From 843b2b64fcd41be4a9e934ba83a3a499c7aff5c0 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Mon, 12 Sep 2022 18:40:06 +0300
Subject:  Instance of CUDA out of memory on a low-res batch, even with
 --opt-split-attention-v1 (found cause) #255

---
 modules/codeformer_model.py | 32 ++++++++++++++++++--------------
 1 file changed, 18 insertions(+), 14 deletions(-)

(limited to 'modules/codeformer_model.py')

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index 946b4a30..fd1da692 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -5,7 +5,7 @@ import traceback
 import cv2
 import torch
 
-from modules import shared
+from modules import shared, devices
 from modules.paths import script_path
 import modules.shared
 import modules.face_restoration
@@ -51,6 +51,7 @@ def setup_codeformer():
             def create_models(self):
 
                 if self.net is not None and self.face_helper is not None:
+                    self.net.to(shared.device)
                     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)
@@ -61,9 +62,9 @@ def setup_codeformer():
 
                 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
+                self.net = net
+                self.face_helper = face_helper
+                self.net.to(shared.device)
 
                 return net, face_helper
 
@@ -72,20 +73,20 @@ def setup_codeformer():
 
                 original_resolution = np_image.shape[0:2]
 
-                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()
+                self.create_models()
+                self.face_helper.clean_all()
+                self.face_helper.read_image(np_image)
+                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+                self.face_helper.align_warp_face()
 
-                for idx, cropped_face in enumerate(face_helper.cropped_faces):
+                for idx, cropped_face in enumerate(self.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=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
+                            output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
                             restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                         del output
                         torch.cuda.empty_cache()
@@ -94,16 +95,19 @@ def setup_codeformer():
                         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)
+                    self.face_helper.add_restored_face(restored_face)
 
-                face_helper.get_inverse_affine(None)
+                self.face_helper.get_inverse_affine(None)
 
-                restored_img = face_helper.paste_faces_to_input_image()
+                restored_img = self.face_helper.paste_faces_to_input_image()
                 restored_img = restored_img[:, :, ::-1]
 
                 if original_resolution != restored_img.shape[0:2]:
                     restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
 
+                if shared.opts.face_restoration_unload:
+                    self.net.to(devices.cpu)
+
                 return restored_img
 
         global have_codeformer
-- 
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