ldm/models/diffusion/lcm_audio.py

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

from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.ddpm import DDPM, disabled_train, DiffusionWrapper
from omegaconf import ListConfig
from ldm.models.diffusion.scheduling_lcm import LCMSampler
from ldm.models.diffusion.ddim_solver import DDIMSolver
__conditioning_keys__ = {'concat': 'c_concat',
                         'crossattn': 'c_crossattn',
                         'adm': 'y'}

def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
    return x[(...,) + (None,) * dims_to_append]


# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
    c_skip = sigma_data**2 / ((timestep / 0.1) ** 2 + sigma_data**2)
    c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5
    return c_skip, c_out


class LCM_audio(DDPM):
    """main class"""
    def __init__(self,

                 first_stage_config,

                 cond_stage_config,

                 num_timesteps_cond=None,

                 mel_dim=80,

                 mel_length=848,

                 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_lcm=True,

                 num_ddim_timesteps=50,

                 w_min=None,

                 w_max=None,

                 *args, **kwargs):
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs['timesteps']
        # for backwards compatibility after implementation of DiffusionWrapper
        if conditioning_key is None:
            conditioning_key = 'concat' if concat_mode else 'crossattn'
        if cond_stage_config == '__is_unconditional__':
            conditioning_key = None
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
        self.concat_mode = concat_mode
        self.mel_dim = mel_dim
        self.mel_length = mel_length
        self.use_lcm = use_lcm
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key
        try:
            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
        except:
            self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        else:
            self.register_buffer('scale_factor', torch.tensor(scale_factor))
        self.instantiate_first_stage(first_stage_config)
        self.instantiate_cond_stage(cond_stage_config)
        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None  
        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True
        if self.use_lcm:
            ### DDIM Solver
            self.solver = DDIMSolver(self.alphas_cumprod.numpy(),self.num_timesteps, num_ddim_timesteps)
            step_ratio = self.num_timesteps // num_ddim_timesteps
            self.step_ratio = step_ratio
            self.num_ddim_timesteps = num_ddim_timesteps
            self.ddim_timesteps = (np.arange(1, num_ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
            # convert to torch tensors
            self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
            self.model.requires_grad_(False)
            self.unet = DiffusionWrapper(self.unet_config, conditioning_key)
            self.unet.load_state_dict(self.model.state_dict(), strict=False)
            self.unet.train()
            self.target_unet = DiffusionWrapper(self.unet_config, conditioning_key)
            self.target_unet.load_state_dict(self.unet.state_dict())
            self.target_unet.requires_grad_(False)
            self.target_unet.train()
            self.w_min = w_min
            self.w_max = w_max

    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., 'rather not use custom rescaling and std-rescaling simultaneously'
            # set rescale weight to 1./std of encodings
            print("### USING STD-RESCALING ###")
            x = super().get_input(batch, self.first_stage_key)
            x = x.to(self.device)
            encoder_posterior = self.encode_first_stage(x)
            z = self.get_first_stage_encoding(encoder_posterior).detach()# get latent
            del self.scale_factor
            self.register_buffer('scale_factor', 1. / z.flatten().std())# 1/latent.std， get_first_stage_encoding returns self.scale_factor * latent
            print(f"setting self.scale_factor to {self.scale_factor}")
            print("### USING STD-RESCALING ###")

    # def on_train_epoch_start(self):
    #     print("!!!!!!!!!!!!!!!!!!!!!!!!!!on_train_epoch_strat",self.trainer.train_dataloader.batch_sampler,hasattr(self.trainer.train_dataloader.batch_sampler,'set_epoch'))
    #     if hasattr(self.trainer.train_dataloader.batch_sampler,'set_epoch'):
    #         self.trainer.train_dataloader.batch_sampler.set_epoch(self.current_epoch)
    #     return super().on_train_epoch_start()


    def register_schedule(self,

                          given_betas=None, beta_schedule="linear", timesteps=1000,

                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)

