InvokeAI/ldm/models/diffusion/ddpm.py

"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""

import torch

import torch.nn as nn
import os
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig
import urllib

from ldm.modules.textual_inversion_manager import TextualInversionManager
from ldm.util import (
    log_txt_as_img,
    exists,
    default,
    ismap,
    isimage,
    mean_flat,
    count_params,
    instantiate_from_config,
)
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import (
    normal_kl,
    DiagonalGaussianDistribution,
)
from ldm.models.autoencoder import (
    VQModelInterface,
    IdentityFirstStage,
    AutoencoderKL,
)
from ldm.modules.diffusionmodules.util import (
    make_beta_schedule,
    extract_into_tensor,
    noise_like,
)
from ldm.models.diffusion.ddim import DDIMSampler


__conditioning_keys__ = {
    'concat': 'c_concat',
    'crossattn': 'c_crossattn',
    'adm': 'y',
}


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


def uniform_on_device(r1, r2, shape, device):
    return (r1 - r2) * torch.rand(*shape, device=device) + r2


class DDPM(pl.LightningModule):
    # classic DDPM with Gaussian diffusion, in image space
    def __init__(
        self,
        unet_config,
        timesteps=1000,
        beta_schedule='linear',
        loss_type='l2',
        ckpt_path=None,
        ignore_keys=[],
        load_only_unet=False,
        monitor='val/loss',
        use_ema=True,
        first_stage_key='image',
        image_size=256,
        channels=3,
        log_every_t=100,
        clip_denoised=True,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
        given_betas=None,
        original_elbo_weight=0.0,
        embedding_reg_weight=0.0,
        v_posterior=0.0,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
        l_simple_weight=1.0,
        conditioning_key=None,
        parameterization='eps',  # all assuming fixed variance schedules
        scheduler_config=None,
        use_positional_encodings=False,
        learn_logvar=False,
        logvar_init=0.0,
    ):
        super().__init__()
        assert parameterization in [
            'eps',
            'x0',
        ], 'currently only supporting "eps" and "x0"'
        self.parameterization = parameterization
        print(
            f'   | {self.__class__.__name__}: Running in {self.parameterization}-prediction mode'
        )
        self.cond_stage_model = None
        self.clip_denoised = clip_denoised
        self.log_every_t = log_every_t
        self.first_stage_key = first_stage_key
        self.image_size = image_size  # try conv?
        self.channels = channels
        self.use_positional_encodings = use_positional_encodings
        self.model = DiffusionWrapper(unet_config, conditioning_key)
        count_params(self.model, verbose=True)
        self.use_ema = use_ema
        if self.use_ema:
            self.model_ema = LitEma(self.model)
            print(f'   | Keeping EMAs of {len(list(self.model_ema.buffers()))}.')

        self.use_scheduler = scheduler_config is not None
        if self.use_scheduler:
            self.scheduler_config = scheduler_config

        self.v_posterior = v_posterior
        self.original_elbo_weight = original_elbo_weight
        self.l_simple_weight = l_simple_weight
        self.embedding_reg_weight = embedding_reg_weight

        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(
                ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
            )

        self.register_schedule(
            given_betas=given_betas,
            beta_schedule=beta_schedule,
            timesteps=timesteps,
            linear_start=linear_start,
            linear_end=linear_end,
            cosine_s=cosine_s,
        )

        self.loss_type = loss_type

        self.learn_logvar = learn_logvar
        self.logvar = torch.full(
            fill_value=logvar_init, size=(self.num_timesteps,)
        )
        if self.learn_logvar:
            self.logvar = nn.Parameter(self.logvar, requires_grad=True)

    def register_schedule(
        self,
        given_betas=None,
        beta_schedule='linear',
        timesteps=1000,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
    ):
        if exists(given_betas):
            betas = given_betas
        else:
            betas = make_beta_schedule(
                beta_schedule,
                timesteps,
                linear_start=linear_start,
                linear_end=linear_end,
                cosine_s=cosine_s,
            )
        alphas = 1.0 - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])

        (timesteps,) = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end
        assert (
            alphas_cumprod.shape[0] == self.num_timesteps
        ), 'alphas have to be defined for each timestep'

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer('betas', to_torch(betas))
        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
        self.register_buffer(
            'alphas_cumprod_prev', to_torch(alphas_cumprod_prev)
        )

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer(
            'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))
        )
        self.register_buffer(
            'sqrt_one_minus_alphas_cumprod',
            to_torch(np.sqrt(1.0 - alphas_cumprod)),
        )
        self.register_buffer(
            'log_one_minus_alphas_cumprod',
            to_torch(np.log(1.0 - alphas_cumprod)),
        )
        self.register_buffer(
            'sqrt_recip_alphas_cumprod',
            to_torch(np.sqrt(1.0 / alphas_cumprod)),
        )
        self.register_buffer(
            'sqrt_recipm1_alphas_cumprod',
            to_torch(np.sqrt(1.0 / alphas_cumprod - 1)),
        )

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = (1 - self.v_posterior) * betas * (
            1.0 - alphas_cumprod_prev
        ) / (1.0 - alphas_cumprod) + self.v_posterior * betas
        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
        self.register_buffer(
            'posterior_variance', to_torch(posterior_variance)
        )
        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
        self.register_buffer(
            'posterior_log_variance_clipped',
            to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
        )
        self.register_buffer(
            'posterior_mean_coef1',
            to_torch(
                betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
            ),
        )
        self.register_buffer(
            'posterior_mean_coef2',
            to_torch(
                (1.0 - alphas_cumprod_prev)
                * np.sqrt(alphas)
                / (1.0 - alphas_cumprod)
            ),
        )

        if self.parameterization == 'eps':
            lvlb_weights = self.betas**2 / (
                2
                * self.posterior_variance
                * to_torch(alphas)
                * (1 - self.alphas_cumprod)
            )
        elif self.parameterization == 'x0':
            lvlb_weights = (
                0.5
                * np.sqrt(torch.Tensor(alphas_cumprod))
                / (2.0 * 1 - torch.Tensor(alphas_cumprod))
            )
        else:
            raise NotImplementedError('mu not supported')
        # TODO how to choose this term
        lvlb_weights[0] = lvlb_weights[1]
        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
        assert not torch.isnan(self.lvlb_weights).all()

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.model.parameters())
            self.model_ema.copy_to(self.model)
            if context is not None:
                print(f'{context}: Switched to EMA weights')
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.model.parameters())
                if context is not None:
                    print(f'{context}: Restored training weights')

