Commit e8e7fe11 authored by AUTOMATIC's avatar AUTOMATIC
Browse files

updates for the noise schedule settings

parent 654234ec

modules/generation_parameters_copypaste.py

+12 −12
Original line number Diff line number Diff line
@@ -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'),

modules/sd_samplers_kdiffusion.py

+16 −14
Original line number Diff line number Diff line
@@ -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



            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':

            p.extra_generation_params["Schedule type"] = opts.k_sched_type



            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':

modules/shared.py

+4 −4
Original line number Diff line number Diff line
@@ -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"]}),

scripts/xyz_grid.py

+4 −4
Original line number Diff line number Diff line
@@ -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")),

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