test_ddgan.py

# ---------------------------------------------------------------
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# for Denoising Diffusion GAN. To view a copy of this license, see the LICENSE file.
# ---------------------------------------------------------------
import argparse
import torch
import numpy as np
import time
import os
import json
import torchvision
from score_sde.models.ncsnpp_generator_adagn import NCSNpp
from encoder import build_encoder

#%% Diffusion coefficients 
def var_func_vp(t, beta_min, beta_max):
    log_mean_coeff = -0.25 * t ** 2 * (beta_max - beta_min) - 0.5 * t * beta_min
    var = 1. - torch.exp(2. * log_mean_coeff)
    return var

def var_func_geometric(t, beta_min, beta_max):
    return beta_min * ((beta_max / beta_min) ** t)

def extract(input, t, shape):
    out = torch.gather(input, 0, t)
    reshape = [shape[0]] + [1] * (len(shape) - 1)
    out = out.reshape(*reshape)

    return out

def get_time_schedule(args, device):
    n_timestep = args.num_timesteps
    eps_small = 1e-3
    t = np.arange(0, n_timestep + 1, dtype=np.float64)
    t = t / n_timestep
    t = torch.from_numpy(t) * (1. - eps_small)  + eps_small
    return t.to(device)

def get_sigma_schedule(args, device):
    n_timestep = args.num_timesteps
    beta_min = args.beta_min
    beta_max = args.beta_max
    eps_small = 1e-3
   
    t = np.arange(0, n_timestep + 1, dtype=np.float64)
    t = t / n_timestep
    t = torch.from_numpy(t) * (1. - eps_small) + eps_small
    
    if args.use_geometric:
        var = var_func_geometric(t, beta_min, beta_max)
    else:
        var = var_func_vp(t, beta_min, beta_max)
    alpha_bars = 1.0 - var
    betas = 1 - alpha_bars[1:] / alpha_bars[:-1]
    
    first = torch.tensor(1e-8)
    betas = torch.cat((first[None], betas)).to(device)
    betas = betas.type(torch.float32)
    sigmas = betas**0.5
    a_s = torch.sqrt(1-betas)
    return sigmas, a_s, betas

#%% posterior sampling
class Posterior_Coefficients():
    def __init__(self, args, device):
        
        _, _, self.betas = get_sigma_schedule(args, device=device)
        
        #we don't need the zeros
        self.betas = self.betas.type(torch.float32)[1:]
        
        self.alphas = 1 - self.betas
        self.alphas_cumprod = torch.cumprod(self.alphas, 0)
        self.alphas_cumprod_prev = torch.cat(
                                    (torch.tensor([1.], dtype=torch.float32,device=device), self.alphas_cumprod[:-1]), 0
                                        )               
        self.posterior_variance = self.betas * (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod)
        
        self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod)
        self.sqrt_recip_alphas_cumprod = torch.rsqrt(self.alphas_cumprod)
        self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1 / self.alphas_cumprod - 1)
        
        self.posterior_mean_coef1 = (self.betas * torch.sqrt(self.alphas_cumprod_prev) / (1 - self.alphas_cumprod))
        self.posterior_mean_coef2 = ((1 - self.alphas_cumprod_prev) * torch.sqrt(self.alphas) / (1 - self.alphas_cumprod))
        
        self.posterior_log_variance_clipped = torch.log(self.posterior_variance.clamp(min=1e-20))

def predict_q_posterior(coefficients, x_0, x_t, t):
    mean = (
        extract(coefficients.posterior_mean_coef1, t, x_t.shape) * x_0
        + extract(coefficients.posterior_mean_coef2, t, x_t.shape) * x_t
    )
    var = extract(coefficients.posterior_variance, t, x_t.shape)
    log_var_clipped = extract(coefficients.posterior_log_variance_clipped, t, x_t.shape)
    return mean, var, log_var_clipped



def sample_posterior(coefficients, x_0,x_t, t):
    
