cldm/ddim_hacked_sag.py

"""SAMPLING ONLY."""

import torch
import numpy as np
from tqdm import tqdm

from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
import torch.nn.functional as F

import cv2
# Gaussian blur
def gaussian_blur_2d(img, kernel_size, sigma):
    ksize_half = (kernel_size - 1) * 0.5

    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)

    pdf = torch.exp(-0.5 * (x / sigma).pow(2))

    x_kernel = pdf / pdf.sum()
    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)

    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])

    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]

    img = F.pad(img, padding, mode="reflect")
    img = F.conv2d(img, kernel2d, groups=img.shape[-3])

    return img

# processes and stores attention probabilities
class CrossAttnStoreProcessor:
    def __init__(self):
        self.attention_probs = None

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
    ):
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        self.attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(self.attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states

class DDIMSampler(object):
    def __init__(self, model, schedule="linear", **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule

    def register_buffer(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device("cuda"):
                attr = attr.to(torch.device("cuda"))
        setattr(self, name, attr)

    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
        alphas_cumprod = self.model.alphas_cumprod
        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)

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

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

        # ddim sampling parameters
        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
                                                                                   ddim_timesteps=self.ddim_timesteps,
                                                                                   eta=ddim_eta,verbose=verbose)
        self.register_buffer('ddim_sigmas', ddim_sigmas)
        self.register_buffer('ddim_alphas', ddim_alphas)
        self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)

    @torch.no_grad()
    def sample(self,
               S,
               batch_size,
               shape,
               conditioning=None,
               callback=None,
               normals_sequence=None,
               img_callback=None,
               quantize_x0=False,
               eta=0.,
               mask=None,
               masked_image_latents=None,
               x0=None,
               temperature=1.,
               noise_dropout=0.,
               score_corrector=None,
               corrector_kwargs=None,
               verbose=True,
               x_T=None,
               log_every_t=100,
               unconditional_guidance_scale=1.,
               sag_scale=0.75,
               SAG_influence_step=600,
               noise = None,
               unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
               dynamic_threshold=None,
               ucg_schedule=None,
               **kwargs
               ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                ctmp = conditioning[list(conditioning.keys())[0]]
                while isinstance(ctmp, list): ctmp = ctmp[0]
                cbs = ctmp.shape[0]
                if cbs != batch_size:
                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")

            elif isinstance(conditioning, list):
                for ctmp in conditioning:
                    if ctmp.shape[0] != batch_size:
                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")

            else:
                if conditioning.shape[0] != batch_size:
                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")

        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
        # sampling
        C, H, W = shape
        size = (batch_size, C, H, W)
        print(f'Data shape for DDIM sampling is {size}, eta {eta}')

        samples, intermediates = self.ddim_sampling(conditioning, size,
                                                    callback=callback,
                                                    img_callback=img_callback,
                                                    quantize_denoised=quantize_x0,
                                                    mask=mask,masked_image_latents=masked_image_latents, x0=x0,
                                                    ddim_use_original_steps=False,
                                                    noise_dropout=noise_dropout,
                                                    temperature=temperature,
                                                    score_corrector=score_corrector,
                                                    corrector_kwargs=corrector_kwargs,
                                                    x_T=x_T,
                                                    log_every_t=log_every_t,
                                                    unconditional_guidance_scale=unconditional_guidance_scale,
                                                    sag_scale = sag_scale,
                                                    SAG_influence_step = SAG_influence_step,
                                                    noise = noise,
                                                    unconditional_conditioning=unconditional_conditioning,
                                                    dynamic_threshold=dynamic_threshold,
                                                    ucg_schedule=ucg_schedule
                                                    )
        return samples, intermediates
    
    def add_noise(self,
        original_samples: torch.FloatTensor,
        noise: torch.FloatTensor,
        timesteps: torch.IntTensor,
    ) -> torch.FloatTensor:
        betas = torch.linspace(0.00085, 0.0120, 1000, dtype=torch.float32)
        alphas = 1.0 - betas
        alphas_cumprod = torch.cumprod(alphas, dim=0)
        alphas_cumprod = alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
        timesteps = timesteps.to(original_samples.device)

        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)

        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)

        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise

        return noisy_samples
    # def add_noise(
    #     self,
    #     original_samples: torch.FloatTensor,
    #     noise: torch.FloatTensor,
    #     timesteps: torch.FloatTensor,
    #     sigma_t,
    # ) -> torch.FloatTensor:
        
    #     # Make sure sigmas and timesteps have the same device and dtype as original_samples
        
    #     sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
    #     if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
    #         # mps does not support float64
    #         schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
    #         timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
    #     else:
    #         schedule_timesteps = self.timesteps.to(original_samples.device)
    #         timesteps = timesteps.to(original_samples.device)

