From cc53db6652b11e6f7bca42c3aa93bd6761ed3d3f Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 30 Jul 2023 15:30:33 +0300
Subject: [PATCH] this time for sure

---
 modules/processing.py | 16 +++++++++++++---
 1 file changed, 13 insertions(+), 3 deletions(-)

diff --git a/modules/processing.py b/modules/processing.py
index c4da208f6..3190b9640 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -538,8 +538,12 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+class DecodedSamples(list):
+    already_decoded = True
+
+
 def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
-    samples = []
+    samples = DecodedSamples()
 
     for i in range(batch.shape[0]):
         sample = decode_first_stage(model, batch[i:i + 1])[0]
@@ -793,7 +797,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
-            x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+            if getattr(samples_ddim, 'already_decoded', False):
+                x_samples_ddim = samples_ddim
+            else:
+                x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+
             x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -1161,9 +1169,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
 
+        decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
+
         self.is_hr_pass = False
 
-        return samples
+        return decoded_samples
 
     def close(self):
         super().close()