    def q_posterior(x_0, x_t, t):
        mean = (
            extract(coefficients.posterior_mean_coef1, t, x_t.shape) * x_0
            + extract(coefficients.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        var = extract(coefficients.posterior_variance, t, x_t.shape)
        log_var_clipped = extract(coefficients.posterior_log_variance_clipped, t, x_t.shape)
        return mean, var, log_var_clipped
    
  
    def p_sample(x_0, x_t, t):
        mean, _, log_var = q_posterior(x_0, x_t, t)
        
        noise = torch.randn_like(x_t)
        
        nonzero_mask = (1 - (t == 0).type(torch.float32))

        return mean + nonzero_mask[:,None,None,None] * torch.exp(0.5 * log_var) * noise
            
    sample_x_pos = p_sample(x_0, x_t, t)
    
    return sample_x_pos

def sample_from_model(coefficients, generator, n_time, x_init, T, opt, cond=None):
    x = x_init
    with torch.no_grad():
        for i in reversed(range(n_time)):
            t = torch.full((x.size(0),), i, dtype=torch.int64).to(x.device)
            
            t_time = t
            latent_z = torch.randn(x.size(0), opt.nz, device=x.device)#.to(x.device)
            x_0 = generator(x, t_time, latent_z, cond=cond)
            x_new = sample_posterior(coefficients, x_0, x, t)
            x = x_new.detach()
        
    return x


def sample_from_model_classifier_free_guidance(coefficients, generator, n_time, x_init, T, opt, text_encoder, cond=None, guidance_scale=0):
    x = x_init
    null = text_encoder([""] * len(x_init), return_only_pooled=False)
    #latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
    with torch.no_grad():
        for i in reversed(range(n_time)):
            t = torch.full((x.size(0),), i, dtype=torch.int64).to(x.device)
            t_time = t
            
            latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
            
            x_0_uncond = generator(x, t_time, latent_z, cond=null)
            
            #latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
            
            x_0_cond = generator(x, t_time, latent_z, cond=cond)

            eps_uncond = (x - torch.sqrt(coefficients.alphas_cumprod[i]) * x_0_uncond) / torch.sqrt(1 - coefficients.alphas_cumprod[i])
            eps_cond = (x - torch.sqrt(coefficients.alphas_cumprod[i]) * x_0_cond) / torch.sqrt(1 - coefficients.alphas_cumprod[i])
            
            # eps = eps_uncond + guidance_scale * (eps_cond - eps_uncond)
            eps = eps_uncond * (1 - guidance_scale) + eps_cond * guidance_scale
            x_0 = (1/torch.sqrt(coefficients.alphas_cumprod[i])) * (x - torch.sqrt(1 - coefficients.alphas_cumprod[i]) * eps)
            #x_0 = x_0_uncond * (1 - guidance_scale) + x_0_cond * guidance_scale

            # Dynamic thresholding
            q = opt.dynamic_thresholding_quantile
            #print("Before", x_0.min(), x_0.max())
            if q:
                shape = x_0.shape
                x_0_v = x_0.view(shape[0], -1)
                d = torch.quantile(torch.abs(x_0_v), q, dim=1, keepdim=True)
                d.clamp_(min=1)
                x_0_v = x_0_v.clamp(-d, d) / d
                x_0 = x_0_v.view(shape)
            #print("After", x_0.min(), x_0.max())
            
            x_new = sample_posterior(coefficients, x_0, x, t)
            
