Fix memory leak and reduce memory usage

5 files changed, 39 insertions, 16 deletions

diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index 8fbdea24..2177291a 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -89,7 +89,7 @@ def setup_codeformer():
                             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()

+                        devices.torch_gc()

                     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))

@@ -106,7 +106,9 @@ def setup_codeformer():
                     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)

+                    self.net = None

+                    self.face_helper = None

+                    devices.torch_gc()

 

                 return restored_img

 

diff --git a/modules/devices.py b/modules/devices.py
index 07bb2339..df63dd88 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,4 +1,5 @@
 import torch
+import gc
 
 # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
 from modules import errors
@@ -17,8 +18,8 @@ def get_optimal_device():
 
     return cpu
 
-
 def torch_gc():
+    gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
diff --git a/modules/extras.py b/modules/extras.py
index 9a825530..38b86167 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -98,6 +98,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
 

         outputs.append(image)

 

+    devices.torch_gc()

+

     return outputs, plaintext_to_html(info), ''

 

 

diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 44c5dc6c..b1288f0c 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -49,6 +49,7 @@ def gfpgan():
 

 

 def gfpgan_fix_faces(np_image):

+    global loaded_gfpgan_model

     model = gfpgan()

 

     np_image_bgr = np_image[:, :, ::-1]

@@ -56,7 +57,9 @@ def gfpgan_fix_faces(np_image):
     np_image = gfpgan_output_bgr[:, :, ::-1]

 

     if shared.opts.face_restoration_unload:

-        model.gfpgan.to(devices.cpu)

+        del model

+        loaded_gfpgan_model = None

+        devices.torch_gc()

 

     return np_image

 

@@ -83,11 +86,7 @@ def setup_gfpgan():
                 return "GFPGAN"

 

             def restore(self, np_image):

-                np_image_bgr = np_image[:, :, ::-1]

-                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)

-                np_image = gfpgan_output_bgr[:, :, ::-1]

-

-                return np_image

+                return gfpgan_fix_faces(np_image)

 

         shared.face_restorers.append(FaceRestorerGFPGAN())

     except Exception:

diff --git a/modules/processing.py b/modules/processing.py
index 4ecdfcd2..de5cda79 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -12,7 +12,7 @@ import cv2
 from skimage import exposure

 

 import modules.sd_hijack

-from modules import devices, prompt_parser, masking

+from modules import devices, prompt_parser, masking, lowvram

 from modules.sd_hijack import model_hijack

 from modules.sd_samplers import samplers, samplers_for_img2img

 from modules.shared import opts, cmd_opts, state

@@ -335,7 +335,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         if state.job_count == -1:

             state.job_count = p.n_iter

 

-        for n in range(p.n_iter):

+    for n in range(p.n_iter):

+        with torch.no_grad(), precision_scope("cuda"), ema_scope():

             if state.interrupted:

                 break

 

@@ -368,22 +369,32 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)

             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

 

+            del samples_ddim

+

+            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

+                lowvram.send_everything_to_cpu()

+

+            devices.torch_gc()

+

             if opts.filter_nsfw:

                 import modules.safety as safety

                 x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)

 

-            for i, x_sample in enumerate(x_samples_ddim):

+        for i, x_sample in enumerate(x_samples_ddim):

+            with torch.no_grad(), precision_scope("cuda"), ema_scope():

                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

                 x_sample = x_sample.astype(np.uint8)

 

-                if p.restore_faces:

+            if p.restore_faces:

+                with torch.no_grad(), precision_scope("cuda"), ema_scope():

                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:

                         images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")

 

-                    devices.torch_gc()

-

                     x_sample = modules.face_restoration.restore_faces(x_sample)

 

+                devices.torch_gc()

+

+            with torch.no_grad(), precision_scope("cuda"), ema_scope():

                 image = Image.fromarray(x_sample)

 

                 if p.color_corrections is not None and i < len(p.color_corrections):

@@ -411,8 +422,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 infotexts.append(infotext(n, i))

                 output_images.append(image)

 

-            state.nextjob()

+        del x_samples_ddim 

 

+        devices.torch_gc()

+

+        state.nextjob()

+

+    with torch.no_grad(), precision_scope("cuda"), ema_scope():

         p.color_corrections = None

 

         index_of_first_image = 0

@@ -648,4 +664,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         if self.mask is not None:

             samples = samples * self.nmask + self.init_latent * self.mask

 

+        del x

+        devices.torch_gc()

+

         return samples