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Switch to a continous blend for cond. image.
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@ -769,9 +769,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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# Create another latent image, this time with a masked version of the original input.
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conditioning_mask = conditioning_mask.to(image.device)
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conditioning_image = image
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if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image):
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conditioning_image = conditioning_image * (1.0 - conditioning_mask)
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# Smoothly interpolate between the masked and unmasked latent conditioning image.
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conditioning_image = torch.lerp(
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image,
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image * (1.0 - conditioning_mask),
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getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
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)
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
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@ -320,7 +320,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
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's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
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"inpainting_mask_image": OptionInfo(True, "Mask original image for conditioning used by inpainting model."),
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"inpainting_mask_weight": OptionInfo(1.0, "Blend betweeen an unmasked and masked conditioning image for inpainting models.", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
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}))
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@ -153,9 +153,6 @@ def str_permutations(x):
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"""dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
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return x
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def str_to_bool(x):
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return "true" in x.lower().strip()
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AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
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AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
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@ -180,7 +177,7 @@ axis_options = [
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AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
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AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
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AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
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AxisOption("Mask Conditioning Image", str_to_bool, apply_field("inpainting_mask_image"), format_value_add_label, None),
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AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
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]
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