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@@ -1,41 +1,28 @@
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import torch
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import torch
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import inspect
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import inspect
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import k_diffusion.sampling
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import k_diffusion.sampling
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-from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
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+from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser, sd_schedulers
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from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
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from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
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-from modules.sd_samplers_custom_schedulers import sgm_uniform
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from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
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from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
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from modules.shared import opts
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from modules.shared import opts
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import modules.shared as shared
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import modules.shared as shared
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samplers_k_diffusion = [
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samplers_k_diffusion = [
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- ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
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- ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
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- ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
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- ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
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+ ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {'scheduler': 'karras'}),
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+ ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
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+ ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde'], {'scheduler': 'exponential', "brownian_noise": True}),
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+ ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
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+ ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
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+ ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
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('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
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('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
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('Euler', 'sample_euler', ['k_euler'], {}),
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('Euler', 'sample_euler', ['k_euler'], {}),
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('LMS', 'sample_lms', ['k_lms'], {}),
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('LMS', 'sample_lms', ['k_lms'], {}),
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('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
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('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
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- ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
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- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
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- ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
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- ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
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- ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
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- ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
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- ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
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- ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
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- ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
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- ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
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- ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
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- ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
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+ ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "second_order": True}),
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+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
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- ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
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- ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
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- ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
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- ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
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('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
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('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
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]
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]
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@@ -59,13 +46,7 @@ sampler_extra_params = {
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}
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}
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k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
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k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
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-k_diffusion_scheduler = {
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- 'Automatic': None,
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- 'karras': k_diffusion.sampling.get_sigmas_karras,
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- 'exponential': k_diffusion.sampling.get_sigmas_exponential,
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- 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential,
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- 'sgm_uniform' : sgm_uniform,
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-}
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+k_diffusion_scheduler = {x.name: x.function for x in sd_schedulers.schedulers}
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class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
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class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
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@@ -98,47 +79,44 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
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steps += 1 if discard_next_to_last_sigma else 0
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steps += 1 if discard_next_to_last_sigma else 0
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+ scheduler_name = p.scheduler or 'Automatic'
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+ if scheduler_name == 'Automatic':
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+ scheduler_name = self.config.options.get('scheduler', None)
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+
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+ scheduler = sd_schedulers.schedulers_map.get(scheduler_name)
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+
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+ m_sigma_min, m_sigma_max = self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()
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+ sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
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+
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if p.sampler_noise_scheduler_override:
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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sigmas = p.sampler_noise_scheduler_override(steps)
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- elif opts.k_sched_type != "Automatic":
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- m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
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- sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
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- sigmas_kwargs = {
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- 'sigma_min': sigma_min,
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- 'sigma_max': sigma_max,
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- }
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-
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- sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
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- p.extra_generation_params["Schedule type"] = opts.k_sched_type
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-
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- if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
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+ elif scheduler is None or scheduler.function is None:
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+ sigmas = self.model_wrap.get_sigmas(steps)
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+ else:
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+ sigmas_kwargs = {'sigma_min': sigma_min, 'sigma_max': sigma_max}
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+
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+ p.extra_generation_params["Schedule type"] = scheduler.label
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+
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+ if opts.sigma_min != 0 and opts.sigma_min != m_sigma_min:
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sigmas_kwargs['sigma_min'] = opts.sigma_min
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sigmas_kwargs['sigma_min'] = opts.sigma_min
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p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
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p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
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- if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
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+
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+ if opts.sigma_max != 0 and opts.sigma_max != m_sigma_max:
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sigmas_kwargs['sigma_max'] = opts.sigma_max
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sigmas_kwargs['sigma_max'] = opts.sigma_max
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p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
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p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
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- default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
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-
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- if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
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+ if scheduler.default_rho != -1 and opts.rho != 0 and opts.rho != scheduler.default_rho:
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sigmas_kwargs['rho'] = opts.rho
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sigmas_kwargs['rho'] = opts.rho
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p.extra_generation_params["Schedule rho"] = opts.rho
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p.extra_generation_params["Schedule rho"] = opts.rho
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- if opts.k_sched_type == 'sgm_uniform':
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- # Ensure the "step" will be target step + 1
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- steps += 1 if not discard_next_to_last_sigma else 0
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+
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+ if scheduler.need_inner_model:
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sigmas_kwargs['inner_model'] = self.model_wrap
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sigmas_kwargs['inner_model'] = self.model_wrap
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- sigmas_kwargs.pop('rho', None)
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- sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
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- elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
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- sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
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+ if scheduler.name == "sgm_uniform": # XXX check this
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+ # Ensure the "step" will be target step + 1
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+ steps += 1 if not discard_next_to_last_sigma else 0
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- sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
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- elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
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- m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
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- sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
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- else:
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- sigmas = self.model_wrap.get_sigmas(steps)
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+ sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=shared.device)
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if discard_next_to_last_sigma:
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if discard_next_to_last_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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