# ---------------------------------------------------------------
# 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_next_path = './saved_info/dd_gan/{}/{}/netG_{}.pth'.format(args.dataset, args.exp, args.epoch_id+2)
if not os.path.exists(path):
continue
if not os.path.exists(next_next_path):
break
print(path)
#if not os.path.exists(next_path):
# print(f"STOP at {epoch}")
# break
try:
ckpt = torch.load(path, map_location=device)
except Exception:
continue
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)