"""
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 numpy as np
import torch
import torch.nn as nn

try:
    import lightning.pytorch as pl
    from lightning.pytorch.utilities import rank_zero_info, rank_zero_only
except:
    import pytorch_lightning as pl
    from pytorch_lightning.utilities import rank_zero_only, rank_zero_info

import itertools
from contextlib import contextmanager, nullcontext
from functools import partial

from einops import rearrange, repeat
from ldm.lr_scheduler import LambdaLinearScheduler
from ldm.models.autoencoder import *
from ldm.models.autoencoder import AutoencoderKL, IdentityFirstStage
from ldm.models.diffusion.ddim import *
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.modules.diffusionmodules.model import *
from ldm.modules.diffusionmodules.openaimodel import *
from ldm.modules.diffusionmodules.openaimodel import UNetModel
from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule, noise_like
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution, normal_kl
from ldm.modules.ema import LitEma
from ldm.modules.encoders.modules import *
from ldm.modules.midas.api import MiDaSInference
from ldm.util import count_params, default, exists, isimage, ismap, log_txt_as_img, mean_flat
from omegaconf import ListConfig
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm

__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=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,
        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,
        use_fp16=True,
        make_it_fit=False,
        ucg_training=None,
        reset_ema=False,
        reset_num_ema_updates=False,
    ):
        super().__init__()
        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
        self.parameterization = parameterization
        rank_zero_info(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
        self.channels = channels
        self.use_positional_encodings = use_positional_encodings

        self.unet_config = unet_config
        self.conditioning_key = conditioning_key
        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)
            rank_zero_info(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

        if monitor is not None:
            self.monitor = monitor
        self.make_it_fit = make_it_fit
        self.ckpt = ckpt
        self.ignore_keys = ignore_keys
        self.load_only_unet = load_only_unet
        self.reset_ema = reset_ema
        self.reset_num_ema_updates = reset_num_ema_updates

        if reset_ema:
            assert exists(ckpt)
        """
        Uncomment if you Use DDP Strategy
        """
        # if ckpt is not None:
        #     self.init_from_ckpt(ckpt, ignore_keys=ignore_keys, only_model=load_only_unet)
        #     if reset_ema:
        #         assert self.use_ema
        #         rank_zero_info(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
        #         self.model_ema = LitEma(self.model)

        if reset_num_ema_updates:
            rank_zero_info(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
            assert self.use_ema
            self.model_ema.reset_num_updates()

        self.timesteps = timesteps
        self.beta_schedule = beta_schedule
        self.given_betas = given_betas
        self.linear_start = linear_start
        self.linear_end = linear_end
        self.cosine_s = cosine_s

        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.logvar_init = logvar_init
        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)
        self.use_fp16 = use_fp16
        self.ucg_training = ucg_training or dict()
        if self.ucg_training:
            self.ucg_prng = np.random.RandomState()

    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))
        elif self.parameterization == "v":
            lvlb_weights = torch.ones_like(
                self.betas**2 / (2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
            )
        else:
            raise NotImplementedError("mu not supported")
        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:
                rank_zero_info(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:
                    rank_zero_info(f"{context}: Restored training weights")

    @torch.no_grad()
    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):
                    rank_zero_info("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        if self.make_it_fit:
            n_params = len([name for name, _ in itertools.chain(self.named_parameters(), self.named_buffers())])
            for name, param in tqdm(
                itertools.chain(self.named_parameters(), self.named_buffers()),
                desc="Fitting old weights to new weights",
                total=n_params,
            ):
                if not name in sd:
                    continue
                old_shape = sd[name].shape
                new_shape = param.shape
                assert len(old_shape) == len(new_shape)
                if len(new_shape) > 2:
                    # we only modify first two axes
                    assert new_shape[2:] == old_shape[2:]
                # assumes first axis corresponds to output dim
                if not new_shape == old_shape:
                    new_param = param.clone()
                    old_param = sd[name]
                    if len(new_shape) == 1:
                        for i in range(new_param.shape[0]):
                            new_param[i] = old_param[i % old_shape[0]]
                    elif len(new_shape) >= 2:
                        for i in range(new_param.shape[0]):
                            for j in range(new_param.shape[1]):
                                new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]

                        n_used_old = torch.ones(old_shape[1])
                        for j in range(new_param.shape[1]):
                            n_used_old[j % old_shape[1]] += 1
                        n_used_new = torch.zeros(new_shape[1])
                        for j in range(new_param.shape[1]):
                            n_used_new[j] = n_used_old[j % old_shape[1]]