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location='cpu')
        if 'state_dict' in list(sd.keys()):
            sd = sd['state_dict']
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print('Deleting key {} from state_dict.'.format(k))
                    del sd[k]
        missing, unexpected = (
            self.load_state_dict(sd, strict=False)
            if not only_model
            else self.model.load_state_dict(sd, strict=False)
        )
        print(
            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
        )
        if len(missing) > 0:
            print(f'Missing Keys: {missing}')
        if len(unexpected) > 0:
            print(f'Unexpected Keys: {unexpected}')

    def q_mean_variance(self, x_start, t):
        """
        Get the distribution q(x_t | x_0).
        :param x_start: the [N x C x ...] tensor of noiseless inputs.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
        """
        mean = (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape)
            * x_start
        )
        variance = extract_into_tensor(
            1.0 - self.alphas_cumprod, t, x_start.shape
        )
        log_variance = extract_into_tensor(
            self.log_one_minus_alphas_cumprod, t, x_start.shape
        )
        return mean, variance, log_variance

    def predict_start_from_noise(self, x_t, t, noise):
        return (
            extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape)
            * x_t
            - extract_into_tensor(
                self.sqrt_recipm1_alphas_cumprod, t, x_t.shape
            )
            * noise
        )

    def q_posterior(self, x_start, x_t, t):
        posterior_mean = (
            extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape)
            * x_start
            + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape)
            * x_t
        )
        posterior_variance = extract_into_tensor(
            self.posterior_variance, t, x_t.shape
        )
        posterior_log_variance_clipped = extract_into_tensor(
            self.posterior_log_variance_clipped, t, x_t.shape
        )
        return (
            posterior_mean,
            posterior_variance,
            posterior_log_variance_clipped,
        )

    def p_mean_variance(self, x, t, clip_denoised: bool):
        model_out = self.model(x, t)
        if self.parameterization == 'eps':
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == 'x0':
            x_recon = model_out
        if clip_denoised:
            x_recon.clamp_(-1.0, 1.0)

        (
            model_mean,
            posterior_variance,
            posterior_log_variance,
        ) = self.q_posterior(x_start=x_recon, x_t=x, t=t)
        return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
        b, *_, device = *x.shape, x.device
        model_mean, _, model_log_variance = self.p_mean_variance(
            x=x, t=t, clip_denoised=clip_denoised
        )
        noise = noise_like(x.shape, device, repeat_noise)
        # no noise when t == 0
        nonzero_mask = (1 - (t == 0).float()).reshape(
            b, *((1,) * (len(x.shape) - 1))
        )
        return (
            model_mean
            + nonzero_mask * (0.5 * model_log_variance).exp() * noise
        )

    @torch.no_grad()
    def p_sample_loop(self, shape, return_intermediates=False):
        device = self.betas.device
        b = shape[0]
        img = torch.randn(shape, device=device)
        intermediates = [img]
        for i in tqdm(
            reversed(range(0, self.num_timesteps)),
            desc='Sampling t',
            total=self.num_timesteps,
            dynamic_ncols=True,
        ):
            img = self.p_sample(
                img,
                torch.full((b,), i, device=device, dtype=torch.long),
                clip_denoised=self.clip_denoised,
            )
            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
                intermediates.append(img)
        if return_intermediates:
            return img, intermediates
        return img

    @torch.no_grad()
    def sample(self, batch_size=16, return_intermediates=False):
        image_size = self.image_size
        channels = self.channels
        return self.p_sample_loop(
            (batch_size, channels, image_size, image_size),
            return_intermediates=return_intermediates,
        )

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape)
            * x_start
            + extract_into_tensor(
                self.sqrt_one_minus_alphas_cumprod, t, x_start.shape
            )
            * noise
        )

    def get_loss(self, pred, target, mean=True):
        if self.loss_type == 'l1':
            loss = (target - pred).abs()
            if mean:
                loss = loss.mean()
        elif self.loss_type == 'l2':
            if mean:
                loss = torch.nn.functional.mse_loss(target, pred)
            else:
                loss = torch.nn.functional.mse_loss(
                    target, pred, reduction='none'
                )
        else:
            raise NotImplementedError("unknown loss type '{loss_type}'")

        return loss

    def p_losses(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        model_out = self.model(x_noisy, t)

        loss_dict = {}
        if self.parameterization == 'eps':
            target = noise
        elif self.parameterization == 'x0':
            target = x_start
        else:
            raise NotImplementedError(
                f'Paramterization {self.parameterization} not yet supported'
            )

        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])

        log_prefix = 'train' if self.training else 'val'

        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
        loss_simple = loss.mean() * self.l_simple_weight

        loss_vlb = (self.lvlb_weights[t] * loss).mean()
        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})

        loss = loss_simple + self.original_elbo_weight * loss_vlb

        loss_dict.update({f'{log_prefix}/loss': loss})

        return loss, loss_dict

    def forward(self, x, *args, **kwargs):
        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
        t = torch.randint(
            0, self.num_timesteps, (x.shape[0],), device=self.device
        ).long()
        return self.p_losses(x, t, *args, **kwargs)

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = rearrange(x, 'b h w c -> b c h w')
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    def shared_step(self, batch):
        x = self.get_input(batch, self.first_stage_key)
        loss, loss_dict = self(x)
        return loss, loss_dict

    def training_step(self, batch, batch_idx):
        loss, loss_dict = self.shared_step(batch)

        self.log_dict(
            loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
        )

        self.log(
            'global_step',
            self.global_step,
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=False,
        )

        if self.use_scheduler:
            lr = self.optimizers().param_groups[0]['lr']
            self.log(
                'lr_abs',
                lr,
                prog_bar=True,
                logger=True,
                on_step=True,
                on_epoch=False,
            )

        return loss

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        _, loss_dict_no_ema = self.shared_step(batch)
        with self.ema_scope():
            _, loss_dict_ema = self.shared_step(batch)
            loss_dict_ema = {
                key + '_ema': loss_dict_ema[key] for key in loss_dict_ema
            }
        self.log_dict(
            loss_dict_no_ema,
            prog_bar=False,
            logger=True,
            on_step=False,
            on_epoch=True,
        )
        self.log_dict(
            loss_dict_ema,
            prog_bar=False,
            logger=True,
            on_step=False,
            on_epoch=True,
        )

    def on_train_batch_end(self, *args, **kwargs):
        if self.use_ema:
            self.model_ema(self.model)

    def _get_rows_from_list(self, samples):
        n_imgs_per_row = len(samples)
        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
        return denoise_grid

    @torch.no_grad()
    def log_images(
        self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs
    ):
        log = dict()
        x = self.get_input(batch, self.first_stage_key)
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        x = x.to(self.device)[:N]
        log['inputs'] = x

        # get diffusion row
        diffusion_row = list()
        x_start = x[:n_row]

        for t in range(self.num_timesteps):
            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
                t = t.to(self.device).long()
                noise = torch.randn_like(x_start)
                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
                diffusion_row.append(x_noisy)

        log['diffusion_row'] = self._get_rows_from_list(diffusion_row)

        if sample:
            # get denoise row
            with self.ema_scope('Plotting'):
                samples, denoise_row = self.sample(
                    batch_size=N, return_intermediates=True
                )

            log['samples'] = samples
            log['denoise_row'] = self._get_rows_from_list(denoise_row)

        if return_keys:
            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
                return log
            else:
                return {key: log[key] for key in return_keys}
        return log

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())
        if self.learn_logvar:
            params = params + [self.logvar]
        opt = torch.optim.AdamW(params, lr=lr)
        return opt


class LatentDiffusion(DDPM):
    """main class"""

    def __init__(
        self,
        first_stage_config,
        cond_stage_config,
        personalization_config,
        num_timesteps_cond=None,
        cond_stage_key='image',
        cond_stage_trainable=False,
        concat_mode=True,
        cond_stage_forward=None,
        conditioning_key=None,
        scale_factor=1.0,
        scale_by_std=False,
        *args,
        **kwargs,
    ):

