Split history sd_samplers.py to sd_samplers_common.py

from collections import namedtuple, deque

import numpy as np

from math import floor

import torch

import tqdm

from PIL import Image

import inspect

import k_diffusion.sampling

import torchsde._brownian.brownian_interval

import ldm.models.diffusion.ddim

import ldm.models.diffusion.plms

from modules import prompt_parser, devices, processing, images, sd_vae_approx

from modules.shared import opts, cmd_opts, state

import modules.shared as shared

from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback

SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])

samplers_k_diffusion = [

    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),

    ('Euler', 'sample_euler', ['k_euler'], {}),

    ('LMS', 'sample_lms', ['k_lms'], {}),

    ('Heun', 'sample_heun', ['k_heun'], {}),

    ('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}),

    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),

    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),

    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),

    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),

    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),

    ('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}),

    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),

    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),

    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),

    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),

]

samplers_data_k_diffusion = [

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

    for label, funcname, aliases, options in samplers_k_diffusion

    if hasattr(k_diffusion.sampling, funcname)

]

all_samplers = [

    *samplers_data_k_diffusion,

    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),

    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),

]

all_samplers_map = {x.name: x for x in all_samplers}

samplers = []

samplers_for_img2img = []

samplers_map = {}

def create_sampler(name, model):

    if name is not None:

        config = all_samplers_map.get(name, None)

    else:

        config = all_samplers[0]

    assert config is not None, f'bad sampler name: {name}'

    sampler = config.constructor(model)

    sampler.config = config

    return sampler

def set_samplers():

    global samplers, samplers_for_img2img

    hidden = set(opts.hide_samplers)

    hidden_img2img = set(opts.hide_samplers + ['PLMS'])

    samplers = [x for x in all_samplers if x.name not in hidden]

    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]

    samplers_map.clear()

    for sampler in all_samplers:

        samplers_map[sampler.name.lower()] = sampler.name

        for alias in sampler.aliases:

            samplers_map[alias.lower()] = sampler.name

set_samplers()

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'],

}

def setup_img2img_steps(p, steps=None):

    if opts.img2img_fix_steps or steps is not None:

        requested_steps = (steps or p.steps)

        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0

        t_enc = requested_steps - 1

    else:

        steps = p.steps

        t_enc = int(min(p.denoising_strength, 0.999) * steps)

    return steps, t_enc

approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}

def single_sample_to_image(sample, approximation=None):

    if approximation is None:

        approximation = approximation_indexes.get(opts.show_progress_type, 0)

    if approximation == 2:

        x_sample = sd_vae_approx.cheap_approximation(sample)

    elif approximation == 1:

        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()

    else:

        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]

    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)

    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

    x_sample = x_sample.astype(np.uint8)

    return Image.fromarray(x_sample)

def sample_to_image(samples, index=0, approximation=None):

    return single_sample_to_image(samples[index], approximation)

def samples_to_image_grid(samples, approximation=None):

    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])

def store_latent(decoded):

    state.current_latent = decoded

    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:

        if not shared.parallel_processing_allowed:

            shared.state.assign_current_image(sample_to_image(decoded))

class InterruptedException(BaseException):

    pass

class VanillaStableDiffusionSampler:

    def __init__(self, constructor, sd_model):

        self.sampler = constructor(sd_model)

        self.is_plms = hasattr(self.sampler, 'p_sample_plms')

        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim

        self.mask = None

        self.nmask = None

        self.init_latent = None

        self.sampler_noises = None

        self.step = 0

        self.stop_at = None

        self.eta = None

        self.default_eta = 0.0

        self.config = None

        self.last_latent = None

        self.conditioning_key = sd_model.model.conditioning_key

    def number_of_needed_noises(self, p):

        return 0

    def launch_sampling(self, steps, func):

        state.sampling_steps = steps

        state.sampling_step = 0

        try:

            return func()

        except InterruptedException:

            return self.last_latent

    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):

        if state.interrupted or state.skipped:

            raise InterruptedException

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

            raise InterruptedException

        # Have to unwrap the inpainting conditioning here to perform pre-processing

        image_conditioning = None

        if isinstance(cond, dict):

            image_conditioning = cond["c_concat"][0]

            cond = cond["c_crossattn"][0]

            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]

        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)

        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'

        cond = tensor

        # for DDIM, shapes must match, we can't just process cond and uncond independently;

        # filling unconditional_conditioning with repeats of the last vector to match length is

        # not 100% correct but should work well enough

        if unconditional_conditioning.shape[1] < cond.shape[1]:

            last_vector = unconditional_conditioning[:, -1:]

            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])

            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])

        elif unconditional_conditioning.shape[1] > cond.shape[1]:

            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]

        if self.mask is not None:

            img_orig = self.sampler.model.q_sample(self.init_latent, ts)

            x_dec = img_orig * self.mask + self.nmask * x_dec

        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.

