /dev/null
+++ b/modules/codeformer_model.py
@@ -0,0 +1,108 @@
+import os
+import sys
+import traceback
+import torch
+
+from modules import shared
+from modules.paths import script_path
+import modules.shared
+import modules.face_restoration
+from importlib import reload
+
+# codeformer people made a choice to include modified basicsr librry to their projectwhich makes
+# it utterly impossiblr to use it alongside with other libraries that also use basicsr, like GFPGAN.
+# I am making a choice to include some files from codeformer to work around this issue.
+
+pretrain_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
+
+have_codeformer = False
+
+def setup_codeformer():
+    path = modules.paths.paths.get("CodeFormer", None)
+    if path is None:
+        return
+
+
+    # both GFPGAN and CodeFormer use bascisr, one has it installed from pip the other uses its own
+    #stored_sys_path = sys.path
+    #sys.path = [path] + sys.path
+
+    try:
+        from torchvision.transforms.functional import normalize
+        from modules.codeformer.codeformer_arch import CodeFormer
+        from basicsr.utils.download_util import load_file_from_url
+        from basicsr.utils import imwrite, img2tensor, tensor2img
+        from facelib.utils.face_restoration_helper import FaceRestoreHelper
+        from modules.shared import cmd_opts
+
+        net_class = CodeFormer
+
+        class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
+            def name(self):
+                return "CodeFormer"
+
+            def __init__(self):
+                self.net = None
+                self.face_helper = None
+
+            def create_models(self):
+
+                if self.net is not None and self.face_helper is not None:
+                    return self.net, self.face_helper
+
+                net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(shared.device)
+                ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True)
+                checkpoint = torch.load(ckpt_path)['params_ema']
+                net.load_state_dict(checkpoint)
+                net.eval()
+
+                face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=shared.device)
+
+                if not cmd_opts.unload_gfpgan:
+                    self.net = net
+                    self.face_helper = face_helper
+
+                return net, face_helper
+
+            def restore(self, np_image):
+                np_image = np_image[:, :, ::-1]
+
+                net, face_helper = self.create_models()
+                face_helper.clean_all()
+                face_helper.read_image(np_image)
+                face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+                face_helper.align_warp_face()
+
+                for idx, cropped_face in enumerate(face_helper.cropped_faces):
+                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+                    cropped_face_t = cropped_face_t.unsqueeze(0).to(shared.device)
+
+                    try:
+                        with torch.no_grad():
+                            output = net(cropped_face_t, w=shared.opts.code_former_weight, adain=True)[0]
+                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+                        del output
+                        torch.cuda.empty_cache()
+                    except Exception as error:
+                        print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
+                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+
+                    restored_face = restored_face.astype('uint8')
+                    face_helper.add_restored_face(restored_face)
+
+                face_helper.get_inverse_affine(None)
+
+                restored_img = face_helper.paste_faces_to_input_image()
+                restored_img = restored_img[:, :, ::-1]
+                return restored_img
+
+        global have_codeformer
+        have_codeformer = True
+        shared.face_restorers.append(FaceRestorerCodeFormer())
+
+    except Exception:
+        print("Error setting up CodeFormer:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+   # sys.path = stored_sys_path
-- 
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