Different approach to skip/interrupt with highres fix

1 files changed, 4 insertions, 0 deletions

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