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scheduler selection in main UI

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AUTOMATIC1111
2024-03-20 09:17:11 +03:00
parent 060e55dfe3
commit 25cd53d775
11 changed files with 175 additions and 108 deletions
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modules/sd_samplers_kdiffusion.py
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@@ -1,41 +1,28 @@
import torch
import inspect
import k_diffusion.sampling
from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser, sd_schedulers
from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
from modules.sd_samplers_custom_schedulers import sgm_uniform
from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
from modules.shared import opts
import modules.shared as shared
samplers_k_diffusion = [
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {'scheduler': 'karras'}),
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde'], {'scheduler': 'exponential', "brownian_noise": True}),
('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
('Euler', 'sample_euler', ['k_euler'], {}),
('LMS', 'sample_lms', ['k_lms'], {}),
('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "second_order": True}),
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
('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}),
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
]
@@ -59,13 +46,7 @@ sampler_extra_params = {
}
k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
k_diffusion_scheduler = {
'Automatic': None,
'karras': k_diffusion.sampling.get_sigmas_karras,
'exponential': k_diffusion.sampling.get_sigmas_exponential,
'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential,
'sgm_uniform' : sgm_uniform,
}
k_diffusion_scheduler = {x.name: x.function for x in sd_schedulers.schedulers}
class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
@@ -98,47 +79,44 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
steps += 1 if discard_next_to_last_sigma else 0
scheduler_name = p.scheduler or 'Automatic'
if scheduler_name == 'Automatic':
scheduler_name = self.config.options.get('scheduler', None)
scheduler = sd_schedulers.schedulers_map.get(scheduler_name)
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) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
if p.sampler_noise_scheduler_override:
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) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
sigmas_kwargs = {
'sigma_min': sigma_min,
'sigma_max': sigma_max,
}
elif scheduler is None or scheduler.function is None:
sigmas = self.model_wrap.get_sigmas(steps)
else:
sigmas_kwargs = {'sigma_min': sigma_min, 'sigma_max': sigma_max}
sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
p.extra_generation_params["Schedule type"] = opts.k_sched_type
p.extra_generation_params["Schedule type"] = scheduler.label
if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
if opts.sigma_min != 0 and opts.sigma_min != m_sigma_min:
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:
if opts.sigma_max != 0 and opts.sigma_max != m_sigma_max:
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:
if scheduler.default_rho != -1 and opts.rho != 0 and opts.rho != scheduler.default_rho:
sigmas_kwargs['rho'] = opts.rho
p.extra_generation_params["Schedule rho"] = opts.rho
if opts.k_sched_type == 'sgm_uniform':
if scheduler.need_inner_model:
sigmas_kwargs['inner_model'] = self.model_wrap
if scheduler.name == "sgm_uniform": # XXX check this
# Ensure the "step" will be target step + 1
steps += 1 if not discard_next_to_last_sigma else 0
sigmas_kwargs['inner_model'] = self.model_wrap
sigmas_kwargs.pop('rho', None)
sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
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())
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
else:
sigmas = self.model_wrap.get_sigmas(steps)
sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=shared.device)
if discard_next_to_last_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])