                        n_used_new = n_used_new[None, :]
                        while len(n_used_new.shape) < len(new_shape):
                            n_used_new = n_used_new.unsqueeze(-1)
                        new_param /= n_used_new

                    sd[name] = new_param

        missing, unexpected = (
            self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False)
        )
        rank_zero_info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            rank_zero_info(f"Missing Keys:\n {missing}")
        if len(unexpected) > 0:
            rank_zero_info(f"\nUnexpected Keys:\n {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 predict_start_from_z_and_v(self, x_t, t, v):
        # 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. - alphas_cumprod)))
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t
            - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
        )

    def predict_eps_from_z_and_v(self, x_t, t, v):
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v
            + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
        )

    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):
            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_v(self, x, noise, t):
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise
            - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
        )

    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
        elif self.parameterization == "v":
            target = self.get_v(x_start, noise, t)
        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")
        if self.use_fp16:
            x = x.to(memory_format=torch.contiguous_format).half()
        else:
            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):
        for k in self.ucg_training:
            p = self.ucg_training[k]["p"]
            val = self.ucg_training[k]["val"]
            if val is None:
                val = ""
            for i in range(len(batch[k])):
                if self.ucg_prng.choice(2, p=[1 - p, p]):
                    batch[k][i] = val

        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,
        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,
        use_fp16=True,
        force_null_conditioning=False,
        *args,
        **kwargs,
    ):
        self.force_null_conditioning = force_null_conditioning
        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__" and not self.force_null_conditioning:
            conditioning_key = None

        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.first_stage_config = first_stage_config
        self.cond_stage_config = cond_stage_config
        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
        """
        Uncomment if you Use DDP Strategy
        """
        # self.restarted_from_ckpt = False
        # if self.ckpt is not None:
        #     self.init_from_ckpt(self.ckpt, self.ignore_keys)
        #     self.restarted_from_ckpt = True
        #     if self.reset_ema:
        #         assert self.use_ema
        #         rank_zero_info(
        #             f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
        #         self.model_ema = LitEma(self.model)
        if self.reset_num_ema_updates:
            rank_zero_info(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
            assert self.use_ema
            self.model_ema.reset_num_updates()

    def configure_sharded_model(self) -> None:
        rank_zero_info("Configure sharded model for LatentDiffusion")
        self.model = DiffusionWrapper(self.unet_config, self.conditioning_key)
        count_params(self.model, verbose=True)
        if self.use_ema:
            self.model_ema = LitEma(self.model)

        if self.ckpt is not None:
            self.init_from_ckpt(self.ckpt, ignore_keys=self.ignore_keys, only_model=self.load_only_unet)
            if self.reset_ema:
                assert self.use_ema
                rank_zero_info(
                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint."
                )
                self.model_ema = LitEma(self.model)

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

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

        self.instantiate_first_stage(self.first_stage_config)
        self.instantiate_cond_stage(self.cond_stage_config)
        if self.ckpt is not None:
            self.init_from_ckpt(self.ckpt, self.ignore_keys)
            self.restarted_from_ckpt = True
            if self.reset_ema:
                assert self.use_ema
                rank_zero_info(
                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint."
                )
                self.model_ema = LitEma(self.model)

    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):
        # 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
            rank_zero_info("### 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())
            rank_zero_info(f"setting self.scale_factor to {self.scale_factor}")
            rank_zero_info("### 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 = AutoencoderKL(**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__":
                rank_zero_info("Using first stage also as cond stage.")
                self.cond_stage_model = self.first_stage_model
            elif config == "__is_unconditional__":
                rank_zero_info(f"Training {self.__class__.__name__} as an unconditional model.")
                self.cond_stage_model = None
                # self.be_unconditional = True
            else:
                model = FrozenOpenCLIPEmbedder(**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:
            model = FrozenOpenCLIPEmbedder(**config)
            self.cond_stage_model = 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.half() if self.use_fp16 else self.scale_factor * z

    def get_learned_conditioning(self, c):
        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)
                if isinstance(c, DiagonalGaussianDistribution):
                    c = c.mode()
            else:
                c = self.cond_stage_model(c)
        else:
            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
        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,
        return_x=False,
    ):
        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 and not self.force_null_conditioning:
            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", "txt"]:
                    xc = batch[cond_key]
                elif cond_key in ["class_label", "cls"]:
                    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):
                    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_x:
            out.extend([x])
        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
        return self.first_stage_model.decode(z)

    @torch.no_grad()
    def encode_first_stage(self, x):
        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 apply_model(self, x_noisy, t, cond, return_ids=False):
        if isinstance(cond, dict):
            # hybrid case, cond is expected 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}

