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updates for the noise schedule settings

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AUTOMATIC 2023-05-27 19:53:09 +03:00
parent 654234ec56
commit e8e7fe11e9
4 changed files with 35 additions and 33 deletions
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24
modules/generation_parameters_copypaste.py
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@ -306,17 +306,17 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if "RNG" not in res:
res["RNG"] = "GPU"
if "KDiff Schedule Type" not in res:
res["KDiff Schedule Type"] = "Automatic"
if "Schedule type" not in res:
res["Schedule type"] = "Automatic"
if "KDiff Schedule max sigma" not in res:
res["KDiff Schedule max sigma"] = 14.6
if "Schedule max sigma" not in res:
res["Schedule max sigma"] = 0
if "KDiff Schedule min sigma" not in res:
res["KDiff Schedule min sigma"] = 0.3
if "Schedule min sigma" not in res:
res["Schedule min sigma"] = 0
if "KDiff Schedule rho" not in res:
res["KDiff Schedule rho"] = 7.0
if "Schedule rho" not in res:
res["Schedule rho"] = 0
return res
@ -330,10 +330,10 @@ infotext_to_setting_name_mapping = [
('Conditional mask weight', 'inpainting_mask_weight'),
('Model hash', 'sd_model_checkpoint'),
('ENSD', 'eta_noise_seed_delta'),
('KDiff Schedule Type', 'k_sched_type'),
('KDiff Schedule max sigma', 'sigma_max'),
('KDiff Schedule min sigma', 'sigma_min'),
('KDiff Schedule rho', 'rho'),
('Schedule type', 'k_sched_type'),
('Schedule max sigma', 'sigma_max'),
('Schedule min sigma', 'sigma_min'),
('Schedule rho', 'rho'),
('Noise multiplier', 'initial_noise_multiplier'),
('Eta', 'eta_ancestral'),
('Eta DDIM', 'eta_ddim'),

28
modules/sd_samplers_kdiffusion.py
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@ -321,25 +321,27 @@ class KDiffusionSampler:
sigmas = p.sampler_noise_scheduler_override(steps)
elif opts.k_sched_type != "Automatic":
m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
sigma_min, sigma_max = (0.1, 10)
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
sigmas_kwargs = {
'sigma_min': sigma_min if opts.use_old_karras_scheduler_sigmas else m_sigma_min,
'sigma_max': sigma_max if opts.use_old_karras_scheduler_sigmas else m_sigma_max
'sigma_min': sigma_min,
'sigma_max': sigma_max,
}
sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
p.extra_generation_params["KDiff Schedule Type"] = opts.k_sched_type
p.extra_generation_params["Schedule type"] = opts.k_sched_type
if opts.sigma_min != 0.3:
# take 0.0 as model default
sigmas_kwargs['sigma_min'] = opts.sigma_min or m_sigma_min
p.extra_generation_params["KDiff Schedule min sigma"] = opts.sigma_min
if opts.sigma_max != 14.6:
sigmas_kwargs['sigma_max'] = opts.sigma_max or m_sigma_max
p.extra_generation_params["KDiff Schedule max sigma"] = opts.sigma_max
if opts.k_sched_type != 'exponential':
if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
sigmas_kwargs['sigma_min'] = opts.sigma_min
p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
sigmas_kwargs['sigma_max'] = opts.sigma_max
p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
sigmas_kwargs['rho'] = opts.rho
p.extra_generation_params["KDiff Schedule rho"] = opts.rho
p.extra_generation_params["Schedule rho"] = opts.rho
sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':

8
modules/shared.py
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@ -518,10 +518,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_churn': OptionInfo(0.0, "sigma churn", 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}),
'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}),
'sigma_max': OptionInfo(14.6, "sigma max", gr.Number).info("the maximum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),
'sigma_min': OptionInfo(0.3, "sigma min", gr.Number).info("the minimum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),
'rho': OptionInfo(7.0, "rho", gr.Number).info("higher will make a more steep noise scheduler (decrease faster). default for karras is 7.0, for polyexponential is 1.0"),
'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),

8
scripts/xyz_grid.py
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@ -220,10 +220,10 @@ axis_options = [
AxisOption("Sigma min", float, apply_field("s_tmin")),
AxisOption("Sigma max", float, apply_field("s_tmax")),
AxisOption("Sigma noise", float, apply_field("s_noise")),
AxisOption("KDiff Schedule Type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),
AxisOption("KDiff Schedule min sigma", float, apply_override("sigma_min")),
AxisOption("KDiff Schedule max sigma", float, apply_override("sigma_max")),
AxisOption("KDiff Schedule rho", float, apply_override("rho")),
AxisOption("Schedule type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),
AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
AxisOption("Schedule rho", float, apply_override("rho")),
AxisOption("Eta", float, apply_field("eta")),
AxisOption("Clip skip", int, apply_clip_skip),
AxisOption("Denoising", float, apply_field("denoising_strength")),