ldm/models/diffusion/ddim.py

"""SAMPLING ONLY."""

from confs import *
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from src.Face_models.encoders.model_irse import Backbone
import torch.nn as nn
import torchvision.transforms.functional as TF

from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
    extract_into_tensor

def un_norm_clip(x1):
    x = x1*1.0 # to avoid changing the original tensor or clone() can be used
    reduce=False
    if len(x.shape)==3:
        x = x.unsqueeze(0)
        reduce=True
    x[:,0,:,:] = x[:,0,:,:] * 0.26862954 + 0.48145466
    x[:,1,:,:] = x[:,1,:,:] * 0.26130258 + 0.4578275
    x[:,2,:,:] = x[:,2,:,:] * 0.27577711 + 0.40821073
    
    if reduce:
        x = x.squeeze(0)
    return x

class IDLoss(nn.Module):
    def __init__(self,path="Other_dependencies/arcface/model_ir_se50.pth",multiscale=False):
        super(IDLoss, self).__init__()
        print('Loading ResNet ArcFace')
        
        self.multiscale = multiscale
        self.face_pool_1 = torch.nn.AdaptiveAvgPool2d((256, 256))
        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
        # self.facenet=iresnet100(pretrained=False, fp16=False) # changed by sanoojan
        
        self.facenet.load_state_dict(torch.load(path))
        
        self.face_pool_2 = torch.nn.AdaptiveAvgPool2d((112, 112))
        self.facenet.eval()
        
        self.set_requires_grad(False)
            
    def set_requires_grad(self, flag=True):
        for p in self.parameters():
            p.requires_grad = flag
    
    def extract_feats(self, x,clip_img=True):
        # breakpoint()
        if clip_img:
            x = un_norm_clip(x)
            x = TF.normalize(x, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
        x = self.face_pool_1(x)  if x.shape[2]!=256 else  x # (1) resize to 256 if needed
        x = x[:, :, 35:223, 32:220]  # (2) Crop interesting region
        x = self.face_pool_2(x) # (3) resize to 112 to fit pre-trained model
        # breakpoint()
        x_feats = self.facenet(x, multi_scale=self.multiscale )
        
        # x_feats = self.facenet(x) # changed by sanoojan
        return x_feats

    

    def forward(self, y_hat, y,clip_img=True,return_seperate=False):
        n_samples = y.shape[0]
        y_feats_ms = self.extract_feats(y,clip_img=clip_img)  # Otherwise use the feature from there

        y_hat_feats_ms = self.extract_feats(y_hat,clip_img=clip_img)
        y_feats_ms = [y_f.detach() for y_f in y_feats_ms]
        
        loss_all = 0
        sim_improvement_all = 0
        seperate_losses=[]
        for y_hat_feats, y_feats in zip(y_hat_feats_ms, y_feats_ms):
 
            loss = 0
            sim_improvement = 0
            count = 0
            
            for i in range(n_samples):
                sim_target = y_hat_feats[i].dot(y_feats[i])
                sim_views = y_feats[i].dot(y_feats[i])

                seperate_losses.append(1-sim_target)
                loss += 1 - sim_target  # id loss
                sim_improvement +=  float(sim_target) - float(sim_views)
                count += 1
            
            loss_all += loss / count
            sim_improvement_all += sim_improvement / count
    
        return loss_all, sim_improvement_all, None
    

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
        # self.ID_LOSS=IDLoss()

    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,
               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.,
               unconditional_conditioning=None,
               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
               **kwargs
               ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                if cbs != 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, 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,
                                                    unconditional_conditioning=unconditional_conditioning,
                                                    **kwargs
                                                    )
        return samples, intermediates

    @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, x0=None, img_callback=None, log_every_t=100,
                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                      unconditional_guidance_scale=1., unconditional_conditioning=None,
        z_ref=None,
        **kwargs):
        device = self.model.betas.device
        