        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs['timesteps']
        # for backwards compatibility after implementation of DiffusionWrapper
        if conditioning_key is None:
            conditioning_key = 'concat' if concat_mode else 'crossattn'
        if cond_stage_config == '__is_unconditional__':
            conditioning_key = None
        ckpt_path = kwargs.pop('ckpt_path', None)
        ignore_keys = kwargs.pop('ignore_keys', [])
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
        self.concat_mode = concat_mode
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key

        try:
            self.num_downs = (
                len(first_stage_config.params.ddconfig.ch_mult) - 1
            )
        except:
            self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        else:
            self.register_buffer('scale_factor', torch.tensor(scale_factor))
        self.instantiate_first_stage(first_stage_config)
        self.instantiate_cond_stage(cond_stage_config)

        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None

        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True

        self.cond_stage_model.train = disabled_train
        for param in self.cond_stage_model.parameters():
            param.requires_grad = False

        self.model.eval()
        self.model.train = disabled_train
        for param in self.model.parameters():
            param.requires_grad = False

        self.embedding_manager = self.instantiate_embedding_manager(
            personalization_config, self.cond_stage_model
        )
        self.textual_inversion_manager = TextualInversionManager(
            tokenizer = self.cond_stage_model.tokenizer,
            text_encoder = self.cond_stage_model.transformer,
            full_precision = True
        )
        # this circular component dependency is gross and bad, needs to be rethought
        self.cond_stage_model.set_textual_inversion_manager(self.textual_inversion_manager)

        self.emb_ckpt_counter = 0

        # if self.embedding_manager.is_clip:
        #     self.cond_stage_model.update_embedding_func(self.embedding_manager)

        for param in self.embedding_manager.embedding_parameters():
            param.requires_grad = True

    def make_cond_schedule(
        self,
    ):
        self.cond_ids = torch.full(
            size=(self.num_timesteps,),
            fill_value=self.num_timesteps - 1,
            dtype=torch.long,
        )
        ids = torch.round(
            torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
        ).long()
        self.cond_ids[: self.num_timesteps_cond] = ids

    @rank_zero_only
    @torch.no_grad()
    def on_train_batch_start(self, batch, batch_idx, dataloader_idx=None):
        # only for very first batch
        if (
            self.scale_by_std
            and self.current_epoch == 0
            and self.global_step == 0
            and batch_idx == 0
            and not self.restarted_from_ckpt
        ):
            assert (
                self.scale_factor == 1.0
            ), 'rather not use custom rescaling and std-rescaling simultaneously'
            # set rescale weight to 1./std of encodings
            print('### USING STD-RESCALING ###')
            x = super().get_input(batch, self.first_stage_key)
            x = x.to(self.device)
            encoder_posterior = self.encode_first_stage(x)
            z = self.get_first_stage_encoding(encoder_posterior).detach()
            del self.scale_factor
            self.register_buffer('scale_factor', 1.0 / z.flatten().std())
            print(f'setting self.scale_factor to {self.scale_factor}')
            print('### USING STD-RESCALING ###')

    def register_schedule(
        self,
        given_betas=None,
        beta_schedule='linear',
        timesteps=1000,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
    ):
        super().register_schedule(
            given_betas,
            beta_schedule,
            timesteps,
            linear_start,
            linear_end,
            cosine_s,
        )

        self.shorten_cond_schedule = self.num_timesteps_cond > 1
        if self.shorten_cond_schedule:
            self.make_cond_schedule()

    def instantiate_first_stage(self, config):
        model = instantiate_from_config(config)
        self.first_stage_model = model.eval()
        self.first_stage_model.train = disabled_train
        for param in self.first_stage_model.parameters():
            param.requires_grad = False

    def instantiate_cond_stage(self, config):
        if not self.cond_stage_trainable:
            if config == '__is_first_stage__':
                print('Using first stage also as cond stage.')
                self.cond_stage_model = self.first_stage_model
            elif config == '__is_unconditional__':
                print(
                    f'Training {self.__class__.__name__} as an unconditional model.'
                )
                self.cond_stage_model = None
                # self.be_unconditional = True
            else:
                model = instantiate_from_config(config)
                self.cond_stage_model = model.eval()
                self.cond_stage_model.train = disabled_train
                for param in self.cond_stage_model.parameters():
                    param.requires_grad = False
        else:
            assert config != '__is_first_stage__'
            assert config != '__is_unconditional__'
            try:
                model = instantiate_from_config(config)
            except urllib.error.URLError:
                raise SystemExit(
                    "* Couldn't load a dependency. Try running scripts/preload_models.py from an internet-conected machine."
                )
            self.cond_stage_model = model

    def instantiate_embedding_manager(self, config, embedder):
        model = instantiate_from_config(config, embedder=embedder)

        if config.params.get(
            'embedding_manager_ckpt', None
        ):   # do not load if missing OR empty string
            model.load(config.params.embedding_manager_ckpt)

        return model

    def _get_denoise_row_from_list(
        self, samples, desc='', force_no_decoder_quantization=False
    ):
        denoise_row = []
        for zd in tqdm(samples, desc=desc):
            denoise_row.append(
                self.decode_first_stage(
                    zd.to(self.device),
                    force_not_quantize=force_no_decoder_quantization,
                )
            )
        n_imgs_per_row = len(denoise_row)
        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
        return denoise_grid

    def get_first_stage_encoding(self, encoder_posterior):
        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
            z = encoder_posterior.sample()
        elif isinstance(encoder_posterior, torch.Tensor):
            z = encoder_posterior
        else:
            raise NotImplementedError(
                f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
            )
        return self.scale_factor * z

    def get_learned_conditioning(self, c, **kwargs):
        if self.cond_stage_forward is None:
            if hasattr(self.cond_stage_model, 'encode') and callable(
                self.cond_stage_model.encode
            ):
                c = self.cond_stage_model.encode(
                    c, embedding_manager=self.embedding_manager,**kwargs
                )
                if isinstance(c, DiagonalGaussianDistribution):
                    c = c.mode()
            else:
                c = self.cond_stage_model(c, **kwargs)
        else:
            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c, **kwargs)
        return c

    def meshgrid(self, h, w):
        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)

        arr = torch.cat([y, x], dim=-1)
        return arr

    def delta_border(self, h, w):
        """
        :param h: height
        :param w: width
        :return: normalized distance to image border,
         wtith min distance = 0 at border and max dist = 0.5 at image center
        """
        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
        arr = self.meshgrid(h, w) / lower_right_corner
        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
        edge_dist = torch.min(
            torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1
        )[0]
        return edge_dist

    def get_weighting(self, h, w, Ly, Lx, device):
        weighting = self.delta_border(h, w)
        weighting = torch.clip(
            weighting,
            self.split_input_params['clip_min_weight'],
            self.split_input_params['clip_max_weight'],
        )
        weighting = (
            weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
        )

        if self.split_input_params['tie_braker']:
            L_weighting = self.delta_border(Ly, Lx)
            L_weighting = torch.clip(
                L_weighting,
                self.split_input_params['clip_min_tie_weight'],
                self.split_input_params['clip_max_tie_weight'],
            )

            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
            weighting = weighting * L_weighting
        return weighting

    def get_fold_unfold(
        self, x, kernel_size, stride, uf=1, df=1
    ):  # todo load once not every time, shorten code
        """
        :param x: img of size (bs, c, h, w)
        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
        """
        bs, nc, h, w = x.shape