    #     step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

    #     sigma = sigmas[step_indices].flatten()
    #     while len(sigma.shape) < len(original_samples.shape):
    #         sigma = sigma.unsqueeze(-1)
    #     # print(sigma_t)
    #     noisy_samples = original_samples + noise * sigma_t
    #     return noisy_samples
    
    
    def sag_masking(self, original_latents,model_output,x, attn_map, map_size, t, eps):
        # Same masking process as in SAG paper: https://arxiv.org/pdf/2210.00939.pdf
        bh, hw1, hw2 = attn_map.shape
        b, latent_channel, latent_h, latent_w = original_latents.shape
        h = 4 #self.unet.config.attention_head_dim
        if isinstance(h, list):
            h = h[-1]
        # print(attn_map.shape)
        # print(original_latents.shape)
        # print(map_size)
        # Produce attention mask
        attn_map = attn_map.reshape(b, h, hw1, hw2)
        attn_mask = attn_map.mean(1, keepdim=False).sum(1, keepdim=False) > 1.0
        # print(attn_mask.shape)
        attn_mask = (
            attn_mask.reshape(b, map_size[0], map_size[1])
            .unsqueeze(1)
            .repeat(1, latent_channel, 1, 1)
            .type(attn_map.dtype)
        )
        attn_mask = F.interpolate(attn_mask, (latent_h, latent_w))
        # print(attn_mask.shape)
        # cv2.imwrite("attn_mask.png",attn_mask)
        # Blur according to the self-attention mask
        degraded_latents = gaussian_blur_2d(original_latents, kernel_size=9, sigma=1.0)
        # degraded_latents = self.add_noise(degraded_latents, noise=eps, timesteps=t)#,sigma_t=sigma_t)
        degraded_latents = degraded_latents * attn_mask + original_latents * (1 - attn_mask) #x#original_latents
        # degraded_latents = self.model.get_x_t_from_start_and_t(degraded_latents,t,model_output)
        # print(original_latents.shape)
        # print(eps.shape)
        # Noise it again to match the noise level
        # print("t",t)
        # degraded_latents = self.add_noise(degraded_latents, noise=eps, timesteps=t)#,sigma_t=sigma_t)

        return degraded_latents
    
    def pred_epsilon(self, sample, model_output, timestep):
        alpha_prod_t = timestep

        beta_prod_t = 1 - alpha_prod_t
        # print(self.model.parameterization)#eps
        if self.model.parameterization == "eps":
            pred_eps = model_output
        elif self.model.parameterization == "sample":
            pred_eps = (sample - (alpha_prod_t**0.5) * model_output) / (beta_prod_t**0.5)
        elif self.model.parameterization == "v":
            pred_eps = (beta_prod_t**0.5) * sample + (alpha_prod_t**0.5) * model_output
        else:
            raise ValueError(
                f"prediction_type given as {self.scheduler.config.prediction_type} must be one of `eps`, `sample`,"
                " or `v`"
            )

        return pred_eps
    
    @torch.no_grad()
    def ddim_sampling(self, cond, shape,
                      x_T=None, ddim_use_original_steps=False,
                      callback=None, timesteps=None, quantize_denoised=False,
                      mask=None,masked_image_latents=None, x0=None, img_callback=None, log_every_t=100,
                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                      unconditional_guidance_scale=1.,sag_scale = 0.75, SAG_influence_step=600, sag_enable = True, noise = None, unconditional_conditioning=None, dynamic_threshold=None,
                      ucg_schedule=None):
        device = self.model.betas.device
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T
        # timesteps =100
        if timesteps is None:
            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
        elif timesteps is not None and not ddim_use_original_steps:
            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
            timesteps = self.ddim_timesteps[:subset_end]
        # timesteps=timesteps[:-3]
        # print("timesteps",timesteps)
        intermediates = {'x_inter': [img], 'pred_x0': [img]}
        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
        print(f"Running DDIM Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)

        for i, step in enumerate(iterator):
            print(step)
            if step > SAG_influence_step:
                sag_enable_t=True
            else:
                sag_enable_t=False
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)

            # if mask is not None:
            #     assert x0 is not None
            #     img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
            #     img = img_orig * mask + (1. - mask) * img

            if ucg_schedule is not None:
                assert len(ucg_schedule) == len(time_range)
                unconditional_guidance_scale = ucg_schedule[i]

            outs = self.p_sample_ddim(img,mask,masked_image_latents, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
                                      quantize_denoised=quantize_denoised, temperature=temperature,
                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
                                      corrector_kwargs=corrector_kwargs,
                                      unconditional_guidance_scale=unconditional_guidance_scale,
                                      sag_scale = sag_scale,
                                      sag_enable=sag_enable_t,
                                      noise =noise,
                                      unconditional_conditioning=unconditional_conditioning,
                                      dynamic_threshold=dynamic_threshold)
            img, pred_x0 = outs
            if callback: callback(i)
            if img_callback: img_callback(pred_x0, i)

            if index % log_every_t == 0 or index == total_steps - 1:
                intermediates['x_inter'].append(img)
                intermediates['pred_x0'].append(pred_x0)

        return img, intermediates
    
    @torch.no_grad()
    def p_sample_ddim(self, x,mask,masked_image_latents, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                      unconditional_guidance_scale=1.,sag_scale = 0.75, sag_enable=True, noise=None, unconditional_conditioning=None,
                      dynamic_threshold=None):
        b, *_, device = *x.shape, x.device

