AltDiffusion.py

import sys
import torch
import torch.nn as nn
import numpy as np
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 flagai.model.mm.utils import exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config, log_txt_as_img
from flagai.model.mm.autoencoders import VQModelInterface, IdentityFirstStage, AutoencoderKL
from flagai.model.mm.utils import make_beta_schedule, extract_into_tensor, noise_like
from flagai.model.mm.Sampler import DDIMSampler
from flagai.model.base_model import BaseModel
from torch.cuda.amp import autocast as autocast

__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(BaseModel):
    # 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=False,
        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.,
        v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
        l_simple_weight=1.,
        conditioning_key=None,
        parameterization="eps",  # all assuming fixed variance schedules
        scheduler_config=None,
        use_positional_encodings=False,
        learn_logvar=False,
        logvar_init=0.,
        **kwargs,
    ):
        super(DDPM, self).__init__(unet_config, **kwargs)
        unet_config.params.update(kwargs)
        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)
        self.use_ema = use_ema
        count_params(self.model, verbose=True)

        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
        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. - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1., 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. - alphas_cumprod)))
        self.register_buffer('log_one_minus_alphas_cumprod',
                             to_torch(np.log(1. - alphas_cumprod)))
        self.register_buffer('sqrt_recip_alphas_cumprod',
                             to_torch(np.sqrt(1. / alphas_cumprod)))
        self.register_buffer('sqrt_recipm1_alphas_cumprod',
                             to_torch(np.sqrt(1. / alphas_cumprod - 1)))

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = (1 - self.v_posterior) * betas * (
            1. - alphas_cumprod_prev) / (
                1. - 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. - alphas_cumprod)))
        self.register_buffer(
            'posterior_mean_coef2',
            to_torch((1. - alphas_cumprod_prev) * np.sqrt(alphas) /
                     (1. - 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. * 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., 1.)

        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_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))
        # print(f"after get noise: {torch.cuda.memory_allocated()/1024/1024/1024:.2f}GB")
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        # print(f"after sampled noise: {torch.cuda.memory_allocated()/1024/1024/1024:.2f}GB")
        model_out = self.model(x_noisy, t)
        # print(f"after predict noise: {torch.cuda.memory_allocated()/1024/1024/1024:.2f}GB")

        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])
        # print(f"after get loss: {torch.cuda.memory_allocated()/1024/1024/1024:.2f}GB")

        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):
        # The k here stands for key, and is primarily used to handle the case that batch contains both image and caption
        x = batch[k]
        # In unsupervised learning, k is not used
        #x = batch
        if len(x.shape) == 3:
            x = x[..., None]
        # dataseet中已经处理了图片顺序，不需要再做rearrange
        # 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)
        try:
            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)
        except:
            return loss

        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


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

    def to(self, device):
        self.device = device
        self.cond_stage_model.to(device)
        super().to(device)

    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,
                 tokenizer=None,
                 *args,
                 **kwargs):
        self.device = "cpu"
        self.tokenizer = tokenizer
        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
        self.conditioning_key = conditioning_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
        if self.cond_stage_forward is None:
            self.set_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

    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

    # In the preprocessing before the start of training, an error about positional parameter will appear
    @rank_zero_only
    @torch.no_grad()
    #def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
    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., '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)
            # There will be an compression of x
            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. / 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):
        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
    
    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")
            z = encoder_posterior.sample()
        return self.scale_factor * z
    def set_cond_stage_forward(self):
        def func(c):
            device = next(self.cond_stage_model.parameters()).device
            text = self.tokenizer(c,
                            truncation=True,
                            max_length=77,
                            return_length=False,
                            return_overflowing_tokens=False,
                            padding="max_length",
                            return_tensors="pt")
            text["input_ids"] = text["input_ids"].clone().detach().to(device)
            text["attention_mask"] = text['attention_mask'].clone().detach().to(device)
            features = self.cond_stage_model(**text)
            return features['projection_state']
        self.cond_stage_forward = func
    def get_learned_conditioning(self, c):
        # C will be directly returned if it is None
        if c is None:
            return None
        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, self.tokenizer)
                if isinstance(c, DiagonalGaussianDistribution):
                    c = c.mode()
            else:
                c = self.cond_stage_model(c)
        else:
            c = 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):
        x = super().get_input(batch, k)
        if bs is not None:
            x = x[:bs]
        x = x.to(self.device)
        # VAE-style compression by default
        encoder_posterior = self.encode_first_stage(x)

        z = self.get_first_stage_encoding(encoder_posterior).detach()
        # self.model.conditioning_key is of type str by default. Note that setting it to None in config file will return string 'None'
        # Comment it in yaml if you do not want to set it

