diffusion.py

"""
This code started out as a PyTorch port of Ho et al's diffusion models:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
"""

import enum
import math

import numpy as np
import torch as th
import torch
from models.nn import mean_flat
from models.losses import normal_kl, discretized_gaussian_log_likelihood
from types import *

import torch
import tqdm

def compute_alpha(beta, t):
    beta = torch.cat([torch.zeros(1).to(beta.device), beta], dim=0)
    a = (1 - beta).cumprod(dim=0).index_select(0, t + 1).view(-1, 1, 1, 1)
    return a


def ddim_steps(x, seq, model, b, x_cond, diffusion = None, **kwargs):
    x_cond[0] = [model.encode(x_cond[0])['cond'], model.encode(torch.zeros_like(x_cond[0]))['cond']]
    
    with torch.no_grad():
        n = x.size(0)
        seq_next = [-1] + list(seq[:-1])
        x0_preds = []
        xs = [x]
        xt = x
        for i, j in tqdm.tqdm(zip(reversed(seq), reversed(seq_next))):
            t = (torch.ones(n) * i).to(x.device)
            next_t = (torch.ones(n) * j).to(x.device)
            at = compute_alpha(b, t.long())
            at_next = compute_alpha(b, next_t.long())
        
            [cond, target_pose] = x_cond[:2]
            et = model.forward_with_cond_scale(x = torch.cat([xt, target_pose],1), t = t, cond = cond, cond_scale = 2)[0]
            et, model_var_values = torch.split(et, 3, dim=1)
            x0_t = (xt - et * (1 - at).sqrt()) / at.sqrt()
            #x0_preds.append(x0_t.to('cpu'))
            c1 = (
                kwargs.get("eta", 0) * ((1 - at / at_next) * (1 - at_next) / (1 - at)).sqrt()
            )
            c2 = ((1 - at_next) - c1 ** 2).sqrt()
            xt = at_next.sqrt() * x0_t + c1 * torch.randn_like(x) + c2 * et

            if len(x_cond) == 4:
                [_,_,ref,mask] = x_cond
                xt = xt*mask + diffusion.q_sample(ref, t.long())*(1-mask)
            #xs.append(xt_next.to('cpu'))

    return [xt], x0_preds




def make_beta_schedule(
    schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
):
    if schedule == "quad":
        betas = (
            torch.linspace(
                linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64
            )
            ** 2
        )

    elif schedule == "linear":
        betas = torch.linspace(
            linear_start, linear_end, n_timestep, dtype=torch.float64
        )

    elif schedule == "cosine":
        timesteps = (
            torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
        )
        alphas = timesteps / (1 + cosine_s) * math.pi / 2
        alphas = torch.cos(alphas).pow(2)
        alphas = alphas / alphas[0]
        betas = 1 - alphas[1:] / alphas[:-1]
        betas = betas.clamp(max=0.999)

    return betas



def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function,
    which defines the cumulative product of (1-beta) over time from t = [0,1].
    :param num_diffusion_timesteps: the number of betas to produce.
    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                      produces the cumulative product of (1-beta) up to that
                      part of the diffusion process.
    :param max_beta: the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    """
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
    return np.array(betas)


class ModelMeanType(enum.Enum):
    """
    Which type of output the model predicts.
    """

    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
    START_X = enum.auto()  # the model predicts x_0
    EPSILON = enum.auto()  # the model predicts epsilon


class ModelVarType(enum.Enum):
    """
    What is used as the model's output variance.
    The LEARNED_RANGE option has been added to allow the model to predict
    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
    """

