split sd_samplers_kdiffusion into two

from collections import deque

import torch

import inspect

import k_diffusion.sampling

from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra

from modules import prompt_parser, devices, sd_samplers_common

from modules.processing import StableDiffusionProcessing

from modules.shared import opts, state

import modules.shared as shared

from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback

from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback

from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback

samplers_k_diffusion = [

    ('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}),

    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": 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 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}),

    ('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 Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),

    ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),

    ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),

]

samplers_data_k_diffusion = [

    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)

    for label, funcname, aliases, options in samplers_k_diffusion

    if callable(funcname) or hasattr(k_diffusion.sampling, funcname)

]

sampler_extra_params = {

    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],

}

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

}

def catenate_conds(conds):

    if not isinstance(conds[0], dict):

        return devices.randn_like(x)

class KDiffusionSampler:

    def __init__(self, funcname, sd_model):

        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser

        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)

        self.funcname = funcname

        self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)

        self.extra_params = sampler_extra_params.get(funcname, [])

        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)

        self.sampler_noises = None

        self.stop_at = None

        self.eta = None

        self.config = None  # set by the function calling the constructor

        self.last_latent = None

        self.s_min_uncond = None

        # NOTE: These are also defined in the StableDiffusionProcessing class.

        # They should have been here to begin with but we're going to

        # leave that class __init__ signature alone.

        self.s_churn = 0.0

        self.s_tmin = 0.0

        self.s_tmax = float('inf')

        self.s_noise = 1.0

        self.conditioning_key = sd_model.model.conditioning_key

    def callback_state(self, d):

        step = d['i']

        latent = d["denoised"]

        if opts.live_preview_content == "Combined":

            sd_samplers_common.store_latent(latent)

        self.last_latent = latent

        if self.stop_at is not None and step > self.stop_at:

            raise sd_samplers_common.InterruptedException

        state.sampling_step = step

        shared.total_tqdm.update()

    def launch_sampling(self, steps, func):

        state.sampling_steps = steps

        state.sampling_step = 0

        try:

            return func()

        except RecursionError:

            print(

                'Encountered RecursionError during sampling, returning last latent. '

                'rho >5 with a polyexponential scheduler may cause this error. '

                'You should try to use a smaller rho value instead.'

            )

            return self.last_latent

        except sd_samplers_common.InterruptedException:

            return self.last_latent

    def number_of_needed_noises(self, p):

        return p.steps

    def initialize(self, p: StableDiffusionProcessing):

        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None

        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None

        self.model_wrap_cfg.step = 0

        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)

        self.eta = p.eta if p.eta is not None else opts.eta_ancestral

        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)

        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])

        extra_params_kwargs = {}

        for param_name in self.extra_params:

            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:

                extra_params_kwargs[param_name] = getattr(p, param_name)

        if 'eta' in inspect.signature(self.func).parameters:

            if self.eta != 1.0:

                p.extra_generation_params["Eta"] = self.eta

            extra_params_kwargs['eta'] = self.eta

        if len(self.extra_params) > 0:

            s_churn = getattr(opts, 's_churn', p.s_churn)

            s_tmin = getattr(opts, 's_tmin', p.s_tmin)

            s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf

            s_noise = getattr(opts, 's_noise', p.s_noise)

            if s_churn != self.s_churn:

                extra_params_kwargs['s_churn'] = s_churn

                p.s_churn = s_churn

                p.extra_generation_params['Sigma churn'] = s_churn

            if s_tmin != self.s_tmin:

                extra_params_kwargs['s_tmin'] = s_tmin

                p.s_tmin = s_tmin

                p.extra_generation_params['Sigma tmin'] = s_tmin

            if s_tmax != self.s_tmax:

                extra_params_kwargs['s_tmax'] = s_tmax

                p.s_tmax = s_tmax

                p.extra_generation_params['Sigma tmax'] = s_tmax

            if s_noise != self.s_noise:

                extra_params_kwargs['s_noise'] = s_noise

                p.s_noise = s_noise

                p.extra_generation_params['Sigma noise'] = s_noise

        return extra_params_kwargs

    def get_sigmas(self, p, steps):

        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)

        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:

            discard_next_to_last_sigma = True

            p.extra_generation_params["Discard penultimate sigma"] = True

        steps += 1 if discard_next_to_last_sigma else 0

        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,

            }

            sigmas_func = k_diffusion_scheduler[opts.k_sched_type]

            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["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':

            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)

        if discard_next_to_last_sigma:

            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])

        return sigmas

    def create_noise_sampler(self, x, sigmas, p):

        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""

        if shared.opts.no_dpmpp_sde_batch_determinism:

            return None

        from k_diffusion.sampling import BrownianTreeNoiseSampler

        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()

        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]

        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)

    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)

        sigmas = self.get_sigmas(p, steps)

        sigma_sched = sigmas[steps - t_enc - 1:]

        xi = x + noise * sigma_sched[0]

        extra_params_kwargs = self.initialize(p)

        parameters = inspect.signature(self.func).parameters

        if 'sigma_min' in parameters:

            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last

            extra_params_kwargs['sigma_min'] = sigma_sched[-2]

        if 'sigma_max' in parameters:

            extra_params_kwargs['sigma_max'] = sigma_sched[0]

        if 'n' in parameters:

            extra_params_kwargs['n'] = len(sigma_sched) - 1

        if 'sigma_sched' in parameters:

            extra_params_kwargs['sigma_sched'] = sigma_sched

        if 'sigmas' in parameters:

            extra_params_kwargs['sigmas'] = sigma_sched

        if self.config.options.get('brownian_noise', False):

            noise_sampler = self.create_noise_sampler(x, sigmas, p)

            extra_params_kwargs['noise_sampler'] = noise_sampler

        self.model_wrap_cfg.init_latent = x

        self.last_latent = x

        extra_args = {

            'cond': conditioning,

            'image_cond': image_conditioning,

            'uncond': unconditional_conditioning,

            'cond_scale': p.cfg_scale,

            's_min_uncond': self.s_min_uncond

        }

        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))

        if self.model_wrap_cfg.padded_cond_uncond:

            p.extra_generation_params["Pad conds"] = True

        return samples

    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):

        steps = steps or p.steps

        sigmas = self.get_sigmas(p, steps)

        x = x * sigmas[0]

        extra_params_kwargs = self.initialize(p)

        parameters = inspect.signature(self.func).parameters

        if 'sigma_min' in parameters:

            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()

            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()

            if 'n' in parameters:

                extra_params_kwargs['n'] = steps

        else:

            extra_params_kwargs['sigmas'] = sigmas

        if self.config.options.get('brownian_noise', False):

            noise_sampler = self.create_noise_sampler(x, sigmas, p)

            extra_params_kwargs['noise_sampler'] = noise_sampler

        self.last_latent = x

        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={

            'cond': conditioning,

            'image_cond': image_conditioning,

            'uncond': unconditional_conditioning,

            'cond_scale': p.cfg_scale,

            's_min_uncond': self.s_min_uncond

        }, disable=False, callback=self.callback_state, **extra_params_kwargs))

        if self.model_wrap_cfg.padded_cond_uncond:

            p.extra_generation_params["Pad conds"] = True

        return samples