        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
        elif self.parameterization == "v":
            target = self.get_v(x_start, noise, t)
        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].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})

        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:
            if self.cond_stage_key in ["class_label", "cls"]:
                xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
                return self.get_learned_conditioning(xc)
            else:
                raise NotImplementedError("todo")
        if isinstance(c, list):  # in case the encoder gives us a list
            for i in range(len(c)):
                c[i] = repeat(c[i], "1 ... -> b ...", b=batch_size).to(self.device)
        else:
            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=0.0,
        return_keys=None,
        quantize_denoised=True,
        inpaint=True,
        plot_denoise_rows=False,
        plot_progressive_rows=True,
        plot_diffusion_rows=True,
        unconditional_guidance_scale=1.0,
        unconditional_guidance_label=None,
        use_ema_scope=True,
        **kwargs,
    ):
        ema_scope = self.ema_scope if use_ema_scope else nullcontext
        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", "txt"]:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
                log["conditioning"] = xc
            elif self.cond_stage_key in ["class_label", "cls"]:
                try:
                    xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
                    log["conditioning"] = xc
                except KeyError:
                    # probably no "human_label" in batch
                    pass
            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 ema_scope("Sampling"):
                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

            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 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 unconditional_guidance_scale > 1.0:
            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
            if self.model.conditioning_key == "crossattn-adm":
                uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
            with ema_scope("Sampling with classifier-free guidance"):
                samples_cfg, _ = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                    unconditional_guidance_scale=unconditional_guidance_scale,
                    unconditional_conditioning=uc,
                )
                x_samples_cfg = self.decode_first_stage(samples_cfg)
                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg

        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 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
            mask = 1.0 - mask
            with 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 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
        params = list(self.model.parameters())
        if self.cond_stage_trainable:
            rank_zero_info(f"{self.__class__.__name__}: Also optimizing conditioner params!")
            params = params + list(self.cond_stage_model.parameters())
        if self.learn_logvar:
            rank_zero_info("Diffusion model optimizing logvar")
            params.append(self.logvar)

        from colossalai.nn.optimizer import HybridAdam

        opt = HybridAdam(params, lr=lr)

        # opt = torch.optim.AdamW(params, lr=lr)
        if self.use_scheduler:
            scheduler = LambdaLinearScheduler(**self.scheduler_config)

            rank_zero_info("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


class DiffusionWrapper(pl.LightningModule):
    def __init__(self, diff_model_config, conditioning_key):
        super().__init__()
        self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
        self.diffusion_model = UNetModel(**diff_model_config)
        self.conditioning_key = conditioning_key
        assert self.conditioning_key in [None, "concat", "crossattn", "hybrid", "adm", "hybrid-adm", "crossattn-adm"]

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

        return out


class LatentUpscaleDiffusion(LatentDiffusion):
    def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
        super().__init__(*args, **kwargs)
        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
        assert not self.cond_stage_trainable
        self.instantiate_low_stage(low_scale_config)
        self.low_scale_key = low_scale_key
        self.noise_level_key = noise_level_key

    def instantiate_low_stage(self, config):
        model = ImageConcatWithNoiseAugmentation(**config)
        self.low_scale_model = model.eval()
        self.low_scale_model.train = disabled_train
        for param in self.low_scale_model.parameters():
            param.requires_grad = False

    @torch.no_grad()
    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
        if not log_mode:
            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
        else:
            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,
            )
        x_low = batch[self.low_scale_key][:bs]
        x_low = rearrange(x_low, "b h w c -> b c h w")
        if self.use_fp16:
            x_low = x_low.to(memory_format=torch.contiguous_format).half()
        else:
            x_low = x_low.to(memory_format=torch.contiguous_format).float()
        zx, noise_level = self.low_scale_model(x_low)
        if self.noise_level_key is not None:
            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
            raise NotImplementedError("TODO")

        all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
        if log_mode:
            # TODO: maybe disable if too expensive
            x_low_rec = self.low_scale_model.decode(zx)
            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
        return z, all_conds

