michael/stable-diffusion-webui
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Switch to a continous blend for cond. image.

Browse source
This commit is contained in:
random_thoughtss 2022-10-25 13:15:08 -07:00
parent 605d27687f
commit 8b4f32779f
3 changed files with 8 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

9
modules/processing.py
View file

@ -769,9 +769,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
# Create another latent image, this time with a masked version of the original input. # Create another latent image, this time with a masked version of the original input.
conditioning_mask = conditioning_mask.to(image.device) conditioning_mask = conditioning_mask.to(image.device)
conditioning_image = image # Smoothly interpolate between the masked and unmasked latent conditioning image.
if getattr(self, "inpainting_mask_image", shared.opts.inpainting_mask_image): conditioning_image = torch.lerp(
conditioning_image = conditioning_image * (1.0 - conditioning_mask) image,
image * (1.0 - conditioning_mask),
getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
)
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))

2
modules/shared.py
View file

@ -320,7 +320,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}), 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
"inpainting_mask_image": OptionInfo(True, "Mask original image for conditioning used by inpainting model."), "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}),
})) }))

5
scripts/xy_grid.py
View file

@ -153,9 +153,6 @@ def str_permutations(x):
"""dummy function for specifying it in AxisOption's type when you want to get a list of permutations""" """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
return x return x
def str_to_bool(x):
return "true" in x.lower().strip()
AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"]) AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"]) AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
@ -180,7 +177,7 @@ axis_options = [
AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None), AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None), AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None), AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
AxisOption("Mask Conditioning Image", str_to_bool, apply_field("inpainting_mask_image"), format_value_add_label, None), AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
] ]