            # Dynamic thresholding
            # q = args.dynamic_thresholding_percentile
            # shape = x_new.shape
            # x_new_v = x_new.view(shape[0], -1)
            # d = torch.quantile(torch.abs(x_new_v), q, dim=1, keepdim=True)
            # d = torch.maximum(d, torch.ones_like(d))
            # d.clamp_(min = 1.)
            # x_new_v = torch.clamp(x_new_v, -d, d) / d
            # x_new = x_new_v.view(shape)
            x = x_new.detach()
        
    return x


def sample_from_model_classifier_free_guidance_convolutional(coefficients, generator, n_time, x_init, T, opt, text_encoder, cond=None, guidance_scale=0, split_input_params=None):
    x = x_init
    null = text_encoder([""] * len(x_init), return_only_pooled=False)
    #latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
    ks = split_input_params["ks"]  # eg. (128, 128)
    stride = split_input_params["stride"]  # eg. (64, 64)
    uf = split_input_params["vqf"]
    with torch.no_grad():
        for i in reversed(range(n_time)):
            t = torch.full((x.size(0),), i, dtype=torch.int64).to(x.device)
            t_time = t
            latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
            
            fold, unfold, normalization, weighting = get_fold_unfold(x, ks, stride, split_input_params, uf=uf)
            x = unfold(x)
            x = x.view((x.shape[0], -1, ks[0], ks[1], x.shape[-1])) 
            x_new_list = []
            for j in range(x.shape[-1]):
                x_0_uncond = generator(x[:,:,:,:,j], t_time, latent_z, cond=null)            
                x_0_cond = generator(x[:,:,:,:,j], t_time, latent_z, cond=cond)

                eps_uncond = (x[:,:,:,:,j] - torch.sqrt(coefficients.alphas_cumprod[i]) * x_0_uncond) / torch.sqrt(1 - coefficients.alphas_cumprod[i])
                eps_cond = (x[:,:,:,:,j] - torch.sqrt(coefficients.alphas_cumprod[i]) * x_0_cond) / torch.sqrt(1 - coefficients.alphas_cumprod[i])
                
                eps = eps_uncond * (1 - guidance_scale) + eps_cond * guidance_scale
                x_0 = (1/torch.sqrt(coefficients.alphas_cumprod[i])) * (x[:,:,:,:,j] - torch.sqrt(1 - coefficients.alphas_cumprod[i]) * eps)
                q = args.dynamic_thresholding_quantile
                if q:
                    shape = x_0.shape
                    x_0_v = x_0.view(shape[0], -1)
                    d = torch.quantile(torch.abs(x_0_v), q, dim=1, keepdim=True)
                    d.clamp_(min=1)
                    x_0_v = x_0_v.clamp(-d, d) / d
                    x_0 = x_0_v.view(shape)
                x_new = sample_posterior(coefficients, x_0, x[:,:,:,:,j], t)
                x_new_list.append(x_new)
            
            o = torch.stack(x_new_list, axis=-1) 
            #o = o * weighting
            o = o.view((o.shape[0], -1, o.shape[-1])) 
            decoded = fold(o)
            decoded = decoded / normalization
            x = decoded.detach()
        
    return x

def sample_from_model_clip_guidance(coefficients, generator, clip_model, n_time, x_init, T, opt, texts, cond=None, guidance_scale=0):
    x = x_init
    text_features = torch.nn.functional.normalize(clip_model.forward_text(texts), dim=1)
    n_time = 16
    for i in reversed(range(n_time)):
        t = torch.full((x.size(0),), i%4, dtype=torch.int64).to(x.device)
        t_time = t            
        latent_z = torch.randn(x.size(0), opt.nz, device=x.device)
        x.requires_grad = True
        x_0 = generator(x, t_time, latent_z, cond=cond)
        x_new = sample_posterior(coefficients, x_0, x, t)
        x_new_n = (x_new + 1) / 2
        image_features = torch.nn.functional.normalize(clip_model.forward_image(x_new_n), dim=1)
        loss = (image_features*text_features).sum(dim=1).mean()
        x_grad, = torch.autograd.grad(loss, x)
        lr = 3000
        x = x.detach()
        print(x.min(),x.max(), lr*x_grad.min(), lr*x_grad.max())
        x += x_grad * lr

        with torch.no_grad():
            x_0 = generator(x, t_time, latent_z, cond=cond)
            x_new = sample_posterior(coefficients, x_0, x, t)

        x = x_new.detach()
        print(i)
    return x

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

    arr = torch.cat([y, x], dim=-1)
    return arr
def delta_border(h, w):
        """
        :param h: height
        :param w: width
        :return: normalized distance to image border,
         wtith min distance = 0 at border and max dist = 0.5 at image center
        """
        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
        arr = meshgrid(h, w) / lower_right_corner
        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
        return edge_dist

