Removing parts no longer needed to fix vram

2 files changed, 9 insertions, 15 deletions

diff --git a/modules/devices.py b/modules/devices.py
index 6db4e57c..0158b11f 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -1,7 +1,6 @@
 import contextlib
 
 import torch
-import gc
 
 from modules import errors
 
@@ -20,8 +19,8 @@ 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/processing.py b/modules/processing.py
index e7f9c85e..f666ba81 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -345,8 +345,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         if state.job_count == -1:

             state.job_count = p.n_iter

 

-    for n in range(p.n_iter):

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

+        for n in range(p.n_iter):

             if state.interrupted:

                 break

 

@@ -395,22 +394,19 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 import modules.safety as safety

                 x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)

 

-        for i, x_sample in enumerate(x_samples_ddim):

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

+            for i, x_sample in enumerate(x_samples_ddim):

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

                 x_sample = x_sample.astype(np.uint8)

 

-            if p.restore_faces:

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

+                if p.restore_faces:

                     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")

 

-                    x_sample = modules.face_restoration.restore_faces(x_sample)

                     devices.torch_gc()

 

-                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):

@@ -438,13 +434,12 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 infotexts.append(infotext(n, i))

                 output_images.append(image)

 

-        del x_samples_ddim 

+            del x_samples_ddim 

 

-        devices.torch_gc()

+            devices.torch_gc()

 

-        state.nextjob()

+            state.nextjob()

 

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

         p.color_corrections = None

 

         index_of_first_image = 0