        # map_size = None
        # def get_map_size(module, input, output):
        #     nonlocal map_size
        #     map_size = output.shape[-2:]
           
        # store_processor = CrossAttnStoreProcessor()
        # for name, param in self.model.model.diffusion_model.named_parameters():
        #     print(name)
        # self.model.control_model.middle_block[1].transformer_blocks[0].attn1.processor = store_processor
        # print(self.model.model.diffusion_model.middle_block[1].transformer_blocks[0].attn1)
        # self.model.model.diffusion_model.middle_block[1].transformer_blocks[0].attn1 = store_processor
        
        # with self.model.model.diffusion_model.middle_block[1].register_forward_hook(get_map_size):
        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
            model_output = self.model.apply_model(x,mask,masked_image_latents, t, c)
        else:
            model_t = self.model.apply_model(x,mask,masked_image_latents, t, c)
            model_uncond = self.model.apply_model(x,mask,masked_image_latents, t, unconditional_conditioning)
            model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)

        if self.model.parameterization == "v":
            e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
        else:
            e_t = model_output

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

        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
        # select parameters corresponding to the currently considered timestep
        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)

        # current prediction for x_0
        if self.model.parameterization != "v":
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        else:
            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)

        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)

        if dynamic_threshold is not None:
            raise NotImplementedError()
        if sag_enable ==  True:
            uncond_attn, cond_attn = self.model.model.diffusion_model.middle_block[1].transformer_blocks[0].attn1.attention_probs.chunk(2)
            # self-attention-based degrading of latents
            map_size = self.model.model.diffusion_model.middle_block[1].map_size
            degraded_latents = self.sag_masking(
                pred_x0,model_output,x,uncond_attn, map_size, t,  eps = noise, #self.pred_epsilon(x, model_uncond, self.model.alphas_cumprod[t]),#noise
            )
            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
                degraded_model_output = self.model.apply_model(degraded_latents,mask,masked_image_latents, t, c)
            else:
                degraded_model_t = self.model.apply_model(degraded_latents,mask,masked_image_latents, t, c)
                degraded_model_uncond = self.model.apply_model(degraded_latents,mask,masked_image_latents, t, unconditional_conditioning)
                degraded_model_output = degraded_model_uncond + unconditional_guidance_scale * (degraded_model_t - degraded_model_uncond)
            # print("sag_scale",sag_scale)
            model_output += sag_scale * (model_output - degraded_model_output)  
            # model_output = (1-sag_scale) * model_output + sag_scale * degraded_model_output
        
        # current prediction for x_0
        if self.model.parameterization != "v":
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        else:
            pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)

        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)

        if dynamic_threshold is not None:
            raise NotImplementedError()
        
        # direction pointing to x_t
        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
        return x_prev, pred_x0

    @torch.no_grad()
    def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
               unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
        num_reference_steps = timesteps.shape[0]

        assert t_enc <= num_reference_steps
        num_steps = t_enc

        if use_original_steps:
            alphas_next = self.alphas_cumprod[:num_steps]
            alphas = self.alphas_cumprod_prev[:num_steps]
        else:
            alphas_next = self.ddim_alphas[:num_steps]
            alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])

        x_next = x0
        intermediates = []
        inter_steps = []
        for i in tqdm(range(num_steps), desc='Encoding Image'):
            t = torch.full((x0.shape[0],), timesteps[i], device=self.model.device, dtype=torch.long)
            if unconditional_guidance_scale == 1.:
                noise_pred = self.model.apply_model(x_next, t, c)
            else:
                assert unconditional_conditioning is not None
                e_t_uncond, noise_pred = torch.chunk(
                    self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
                                           torch.cat((unconditional_conditioning, c))), 2)
                noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)

            xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
            weighted_noise_pred = alphas_next[i].sqrt() * (
                    (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
            x_next = xt_weighted + weighted_noise_pred
            if return_intermediates and i % (
                    num_steps // return_intermediates) == 0 and i < num_steps - 1:
                intermediates.append(x_next)
                inter_steps.append(i)
            elif return_intermediates and i >= num_steps - 2:
                intermediates.append(x_next)
                inter_steps.append(i)
            if callback: callback(i)

        out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
        if return_intermediates:
            out.update({'intermediates': intermediates})
        return x_next, out

    @torch.no_grad()
    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
        # fast, but does not allow for exact reconstruction
        # t serves as an index to gather the correct alphas
        if use_original_steps:
            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
        else:
            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas

        if noise is None:
            noise = torch.randn_like(x0)
        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)

    @torch.no_grad()
    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
               use_original_steps=False, callback=None):

        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
        timesteps = timesteps[:t_start]

        time_range = np.flip(timesteps)
        total_steps = timesteps.shape[0]
        print(f"Running DDIM Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
        x_dec = x_latent
        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
                                          unconditional_guidance_scale=unconditional_guidance_scale,
                                          unconditional_conditioning=unconditional_conditioning)
            if callback: callback(i)
        return x_dec