fix for img2img color correction in a batch #3218
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@ -403,8 +403,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if (len(prompts) == 0):
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if (len(prompts) == 0):
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break
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break
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#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
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#c = p.sd_model.get_learned_conditioning(prompts)
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with devices.autocast():
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with devices.autocast():
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uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
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uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
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c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
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c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
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@ -716,6 +714,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
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batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
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if self.overlay_images is not None:
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if self.overlay_images is not None:
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self.overlay_images = self.overlay_images * self.batch_size
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self.overlay_images = self.overlay_images * self.batch_size
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if self.color_corrections is not None and len(self.color_corrections) == 1:
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self.color_corrections = self.color_corrections * self.batch_size
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elif len(imgs) <= self.batch_size:
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elif len(imgs) <= self.batch_size:
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self.batch_size = len(imgs)
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self.batch_size = len(imgs)
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batch_images = np.array(imgs)
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batch_images = np.array(imgs)
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