    LEARNED = enum.auto()
    FIXED_SMALL = enum.auto()
    FIXED_LARGE = enum.auto()
    LEARNED_RANGE = enum.auto()


class LossType(enum.Enum):
    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
    RESCALED_MSE = (
        enum.auto()
    )  # use raw MSE loss (with RESCALED_KL when learning variances)
    KL = enum.auto()  # use the variational lower-bound
    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB

    def is_vb(self):
        return self == LossType.KL or self == LossType.RESCALED_KL


class GaussianDiffusion:
    """
    Utilities for training and sampling diffusion models.
    Ported directly from here, and then adapted over time to further experimentation.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
    :param betas: a 1-D numpy array of betas for each diffusion timestep,
                  starting at T and going to 1.
    :param model_mean_type: a ModelMeanType determining what the model outputs.
    :param model_var_type: a ModelVarType determining how variance is output.
    :param loss_type: a LossType determining the loss function to use.
    :param rescale_timesteps: if True, pass floating point timesteps into the
                              model so that they are always scaled like in the
                              original paper (0 to 1000).
    """

    def __init__(
        self,
        *,
        betas,
        model_mean_type,
        model_var_type,
        loss_type,
        rescale_timesteps=False,
    ):
        self.model_mean_type = model_mean_type
        self.model_var_type = model_var_type
        self.loss_type = loss_type
        self.rescale_timesteps = rescale_timesteps

        # Use float64 for accuracy.
        betas = np.array(betas, dtype=np.float64)
        self.betas = betas
        assert len(betas.shape) == 1, "betas must be 1-D"
        assert (betas > 0).all() and (betas <= 1).all()

        self.num_timesteps = int(betas.shape[0])

        alphas = 1.0 - betas
        self.alphas_cumprod = np.cumprod(alphas, axis=0)
        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        self.posterior_variance = (
            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
        )
        # log calculation clipped because the posterior variance is 0 at the
        # beginning of the diffusion chain.
        self.posterior_log_variance_clipped = np.log(
            np.append(self.posterior_variance[1], self.posterior_variance[1:])
        )
        self.posterior_mean_coef1 = (
            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
        )
        self.posterior_mean_coef2 = (
            (1.0 - self.alphas_cumprod_prev)
            * np.sqrt(alphas)
            / (1.0 - self.alphas_cumprod)
        )

    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 q_sample(self, x_start, t, noise=None):
        """
        Diffuse the data for a given number of diffusion steps.
        In other words, sample from q(x_t | x_0).
        :param x_start: the initial data batch.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :param noise: if specified, the split-out normal noise.
        :return: A noisy version of x_start.
        """
        if noise is None:
            noise = th.randn_like(x_start)
        assert noise.shape == x_start.shape
        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 q_posterior_mean_variance(self, x_start, x_t, t):
        """
        Compute the mean and variance of the diffusion posterior:
            q(x_{t-1} | x_t, x_0)
        """
        assert x_start.shape == x_t.shape
        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
        )
        assert (
            posterior_mean.shape[0]
            == posterior_variance.shape[0]
            == posterior_log_variance_clipped.shape[0]
            == x_start.shape[0]
        )
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def p_mean_variance(
        self, model, x, t, x_cond = None, cond_scale = 1, clip_denoised=True, denoised_fn=None, model_kwargs=None, normalize=False
    ):
        """
        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
        the initial x, x_0.
        :param model: the model, which takes a signal and a batch of timesteps
                      as input.
        :param x: the [N x C x ...] tensor at time t.
        :param t: a 1-D Tensor of timesteps.
        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample. Applies before
            clip_denoised.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict with the following keys:
                 - 'mean': the model mean output.
                 - 'variance': the model variance output.
                 - 'log_variance': the log of 'variance'.
                 - 'pred_xstart': the prediction for x_0.
        """
        if model_kwargs is None:
            model_kwargs = {}

        def process_xstart(x):
            if denoised_fn is not None:
                x = denoised_fn(x)
            if clip_denoised:
                return x.clamp(-1, 1)
            return x

        def get_mean_var_from_eps(model_output):
            model_output, model_var_values = th.split(model_output, C, dim=1)

            min_log = _extract_into_tensor(
                self.posterior_log_variance_clipped, t, x.shape
            )
            max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
            # The model_var_values is [-1, 1] for [min_var, max_var].
            frac = (model_var_values + 1) / 2
            model_log_variance = frac * max_log + (1 - frac) * min_log
            model_variance = th.exp(model_log_variance)
            pred_xstart = process_xstart(
                self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
            )
            model_mean, _, _ = self.q_posterior_mean_variance(
                x_start=pred_xstart, x_t=x, t=t
            )
            return model_mean, model_variance, model_log_variance, pred_xstart