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

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

    def instantiate_cond_stage(self, config):
        if not self.cond_stage_trainable:
            if config == "__is_first_stage__":
                print("Using first stage also as cond stage.")
                self.cond_stage_model = self.first_stage_model
            elif config == "__is_unconditional__":
                print(f"Training {self.__class__.__name__} as an unconditional model.")
                self.cond_stage_model = None
            else:
                model = instantiate_from_config(config)
                self.cond_stage_model = model.eval()
                self.cond_stage_model.train = disabled_train
                for param in self.cond_stage_model.parameters():
                    param.requires_grad = False
        else:
            assert config != '__is_first_stage__'
            assert config != '__is_unconditional__'
            model = instantiate_from_config(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)
        if len(denoise_row[0].shape) == 3:
            denoise_row = [x.unsqueeze(1) for x in 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

    #@profile
    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


    @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

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

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

    def delta_border(self, h, w):
        """

        :param h: height

        :param w: width

        :return: normalized distance to image border,

         wtith min distance = 0 at border and max dist = 0.5 at image center

        """
        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
        arr = self.meshgrid(h, w) / lower_right_corner
        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
        return edge_dist

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

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

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

    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
        """

        :param x: img of size (bs, c, h, w)

        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])

        """
        bs, nc, h, w = x.shape

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

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

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

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

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

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

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

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

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

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

        else:
            raise NotImplementedError

        return fold, unfold, normalization, weighting

    @torch.no_grad()
    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,

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

        if self.model.conditioning_key is not None:
            if cond_key is None:
                cond_key = self.cond_stage_key
            if cond_key != self.first_stage_key:
                if cond_key in ['caption', 'coordinates_bbox']:
                    xc = batch[cond_key]
                elif cond_key == 'class_label':
                    xc = batch
                else:
                    xc = super().get_input(batch, cond_key).to(self.device)
            else:
                xc = x
            if not self.cond_stage_trainable or force_c_encode:
                if isinstance(xc, dict) or isinstance(xc, list):
                    # import pudb; pudb.set_trace()
                    c = self.get_learned_conditioning(xc)
                else:
                    c = self.get_learned_conditioning(xc.to(self.device))
            else:
                c = xc
            if bs is not None:
                c = c[:bs]
            # Testing #
            if cond_key == 'masked_image':
                mask = super().get_input(batch, "mask")
                cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:]) # [B, 1, 10, 106]
                c = torch.cat((c, cc), dim=1) # [B, 5, 10, 106]
            # Testing #
            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 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 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):
        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 test_step(self,batch,batch_idx):
        cond = batch[self.cond_stage_key] #  * self.test_repeat
        cond = self.get_learned_conditioning(cond) # c: string -> [B, T, Context_dim]
        batch_size = len(cond)
        enc_emb = self.sample(cond,batch_size,timesteps=self.num_timesteps)# shape = [batch_size,self.channels,self.mel_dim,self.mel_length]
        xrec = self.decode_first_stage(enc_emb)
        # reconstructions = (xrec + 1)/2 # to mel scale
        # test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
        # savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
        # if not os.path.exists(savedir):
        #     os.makedirs(savedir)

        # file_names = batch['f_name']
        # nfiles = len(file_names)
        # reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim
        # for k in range(reconstructions.shape[0]):
        #     b,repeat = k % nfiles, k // nfiles
        #     vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
        #     v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
        #     save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition
        #     np.save(save_img_path,reconstructions[b])
        return None

    def forward(self, x, cond, *args, **kwargs):

        if self.use_lcm:
            index = torch.randint(0, self.num_ddim_timesteps, (x.shape[0],), device=self.device).long()
            t = self.ddim_timesteps[index].to(self.device)
            # t = torch.randint(0, self.ddim_timesteps, (x.shape[0],), device=self.device).long()
        else:
            t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
        if self.model.conditioning_key is not None:
            assert cond is not None
            cond = cond.to(x.device)

            if self.cond_stage_trainable:
                cond = self.get_learned_conditioning(cond) # c: string -> [B, T, Context_dim]
            if self.shorten_cond_schedule:  # TODO: drop this option
                tc = self.cond_ids[t].to(self.device)
                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond.float()))
        if self.use_lcm:
            return self.lcm_losses(x, cond, t, index, *args, **kwargs)
            
        return self.p_losses(x, cond, t, *args, **kwargs)


    def apply_model(self, x_noisy, t, cond, model, w_cond=None, return_ids=False):

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


        x_recon = model(x_noisy, t, **cond, w_cond=w_cond)

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

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

    def _prior_bpd(self, x_start):
        """

        Get the prior KL term for the variational lower-bound, measured in

        bits-per-dim.