        # number of crops in image
        Ly = (h - kernel_size[0]) // stride[0] + 1
        Lx = (w - kernel_size[1]) // stride[1] + 1

        if uf == 1 and df == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride
            )
            unfold = torch.nn.Unfold(**fold_params)

            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)

            weighting = self.get_weighting(
                kernel_size[0], kernel_size[1], Ly, Lx, x.device
            ).to(x.dtype)
            normalization = fold(weighting).view(
                1, 1, h, w
            )  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0], kernel_size[1], Ly * Lx)
            )

        elif uf > 1 and df == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride
            )
            unfold = torch.nn.Unfold(**fold_params)

            fold_params2 = dict(
                kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
                dilation=1,
                padding=0,
                stride=(stride[0] * uf, stride[1] * uf),
            )
            fold = torch.nn.Fold(
                output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
            )

            weighting = self.get_weighting(
                kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device
            ).to(x.dtype)
            normalization = fold(weighting).view(
                1, 1, h * uf, w * uf
            )  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)
            )

        elif df > 1 and uf == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride
            )
            unfold = torch.nn.Unfold(**fold_params)

            fold_params2 = dict(
                kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
                dilation=1,
                padding=0,
                stride=(stride[0] // df, stride[1] // df),
            )
            fold = torch.nn.Fold(
                output_size=(x.shape[2] // df, x.shape[3] // df),
                **fold_params2,
            )

            weighting = self.get_weighting(
                kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
            ).to(x.dtype)
            normalization = fold(weighting).view(
                1, 1, h // df, w // df
            )  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
            )

        else:
            raise NotImplementedError

        return fold, unfold, normalization, weighting

    @torch.no_grad()
    def get_input(
        self,
        batch,
        k,
        return_first_stage_outputs=False,
        force_c_encode=False,
        cond_key=None,
        return_original_cond=False,
        bs=None,
    ):
        x = super().get_input(batch, k)
        if bs is not None:
            x = x[:bs]
        x = x.to(self.device)
        encoder_posterior = self.encode_first_stage(x)
        z = self.get_first_stage_encoding(encoder_posterior).detach()

        if self.model.conditioning_key is not None:
            if cond_key is None:
                cond_key = self.cond_stage_key
            if cond_key != self.first_stage_key:
                if cond_key in ['caption', 'coordinates_bbox']:
                    xc = batch[cond_key]
                elif cond_key == 'class_label':
                    xc = batch
                else:
                    xc = super().get_input(batch, cond_key).to(self.device)
            else:
                xc = x
            if not self.cond_stage_trainable or force_c_encode:
                if isinstance(xc, dict) or isinstance(xc, list):
                    # import pudb; pudb.set_trace()
                    c = self.get_learned_conditioning(xc)
                else:
                    c = self.get_learned_conditioning(xc.to(self.device))
            else:
                c = xc
            if bs is not None:
                c = c[:bs]

            if self.use_positional_encodings:
                pos_x, pos_y = self.compute_latent_shifts(batch)
                ckey = __conditioning_keys__[self.model.conditioning_key]
                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}

        else:
            c = None
            xc = None
            if self.use_positional_encodings:
                pos_x, pos_y = self.compute_latent_shifts(batch)
                c = {'pos_x': pos_x, 'pos_y': pos_y}
        out = [z, c]
        if return_first_stage_outputs:
            xrec = self.decode_first_stage(z)
            out.extend([x, xrec])
        if return_original_cond:
            out.append(xc)
        return out

    @torch.no_grad()
    def decode_first_stage(
        self, z, predict_cids=False, force_not_quantize=False
    ):
        if predict_cids:
            if z.dim() == 4:
                z = torch.argmax(z.exp(), dim=1).long()
            z = self.first_stage_model.quantize.get_codebook_entry(
                z, shape=None
            )
            z = rearrange(z, 'b h w c -> b c h w').contiguous()

        z = 1.0 / self.scale_factor * z

        if hasattr(self, 'split_input_params'):
            if self.split_input_params['patch_distributed_vq']:
                ks = self.split_input_params['ks']  # eg. (128, 128)
                stride = self.split_input_params['stride']  # eg. (64, 64)
                uf = self.split_input_params['vqf']
                bs, nc, h, w = z.shape
                if ks[0] > h or ks[1] > w:
                    ks = (min(ks[0], h), min(ks[1], w))
                    print('reducing Kernel')

                if stride[0] > h or stride[1] > w:
                    stride = (min(stride[0], h), min(stride[1], w))
                    print('reducing stride')

                fold, unfold, normalization, weighting = self.get_fold_unfold(
                    z, ks, stride, uf=uf
                )

                z = unfold(z)  # (bn, nc * prod(**ks), L)
                # 1. Reshape to img shape
                z = z.view(
                    (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                # 2. apply model loop over last dim
                if isinstance(self.first_stage_model, VQModelInterface):
                    output_list = [
                        self.first_stage_model.decode(
                            z[:, :, :, :, i],
                            force_not_quantize=predict_cids
                            or force_not_quantize,
                        )
                        for i in range(z.shape[-1])
                    ]
                else:

                    output_list = [
                        self.first_stage_model.decode(z[:, :, :, :, i])
                        for i in range(z.shape[-1])
                    ]

                o = torch.stack(
                    output_list, axis=-1
                )  # # (bn, nc, ks[0], ks[1], L)
                o = o * weighting
                # Reverse 1. reshape to img shape
                o = o.view(
                    (o.shape[0], -1, o.shape[-1])
                )  # (bn, nc * ks[0] * ks[1], L)
                # stitch crops together
                decoded = fold(o)
                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
                return decoded
            else:
                if isinstance(self.first_stage_model, VQModelInterface):
                    return self.first_stage_model.decode(
                        z,
                        force_not_quantize=predict_cids or force_not_quantize,
                    )
                else:
                    return self.first_stage_model.decode(z)

        else:
            if isinstance(self.first_stage_model, VQModelInterface):
                return self.first_stage_model.decode(
                    z, force_not_quantize=predict_cids or force_not_quantize
                )
            else:
                return self.first_stage_model.decode(z)

    # same as above but without decorator
    def differentiable_decode_first_stage(
        self, z, predict_cids=False, force_not_quantize=False
    ):
        if predict_cids:
            if z.dim() == 4:
                z = torch.argmax(z.exp(), dim=1).long()
            z = self.first_stage_model.quantize.get_codebook_entry(
                z, shape=None
            )
            z = rearrange(z, 'b h w c -> b c h w').contiguous()

        z = 1.0 / self.scale_factor * z

        if hasattr(self, 'split_input_params'):
            if self.split_input_params['patch_distributed_vq']:
                ks = self.split_input_params['ks']  # eg. (128, 128)
                stride = self.split_input_params['stride']  # eg. (64, 64)
                uf = self.split_input_params['vqf']
                bs, nc, h, w = z.shape
                if ks[0] > h or ks[1] > w:
                    ks = (min(ks[0], h), min(ks[1], w))
                    print('reducing Kernel')

                if stride[0] > h or stride[1] > w:
                    stride = (min(stride[0], h), min(stride[1], w))
                    print('reducing stride')

                fold, unfold, normalization, weighting = self.get_fold_unfold(
                    z, ks, stride, uf=uf
                )