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

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

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

def get_fold_unfold(x, kernel_size, stride, split_input_params, uf=1, df=1):  # todo load once not every time, shorten code
    """
    :param x: img of size (bs, c, h, w)
    :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
    """
    bs, nc, h, w = x.shape

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

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

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

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

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

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

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

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

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

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

    else:
        raise NotImplementedError

    return fold, unfold, normalization, weighting



#%%
def sample_and_test(args):
    torch.manual_seed(args.seed)

    device = 'cuda:0'
    text_encoder  =build_encoder(name=args.text_encoder, masked_mean=args.masked_mean).to(device)
    args.cond_size = text_encoder.output_size
    if args.dataset == 'cifar10':
        real_img_dir = 'pytorch_fid/cifar10_train_stat.npy'
    elif args.dataset == 'celeba_256':
        real_img_dir = 'pytorch_fid/celeba_256_stat.npy'
    elif args.dataset == 'lsun':
        real_img_dir = 'pytorch_fid/lsun_church_stat.npy'
    else:
        real_img_dir = args.real_img_dir
    
    to_range_0_1 = lambda x: (x + 1.) / 2.

    print(vars(args)) 
    netG = NCSNpp(args).to(device)
     
    if args.epoch_id == -1:
        epochs = range(1000)
    else:
        epochs = [args.epoch_id]
    
    for epoch in epochs:
        args.epoch_id = epoch
        path = './saved_info/dd_gan/{}/{}/netG_{}.pth'.format(args.dataset, args.exp, args.epoch_id)
        next_path = './saved_info/dd_gan/{}/{}/netG_{}.pth'.format(args.dataset, args.exp, args.epoch_id+1)
        if not os.path.exists(path):
            continue
        print(path)

        #if not os.path.exists(next_path):
        #    print(f"STOP at {epoch}")
        #    break
        ckpt = torch.load(path, map_location=device)
        suffix = '_' + args.eval_name if args.eval_name else ""
        dest = './saved_info/dd_gan/{}/{}/eval_{}{}.json'.format(args.dataset, args.exp, args.epoch_id, suffix)
        next_dest = './saved_info/dd_gan/{}/{}/eval_{}{}.json'.format(args.dataset, args.exp, args.epoch_id+1, suffix)

        if (args.compute_fid or args.compute_clip_score) and  os.path.exists(dest):
            continue
        print("Eval Epoch", args.epoch_id)
        #loading weights from ddp in single gpu
        #print(ckpt.keys())
        for key in list(ckpt.keys()):
            if key.startswith("module"):
                ckpt[key[7:]] = ckpt.pop(key)
        netG.load_state_dict(ckpt)
        netG.eval()
        
        
        T = get_time_schedule(args, device)
        
        pos_coeff = Posterior_Coefficients(args, device)
            
        
        save_dir = "./generated_samples/{}".format(args.dataset)
        