        This term can't be optimized, as it only depends on the encoder.

        :param x_start: the [N x C x ...] tensor of inputs.

        :return: a batch of [N] KL values (in bits), one per batch element.

        """
        batch_size = x_start.shape[0]
        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
        return mean_flat(kl_prior) / np.log(2.0)

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

        loss_dict = {}
        prefix = 'train' if self.training else 'val'
 
        if self.parameterization == "x0":
            target = x_start
        elif self.parameterization == "eps":
            target = noise
        else:
            raise NotImplementedError()
        
        mean_dims = list(range(1,len(target.shape)))
        loss_simple = self.get_loss(model_output, target, mean=False).mean(dim=mean_dims)
        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=mean_dims)
        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

    # From LatentConsistencyModel.get_guidance_scale_embedding
    def guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
        """

        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298



        Args:

            timesteps (`torch.Tensor`):

                generate embedding vectors at these timesteps

            embedding_dim (`int`, *optional*, defaults to 512):

                dimension of the embeddings to generate

            dtype:

                data type of the generated embeddings



        Returns:

            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`

        """
        assert len(w.shape) == 1
        w = w * 1000.0

        half_dim = embedding_dim // 2
        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
        emb = w.to(dtype)[:, None] * emb[None, :]
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
        if embedding_dim % 2 == 1:  # zero pad
            emb = torch.nn.functional.pad(emb, (0, 1))
        assert emb.shape == (w.shape[0], embedding_dim)
        return emb

    def lcm_losses(self, x_start, cond, t, index, noise=None):
        # 20.4.4. Get boundary scalings for start_timesteps and (end) timesteps.
        c_skip_start, c_out_start = scalings_for_boundary_conditions(t)
        c_skip_start, c_out_start = [append_dims(x, x_start.ndim) for x in [c_skip_start, c_out_start]]
        timesteps = t - self.step_ratio
        timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
        c_skip, c_out = scalings_for_boundary_conditions(timesteps)
        c_skip, c_out = [append_dims(x, x_start.ndim) for x in [c_skip, c_out]]
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        bsz = x_start.shape[0]
        # 20.4.6. Sample a random guidance scale w from U[w_min, w_max] and embed it
        w = (self.w_max - self.w_min) * torch.rand((bsz,)) + self.w_min
        w_embedding = self.guidance_scale_embedding(w, embedding_dim=256)
        w = w.reshape(bsz, 1, 1)
        # Move to U-Net device and dtype
        w = w.to(device=x_start.device, dtype=x_start.dtype)
        w_embedding = w_embedding.to(device=x_start.device, dtype=x_start.dtype)

        # import ipdb
        # ipdb.set_trace()
        model_output = self.apply_model(x_noisy, t, cond, self.unet, w_cond=w_embedding)
        pred_x_0 = self.predict_start_from_noise(x_noisy,t,model_output)
        model_pred = c_skip_start * x_noisy + c_out_start * pred_x_0

        # 20.4.10. Use the ODE solver to predict the kth step in the augmented PF-ODE trajectory after
        # noisy_latents with both the conditioning embedding c and unconditional embedding 0
        # Get teacher model prediction on noisy_latents and conditional embedding
        with torch.no_grad():
            with torch.autocast("cuda"):

                teacher_output = self.apply_model(x_noisy, t, cond, self.model)
                teacher_pred_x0 = self.predict_start_from_noise(x_noisy,t,teacher_output)
                uncond = self.get_learned_conditioning({'ori_caption': [""] * bsz,"struct_caption":[""] * bsz})
                uncond_teacher_output = self.apply_model(x_noisy, t, uncond, self.model)
                uncond_teacher_pred_x0 = self.predict_start_from_noise(x_noisy,t,uncond_teacher_output)
                pred_x0 = teacher_pred_x0 + w * (teacher_pred_x0 - uncond_teacher_pred_x0)
                pred_noise = teacher_output + w * (teacher_output - uncond_teacher_output)

                x_prev = self.solver.ddim_step(pred_x0, pred_noise, index)

        # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n
        with torch.no_grad():
            with torch.autocast("cuda"):
                target_noise_pred = self.apply_model(x_prev.float(), timesteps, cond, self.target_unet, w_cond=w_embedding)
            pred_x_0 = self.predict_start_from_noise(x_prev,timesteps,target_noise_pred)
            target = c_skip * x_prev + c_out * pred_x_0

        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()
        
        # mean_dims = list(range(1,len(target.shape)))
        # loss_simple = self.get_loss(model_pred, target, mean=False).mean(dim=mean_dims)
        # loss = self.get_loss(model_pred, target, mean=True)
        # loss_dict.update({f'{prefix}/loss_simple': loss.mean()})
        # loss = torch.nn.functional.mse_loss(model_pred.float(), target.float(), reduction="mean")
        loss = torch.mean(
                        torch.sqrt((model_pred.float() - target.float()) ** 2 + 0.001**2) - 0.001
                    )
        loss_dict.update({f'{prefix}/loss': loss.mean()})

        loss = self.l_simple_weight * loss.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_pred, target, mean=False).mean(dim=mean_dims)
        # 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

    @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:
            if self.channels > 0:
                shape = (batch_size, self.channels, self.mel_dim, self.mel_length)
            else:
                shape = (batch_size, self.mel_dim, self.mel_length)
        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):
        
        lcm_sampler = LCMSampler(self)
        shape = (self.channels, self.mel_dim, self.mel_length) if self.channels > 0 else (self.mel_dim, self.mel_length)
        samples, intermediates = lcm_sampler.sample(ddim_steps,batch_size,
                                                        shape,cond,verbose=False,**kwargs)
        # if ddim:
        #     ddim_sampler = DDIMSampler(self)
        #     shape = (self.channels, self.mel_dim, self.mel_length) if self.channels > 0 else (self.mel_dim, self.mel_length)
        #     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=50, ddim_eta=1., return_keys=None,

                   quantize_denoised=True, inpaint=False, 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) # z is latent,c is condition embedding, xc is condition(caption) list
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log["inputs"] = x if len(x.shape)==4 else x.unsqueeze(1)
        log["reconstruction"] = xrec if len(xrec.shape)==4 else xrec.unsqueeze(1)
        if self.model.conditioning_key is not None:
            if hasattr(self.cond_stage_model, "decode") and self.cond_stage_key != "masked_image":
                xc = self.cond_stage_model.decode(c)
                log["conditioning"] = xc
            elif self.cond_stage_key == "masked_image":
                log["mask"] = c[:, -1, :, :][:, None, :, :]
                xc = self.cond_stage_model.decode(c[:, :self.cond_stage_model.embed_dim, :, :])
                log["conditioning"] = xc
            elif self.cond_stage_key in ["caption"]:
                pass
                # xc = log_txt_as_img((256, 256), 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 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))
            if len(diffusion_row[0].shape) == 3:
                diffusion_row = [x.unsqueeze(1) for x in diffusion_row]
            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 len(x_samples.shape)==4 else x_samples.unsqueeze(1)
            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 len(x_samples.shape)==4 else x_samples.unsqueeze(1)

            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_inpainting"] = mask

                # outpaint
                mask = 1 - mask
                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
                log["mask_outpainting"] = mask

        # if plot_progressive_rows:
        #     with self.ema_scope("Plotting Progressives"):
        #         shape = (self.channels, self.mel_dim, self.mel_length) if self.channels > 0 else (self.mel_dim, self.mel_length)
        #         img, progressives = self.progressive_denoising(c,
        #                                                        shape=shape,
        #                                                        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.unet.parameters())
        if self.cond_stage_trainable:
            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
            params = params + list(self.cond_stage_model.parameters())
        if self.learn_logvar:
            print('Diffusion model optimizing logvar')
            params.append(self.logvar)
        opt = torch.optim.AdamW(params, lr=lr)
        if self.use_scheduler:
            assert 'target' in self.scheduler_config
            scheduler = instantiate_from_config(self.scheduler_config)

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

    def on_train_batch_end(self, *args, **kwargs):
        rate = 0.95
        for targ, src in zip(self.target_unet.parameters(), self.unet.parameters()):
            targ.detach().mul_(rate).add_(src, alpha=1 - rate)