                z = unfold(z)  # (bn, nc * prod(**ks), L)
                # 1. Reshape to img shape
                z = z.view(
                    (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                # 2. apply model loop over last dim
                if isinstance(self.first_stage_model, VQModelInterface):
                    output_list = [
                        self.first_stage_model.decode(
                            z[:, :, :, :, i],
                            force_not_quantize=predict_cids
                            or force_not_quantize,
                        )
                        for i in range(z.shape[-1])
                    ]
                else:

                    output_list = [
                        self.first_stage_model.decode(z[:, :, :, :, i])
                        for i in range(z.shape[-1])
                    ]

                o = torch.stack(
                    output_list, axis=-1
                )  # # (bn, nc, ks[0], ks[1], L)
                o = o * weighting
                # Reverse 1. reshape to img shape
                o = o.view(
                    (o.shape[0], -1, o.shape[-1])
                )  # (bn, nc * ks[0] * ks[1], L)
                # stitch crops together
                decoded = fold(o)
                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
                return decoded
            else:
                if isinstance(self.first_stage_model, VQModelInterface):
                    return self.first_stage_model.decode(
                        z,
                        force_not_quantize=predict_cids or force_not_quantize,
                    )
                else:
                    return self.first_stage_model.decode(z)

        else:
            if isinstance(self.first_stage_model, VQModelInterface):
                return self.first_stage_model.decode(
                    z, force_not_quantize=predict_cids or force_not_quantize
                )
            else:
                return self.first_stage_model.decode(z)

    @torch.no_grad()
    def encode_first_stage(self, x):
        if hasattr(self, 'split_input_params'):
            if self.split_input_params['patch_distributed_vq']:
                ks = self.split_input_params['ks']  # eg. (128, 128)
                stride = self.split_input_params['stride']  # eg. (64, 64)
                df = self.split_input_params['vqf']
                self.split_input_params['original_image_size'] = x.shape[-2:]
                bs, nc, h, w = x.shape
                if ks[0] > h or ks[1] > w:
                    ks = (min(ks[0], h), min(ks[1], w))
                    print('reducing Kernel')

                if stride[0] > h or stride[1] > w:
                    stride = (min(stride[0], h), min(stride[1], w))
                    print('reducing stride')

                fold, unfold, normalization, weighting = self.get_fold_unfold(
                    x, ks, stride, df=df
                )
                z = unfold(x)  # (bn, nc * prod(**ks), L)
                # Reshape to img shape
                z = z.view(
                    (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                output_list = [
                    self.first_stage_model.encode(z[:, :, :, :, i])
                    for i in range(z.shape[-1])
                ]

                o = torch.stack(output_list, axis=-1)
                o = o * weighting

                # Reverse reshape to img shape
                o = o.view(
                    (o.shape[0], -1, o.shape[-1])
                )  # (bn, nc * ks[0] * ks[1], L)
                # stitch crops together
                decoded = fold(o)
                decoded = decoded / normalization
                return decoded

            else:
                return self.first_stage_model.encode(x)
        else:
            return self.first_stage_model.encode(x)

    def shared_step(self, batch, **kwargs):
        x, c = self.get_input(batch, self.first_stage_key)
        loss = self(x, c)
        return loss

    def forward(self, x, c, *args, **kwargs):
        t = torch.randint(
            0, self.num_timesteps, (x.shape[0],), device=self.device
        ).long()
        if self.model.conditioning_key is not None:
            assert c is not None
            if self.cond_stage_trainable:
                c = self.get_learned_conditioning(c)
            if self.shorten_cond_schedule:  # TODO: drop this option
                tc = self.cond_ids[t].to(self.device)
                c = self.q_sample(
                    x_start=c, t=tc, noise=torch.randn_like(c.float())
                )

        return self.p_losses(x, c, t, *args, **kwargs)

    def _rescale_annotations(
        self, bboxes, crop_coordinates
    ):  # TODO: move to dataset
        def rescale_bbox(bbox):
            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
            return x0, y0, w, h

        return [rescale_bbox(b) for b in bboxes]

    def apply_model(self, x_noisy, t, cond, return_ids=False):

        if isinstance(cond, dict):
            # hybrid case, cond is exptected to be a dict
            pass
        else:
            if not isinstance(cond, list):
                cond = [cond]
            key = (
                'c_concat'
                if self.model.conditioning_key == 'concat'
                else 'c_crossattn'
            )
            cond = {key: cond}

        if hasattr(self, 'split_input_params'):
            assert (
                len(cond) == 1
            )  # todo can only deal with one conditioning atm
            assert not return_ids
            ks = self.split_input_params['ks']  # eg. (128, 128)
            stride = self.split_input_params['stride']  # eg. (64, 64)

            h, w = x_noisy.shape[-2:]

            fold, unfold, normalization, weighting = self.get_fold_unfold(
                x_noisy, ks, stride
            )

            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
            # Reshape to img shape
            z = z.view(
                (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
            )  # (bn, nc, ks[0], ks[1], L )
            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]

            if (
                self.cond_stage_key
                in ['image', 'LR_image', 'segmentation', 'bbox_img']
                and self.model.conditioning_key
            ):  # todo check for completeness
                c_key = next(iter(cond.keys()))  # get key
                c = next(iter(cond.values()))  # get value
                assert (
                    len(c) == 1
                )  # todo extend to list with more than one elem
                c = c[0]  # get element

                c = unfold(c)
                c = c.view(
                    (c.shape[0], -1, ks[0], ks[1], c.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                cond_list = [
                    {c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])
                ]

            elif self.cond_stage_key == 'coordinates_bbox':
                assert (
                    'original_image_size' in self.split_input_params
                ), 'BoudingBoxRescaling is missing original_image_size'

                # assuming padding of unfold is always 0 and its dilation is always 1
                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
                full_img_h, full_img_w = self.split_input_params[
                    'original_image_size'
                ]
                # as we are operating on latents, we need the factor from the original image size to the
                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
                num_downs = self.first_stage_model.encoder.num_resolutions - 1
                rescale_latent = 2 ** (num_downs)

                # get top left positions of patches as conforming for the bbbox tokenizer, therefore we
                # need to rescale the tl patch coordinates to be in between (0,1)
                tl_patch_coordinates = [
                    (
                        rescale_latent
                        * stride[0]
                        * (patch_nr % n_patches_per_row)
                        / full_img_w,
                        rescale_latent
                        * stride[1]
                        * (patch_nr // n_patches_per_row)
                        / full_img_h,
                    )
                    for patch_nr in range(z.shape[-1])
                ]

                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
                patch_limits = [
                    (
                        x_tl,
                        y_tl,
                        rescale_latent * ks[0] / full_img_w,
                        rescale_latent * ks[1] / full_img_h,
                    )
                    for x_tl, y_tl in tl_patch_coordinates
                ]
                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]

                # tokenize crop coordinates for the bounding boxes of the respective patches
                patch_limits_tknzd = [
                    torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[
                        None
                    ].to(self.device)
                    for bbox in patch_limits
                ]  # list of length l with tensors of shape (1, 2)
                print(patch_limits_tknzd[0].shape)
                # cut tknzd crop position from conditioning
                assert isinstance(
                    cond, dict
                ), 'cond must be dict to be fed into model'
                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
                print(cut_cond.shape)

                adapted_cond = torch.stack(
                    [
                        torch.cat([cut_cond, p], dim=1)
                        for p in patch_limits_tknzd
                    ]
                )
                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
                print(adapted_cond.shape)
                adapted_cond = self.get_learned_conditioning(adapted_cond)
                print(adapted_cond.shape)
                adapted_cond = rearrange(
                    adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]
                )
                print(adapted_cond.shape)

