From d23a46ceaa76af2847f11172f32c92665c268b1b Mon Sep 17 00:00:00 2001
From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com>
Date: Thu, 20 Oct 2022 23:49:14 +0300
Subject: [PATCH] Different approach to skip/interrupt with highres fix

---
 modules/processing.py  | 4 +++-
 modules/sd_samplers.py | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/modules/processing.py b/modules/processing.py
index 6324ca91..bcb0c32c 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -587,7 +587,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
+
+        return samples
 
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index b58e810b..7ff77c01 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler:
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
 
         self.init_latent = x
+        self.last_latent = x
         self.step = 0
 
         samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler:
         self.initialize(p)
 
         self.init_latent = None
+        self.last_latent = x
         self.step = 0
 
         steps = steps or p.steps
@@ -388,6 +390,7 @@ class KDiffusionSampler:
             extra_params_kwargs['sigmas'] = sigma_sched
 
         self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
 
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
@@ -414,6 +417,7 @@ class KDiffusionSampler:
         else:
             extra_params_kwargs['sigmas'] = sigmas
 
+        self.last_latent = x
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         return samples