        B, C = x.shape[:2]
        assert t.shape == (B,)
        if x_cond is None:
            model_output = model(x, self._scale_timesteps(t), **model_kwargs)
        else:
            [cond, target_pose] = x_cond
            model_output, cond_output, uncond_output = model.forward_with_cond_scale(x = torch.cat([x, target_pose],1), t = self._scale_timesteps(t), cond = cond, cond_scale = cond_scale)

        model_mean, model_variance, model_log_variance, pred_xstart = get_mean_var_from_eps(model_output)

        return {
                "mean": model_mean,
                "variance": model_variance,
                "log_variance": model_log_variance,
                "pred_xstart": pred_xstart,
            }
            
                #out_eps[:,:3,:,:] = torch.sqrt(cond_model_variance/out_variance)*(out_eps[:,:3,:,:] - out_mean) + cond_model_mean
            #     #model_output = out_eps
            # else:

            #     return {
            #         "mean": model_mean,
            #         "variance": model_variance,
            #         "log_variance": model_log_variance,
            #         "pred_xstart": pred_xstart}

                

        
        #model_mean, model_variance, model_log_variance, pred_xstart = get_mean_var_from_eps(model_output)



        # if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
        #     assert model_output.shape == (B, C * 2, *x.shape[2:])
        #     model_output, model_var_values = th.split(model_output, C, dim=1)
        #     if self.model_var_type == ModelVarType.LEARNED:
        #         model_log_variance = model_var_values
        #         model_variance = th.exp(model_log_variance)
        #     else:
        #         min_log = _extract_into_tensor(
        #             self.posterior_log_variance_clipped, t, x.shape
        #         )
        #         max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
        #         # The model_var_values is [-1, 1] for [min_var, max_var].
        #         frac = (model_var_values + 1) / 2
        #         model_log_variance = frac * max_log + (1 - frac) * min_log
        #         model_variance = th.exp(model_log_variance)
        # else:
        #     model_variance, model_log_variance = {
        #         # for fixedlarge, we set the initial (log-)variance like so
        #         # to get a better decoder log likelihood.
        #         ModelVarType.FIXED_LARGE: (
        #             np.append(self.posterior_variance[1], self.betas[1:]),
        #             np.log(np.append(self.posterior_variance[1], self.betas[1:])),
        #         ),
        #         ModelVarType.FIXED_SMALL: (
        #             self.posterior_variance,
        #             self.posterior_log_variance_clipped,
        #         ),
        #     }[self.model_var_type]
        #     model_variance = _extract_into_tensor(model_variance, t, x.shape)
        #     model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)


        # if self.model_mean_type == ModelMeanType.PREVIOUS_X:
        #     pred_xstart = process_xstart(
        #         self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
        #     )
        #     model_mean = model_output
        # elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
        #     if self.model_mean_type == ModelMeanType.START_X:
        #         pred_xstart = process_xstart(model_output)
        #     else:
        #         pred_xstart = process_xstart(
        #             self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
        #         )
        #     model_mean, _, _ = self.q_posterior_mean_variance(
        #         x_start=pred_xstart, x_t=x, t=t
        #     )
        # else:
        #     raise NotImplementedError(self.model_mean_type)

        # assert (
        #     model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
        # )


    def _predict_xstart_from_eps(self, x_t, t, eps):
        assert x_t.shape == eps.shape
        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) * eps
        )

    def _predict_xstart_from_xprev(self, x_t, t, xprev):
        assert x_t.shape == xprev.shape
        return (  # (xprev - coef2*x_t) / coef1
            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
            - _extract_into_tensor(
                self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
            )
            * x_t
        )