                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]

            else:
                cond_list = [
                    cond for i in range(z.shape[-1])
                ]  # Todo make this more efficient

            # apply model by loop over crops
            output_list = [
                self.model(z_list[i], t, **cond_list[i])
                for i in range(z.shape[-1])
            ]
            assert not isinstance(
                output_list[0], tuple
            )  # todo cant deal with multiple model outputs check this never happens

            o = torch.stack(output_list, axis=-1)
            o = o * weighting
            # Reverse reshape to img shape
            o = o.view(
                (o.shape[0], -1, o.shape[-1])
            )  # (bn, nc * ks[0] * ks[1], L)
            # stitch crops together
            x_recon = fold(o) / normalization

        else:
            x_recon = self.model(x_noisy, t, **cond)

        if isinstance(x_recon, tuple) and not return_ids:
            return x_recon[0]
        else:
            return x_recon

    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
        return (
            extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape)
            * x_t
            - pred_xstart
        ) / extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)

    def _prior_bpd(self, x_start):
        """
        Get the prior KL term for the variational lower-bound, measured in
        bits-per-dim.
        This term can't be optimized, as it only depends on the encoder.
        :param x_start: the [N x C x ...] tensor of inputs.
        :return: a batch of [N] KL values (in bits), one per batch element.
        """
        batch_size = x_start.shape[0]
        t = torch.tensor(
            [self.num_timesteps - 1] * batch_size, device=x_start.device
        )
        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
        kl_prior = normal_kl(
            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
        )
        return mean_flat(kl_prior) / np.log(2.0)

    def p_losses(self, x_start, cond, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        model_output = self.apply_model(x_noisy, t, cond)

        loss_dict = {}
        prefix = 'train' if self.training else 'val'

        if self.parameterization == 'x0':
            target = x_start
        elif self.parameterization == 'eps':
            target = noise
        else:
            raise NotImplementedError()

        loss_simple = self.get_loss(model_output, target, mean=False).mean(
            [1, 2, 3]
        )
        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})

        logvar_t = self.logvar[t.item()].to(self.device)
        loss = loss_simple / torch.exp(logvar_t) + logvar_t
        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
        if self.learn_logvar:
            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
            loss_dict.update({'logvar': self.logvar.data.mean()})

        loss = self.l_simple_weight * loss.mean()

        loss_vlb = self.get_loss(model_output, target, mean=False).mean(
            dim=(1, 2, 3)
        )
        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
        loss += self.original_elbo_weight * loss_vlb
        loss_dict.update({f'{prefix}/loss': loss})

        if self.embedding_reg_weight > 0:
            loss_embedding_reg = (
                self.embedding_manager.embedding_to_coarse_loss().mean()
            )

            loss_dict.update({f'{prefix}/loss_emb_reg': loss_embedding_reg})

            loss += self.embedding_reg_weight * loss_embedding_reg
            loss_dict.update({f'{prefix}/loss': loss})

        return loss, loss_dict

    def p_mean_variance(
        self,
        x,
        c,
        t,
        clip_denoised: bool,
        return_codebook_ids=False,
        quantize_denoised=False,
        return_x0=False,
        score_corrector=None,
        corrector_kwargs=None,
    ):
        t_in = t
        model_out = self.apply_model(
            x, t_in, c, return_ids=return_codebook_ids
        )

        if score_corrector is not None:
            assert self.parameterization == 'eps'
            model_out = score_corrector.modify_score(
                self, model_out, x, t, c, **corrector_kwargs
            )

        if return_codebook_ids:
            model_out, logits = model_out

        if self.parameterization == 'eps':
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == 'x0':
            x_recon = model_out
        else:
            raise NotImplementedError()

        if clip_denoised:
            x_recon.clamp_(-1.0, 1.0)
        if quantize_denoised:
            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(
                x_recon
            )
        (
            model_mean,
            posterior_variance,
            posterior_log_variance,
        ) = self.q_posterior(x_start=x_recon, x_t=x, t=t)
        if return_codebook_ids:
            return (
                model_mean,
                posterior_variance,
                posterior_log_variance,
                logits,
            )
        elif return_x0:
            return (
                model_mean,
                posterior_variance,
                posterior_log_variance,
                x_recon,
            )
        else:
            return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(
        self,
        x,
        c,
        t,
        clip_denoised=False,
        repeat_noise=False,
        return_codebook_ids=False,
        quantize_denoised=False,
        return_x0=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
    ):
        b, *_, device = *x.shape, x.device
        outputs = self.p_mean_variance(
            x=x,
            c=c,
            t=t,
            clip_denoised=clip_denoised,
            return_codebook_ids=return_codebook_ids,
            quantize_denoised=quantize_denoised,
            return_x0=return_x0,
            score_corrector=score_corrector,
            corrector_kwargs=corrector_kwargs,
        )
        if return_codebook_ids:
            raise DeprecationWarning('Support dropped.')
            model_mean, _, model_log_variance, logits = outputs
        elif return_x0:
            model_mean, _, model_log_variance, x0 = outputs
        else:
            model_mean, _, model_log_variance = outputs

        noise = noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.0:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        # no noise when t == 0
        nonzero_mask = (1 - (t == 0).float()).reshape(
            b, *((1,) * (len(x.shape) - 1))
        )

        if return_codebook_ids:
            return model_mean + nonzero_mask * (
                0.5 * model_log_variance
            ).exp() * noise, logits.argmax(dim=1)
        if return_x0:
            return (
                model_mean
                + nonzero_mask * (0.5 * model_log_variance).exp() * noise,
                x0,
            )
        else:
            return (
                model_mean
                + nonzero_mask * (0.5 * model_log_variance).exp() * noise
            )

    @torch.no_grad()
    def progressive_denoising(
        self,
        cond,
        shape,
        verbose=True,
        callback=None,
        quantize_denoised=False,
        img_callback=None,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        batch_size=None,
        x_T=None,
        start_T=None,
        log_every_t=None,
    ):
        if not log_every_t:
            log_every_t = self.log_every_t
        timesteps = self.num_timesteps
        if batch_size is not None:
            b = batch_size if batch_size is not None else shape[0]
            shape = [batch_size] + list(shape)
        else:
            b = batch_size = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=self.device)
        else:
            img = x_T
        intermediates = []
        if cond is not None:
            if isinstance(cond, dict):
                cond = {
                    key: cond[key][:batch_size]
                    if not isinstance(cond[key], list)
                    else list(map(lambda x: x[:batch_size], cond[key]))
                    for key in cond
                }
            else:
                cond = (
                    [c[:batch_size] for c in cond]
                    if isinstance(cond, list)
                    else cond[:batch_size]
                )

        if start_T is not None:
            timesteps = min(timesteps, start_T)
        iterator = (
            tqdm(
                reversed(range(0, timesteps)),
                desc='Progressive Generation',
                total=timesteps,
            )
            if verbose
            else reversed(range(0, timesteps))
        )
        if type(temperature) == float:
            temperature = [temperature] * timesteps

        for i in iterator:
            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
            if self.shorten_cond_schedule:
                assert self.model.conditioning_key != 'hybrid'
                tc = self.cond_ids[ts].to(cond.device)
                cond = self.q_sample(
                    x_start=cond, t=tc, noise=torch.randn_like(cond)
                )

            img, x0_partial = self.p_sample(
                img,
                cond,
                ts,
                clip_denoised=self.clip_denoised,
                quantize_denoised=quantize_denoised,
                return_x0=True,
                temperature=temperature[i],
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
            )
            if mask is not None:
                assert x0 is not None
                img_orig = self.q_sample(x0, ts)
                img = img_orig * mask + (1.0 - mask) * img

            if i % log_every_t == 0 or i == timesteps - 1:
                intermediates.append(x0_partial)
            if callback:
                callback(i)
            if img_callback:
                img_callback(img, i)
        return img, intermediates