    @torch.no_grad()
    def log_images(
        self,
        batch,
        N=8,
        n_row=4,
        sample=True,
        ddim_steps=200,
        ddim_eta=1.0,
        return_keys=None,
        plot_denoise_rows=False,
        plot_progressive_rows=True,
        plot_diffusion_rows=True,
        unconditional_guidance_scale=1.0,
        unconditional_guidance_label=None,
        use_ema_scope=True,
        **kwargs,
    ):
        ema_scope = self.ema_scope if use_ema_scope else nullcontext
        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(
            batch, self.first_stage_key, bs=N, log_mode=True
        )
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log["inputs"] = x
        log["reconstruction"] = xrec
        log["x_lr"] = x_low
        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
        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", "txt"]:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
                log["conditioning"] = xc
            elif self.cond_stage_key in ["class_label", "cls"]:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
                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 ema_scope("Sampling"):
                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

        if unconditional_guidance_scale > 1.0:
            uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
            # TODO explore better "unconditional" choices for the other keys
            # maybe guide away from empty text label and highest noise level and maximally degraded zx?
            uc = dict()
            for k in c:
                if k == "c_crossattn":
                    assert isinstance(c[k], list) and len(c[k]) == 1
                    uc[k] = [uc_tmp]
                elif k == "c_adm":  # todo: only run with text-based guidance?
                    assert isinstance(c[k], torch.Tensor)
                    # uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
                    uc[k] = c[k]
                elif isinstance(c[k], list):
                    uc[k] = [c[k][i] for i in range(len(c[k]))]
                else:
                    uc[k] = c[k]

            with ema_scope("Sampling with classifier-free guidance"):
                samples_cfg, _ = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                    unconditional_guidance_scale=unconditional_guidance_scale,
                    unconditional_conditioning=uc,
                )
                x_samples_cfg = self.decode_first_stage(samples_cfg)
                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg

        if plot_progressive_rows:
            with 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

        return log


class LatentFinetuneDiffusion(LatentDiffusion):
    """
    Basis for different finetunas, such as inpainting or depth2image
    To disable finetuning mode, set finetune_keys to None
    """

    def __init__(
        self,
        concat_keys: tuple,
        finetune_keys=(
            "model.diffusion_model.input_blocks.0.0.weight",
            "model_ema.diffusion_modelinput_blocks00weight",
        ),
        keep_finetune_dims=4,
        # if model was trained without concat mode before and we would like to keep these channels
        c_concat_log_start=None,  # to log reconstruction of c_concat codes
        c_concat_log_end=None,
        *args,
        **kwargs,
    ):
        ckpt = kwargs.pop("ckpt", None)
        ignore_keys = kwargs.pop("ignore_keys", list())
        super().__init__(*args, **kwargs)
        self.finetune_keys = finetune_keys
        self.concat_keys = concat_keys
        self.keep_dims = keep_finetune_dims
        self.c_concat_log_start = c_concat_log_start
        self.c_concat_log_end = c_concat_log_end
        if exists(self.finetune_keys):
            assert exists(ckpt), "can only finetune from a given checkpoint"
        if exists(ckpt):
            self.init_from_ckpt(ckpt, ignore_keys)

    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):
                    rank_zero_info("Deleting key {} from state_dict.".format(k))
                    del sd[k]

            # make it explicit, finetune by including extra input channels
            if exists(self.finetune_keys) and k in self.finetune_keys:
                new_entry = None
                for name, param in self.named_parameters():
                    if name in self.finetune_keys:
                        rank_zero_info(
                            f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only"
                        )
                        new_entry = torch.zeros_like(param)  # zero init
                assert exists(new_entry), "did not find matching parameter to modify"
                new_entry[:, : self.keep_dims, ...] = sd[k]
                sd[k] = new_entry

        missing, unexpected = (
            self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False)
        )
        rank_zero_info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
        if len(missing) > 0:
            rank_zero_info(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            rank_zero_info(f"Unexpected Keys: {unexpected}")

    @torch.no_grad()
    def log_images(
        self,
        batch,
        N=8,
        n_row=4,
        sample=True,
        ddim_steps=200,
        ddim_eta=1.0,
        return_keys=None,
        quantize_denoised=True,
        inpaint=True,
        plot_denoise_rows=False,
        plot_progressive_rows=True,
        plot_diffusion_rows=True,
        unconditional_guidance_scale=1.0,
        unconditional_guidance_label=None,
        use_ema_scope=True,
        **kwargs,
    ):
        ema_scope = self.ema_scope if use_ema_scope else nullcontext
        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
        c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
        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", "txt"]:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
                log["conditioning"] = xc
            elif self.cond_stage_key in ["class_label", "cls"]:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
                log["conditioning"] = xc
            elif isimage(xc):
                log["conditioning"] = xc
            if ismap(xc):
                log["original_conditioning"] = self.to_rgb(xc)

        if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
            log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start : self.c_concat_log_end])

