Loading modules/generation_parameters_copypaste.py +4 −0 Original line number Original line Diff line number Diff line Loading @@ -328,6 +328,10 @@ infotext_to_setting_name_mapping = [ ('Noise multiplier', 'initial_noise_multiplier'), ('Noise multiplier', 'initial_noise_multiplier'), ('Eta', 'eta_ancestral'), ('Eta', 'eta_ancestral'), ('Eta DDIM', 'eta_ddim'), ('Eta DDIM', 'eta_ddim'), ('Sigma churn', 's_churn'), ('Sigma tmin', 's_tmin'), ('Sigma tmax', 's_tmax'), ('Sigma noise', 's_noise'), ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'), ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'), ('UniPC variant', 'uni_pc_variant'), ('UniPC variant', 'uni_pc_variant'), ('UniPC skip type', 'uni_pc_skip_type'), ('UniPC skip type', 'uni_pc_skip_type'), Loading modules/sd_samplers_kdiffusion.py +33 −1 Original line number Original line Diff line number Diff line Loading @@ -4,6 +4,7 @@ import inspect import k_diffusion.sampling import k_diffusion.sampling from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra from modules.processing import StableDiffusionProcessing from modules.shared import opts, state from modules.shared import opts, state import modules.shared as shared import modules.shared as shared from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback Loading Loading @@ -280,6 +281,14 @@ class KDiffusionSampler: self.last_latent = None self.last_latent = None self.s_min_uncond = None self.s_min_uncond = None # NOTE: These are also defined in the StableDiffusionProcessing class. # They should have been here to begin with but we're going to # leave that class __init__ signature alone. self.s_churn = 0.0 self.s_tmin = 0.0 self.s_tmax = float('inf') self.s_noise = 1.0 self.conditioning_key = sd_model.model.conditioning_key self.conditioning_key = sd_model.model.conditioning_key def callback_state(self, d): def callback_state(self, d): Loading Loading @@ -314,7 +323,7 @@ class KDiffusionSampler: def number_of_needed_noises(self, p): def number_of_needed_noises(self, p): return p.steps return p.steps def initialize(self, p): def initialize(self, p: StableDiffusionProcessing): self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None self.model_wrap_cfg.step = 0 self.model_wrap_cfg.step = 0 Loading @@ -335,6 +344,29 @@ class KDiffusionSampler: extra_params_kwargs['eta'] = self.eta extra_params_kwargs['eta'] = self.eta if len(self.extra_params) > 0: s_churn = p.override_settings.get('s_churn', p.s_churn) s_tmin = p.override_settings.get('s_tmin', p.s_tmin) s_tmax = p.override_settings.get('s_tmax', p.s_tmax) s_noise = p.override_settings.get('s_noise', p.s_noise) if s_churn != self.s_churn: extra_params_kwargs['s_churn'] = s_churn p.s_churn = s_churn p.extra_generation_params['Sigma churn'] = s_churn if s_tmin != self.s_tmin: extra_params_kwargs['s_tmin'] = s_churn p.s_tmin = s_tmin p.extra_generation_params['Sigma tmin'] = s_tmin if s_tmax != self.s_tmax: extra_params_kwargs['s_tmax'] = s_churn p.s_tmax = s_tmax p.extra_generation_params['Sigma tmax'] = s_tmax if s_noise != self.s_noise: extra_params_kwargs['s_noise'] = s_churn p.s_noise = s_noise p.extra_generation_params['Sigma noise'] = s_noise return extra_params_kwargs return extra_params_kwargs def get_sigmas(self, p, steps): def get_sigmas(self, p, steps): Loading Loading
modules/generation_parameters_copypaste.py +4 −0 Original line number Original line Diff line number Diff line Loading @@ -328,6 +328,10 @@ infotext_to_setting_name_mapping = [ ('Noise multiplier', 'initial_noise_multiplier'), ('Noise multiplier', 'initial_noise_multiplier'), ('Eta', 'eta_ancestral'), ('Eta', 'eta_ancestral'), ('Eta DDIM', 'eta_ddim'), ('Eta DDIM', 'eta_ddim'), ('Sigma churn', 's_churn'), ('Sigma tmin', 's_tmin'), ('Sigma tmax', 's_tmax'), ('Sigma noise', 's_noise'), ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'), ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'), ('UniPC variant', 'uni_pc_variant'), ('UniPC variant', 'uni_pc_variant'), ('UniPC skip type', 'uni_pc_skip_type'), ('UniPC skip type', 'uni_pc_skip_type'), Loading
modules/sd_samplers_kdiffusion.py +33 −1 Original line number Original line Diff line number Diff line Loading @@ -4,6 +4,7 @@ import inspect import k_diffusion.sampling import k_diffusion.sampling from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra from modules.processing import StableDiffusionProcessing from modules.shared import opts, state from modules.shared import opts, state import modules.shared as shared import modules.shared as shared from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback Loading Loading @@ -280,6 +281,14 @@ class KDiffusionSampler: self.last_latent = None self.last_latent = None self.s_min_uncond = None self.s_min_uncond = None # NOTE: These are also defined in the StableDiffusionProcessing class. # They should have been here to begin with but we're going to # leave that class __init__ signature alone. self.s_churn = 0.0 self.s_tmin = 0.0 self.s_tmax = float('inf') self.s_noise = 1.0 self.conditioning_key = sd_model.model.conditioning_key self.conditioning_key = sd_model.model.conditioning_key def callback_state(self, d): def callback_state(self, d): Loading Loading @@ -314,7 +323,7 @@ class KDiffusionSampler: def number_of_needed_noises(self, p): def number_of_needed_noises(self, p): return p.steps return p.steps def initialize(self, p): def initialize(self, p: StableDiffusionProcessing): self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None self.model_wrap_cfg.step = 0 self.model_wrap_cfg.step = 0 Loading @@ -335,6 +344,29 @@ class KDiffusionSampler: extra_params_kwargs['eta'] = self.eta extra_params_kwargs['eta'] = self.eta if len(self.extra_params) > 0: s_churn = p.override_settings.get('s_churn', p.s_churn) s_tmin = p.override_settings.get('s_tmin', p.s_tmin) s_tmax = p.override_settings.get('s_tmax', p.s_tmax) s_noise = p.override_settings.get('s_noise', p.s_noise) if s_churn != self.s_churn: extra_params_kwargs['s_churn'] = s_churn p.s_churn = s_churn p.extra_generation_params['Sigma churn'] = s_churn if s_tmin != self.s_tmin: extra_params_kwargs['s_tmin'] = s_churn p.s_tmin = s_tmin p.extra_generation_params['Sigma tmin'] = s_tmin if s_tmax != self.s_tmax: extra_params_kwargs['s_tmax'] = s_churn p.s_tmax = s_tmax p.extra_generation_params['Sigma tmax'] = s_tmax if s_noise != self.s_noise: extra_params_kwargs['s_noise'] = s_churn p.s_noise = s_noise p.extra_generation_params['Sigma noise'] = s_noise return extra_params_kwargs return extra_params_kwargs def get_sigmas(self, p, steps): def get_sigmas(self, p, steps): Loading