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        
        if args.compute_fid or args.compute_clip_score:
            from torch.nn.functional import adaptive_avg_pool2d
            from pytorch_fid.fid_score import calculate_activation_statistics, calculate_fid_given_paths, ImagePathDataset, compute_statistics_of_path, calculate_frechet_distance
            from pytorch_fid.inception import InceptionV3
            import random
            random.seed(args.seed)
            texts = open(args.cond_text).readlines()
            texts = [t.strip() for t in texts]
            if args.nb_images_for_fid:
                random.shuffle(texts)
                texts = texts[0:args.nb_images_for_fid]
            #iters_needed = len(texts) // args.batch_size
            #texts = list(map(lambda s:s.strip(), texts))
            #ntimes = max(30000 // len(texts), 1)
            #texts = texts * ntimes
            print("Text size:", len(texts))
            #print("Iters:", iters_needed)
            i = 0

            if args.compute_fid:
                dims = 2048
                block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
                inceptionv3 = InceptionV3([block_idx]).to(device)

            if args.compute_clip_score:
                import clip
                CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073]
                CLIP_STD = [0.26862954, 0.26130258, 0.27577711]
                clip_model, preprocess = clip.load(args.clip_model, device)
                clip_mean = torch.Tensor(CLIP_MEAN).view(1,-1,1,1).to(device)
                clip_std = torch.Tensor(CLIP_STD).view(1,-1,1,1).to(device)

            if args.compute_fid:
                if not args.real_img_dir.endswith("npz"):
                    real_mu, real_sigma = compute_statistics_of_path(
                        args.real_img_dir, inceptionv3, args.batch_size, dims, device, 
                        resize=args.image_size,
                    )
                    np.savez("inception_statistics.npz", mu=real_mu, sigma=real_sigma)
                else:
                    stats = np.load(args.real_img_dir)
                    real_mu = stats['mu']
                    real_sigma = stats['sigma']

                fake_features = []
            
            if args.compute_clip_score:
                clip_scores = []
            
            for b in range(0, len(texts), args.batch_size):
                text = texts[b:b+args.batch_size]
                with torch.no_grad():
                    cond = text_encoder(text, return_only_pooled=False)
                    bs = len(text)
                    t0 = time.time()
                    x_t_1 = torch.randn(bs, args.num_channels,args.image_size, args.image_size).to(device)
                    if args.guidance_scale:
                        fake_sample = sample_from_model_classifier_free_guidance(pos_coeff, netG, args.num_timesteps, x_t_1,T,  args, text_encoder, cond=cond, guidance_scale=args.guidance_scale)
                    else:
                        fake_sample = sample_from_model(pos_coeff, netG, args.num_timesteps, x_t_1,T,  args, cond=cond)
                    fake_sample = to_range_0_1(fake_sample)
                    """
                    for j, x in enumerate(fake_sample):
                        index = i * args.batch_size + j 
                        torchvision.utils.save_image(x, './generated_samples/{}/{}.jpg'.format(args.dataset, index))
                    """

                    if args.compute_fid:
                        with torch.no_grad():
                            pred = inceptionv3(fake_sample)[0]
                        # If model output is not scalar, apply global spatial average pooling.
                        # This happens if you choose a dimensionality not equal 2048.
                        if pred.size(2) != 1 or pred.size(3) != 1:
                            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
                        pred = pred.squeeze(3).squeeze(2).cpu().numpy()
                        fake_features.append(pred)

                    if args.compute_clip_score:
                        with torch.no_grad():
                            clip_ims = torch.nn.functional.interpolate(fake_sample, (224, 224), mode="bicubic")
                            clip_ims = (clip_ims - clip_mean) / clip_std
                            clip_txt = clip.tokenize(text, truncate=True).to(device)
                            imf = clip_model.encode_image(clip_ims)
                            txtf = clip_model.encode_text(clip_txt)
                            imf = torch.nn.functional.normalize(imf, dim=1)
                            txtf = torch.nn.functional.normalize(txtf, dim=1)
                            clip_scores.append(((imf * txtf).sum(dim=1)).cpu())
                    
                    if i % 10 == 0:
                        print('evaluating batch ', i, time.time() - t0)
                i += 1