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T
        if z_ref is not None:
            tensor_1c = torch.zeros((z_ref.shape[0], 1, z_ref.shape[2], z_ref.shape[3]), device=z_ref.device)
            if REFNET.CH9:
                z_ref = torch.cat([z_ref, z_ref, tensor_1c], dim=1)

        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]

        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):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)

            if mask is not None: # 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 z_ref is not None:
                z_ref_noisy = self.model.q_sample(x_start=z_ref[:,:4], t=ts, )
                if REFNET.CH9:
                    z_ref[:,:4] = z_ref_noisy
            # img and pred_x0 both B,4,64,64; cond/unconditional_conditioning tensors are B,1,768
            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
                z_ref=z_ref,
                                      quantize_denoised=quantize_denoised, temperature=temperature,
                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
                                      corrector_kwargs=corrector_kwargs,
                                      unconditional_guidance_scale=unconditional_guidance_scale,
                                      unconditional_conditioning=unconditional_conditioning,**kwargs)
            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, 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., unconditional_conditioning=None,
        z_ref=None,
        **kwargs):
        """
        0. input param is: (x, c, t, [z_ref] )
        1. x=concat(x,inpaint,mask)
        2. apply_model(x, t, c, [z_ref] )
        (    similar to ddpm.py LatentDiffusion.p_losses()
        """
        b, *_, device = *x.shape, x.device
        if 1:
            z_inpaint      = kwargs['z_inpaint']      # B,4
            z_inpaint_mask = kwargs['z_inpaint_mask'] # B,1
            z9 = kwargs['z9'] # B,9or14
            # x = torch.cat([x, z_inpaint, z_inpaint_mask],dim=1) # B,9,...
            x = torch.cat([x, z9[:,4:]  ],dim=1) # B,9or14,...
        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
            e_t = self.model.apply_model(x, t, c, z_ref=z_ref,)
        else:  # check @ sanoojan
            if MERGE_CFG_in_one_batch:
                # b,... -> 2b,...
                x_in = torch.cat([x] * 2) #x_in: 2,9,64,64
                t_in = torch.cat([t] * 2)
                if z_ref is not None:
                    z_ref_in = torch.cat([z_ref] * 2)
                else:
                    z_ref_in = None
                c_in = torch.cat([unconditional_conditioning, c]) #c_in: 2,1,768
                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in, z_ref=z_ref_in,).chunk(2)
            else:
                # first infer unconditional then conditional (reduces peak CUDA memory)
                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, z_ref=z_ref,)
                e_t = self.model.apply_model(x, t, c, z_ref=z_ref,)
            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) #1,4,64,64

        if score_corrector is not None:
            assert self.model.parameterization == "eps"
            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 x.shape[1]!=4:
            pred_x0 = (x[:,:4,:,:] - sqrt_one_minus_at * e_t) / a_t.sqrt()
        else:
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
        # direction pointing to x_t
        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(dir_xt.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
    
    
    def sample_train(self,
               S,
               batch_size,
               shape,
               conditioning=None,
               callback=None,
               normals_sequence=None,
               img_callback=None,
               quantize_x0=False,
               eta=0.,
               mask=None,
               x0=None,
               temperature=1.,
               noise_dropout=0.,
               t=None,
               score_corrector=None,
               corrector_kwargs=None,
               verbose=True,
               x_T=None,
               log_every_t=100,
               unconditional_guidance_scale=1.,
               unconditional_conditioning=None,
               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
               **kwargs
               ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                if cbs != 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}')
        # for param in self.model.first_stage_model.parameters():
        #     param.requires_grad = False
        samples, intermediates = self.ddim_sampling_train(conditioning, size,
                                                    callback=callback,
                                                    img_callback=img_callback,
                                                    quantize_denoised=quantize_x0,
                                                    mask=mask, 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,ddim_num_steps=S,
                                                    curr_t=t,
                                                    log_every_t=log_every_t,
                                                    unconditional_guidance_scale=unconditional_guidance_scale,
                                                    unconditional_conditioning=unconditional_conditioning,
                                                    **kwargs
                                                    )
        return samples, intermediates