    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 _scale_timesteps(self, t):
        if self.rescale_timesteps:
            return t.float() * (1000.0 / self.num_timesteps)
        return t

    def p_sample(
        self, model, x_cond, x, cond_scale, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
    ):
        """
        Sample x_{t-1} from the model at the given timestep.
        :param model: the model to sample from.
        :param x: the current tensor at x_{t-1}.
        :param t: the value of t, starting at 0 for the first diffusion step.
        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict containing the following keys:
                 - 'sample': a random sample from the model.
                 - 'pred_xstart': a prediction of x_0.
        """
        out = self.p_mean_variance(
            model,
            x,
            t,
            x_cond,
            cond_scale,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
        )
        noise = th.randn_like(x)
        nonzero_mask = (
            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
        )  # no noise when t == 0
        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
        
        return {"sample": sample, "pred_xstart": out["pred_xstart"]}

    def p_sample_loop(
        self,
        model,
        x_cond,
        cond_scale,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        history=False
    ):
        """
        Generate samples from the model.
        :param model: the model module.
        :param shape: the shape of the samples, (N, C, H, W).
        :param noise: if specified, the noise from the encoder to sample.
                      Should be of the same shape as `shape`.
        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :param device: if specified, the device to create the samples on.
                       If not specified, use a model parameter's device.
        :param progress: if True, show a tqdm progress bar.
        :return: a non-differentiable batch of samples.
        """
        shape = x_cond[0].shape
        final = None
        x_cond[0] = [model.encode(x_cond[0])['cond'], model.encode(torch.zeros_like(x_cond[0]))['cond']]

        samples_list = []
        for sample in self.p_sample_loop_progressive(
            model,
            x_cond,
            cond_scale,
            shape,
            noise=noise,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
            device=device,
            progress=progress,
        ):
            final = sample

            if history:
                samples_list.append(sample["sample"])

        if history:
            return samples_list[::100] + [final["sample"]]

        else:
            return final["sample"]

    def p_sample_loop_progressive(
        self,
        model,
        x_cond,
        cond_scale,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
    ):
        """
        Generate samples from the model and yield intermediate samples from
        each timestep of diffusion.
        Arguments are the same as p_sample_loop().
        Returns a generator over dicts, where each dict is the return value of
        p_sample().
        """
        if device is None:
            device = next(model.parameters()).device
        assert isinstance(shape, (tuple, list))
        if noise is not None:
            img = noise
        else:
            img = th.randn(*shape, device=device)
        indices = list(range(self.num_timesteps))[::-1]

        if progress:
            # Lazy import so that we don't depend on tqdm.
            from tqdm.auto import tqdm

            indices = tqdm(indices)

        for i in indices:
            t = th.tensor([i] * shape[0], device=device)
            with th.no_grad():
                out = self.p_sample(
                    model,
                    x_cond[:2],
                    img,
                    cond_scale,
                    t,
                    clip_denoised=clip_denoised,
                    denoised_fn=denoised_fn,
                    model_kwargs=model_kwargs,
                )
                yield out
                if len(x_cond) == 4:
                    [_,_,ref,mask] = x_cond
                    img = out["sample"]*mask + self.q_sample(ref, t)*(1-mask)
                else:
                    img = out["sample"]





    def p_sample_cond(
        self, model, cond_model, x_cond, x, cond_scale, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
    ):
        """
        Sample x_{t-1} from the model at the given timestep.
        :param model: the model to sample from.
        :param x: the current tensor at x_{t-1}.
        :param t: the value of t, starting at 0 for the first diffusion step.
        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict containing the following keys:
                 - 'sample': a random sample from the model.
                 - 'pred_xstart': a prediction of x_0.
        """
        [img, target_pose] = x_cond

        out = self.p_mean_variance(
            model,
            x,
            t,
            img,
            cond_scale,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
        )
        with torch.enable_grad():

            x_t = x.detach().requires_grad_(True)
            logits = cond_model(xt=x_t, ref=img, pose=target_pose, t=t)
            guide_label = torch.ones((logits.shape[0]))*3
            loss =  torch.nn.CrossEntropyLoss()(logits, guide_label.long().cuda())

            grad = torch.autograd.grad(loss, x_t)[0].detach()

        noise = th.randn_like(x)
        nonzero_mask = (
            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
        )  # no noise when t == 0

