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Add missing k-diffusion sigma params to infotext #12358

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merged 4 commits into from
Aug 6, 2023
Merged

Add missing k-diffusion sigma params to infotext #12358

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31506f0
Add sigma params to infotext
catboxanon Aug 6, 2023
1e84823
Merge branch 'dev' into sigma-infotext
catboxanon Aug 6, 2023
ce4be66
Read kdiffusion sigma params from opts
catboxanon Aug 6, 2023
8f31b13
Assume 0 = inf for s_tmax
catboxanon Aug 6, 2023
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4 changes: 4 additions & 0 deletions modules/generation_parameters_copypaste.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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'),
Expand Down
34 changes: 33 additions & 1 deletion modules/sd_samplers_kdiffusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -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
Expand All @@ -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):
Expand Down
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