 
    def ddim_sampling_train(self, cond, shape,
                      x_T=None, ddim_use_original_steps=False,ddim_num_steps=None,
                      callback=None, timesteps=None, quantize_denoised=False,
                      mask=None, x0=None, img_callback=None, log_every_t=100,curr_t=None,
                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                      unconditional_guidance_scale=1., unconditional_conditioning=None,**kwargs):
        device = self.model.betas.device
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T
            
        kwargs['rest']=img[:,4:,:,:]
        img=img[:,:4,:,:]
        

        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]

        curr_t=curr_t.cpu().numpy()
        skip = (curr_t-1) // ddim_num_steps
        # replace all 0s with 1s
        skip[skip == 0] = 1
        if type(skip)!=int:
            seq=[range(1, curr_t[n]-1, skip[n]) for n in range(len(curr_t))]
            min_length = min(len(sublist) for sublist in seq)
            min_length=min(min_length,ddim_num_steps)
            # Create a new list of sublists by truncating each sublist to the minimum length
            truncated_seq = [sublist[:min_length] for sublist in seq]
            seq= np.array(truncated_seq)

            # seq=np.flip(seq)
        #concatenate all sequences
        # seq = np.concatenate(seq)
        seq=torch.from_numpy(seq).to(device)
        seq=torch.flip(seq,dims=[1])

        
        
        intermediates = {'x_inter': [img], 'pred_x0': [img]}
        intermediates = {'x_inter': [], 'pred_x0': []}
        # 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")


        # time_range=np.array([1])
        # iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
        
        total_steps=seq.shape[1] # 4 (ddim 4 steps)
        for i in range(seq.shape[1]):
            index = total_steps - i - 1
            # ts = torch.full((b,), step, device=device, dtype=torch.long)
            ts=seq[:,i].long()
            #make it toech long
            # ts=ts.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
            outs = self.p_sample_ddim_train(img, 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,
                                      unconditional_conditioning=unconditional_conditioning,**kwargs)
            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:
            if i in [ total_steps - 1, ]:
            # if 1: # len_inter 4 (5 if orig rf) => OOM
                intermediates['x_inter'].append(img)
                intermediates['pred_x0'].append(pred_x0)

        return img, intermediates

    
    def p_sample_ddim_train(self, x, 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., unconditional_conditioning=None,return_features=False,**kwargs):
        b, *_, device = *x.shape, x.device
        # if 'test_model_kwargs' in kwargs:
        #     kwargs=kwargs['test_model_kwargs']
        #     x = torch.cat([x, kwargs['inpaint_image'], kwargs['inpaint_mask']],dim=1)
        if 'rest' in kwargs:
            x = torch.cat((x, kwargs['rest']), dim=1)
    
            
        z_ref = kwargs.pop('z_ref',None)
        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
            e_t = self.model.apply_model(x, t, c,return_features=return_features,z_ref=z_ref)
        else:  # check @ sanoojan
            assert 0
            x_in = torch.cat([x] * 2) #x_in: 2,9,64,64
            t_in = torch.cat([t] * 2)
            c_in = torch.cat([unconditional_conditioning, c]) #c_in: 2,1,768
            if return_features:
                e_t_uncond, e_t,features = self.model.apply_model(x_in, t_in, c_in,return_features=return_features).chunk(3)
            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) #1,4,64,64

        if score_corrector is not None:
            assert self.model.parameterization == "eps"
            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 x.shape[1]!=4:
            pred_x0 = (x[:,:4,:,:] - sqrt_one_minus_at * e_t) / a_t.sqrt()
        else:
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
        # direction pointing to x_t
        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(dir_xt.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 decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
               use_original_steps=False):
        assert 0