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add Noise multiplier option to infotext

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AUTOMATIC 2022-12-10 09:51:26 +03:00
parent 8ee1acc1e4
commit bab91b1279
3 changed files with 8 additions and 3 deletions
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1
modules/generation_parameters_copypaste.py
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@ -77,6 +77,7 @@ def integrate_settings_paste_fields(component_dict):
'inpainting_mask_weight': 'Conditional mask weight', 'inpainting_mask_weight': 'Conditional mask weight',
'sd_model_checkpoint': 'Model hash', 'sd_model_checkpoint': 'Model hash',
'eta_noise_seed_delta': 'ENSD', 'eta_noise_seed_delta': 'ENSD',
'initial_noise_multiplier': 'Noise multiplier',
} }
settings_paste_fields = [ settings_paste_fields = [
(component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None))) (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))

8
modules/processing.py
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@ -764,7 +764,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
sampler = None sampler = None
def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs): def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
super().__init__(**kwargs) super().__init__(**kwargs)
self.init_images = init_images self.init_images = init_images
@ -779,6 +779,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.inpaint_full_res = inpaint_full_res self.inpaint_full_res = inpaint_full_res
self.inpaint_full_res_padding = inpaint_full_res_padding self.inpaint_full_res_padding = inpaint_full_res_padding
self.inpainting_mask_invert = inpainting_mask_invert self.inpainting_mask_invert = inpainting_mask_invert
self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
self.mask = None self.mask = None
self.nmask = None self.nmask = None
self.image_conditioning = None self.image_conditioning = None
@ -891,7 +892,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
x = x*shared.opts.initial_noise_multiplier
if self.initial_noise_multiplier != 1.0:
self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
x *= self.initial_noise_multiplier
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning) samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

2
modules/shared.py
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@ -359,7 +359,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"initial_noise_multiplier": OptionInfo(1.0, "Multiply initial noise by this factor, may result in less or more detailed img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }), "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),