        out['mean'] = out['mean'] + out["log_variance"]*grad # torch.clamp(grad, max = 1, min = -1)
        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
        return {"sample": sample, "pred_xstart": out["pred_xstart"]}

    def p_sample_cond_loop(
        self,
        model,
        cond_model,
        x_cond,
        cond_scale,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
    ):
        """
        Generate samples from the model.
        :param model: the model module.
        :param shape: the shape of the samples, (N, C, H, W).
        :param noise: if specified, the noise from the encoder to sample.
                      Should be of the same shape as `shape`.
        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :param device: if specified, the device to create the samples on.
                       If not specified, use a model parameter's device.
        :param progress: if True, show a tqdm progress bar.
        :return: a non-differentiable batch of samples.
        """
        shape = x_cond[0].shape
        final = None
        for sample in self.p_sample_cond_loop_progressive(
            model,
            cond_model,
            x_cond,
            cond_scale,
            shape,
            noise=noise,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
            device=device,
            progress=progress,
        ):
            final = sample
        return final["sample"]

    def p_sample_cond_loop_progressive(
        self,
        model,
        cond_model,
        x_cond,
        cond_scale,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
    ):
        """
        Generate samples from the model and yield intermediate samples from
        each timestep of diffusion.
        Arguments are the same as p_sample_loop().
        Returns a generator over dicts, where each dict is the return value of
        p_sample().
        """
        if device is None:
            device = next(model.parameters()).device
        assert isinstance(shape, (tuple, list))
        if noise is not None:
            img = noise
        else:
            img = th.randn(*shape, device=device)
        indices = list(range(self.num_timesteps))[::-1]

        if progress:
            # Lazy import so that we don't depend on tqdm.
            from tqdm.auto import tqdm

            indices = tqdm(indices)

        for i in indices:
            t = th.tensor([i] * shape[0], device=device)
            with th.no_grad():
                out = self.p_sample_cond(
                    model,
                    cond_model,
                    x_cond,
                    img,
                    cond_scale,
                    t,
                    clip_denoised=clip_denoised,
                    denoised_fn=denoised_fn,
                    model_kwargs=model_kwargs,
                )
                yield out
                img = out["sample"]

  

    def ddim_sample(
        self,
        model,
        x_cond,
        x,
        cond_scale,
        t,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        eta=0.0,
    ):
        """
        Sample x_{t-1} from the model using DDIM.
        Same usage as p_sample().
        """
        out = self.p_mean_variance(
            model,
            x,
            t,
            x_cond,
            cond_scale,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
        )
        # Usually our model outputs epsilon, but we re-derive it
        # in case we used x_start or x_prev prediction.
        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
        sigma = (
            eta
            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
        )
        # Equation 12.
        noise = th.randn_like(x)
        mean_pred = (
            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
        )
        nonzero_mask = (
            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
        )  # no noise when t == 0
        sample = mean_pred + nonzero_mask * sigma * noise
        return {"sample": sample, "pred_xstart": out["pred_xstart"]}

    def ddim_reverse_sample(
        self,
        model,
        x,
        t,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        eta=0.0,
    ):
        """
        Sample x_{t+1} from the model using DDIM reverse ODE.
        """
        assert eta == 0.0, "Reverse ODE only for deterministic path"
        out = self.p_mean_variance(
            model,
            x,
            t,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
        )
        # Usually our model outputs epsilon, but we re-derive it
        # in case we used x_start or x_prev prediction.
        eps = (
            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
            - out["pred_xstart"]
        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)

        # Equation 12. reversed
        mean_pred = (
            out["pred_xstart"] * th.sqrt(alpha_bar_next)
            + th.sqrt(1 - alpha_bar_next) * eps
        )