        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
                # Add the condition for text-image modification
                elif cond_key == "img_and_caption":
                    xc = batch["img_and_caption"]
                else:
                    xc = super().get_input(batch, cond_key).to(self.device)
            else:
                # Obtain xc according to the dictionary
                xc = x

            if not self.cond_stage_trainable or force_c_encode:
                if isinstance(xc, dict) or isinstance(xc, list):
                    # Determine if learning the cond, otherwise it will return
                    # if the image editing is driven by texts, we will process the init_img and caption
                    if cond_key == "img_and_caption":
                        # To prevent xc["init_img"] saved after being encoded, we fetch it from batch at each time
                        c = {}
                        init_img = batch["img_and_caption"]["init_img"]
                        caption = batch["img_and_caption"]["caption"]
                        init_img_posterior = self.encode_first_stage(init_img)
                        init_img_posterior = self.get_first_stage_encoding(
                            init_img_posterior).detach()

                        c["init_img"] = self.get_first_stage_encoding(
                            init_img_posterior)
                        c["caption"] = self.get_learned_conditioning(caption)

                    else:
                        c = self.get_learned_conditioning(xc)
                else:
                    #
                    c = self.get_learned_conditioning(xc.to(self.device))
            else:
                c = xc
            if bs is not None:
                if cond_key == "img_and_caption":
                    c["init_img"] = c["init_img"][:bs]
                    c["caption"] = c["caption"][:bs]
                else:
                    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. / 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. / 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:
                if self.cond_stage_key == "img_and_caption":
                    init_img_posterior = self.encode_first_stage(c["init_img"])
                    c["init_img"] = self.get_first_stage_encoding(
                        init_img_posterior).detach()
                    c["caption"] = self.get_learned_conditioning(c["caption"])
                else:
                    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 = torch.clamp(
                (bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
            y0 = torch.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 self.conditioning_key == "hybrid":
            new_cond = {
                'c_concat': [cond["init_img"]],
                'c_crossattn': [cond["caption"]]
            }
            cond = new_cond
        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 postions 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:
            with autocast():            
                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)
        # 条件图像也要变成噪声图像
        # init_img_noise = self.q_sample(x_start=cond["init_img"], t=t, noise=noise)
        # cond["init_img"] = init_img_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])
        #print(loss_simple)

        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})

        t = t.cpu()
        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., 1.)
        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.,
                 noise_dropout=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.:
            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.,
                              noise_dropout=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. - 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. - 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 log_images(self,
                   batch,
                   N=8,
                   n_row=4,
                   sample=True,
                   ddim_steps=200,
                   ddim_eta=1.,
                   return_keys=None,
                   quantize_denoised=True,
                   inpaint=True,
                   plot_denoise_rows=False,
                   plot_progressive_rows=True,
                   plot_diffusion_rows=True,
                   **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.conditioning_key == 'hybrid':
            # log文本和初始图像
            log["init_img"] = batch["img_and_caption"]["init_img"]
            xc = log_txt_as_img((x.shape[2], x.shape[3]),
                                batch["img_and_caption"]["caption"])
            log["conditioning"] = xc

        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

            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.
                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
        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)
        opt = torch.optim.Adam(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. * (x - x.min()) / (x.max() - x.min()) - 1.
        return x


class DiffusionWrapper(torch.nn.Module):

    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':
            xc = torch.cat([x] + c_concat, dim=1)
            #xc = x + c_concat[0]
            cc = torch.cat(c_crossattn, 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 DiagonalGaussianDistribution(object):

    def __init__(self, parameters, deterministic=False):
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(
                self.mean).to(device=self.parameters.device)

    def sample(self):
        x = self.mean + self.std * torch.randn(
            self.mean.shape).to(device=self.parameters.device)
        return x

    def kl(self, other=None):
        if self.deterministic:
            return torch.Tensor([0.])
        else:
            if other is None:
                return 0.5 * torch.sum(
                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
                    dim=[1, 2, 3])
            else:
                return 0.5 * torch.sum(
                    torch.pow(self.mean - other.mean, 2) / other.var +
                    self.var / other.var - 1.0 - self.logvar + other.logvar,
                    dim=[1, 2, 3])

    def nll(self, sample, dims=[1, 2, 3]):
        if self.deterministic:
            return torch.Tensor([0.])
        logtwopi = np.log(2.0 * np.pi)
        return 0.5 * torch.sum(logtwopi + self.logvar +
                               torch.pow(sample - self.mean, 2) / self.var,
                               dim=dims)

    def mode(self):
        return self.mean


def normal_kl(mean1, logvar1, mean2, logvar2):
    """
    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
    Compute the KL divergence between two gaussians.
    Shapes are automatically broadcasted, so batches can be compared to
    scalars, among other use cases.
    """
    tensor = None
    for obj in (mean1, logvar1, mean2, logvar2):
        if isinstance(obj, torch.Tensor):
            tensor = obj
            break
    assert tensor is not None, "at least one argument must be a Tensor"

    # Force variances to be Tensors. Broadcasting helps convert scalars to
    # Tensors, but it does not work for torch.exp().
    logvar1, logvar2 = [
        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
        for x in (logvar1, logvar2)
    ]

    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) +
                  ((mean1 - mean2)**2) * torch.exp(-logvar2))