            results = {}
            if args.compute_fid:
                fake_features = np.concatenate(fake_features)
                fake_mu = np.mean(fake_features, axis=0)
                fake_sigma = np.cov(fake_features, rowvar=False)
                fid =  calculate_frechet_distance(real_mu, real_sigma, fake_mu, fake_sigma)
                results['fid'] = fid
            if args.compute_clip_score:
                clip_score = torch.cat(clip_scores).mean().item()
                results['clip_score'] = clip_score
            results.update(vars(args))
            with open(dest, "w") as fd:
                json.dump(results, fd)
            print(results)
        else:            
            if args.cond_text.endswith(".txt"):
                texts = open(args.cond_text).readlines()
                texts = [t.strip() for t in texts]
            else:
                texts = [args.cond_text] * args.batch_size
            clip_guidance = False
            if clip_guidance:
                from clip_encoder import CLIPImageEncoder
                cond = text_encoder(texts, return_only_pooled=False)
                clip_image_model = CLIPImageEncoder().to(device)
                x_t_1 = torch.randn(len(texts), args.num_channels,args.image_size*args.scale_factor_h, args.image_size*args.scale_factor_w).to(device)
                fake_sample = sample_from_model_clip_guidance(pos_coeff, netG, clip_image_model, args.num_timesteps, x_t_1,T,  args, texts, cond=cond, guidance_scale=args.guidance_scale)
                fake_sample = to_range_0_1(fake_sample)
                torchvision.utils.save_image(fake_sample, './samples_{}.jpg'.format(args.dataset))

            else:
                cond = text_encoder(texts, return_only_pooled=False)
                x_t_1 = torch.randn(len(texts), args.num_channels,args.image_size*args.scale_factor_h, args.image_size*args.scale_factor_w).to(device)
                t0 = time.time()
                if args.guidance_scale:
                    if args.scale_factor_h > 1 or args.scale_factor_w > 1:
                        if args.scale_method == "convolutional":
                            split_input_params = {
                                "ks": (args.image_size, args.image_size),
                                "stride": (150,  150),
                                "clip_max_tie_weight": 0.5,
                                "clip_min_tie_weight": 0.01,
                                "clip_max_weight": 0.5,
                                "clip_min_weight": 0.01,

                                "tie_braker": True,
                                'vqf': 1,
                            }
                            fake_sample = sample_from_model_classifier_free_guidance_convolutional(pos_coeff, netG, args.num_timesteps, x_t_1,T,  args, text_encoder, cond=cond, guidance_scale=args.guidance_scale, split_input_params=split_input_params)
                        elif args.scale_method == "larger_input":
                            netG.attn_resolutions = [r * args.scale_factor_w for r in netG.attn_resolutions]
                            fake_sample = sample_from_model_classifier_free_guidance(pos_coeff, netG, args.num_timesteps, x_t_1,T,  args, text_encoder, cond=cond, guidance_scale=args.guidance_scale)
                    else:
                        fake_sample = sample_from_model_classifier_free_guidance(pos_coeff, netG, args.num_timesteps, x_t_1,T,  args, text_encoder, cond=cond, guidance_scale=args.guidance_scale)
                else:
                    fake_sample = sample_from_model(pos_coeff, netG, args.num_timesteps, x_t_1,T,  args, cond=cond)

                print(time.time() - t0)
                fake_sample = to_range_0_1(fake_sample)
                torchvision.utils.save_image(fake_sample, './samples_{}.jpg'.format(args.dataset))



    
    
            

if __name__ == '__main__':
    parser = argparse.ArgumentParser('ddgan parameters')
    parser.add_argument('--seed', type=int, default=1024,
                        help='seed used for initialization')
    parser.add_argument('--compute_fid', action='store_true', default=False,
                            help='whether or not compute FID')
    parser.add_argument('--compute_clip_score', action='store_true', default=False,
                            help='whether or not compute CLIP score')
    parser.add_argument('--clip_model', type=str,default="ViT-L/14")
    parser.add_argument('--eval_name', type=str,default="")

