239 lines
12 KiB
Python
239 lines
12 KiB
Python
import torch
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import inspect
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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.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
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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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import modules.shared as shared
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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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('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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('LMS', 'sample_lms', ['k_lms'], {}),
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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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('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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('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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]
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samplers_data_k_diffusion = [
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sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
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for label, funcname, aliases, options in samplers_k_diffusion
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if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
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]
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sampler_extra_params = {
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'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_dpm_fast': ['s_noise'],
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'sample_dpm_2_ancestral': ['s_noise'],
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'sample_dpmpp_2s_ancestral': ['s_noise'],
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'sample_dpmpp_sde': ['s_noise'],
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'sample_dpmpp_2m_sde': ['s_noise'],
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'sample_dpmpp_3m_sde': ['s_noise'],
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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_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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}
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class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
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@property
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def inner_model(self):
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if self.model_wrap is None:
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denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
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self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
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return self.model_wrap
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class KDiffusionSampler(sd_samplers_common.Sampler):
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def __init__(self, funcname, sd_model, options=None):
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super().__init__(funcname)
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self.extra_params = sampler_extra_params.get(funcname, [])
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self.options = options or {}
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self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
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self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
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self.model_wrap = self.model_wrap_cfg.inner_model
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def get_sigmas(self, p, steps):
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discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
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if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
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discard_next_to_last_sigma = True
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p.extra_generation_params["Discard penultimate sigma"] = True
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steps += 1 if discard_next_to_last_sigma else 0
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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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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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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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if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
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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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if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
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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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default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
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if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_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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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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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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if discard_next_to_last_sigma:
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sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
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return sigmas
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
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sigmas = self.get_sigmas(p, steps)
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sigma_sched = sigmas[steps - t_enc - 1:]
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xi = x + noise * sigma_sched[0]
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if opts.img2img_extra_noise > 0:
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p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
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extra_noise_params = ExtraNoiseParams(noise, x)
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extra_noise_callback(extra_noise_params)
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noise = extra_noise_params.noise
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xi += noise * opts.img2img_extra_noise
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extra_params_kwargs = self.initialize(p)
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parameters = inspect.signature(self.func).parameters
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if 'sigma_min' in parameters:
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## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
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extra_params_kwargs['sigma_min'] = sigma_sched[-2]
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if 'sigma_max' in parameters:
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extra_params_kwargs['sigma_max'] = sigma_sched[0]
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if 'n' in parameters:
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extra_params_kwargs['n'] = len(sigma_sched) - 1
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if 'sigma_sched' in parameters:
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extra_params_kwargs['sigma_sched'] = sigma_sched
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if 'sigmas' in parameters:
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extra_params_kwargs['sigmas'] = sigma_sched
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if self.config.options.get('brownian_noise', False):
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noise_sampler = self.create_noise_sampler(x, sigmas, p)
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extra_params_kwargs['noise_sampler'] = noise_sampler
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if self.config.options.get('solver_type', None) == 'heun':
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extra_params_kwargs['solver_type'] = 'heun'
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self.model_wrap_cfg.init_latent = x
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self.last_latent = x
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self.sampler_extra_args = {
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'cond': conditioning,
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'image_cond': image_conditioning,
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'uncond': unconditional_conditioning,
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'cond_scale': p.cfg_scale,
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's_min_uncond': self.s_min_uncond
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}
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samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
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if self.model_wrap_cfg.padded_cond_uncond:
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p.extra_generation_params["Pad conds"] = True
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return samples
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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steps = steps or p.steps
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sigmas = self.get_sigmas(p, steps)
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x = x * sigmas[0]
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extra_params_kwargs = self.initialize(p)
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parameters = inspect.signature(self.func).parameters
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if 'n' in parameters:
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extra_params_kwargs['n'] = steps
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if 'sigma_min' in parameters:
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extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
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extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
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if 'sigmas' in parameters:
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extra_params_kwargs['sigmas'] = sigmas
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if self.config.options.get('brownian_noise', False):
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noise_sampler = self.create_noise_sampler(x, sigmas, p)
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extra_params_kwargs['noise_sampler'] = noise_sampler
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if self.config.options.get('solver_type', None) == 'heun':
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extra_params_kwargs['solver_type'] = 'heun'
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self.last_latent = x
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self.sampler_extra_args = {
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'cond': conditioning,
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'image_cond': image_conditioning,
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'uncond': unconditional_conditioning,
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'cond_scale': p.cfg_scale,
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's_min_uncond': self.s_min_uncond
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}
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samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
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if self.model_wrap_cfg.padded_cond_uncond:
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p.extra_generation_params["Pad conds"] = True
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return samples
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