Fix memory leak and reduce memory usage

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
 

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()

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), ''
 
 

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]
+                return gfpgan_fix_faces(np_image)
-                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
 
         shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception:

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

webui.py

@@ -22,7 +22,10 @@ import modules.txt2img
 import modules.img2img
 import modules.swinir as swinir
 import modules.sd_models
+from torch.nn.functional import silu
+import ldm
 
+ldm.modules.diffusionmodules.model.nonlinearity = silu
 
 modules.codeformer_model.setup_codeformer()
 modules.gfpgan_model.setup_gfpgan()