        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}

    def ddim_sample_loop(
        self,
        model,
        x_cond,
        cond_scale,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        eta=0.0,
    ):
        """
        Generate samples from the model using DDIM.
        Same usage as p_sample_loop().
        """
        shape = x_cond[0].shape
        final = None
        x_cond[0] = [model.encode(x_cond[0])['cond'], model.encode(torch.zeros_like(x_cond[0]))['cond']]

        x_cond[0][0] = [torch.stack([i*feat[0] + (1-i)*feat[-1] for i in torch.linspace(0,1,len(feat))], 0) for feat in x_cond[0][0]]

        for sample in self.ddim_sample_loop_progressive(
            model,
            x_cond,
            cond_scale,
            shape,
            noise=noise,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
            device=device,
            progress=progress,
            eta=eta,
        ):
            final = sample
        return final["sample"]

    def ddim_sample_loop_progressive(
        self,
        model,
        x_cond,
        cond_scale,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        eta=0.0,
    ):
        """
        Use DDIM to sample from the model and yield intermediate samples from
        each timestep of DDIM.
        Same usage as p_sample_loop_progressive().
        """
        if device is None:
            device = next(model.parameters()).device
        assert isinstance(shape, (tuple, list))
        if noise is not None:
            img = noise
        else:
            img = th.randn(*shape, device=device)
        indices = list(range(self.num_timesteps))[::-1]

        if progress:
            # Lazy import so that we don't depend on tqdm.
            from tqdm.auto import tqdm

            indices = tqdm(indices)

        for i in indices:
            t = th.tensor([i] * shape[0], device=device)
            with th.no_grad():
                out = self.ddim_sample(
                    model,
                    x_cond,
                    img,
                    cond_scale,
                    t,
                    clip_denoised=clip_denoised,
                    denoised_fn=denoised_fn,
                    model_kwargs=model_kwargs,
                    eta=eta,
                )
                yield out
                img = out["sample"]

    def _vb_terms_bpd(
        self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
    ):
        """
        Get a term for the variational lower-bound.
        The resulting units are bits (rather than nats, as one might expect).
        This allows for comparison to other papers.
        :return: a dict with the following keys:
                 - 'output': a shape [N] tensor of NLLs or KLs.
                 - 'pred_xstart': the x_0 predictions.
        """
        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
            x_start=x_start, x_t=x_t, t=t
        )
        out = self.p_mean_variance(
            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
        )
        kl = normal_kl(
            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
        )
        kl = mean_flat(kl) / np.log(2.0)

        decoder_nll = -discretized_gaussian_log_likelihood(
            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
        )
        assert decoder_nll.shape == x_start.shape
        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)

        # At the first timestep return the decoder NLL,
        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
        output = th.where((t == 0), decoder_nll, kl)
        return {"output": output, "pred_xstart": out["pred_xstart"]}

    def training_losses(self, model, x_start, cond_input, t, prob, model_kwargs=None, noise=None):
        """
        Compute training losses for a single timestep.
        :param model: the model to evaluate loss on.
        :param x_start: the [N x C x ...] tensor of inputs.
        :param t: a batch of timestep indices.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :param noise: if specified, the specific Gaussian noise to try to remove.
        :return: a dict with the key "loss" containing a tensor of shape [N].
                 Some mean or variance settings may also have other keys.
        """
        if model_kwargs is None:
            model_kwargs = {}
        if noise is None:
            noise = th.randn_like(x_start)
        x_t = self.q_sample(x_start, t, noise=noise)

        [img, target_pose] = cond_input


        terms = {}

        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
            terms["loss"] = self._vb_terms_bpd(
                model=model,
                x_start=x_start,
                x_t=x_t,
                t=t,
                clip_denoised=False,
                model_kwargs=model_kwargs,
            )["output"]
            if self.loss_type == LossType.RESCALED_KL:
                terms["loss"] *= self.num_timesteps
        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
            model_output = model(x = torch.cat([x_t, target_pose],1), t = self._scale_timesteps(t), x_cond = img, prob = prob)
            

            if self.model_var_type in [
                ModelVarType.LEARNED,
                ModelVarType.LEARNED_RANGE,
            ]:
                B, C = x_t.shape[:2]
                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
                model_output, model_var_values = th.split(model_output, C, dim=1)
                # Learn the variance using the variational bound, but don't let
                # it affect our mean prediction.
                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
                terms["vb"] = self._vb_terms_bpd(
                    model=lambda *args, r=frozen_out: r,
                    x_start=x_start,
                    x_t=x_t,
                    t=t,
                    clip_denoised=False,
                )["output"]
                if self.loss_type == LossType.RESCALED_MSE:
                    # Divide by 1000 for equivalence with initial implementation.
                    # Without a factor of 1/1000, the VB term hurts the MSE term.
                    terms["vb"] *= self.num_timesteps / 1000.0

