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@@ -926,9 +926,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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shared.state.nextjob()
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img2img_sampler_name = self.sampler_name
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force_latent_upscaler = shared.opts.data.get('xyz_fallback_sampler')
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if self.sampler_name in ['PLMS']:
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img2img_sampler_name = force_latent_upscaler if force_latent_upscaler != 'None' else shared.opts.fallback_sampler # PLMS does not support img2img, use fallback instead
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if force_latent_upscaler != 'None' and force_latent_upscaler != 'PLMS':
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img2img_sampler_name = force_latent_upscaler
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elif shared.opts.fallback_sampler != 'PLMS':
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img2img_sampler_name = shared.opts.fallback_sampler
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else:
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img2img_sampler_name = 'UniPC'
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self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
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print('HERE', force_latent_upscaler, img2img_sampler_name, self.sampler)
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samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
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noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
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# GC now before running the next img2img to prevent running out of memory
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@@ -942,7 +947,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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# clean patch done by first pass. (clobbering the first patch might be fine? this might be excessive)
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tomesd.remove_patch(self.sd_model)
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log.debug('Temporarily removed token merging optimizations in preparation for next pass')
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sd_models.apply_token_merging(sd_model=self.sd_model, hr=True)
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log.debug('Applied token merging for high-res pass')
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
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