Merge pull request #12358 from catboxanon/sigma-infotext

Add missing k-diffusion sigma params to infotext

modules/generation_parameters_copypaste.py

@@ -328,6 +328,10 @@ def parse_generation_parameters(x: str):
  ('Noise multiplier', 'initial_noise_multiplier'),
  ('Eta', 'eta_ancestral'),
  ('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'),
  ('UniPC variant', 'uni_pc_variant'),
  ('UniPC skip type', 'uni_pc_skip_type'),

modules/sd_samplers_kdiffusion.py

@@ -4,6 +4,7 @@
 import k_diffusion.sampling
 from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
 
+from modules.processing import StableDiffusionProcessing
 from modules.shared import opts, state
 import modules.shared as shared
 from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
@@ -280,6 +281,14 @@ def __init__(self, funcname, sd_model):
  self.last_latent = 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
 
  def callback_state(self, d):
@@ -314,7 +323,7 @@ def launch_sampling(self, steps, func):
  def number_of_needed_noises(self, p):
  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.nmask = p.nmask if hasattr(p, 'nmask') else None
  self.model_wrap_cfg.step = 0
@@ -335,6 +344,29 @@ def initialize(self, p):
 
  extra_params_kwargs['eta'] = self.eta
 
+ if len(self.extra_params) > 0:
+ s_churn = getattr(opts, 's_churn', p.s_churn)
+ s_tmin = getattr(opts, 's_tmin', p.s_tmin)
+ s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
+ s_noise = getattr(opts, '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_tmin
+ 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_tmax
+ 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_noise
+ p.s_noise = s_noise
+ p.extra_generation_params['Sigma noise'] = s_noise
+
  return extra_params_kwargs
 
  def get_sigmas(self, p, steps):