            target = {
                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
                    x_start=x_start, x_t=x_t, t=t
                )[0],
                ModelMeanType.START_X: x_start,
                ModelMeanType.EPSILON: noise,
            }[self.model_mean_type]
            assert model_output.shape == target.shape == x_start.shape
            terms["mse"] = mean_flat((target - model_output) ** 2)
            if "vb" in terms:
                terms["loss"] = terms["mse"] + terms["vb"]
            else:
                terms["loss"] = terms["mse"]
        else:
            raise NotImplementedError(self.loss_type)

        return terms

    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 = th.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 calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
        """
        Compute the entire variational lower-bound, measured in bits-per-dim,
        as well as other related quantities.
        :param model: the model to evaluate loss on.
        :param x_start: the [N x C x ...] tensor of inputs.
        :param clip_denoised: if True, clip denoised samples.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict containing the following keys:
                 - total_bpd: the total variational lower-bound, per batch element.
                 - prior_bpd: the prior term in the lower-bound.
                 - vb: an [N x T] tensor of terms in the lower-bound.
                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
        """
        device = x_start.device
        batch_size = x_start.shape[0]

        vb = []
        xstart_mse = []
        mse = []
        for t in list(range(self.num_timesteps))[::-1]:
            t_batch = th.tensor([t] * batch_size, device=device)
            noise = th.randn_like(x_start)
            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
            # Calculate VLB term at the current timestep
            with th.no_grad():
                out = self._vb_terms_bpd(
                    model,
                    x_start=x_start,
                    x_t=x_t,
                    t=t_batch,
                    clip_denoised=clip_denoised,
                    model_kwargs=model_kwargs,
                )
            vb.append(out["output"])
            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
            mse.append(mean_flat((eps - noise) ** 2))

        vb = th.stack(vb, dim=1)
        xstart_mse = th.stack(xstart_mse, dim=1)
        mse = th.stack(mse, dim=1)

        prior_bpd = self._prior_bpd(x_start)
        total_bpd = vb.sum(dim=1) + prior_bpd
        return {
            "total_bpd": total_bpd,
            "prior_bpd": prior_bpd,
            "vb": vb,
            "xstart_mse": xstart_mse,
            "mse": mse,
        }


def _extract_into_tensor(arr, timesteps, broadcast_shape):
    """
    Extract values from a 1-D numpy array for a batch of indices.
    :param arr: the 1-D numpy array.
    :param timesteps: a tensor of indices into the array to extract.
    :param broadcast_shape: a larger shape of K dimensions with the batch
                            dimension equal to the length of timesteps.
    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
    """
    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
    while len(res.shape) < len(broadcast_shape):
        res = res[..., None]
    return res.expand(broadcast_shape)



def create_gaussian_diffusion(
    betas,
    learn_sigma=True,
    sigma_small=False,
    use_kl=False,
    predict_xstart=False,
    rescale_timesteps=False,
    rescale_learned_sigmas=False,
):

    if use_kl:
        loss_type = LossType.RESCALED_KL
    elif rescale_learned_sigmas:
        loss_type = LossType.RESCALED_MSE
    else:
        loss_type = LossType.MSE

    model_mean_type=(
        ModelMeanType.EPSILON if not predict_xstart else ModelMeanType.START_X
    )

    model_var_type=(
        (
            ModelVarType.FIXED_LARGE
            if not sigma_small
            else ModelVarType.FIXED_SMALL
        )
        if not learn_sigma
        else ModelVarType.LEARNED_RANGE
    )



    return GaussianDiffusion(betas = betas,
    model_mean_type = model_mean_type,
    model_var_type = model_var_type,
    loss_type = loss_type)