        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 ema_scope("Sampling"):
                samples, z_denoise_row = self.sample_log(
                    cond={"c_concat": [c_cat], "c_crossattn": [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

        if unconditional_guidance_scale > 1.0:
            uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
            uc_cat = c_cat
            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
            with ema_scope("Sampling with classifier-free guidance"):
                samples_cfg, _ = self.sample_log(
                    cond={"c_concat": [c_cat], "c_crossattn": [c]},
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                    unconditional_guidance_scale=unconditional_guidance_scale,
                    unconditional_conditioning=uc_full,
                )
                x_samples_cfg = self.decode_first_stage(samples_cfg)
                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg

        return log


class LatentInpaintDiffusion(LatentFinetuneDiffusion):
    """
    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
    e.g. mask as concat and text via cross-attn.
    To disable finetuning mode, set finetune_keys to None
    """

    def __init__(self, concat_keys=("mask", "masked_image"), masked_image_key="masked_image", *args, **kwargs):
        super().__init__(concat_keys, *args, **kwargs)
        self.masked_image_key = masked_image_key
        assert self.masked_image_key in 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:
            if self.use_fp16:
                cc = rearrange(batch[ck], "b h w c -> b c h w").to(memory_format=torch.contiguous_format).half()
            else:
                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

    @torch.no_grad()
    def log_images(self, *args, **kwargs):
        log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
        log["masked_image"] = (
            rearrange(args[0]["masked_image"], "b h w c -> b c h w").to(memory_format=torch.contiguous_format).float()
        )
        return log


class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
    """
    condition on monocular depth estimation
    """

    def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
        super().__init__(concat_keys=concat_keys, *args, **kwargs)
        self.depth_model = MiDaSInference(**depth_stage_config)
        self.depth_stage_key = concat_keys[0]

    @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 depth2img"
        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)
        assert len(self.concat_keys) == 1
        c_cat = list()
        for ck in self.concat_keys:
            cc = batch[ck]
            if bs is not None:
                cc = cc[:bs]
                cc = cc.to(self.device)
            cc = self.depth_model(cc)
            cc = torch.nn.functional.interpolate(
                cc,
                size=z.shape[2:],
                mode="bicubic",
                align_corners=False,
            )

            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(
                cc, dim=[1, 2, 3], keepdim=True
            )
            cc = 2.0 * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.0
            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

    @torch.no_grad()
    def log_images(self, *args, **kwargs):
        log = super().log_images(*args, **kwargs)
        depth = self.depth_model(args[0][self.depth_stage_key])
        depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), torch.amax(
            depth, dim=[1, 2, 3], keepdim=True
        )
        log["depth"] = 2.0 * (depth - depth_min) / (depth_max - depth_min) - 1.0
        return log


class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
    """
    condition on low-res image (and optionally on some spatial noise augmentation)
    """

    def __init__(
        self, concat_keys=("lr",), reshuffle_patch_size=None, low_scale_config=None, low_scale_key=None, *args, **kwargs
    ):
        super().__init__(concat_keys=concat_keys, *args, **kwargs)
        self.reshuffle_patch_size = reshuffle_patch_size
        self.low_scale_model = None
        if low_scale_config is not None:
            rank_zero_info("Initializing a low-scale model")
            assert exists(low_scale_key)
            self.instantiate_low_stage(low_scale_config)
            self.low_scale_key = low_scale_key

    def instantiate_low_stage(self, config):
        model = ImageConcatWithNoiseAugmentation(**config)
        self.low_scale_model = model.eval()
        self.low_scale_model.train = disabled_train
        for param in self.low_scale_model.parameters():
            param.requires_grad = False

    @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 upscaling-ft"
        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)
        assert len(self.concat_keys) == 1
        # optionally make spatial noise_level here
        c_cat = list()
        noise_level = None
        for ck in self.concat_keys:
            cc = batch[ck]
            cc = rearrange(cc, "b h w c -> b c h w")
            if exists(self.reshuffle_patch_size):
                assert isinstance(self.reshuffle_patch_size, int)
                cc = rearrange(
                    cc,
                    "b c (p1 h) (p2 w) -> b (p1 p2 c) h w",
                    p1=self.reshuffle_patch_size,
                    p2=self.reshuffle_patch_size,
                )
            if bs is not None:
                cc = cc[:bs]
                cc = cc.to(self.device)
            if exists(self.low_scale_model) and ck == self.low_scale_key:
                cc, noise_level = self.low_scale_model(cc)
            c_cat.append(cc)
        c_cat = torch.cat(c_cat, dim=1)
        if exists(noise_level):
            all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
        else:
            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

    @torch.no_grad()
    def log_images(self, *args, **kwargs):
        log = super().log_images(*args, **kwargs)
        log["lr"] = rearrange(args[0]["lr"], "b h w c -> b c h w")
        return log