    @torch.no_grad()
    def p_sample_loop(
        self,
        cond,
        shape,
        return_intermediates=False,
        x_T=None,
        verbose=True,
        callback=None,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        start_T=None,
        log_every_t=None,
    ):

        if not log_every_t:
            log_every_t = self.log_every_t
        device = self.betas.device
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

        intermediates = [img]
        if timesteps is None:
            timesteps = self.num_timesteps

        if start_T is not None:
            timesteps = min(timesteps, start_T)
        iterator = (
            tqdm(
                reversed(range(0, timesteps)),
                desc='Sampling t',
                total=timesteps,
            )
            if verbose
            else reversed(range(0, timesteps))
        )

        if mask is not None:
            assert x0 is not None
            assert (
                x0.shape[2:3] == mask.shape[2:3]
            )  # spatial size has to match

        for i in iterator:
            ts = torch.full((b,), i, device=device, dtype=torch.long)
            if self.shorten_cond_schedule:
                assert self.model.conditioning_key != 'hybrid'
                tc = self.cond_ids[ts].to(cond.device)
                cond = self.q_sample(
                    x_start=cond, t=tc, noise=torch.randn_like(cond)
                )

            img = self.p_sample(
                img,
                cond,
                ts,
                clip_denoised=self.clip_denoised,
                quantize_denoised=quantize_denoised,
            )
            if mask is not None:
                img_orig = self.q_sample(x0, ts)
                img = img_orig * mask + (1.0 - mask) * img

            if i % log_every_t == 0 or i == timesteps - 1:
                intermediates.append(img)
            if callback:
                callback(i)
            if img_callback:
                img_callback(img, i)

        if return_intermediates:
            return img, intermediates
        return img

    @torch.no_grad()
    def sample(
        self,
        cond,
        batch_size=16,
        return_intermediates=False,
        x_T=None,
        verbose=True,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        shape=None,
        **kwargs,
    ):
        if shape is None:
            shape = (
                batch_size,
                self.channels,
                self.image_size,
                self.image_size,
            )
        if cond is not None:
            if isinstance(cond, dict):
                cond = {
                    key: cond[key][:batch_size]
                    if not isinstance(cond[key], list)
                    else list(map(lambda x: x[:batch_size], cond[key]))
                    for key in cond
                }
            else:
                cond = (
                    [c[:batch_size] for c in cond]
                    if isinstance(cond, list)
                    else cond[:batch_size]
                )
        return self.p_sample_loop(
            cond,
            shape,
            return_intermediates=return_intermediates,
            x_T=x_T,
            verbose=verbose,
            timesteps=timesteps,
            quantize_denoised=quantize_denoised,
            mask=mask,
            x0=x0,
        )

    @torch.no_grad()
    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):

        if ddim:
            ddim_sampler = DDIMSampler(self)
            shape = (self.channels, self.image_size, self.image_size)
            samples, intermediates = ddim_sampler.sample(
                ddim_steps, batch_size, shape, cond, verbose=False, **kwargs
            )

        else:
            samples, intermediates = self.sample(
                cond=cond,
                batch_size=batch_size,
                return_intermediates=True,
                **kwargs,
            )

        return samples, intermediates

    @torch.no_grad()
    def get_unconditional_conditioning(self, batch_size, null_label=None):
        if null_label is not None:
            xc = null_label
            if isinstance(xc, ListConfig):
                xc = list(xc)
            if isinstance(xc, dict) or isinstance(xc, list):
                c = self.get_learned_conditioning(xc)
            else:
                if hasattr(xc, "to"):
                    xc = xc.to(self.device)
                c = self.get_learned_conditioning(xc)
        else:
            # todo: get null label from cond_stage_model
            raise NotImplementedError()
        c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
        return c

    @torch.no_grad()
    def log_images(
        self,
        batch,
        N=8,
        n_row=4,
        sample=True,
        ddim_steps=50,
        ddim_eta=1.0,
        return_keys=None,
        quantize_denoised=True,
        inpaint=False,
        plot_denoise_rows=False,
        plot_progressive_rows=False,
        plot_diffusion_rows=False,
        **kwargs,
    ):

        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc = self.get_input(
            batch,
            self.first_stage_key,
            return_first_stage_outputs=True,
            force_c_encode=True,
            return_original_cond=True,
            bs=N,
        )
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log['inputs'] = x
        log['reconstruction'] = xrec
        if self.model.conditioning_key is not None:
            if hasattr(self.cond_stage_model, 'decode'):
                xc = self.cond_stage_model.decode(c)
                log['conditioning'] = xc
            elif self.cond_stage_key in ['caption']:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch['caption'])
                log['conditioning'] = xc
            elif self.cond_stage_key == 'class_label':
                xc = log_txt_as_img(
                    (x.shape[2], x.shape[3]), batch['human_label']
                )
                log['conditioning'] = xc
            elif isimage(xc):
                log['conditioning'] = xc
            if ismap(xc):
                log['original_conditioning'] = self.to_rgb(xc)

        if plot_diffusion_rows:
            # get diffusion row
            diffusion_row = list()
            z_start = z[:n_row]
            for t in range(self.num_timesteps):
                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
                    t = t.to(self.device).long()
                    noise = torch.randn_like(z_start)
                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
                    diffusion_row.append(self.decode_first_stage(z_noisy))

            diffusion_row = torch.stack(
                diffusion_row
            )  # n_log_step, n_row, C, H, W
            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
            diffusion_grid = rearrange(
                diffusion_grid, 'b n c h w -> (b n) c h w'
            )
            diffusion_grid = make_grid(
                diffusion_grid, nrow=diffusion_row.shape[0]
            )
            log['diffusion_row'] = diffusion_grid

        if sample:
            # get denoise row
            with self.ema_scope('Plotting'):
                samples, z_denoise_row = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                )
                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
            x_samples = self.decode_first_stage(samples)
            log['samples'] = x_samples
            if plot_denoise_rows:
                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
                log['denoise_row'] = denoise_grid

            uc = self.get_learned_conditioning(len(c) * [''])
            sample_scaled, _ = self.sample_log(
                cond=c,
                batch_size=N,
                ddim=use_ddim,
                ddim_steps=ddim_steps,
                eta=ddim_eta,
                unconditional_guidance_scale=5.0,
                unconditional_conditioning=uc,
            )
            log['samples_scaled'] = self.decode_first_stage(sample_scaled)

