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Different approach to skip/interrupt with highres fix
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@ -587,7 +587,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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x = None
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devices.torch_gc()
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return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
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return samples
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class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler:
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x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
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self.init_latent = x
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self.last_latent = x
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self.step = 0
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samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
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@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler:
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self.initialize(p)
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self.init_latent = None
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self.last_latent = x
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self.step = 0
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steps = steps or p.steps
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@ -388,6 +390,7 @@ class KDiffusionSampler:
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extra_params_kwargs['sigmas'] = sigma_sched
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self.model_wrap_cfg.init_latent = x
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self.last_latent = x
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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))
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@ -414,6 +417,7 @@ class KDiffusionSampler:
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else:
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extra_params_kwargs['sigmas'] = sigmas
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self.last_latent = x
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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))
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return samples
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