add edit_image_conditioning from my earlier edits in case there's an attempt to inegrate pix2pix properly
this allows to use pix2pix model in img2img though it won't work well this way
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@ -185,7 +185,12 @@ class StableDiffusionProcessing:
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conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
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conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
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return conditioning
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return conditioning
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def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):
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def edit_image_conditioning(self, source_image):
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
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return conditioning_image
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def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
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self.is_using_inpainting_conditioning = True
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self.is_using_inpainting_conditioning = True
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# Handle the different mask inputs
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# Handle the different mask inputs
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@ -228,6 +233,9 @@ class StableDiffusionProcessing:
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if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
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if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
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return self.depth2img_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image)
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return self.depth2img_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image)
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if self.sd_model.cond_stage_key == "edit":
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return self.edit_image_conditioning(source_image)
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if self.sampler.conditioning_key in {'hybrid', 'concat'}:
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if self.sampler.conditioning_key in {'hybrid', 'concat'}:
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return self.inpainting_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image, latent_image, image_mask=image_mask)
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return self.inpainting_image_conditioning(source_image.float() if devices.unet_needs_upcast else source_image, latent_image, image_mask=image_mask)
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