            if (
                quantize_denoised
                and not isinstance(self.first_stage_model, AutoencoderKL)
                and not isinstance(self.first_stage_model, IdentityFirstStage)
            ):
                # also display when quantizing x0 while sampling
                with self.ema_scope('Plotting Quantized Denoised'):
                    samples, z_denoise_row = self.sample_log(
                        cond=c,
                        batch_size=N,
                        ddim=use_ddim,
                        ddim_steps=ddim_steps,
                        eta=ddim_eta,
                        quantize_denoised=True,
                    )
                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
                    #                                      quantize_denoised=True)
                x_samples = self.decode_first_stage(samples.to(self.device))
                log['samples_x0_quantized'] = x_samples

            if inpaint:
                # make a simple center square
                b, h, w = z.shape[0], z.shape[2], z.shape[3]
                mask = torch.ones(N, h, w).to(self.device)
                # zeros will be filled in
                mask[:, h // 4 : 3 * h // 4, w // 4 : 3 * w // 4] = 0.0
                mask = mask[:, None, ...]
                with self.ema_scope('Plotting Inpaint'):

                    samples, _ = self.sample_log(
                        cond=c,
                        batch_size=N,
                        ddim=use_ddim,
                        eta=ddim_eta,
                        ddim_steps=ddim_steps,
                        x0=z[:N],
                        mask=mask,
                    )
                x_samples = self.decode_first_stage(samples.to(self.device))
                log['samples_inpainting'] = x_samples
                log['mask'] = mask

                # outpaint
                with self.ema_scope('Plotting Outpaint'):
                    samples, _ = self.sample_log(
                        cond=c,
                        batch_size=N,
                        ddim=use_ddim,
                        eta=ddim_eta,
                        ddim_steps=ddim_steps,
                        x0=z[:N],
                        mask=mask,
                    )
                x_samples = self.decode_first_stage(samples.to(self.device))
                log['samples_outpainting'] = x_samples

        if plot_progressive_rows:
            with self.ema_scope('Plotting Progressives'):
                img, progressives = self.progressive_denoising(
                    c,
                    shape=(self.channels, self.image_size, self.image_size),
                    batch_size=N,
                )
            prog_row = self._get_denoise_row_from_list(
                progressives, desc='Progressive Generation'
            )
            log['progressive_row'] = prog_row

        if return_keys:
            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
                return log
            else:
                return {key: log[key] for key in return_keys}
        return log

    def configure_optimizers(self):
        lr = self.learning_rate

        if self.embedding_manager is not None:
            params = list(self.embedding_manager.embedding_parameters())
            # params = list(self.cond_stage_model.transformer.text_model.embeddings.embedding_manager.embedding_parameters())
        else:
            params = list(self.model.parameters())
            if self.cond_stage_trainable:
                print(
                    f'{self.__class__.__name__}: Also optimizing conditioner params!'
                )
                params = params + list(self.cond_stage_model.parameters())
            if self.learn_logvar:
                print('Diffusion model optimizing logvar')
                params.append(self.logvar)
        opt = torch.optim.AdamW(params, lr=lr)
        if self.use_scheduler:
            assert 'target' in self.scheduler_config
            scheduler = instantiate_from_config(self.scheduler_config)

            print('Setting up LambdaLR scheduler...')
            scheduler = [
                {
                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
                    'interval': 'step',
                    'frequency': 1,
                }
            ]
            return [opt], scheduler
        return opt

    @torch.no_grad()
    def to_rgb(self, x):
        x = x.float()
        if not hasattr(self, 'colorize'):
            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
        x = nn.functional.conv2d(x, weight=self.colorize)
        x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
        return x

    @rank_zero_only
    def on_save_checkpoint(self, checkpoint):
        checkpoint.clear()

        if os.path.isdir(self.trainer.checkpoint_callback.dirpath):
            self.embedding_manager.save(
                os.path.join(
                    self.trainer.checkpoint_callback.dirpath, 'embeddings.pt'
                )
            )

            if (self.global_step - self.emb_ckpt_counter) > 500:
                self.embedding_manager.save(
                    os.path.join(
                        self.trainer.checkpoint_callback.dirpath,
                        f'embeddings_gs-{self.global_step}.pt',
                    )
                )

                self.emb_ckpt_counter += 500


class DiffusionWrapper(pl.LightningModule):
    def __init__(self, diff_model_config, conditioning_key):
        super().__init__()
        self.diffusion_model = instantiate_from_config(diff_model_config)
        self.conditioning_key = conditioning_key
        assert self.conditioning_key in [
            None,
            'concat',
            'crossattn',
            'hybrid',
            'adm',
        ]

    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
        if self.conditioning_key is None:
            out = self.diffusion_model(x, t)
        elif self.conditioning_key == 'concat':
            xc = torch.cat([x] + c_concat, dim=1)
            out = self.diffusion_model(xc, t)
        elif self.conditioning_key == 'crossattn':
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(x, t, context=cc)
        elif self.conditioning_key == 'hybrid':
            cc = torch.cat(c_crossattn, 1)
            xc = torch.cat([x] + c_concat, dim=1)
            out = self.diffusion_model(xc, t, context=cc)
        elif self.conditioning_key == 'adm':
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, y=cc)
        else:
            raise NotImplementedError()

        return out


class Layout2ImgDiffusion(LatentDiffusion):
    # TODO: move all layout-specific hacks to this class
    def __init__(self, cond_stage_key, *args, **kwargs):
        assert (
            cond_stage_key == 'coordinates_bbox'
        ), 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)

    def log_images(self, batch, N=8, *args, **kwargs):
        logs = super().log_images(batch=batch, N=N, *args, **kwargs)

        key = 'train' if self.training else 'validation'
        dset = self.trainer.datamodule.datasets[key]
        mapper = dset.conditional_builders[self.cond_stage_key]

        bbox_imgs = []
        map_fn = lambda catno: dset.get_textual_label(
            dset.get_category_id(catno)
        )
        for tknzd_bbox in batch[self.cond_stage_key][:N]:
            bboximg = mapper.plot(
                tknzd_bbox.detach().cpu(), map_fn, (256, 256)
            )
            bbox_imgs.append(bboximg)

        cond_img = torch.stack(bbox_imgs, dim=0)
        logs['bbox_image'] = cond_img
        return logs

class LatentInpaintDiffusion(LatentDiffusion):
    def __init__(
        self,
        concat_keys=("mask", "masked_image"),
        masked_image_key="masked_image",
        finetune_keys=None,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.masked_image_key = masked_image_key
        assert self.masked_image_key in concat_keys
        self.concat_keys = concat_keys


    @torch.no_grad()
    def get_input(
        self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False
    ):
        # note: restricted to non-trainable encoders currently
        assert (
            not self.cond_stage_trainable
        ), "trainable cond stages not yet supported for inpainting"
        z, c, x, xrec, xc = super().get_input(
            batch,
            self.first_stage_key,
            return_first_stage_outputs=True,
            force_c_encode=True,
            return_original_cond=True,
            bs=bs,
        )

        assert exists(self.concat_keys)
        c_cat = list()
        for ck in self.concat_keys:
            cc = (
                rearrange(batch[ck], "b h w c -> b c h w")
                .to(memory_format=torch.contiguous_format)
                .float()
            )
            if bs is not None:
                cc = cc[:bs]
                cc = cc.to(self.device)
            bchw = z.shape
            if ck != self.masked_image_key:
                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
            else:
                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
            c_cat.append(cc)
        c_cat = torch.cat(c_cat, dim=1)
        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
        if return_first_stage_outputs:
            return z, all_conds, x, xrec, xc
        return z, all_conds