        # Note that they need to be lists because it just concatenates them later.

        if image_conditioning is not None:

            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}

            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)

        if self.mask is not None:

            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]

        else:

            self.last_latent = res[1]

        store_latent(self.last_latent)

        self.step += 1

        state.sampling_step = self.step

        shared.total_tqdm.update()

        return res

    def initialize(self, p):

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

        for fieldname in ['p_sample_ddim', 'p_sample_plms']:

            if hasattr(self.sampler, fieldname):

                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)

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

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

    def adjust_steps_if_invalid(self, p, num_steps):

        if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):

            valid_step = 999 / (1000 // num_steps)

            if valid_step == floor(valid_step):

                return int(valid_step) + 1

        return num_steps

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

        steps, t_enc = setup_img2img_steps(p, steps)

        steps = self.adjust_steps_if_invalid(p, steps)

        self.initialize(p)

        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)

        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)

        self.init_latent = x

        self.last_latent = x

        self.step = 0

        # Wrap the conditioning models with additional image conditioning for inpainting model

        if image_conditioning is not None:

            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}

            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}

        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))

        return samples

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

        self.initialize(p)

        self.init_latent = None

        self.last_latent = x

        self.step = 0

        steps = self.adjust_steps_if_invalid(p, steps or p.steps)

        # Wrap the conditioning models with additional image conditioning for inpainting model

        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape

        if image_conditioning is not None:

            conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}

            unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}

        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])

        return samples_ddim

class CFGDenoiser(torch.nn.Module):

    def __init__(self, model):

        super().__init__()

        self.inner_model = model

        self.mask = None

        self.nmask = None

        self.init_latent = None

        self.step = 0

    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):

        denoised_uncond = x_out[-uncond.shape[0]:]

        denoised = torch.clone(denoised_uncond)

        for i, conds in enumerate(conds_list):

            for cond_index, weight in conds:

                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)

        return denoised

    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):

        if state.interrupted or state.skipped:

            raise InterruptedException

        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)

        batch_size = len(conds_list)

        repeats = [len(conds_list[i]) for i in range(batch_size)]

        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])

        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])

        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])

        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)

        cfg_denoiser_callback(denoiser_params)

        x_in = denoiser_params.x

        image_cond_in = denoiser_params.image_cond

        sigma_in = denoiser_params.sigma

        if tensor.shape[1] == uncond.shape[1]:

            cond_in = torch.cat([tensor, uncond])

            if shared.batch_cond_uncond:

                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})

            else:

                x_out = torch.zeros_like(x_in)

                for batch_offset in range(0, x_out.shape[0], batch_size):

                    a = batch_offset

                    b = a + batch_size

                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})

        else:

            x_out = torch.zeros_like(x_in)

            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size

            for batch_offset in range(0, tensor.shape[0], batch_size):

                a = batch_offset

                b = min(a + batch_size, tensor.shape[0])

                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})

            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})

        devices.test_for_nans(x_out, "unet")

        if opts.live_preview_content == "Prompt":

            store_latent(x_out[0:uncond.shape[0]])

        elif opts.live_preview_content == "Negative prompt":

            store_latent(x_out[-uncond.shape[0]:])

        denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)

        if self.mask is not None:

            denoised = self.init_latent * self.mask + self.nmask * denoised

        self.step += 1

        return denoised

class TorchHijack:

    def __init__(self, sampler_noises):

        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based

        # implementation.

        self.sampler_noises = deque(sampler_noises)

    def __getattr__(self, item):

        if item == 'randn_like':

            return self.randn_like

        if hasattr(torch, item):

            return getattr(torch, item)

        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))

    def randn_like(self, x):

        if self.sampler_noises:

            noise = self.sampler_noises.popleft()

            if noise.shape == x.shape:

                return noise

        if x.device.type == 'mps':

            return torch.randn_like(x, device=devices.cpu).to(x.device)

        else:

            return torch.randn_like(x)

# MPS fix for randn in torchsde

def torchsde_randn(size, dtype, device, seed):

    if device.type == 'mps':

        generator = torch.Generator(devices.cpu).manual_seed(int(seed))

        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)

    else:

        generator = torch.Generator(device).manual_seed(int(seed))

        return torch.randn(size, dtype=dtype, device=device, generator=generator)

torchsde._brownian.brownian_interval._randn = torchsde_randn

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 = 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.default_eta = 1.0

        self.config = None

        self.last_latent = None

        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":

            store_latent(latent)

        self.last_latent = latent

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

            raise 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 InterruptedException:

            return self.last_latent

    def number_of_needed_noises(self, p):

        return p.steps

    def initialize(self, p):

        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.eta = p.eta or opts.eta_ancestral

        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:

            extra_params_kwargs['eta'] = self.eta

        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 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)

        else:

            sigmas = self.model_wrap.get_sigmas(steps)

        if discard_next_to_last_sigma:

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

        return sigmas

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

        steps, t_enc = 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)

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

            extra_params_kwargs['sigma_max'] = sigma_sched[0]

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

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

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

            extra_params_kwargs['sigma_sched'] = sigma_sched

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

            extra_params_kwargs['sigmas'] = sigma_sched

        self.model_wrap_cfg.init_latent = x

        self.last_latent = x

        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={

            'cond': conditioning, 

            'image_cond': image_conditioning, 

            'uncond': unconditional_conditioning, 

            'cond_scale': p.cfg_scale

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

        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)

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

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

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