    parser.add_argument('--epoch_id', type=int,default=1000)
    parser.add_argument('--guidance_scale', type=float,default=0)
    parser.add_argument('--dynamic_thresholding_quantile', type=float,default=0)
    parser.add_argument('--cond_text', type=str,default="0")
    parser.add_argument('--scale_factor_h', type=int,default=1)
    parser.add_argument('--scale_factor_w', type=int,default=1)
    parser.add_argument('--scale_method', type=str,default="convolutional")

    parser.add_argument('--cross_attention', action='store_true',default=False)

    
    parser.add_argument('--num_channels', type=int, default=3,
                            help='channel of image')
    parser.add_argument('--centered', action='store_false', default=True,
                            help='-1,1 scale')
    parser.add_argument('--use_geometric', action='store_true',default=False)
    parser.add_argument('--beta_min', type=float, default= 0.1,
                            help='beta_min for diffusion')
    parser.add_argument('--beta_max', type=float, default=20.,
                            help='beta_max for diffusion')
    
    
    parser.add_argument('--num_channels_dae', type=int, default=128,
                            help='number of initial channels in denosing model')
    parser.add_argument('--n_mlp', type=int, default=3,
                            help='number of mlp layers for z')
    parser.add_argument('--ch_mult', nargs='+', type=int,
                            help='channel multiplier')

    parser.add_argument('--num_res_blocks', type=int, default=2,
                            help='number of resnet blocks per scale')
    parser.add_argument('--attn_resolutions', default=(16,),
                            help='resolution of applying attention')
    parser.add_argument('--dropout', type=float, default=0.,
                            help='drop-out rate')
    parser.add_argument('--resamp_with_conv', action='store_false', default=True,
                            help='always up/down sampling with conv')
    parser.add_argument('--conditional', action='store_false', default=True,
                            help='noise conditional')
    parser.add_argument('--fir', action='store_false', default=True,
                            help='FIR')
    parser.add_argument('--fir_kernel', default=[1, 3, 3, 1],
                            help='FIR kernel')
    parser.add_argument('--skip_rescale', action='store_false', default=True,
                            help='skip rescale')
    parser.add_argument('--resblock_type', default='biggan',
                            help='tyle of resnet block, choice in biggan and ddpm')
    parser.add_argument('--progressive', type=str, default='none', choices=['none', 'output_skip', 'residual'],
                            help='progressive type for output')
    parser.add_argument('--progressive_input', type=str, default='residual', choices=['none', 'input_skip', 'residual'],
                        help='progressive type for input')
    parser.add_argument('--progressive_combine', type=str, default='sum', choices=['sum', 'cat'],
                        help='progressive combine method.')

    parser.add_argument('--embedding_type', type=str, default='positional', choices=['positional', 'fourier'],
                        help='type of time embedding')
    parser.add_argument('--fourier_scale', type=float, default=16.,
                            help='scale of fourier transform')
    parser.add_argument('--not_use_tanh', action='store_true',default=False)
    
    #geenrator and training
    parser.add_argument('--exp', default='experiment_cifar_default', help='name of experiment')
    parser.add_argument('--real_img_dir', default='./pytorch_fid/cifar10_train_stat.npy', help='directory to real images for FID computation')

    parser.add_argument('--dataset', default='cifar10', help='name of dataset')
    parser.add_argument('--image_size', type=int, default=32,
                            help='size of image')

    parser.add_argument('--nz', type=int, default=100)
    parser.add_argument('--num_timesteps', type=int, default=4)
    
    
    parser.add_argument('--z_emb_dim', type=int, default=256)
    parser.add_argument('--t_emb_dim', type=int, default=256)
    parser.add_argument('--batch_size', type=int, default=200, help='sample generating batch size')
    parser.add_argument('--text_encoder', type=str, default="google/t5-v1_1-base")
    parser.add_argument('--masked_mean', action='store_true',default=False)
    parser.add_argument('--nb_images_for_fid', type=int, default=0)




   
    args = parser.parse_args()
    
    sample_and_test(args)