# --------------------------------------------------------------- # 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 import random from score_sde.models.ncsnpp_generator_adagn import NCSNpp from torch.nn.functional import adaptive_avg_pool2d try: 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 except ImportError: pass try: import ImageReward as RM except ImportError: pass try: import clip except ImportError: pass from encoder import build_encoder from clip_encoder import CLIPImageEncoder from model_configs import get_model_config #%% 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(generator, x_init, cond=None): return sample_from_model( generator.pos_coeff, generator, n_time=generator.config.num_timesteps, x_init=x_init, T=generator.time_schedule, opt=generator.config, cond=cond ) 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 class ObjectFromDict: def __init__(self, d): self.__dict__ = d def load_model(config, path, device="cpu"): config = ObjectFromDict(config) text_encoder = build_encoder(name=config.text_encoder, masked_mean=config.masked_mean) config.cond_size = text_encoder.output_size netG = NCSNpp(config) ckpt = torch.load(path, map_location="cpu") for key in list(ckpt.keys()): if key.startswith("module"): ckpt[key[7:]] = ckpt.pop(key) netG.load_state_dict(ckpt) netG.eval() netG.pos_coeff = Posterior_Coefficients(config, device) netG.text_encoder = text_encoder netG.config = config netG.time_schedule = get_time_schedule(config, device) netG = netG.to(device) return netG #%% def sample_and_test(args): torch.manual_seed(args.seed) device = 'cuda' if torch.cuda.is_available() else 'cpu' to_range_0_1 = lambda x: (x + 1.) / 2. if args.epoch_id == -1: epochs = range(1000) else: epochs = [args.epoch_id] if args.compute_image_reward: #image_reward = RM.load("ImageReward-v1.0", download_root=".").to(device) image_reward = RM.load("ImageReward.pt", download_root=".").to(device) cfg = get_model_config(args.name) for epoch in epochs: args.epoch_id = epoch path = './saved_info/dd_gan/{}/{}/netG_{}.pth'.format(cfg['dataset'], args.name, args.epoch_id) next_next_path = './saved_info/dd_gan/{}/{}/netG_{}.pth'.format(cfg['dataset'], args.name, args.epoch_id+2) print(path) if not os.path.exists(path): continue if not os.path.exists(next_next_path): break print("PATH", path) suffix = '_' + args.eval_name if args.eval_name else "" dest = './saved_info/dd_gan/{}/{}/eval_{}{}.json'.format(cfg['dataset'], args.name, args.epoch_id, suffix) if (args.compute_fid or args.compute_clip_score or args.compute_image_reward) and os.path.exists(dest): continue print("Load epoch", args.epoch_id, "checkpoint") netG = load_model(cfg, path, device=device) save_dir = "./generated_samples/{}".format(cfg['dataset']) if not os.path.exists(save_dir): os.makedirs(save_dir) if args.compute_fid or args.compute_clip_score or args.compute_image_reward: # Evaluate 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] print("Text size:", len(texts)) 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: 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 = [] if args.compute_image_reward: image_rewards = [] for b in range(0, len(texts), args.batch_size): text = texts[b:b+args.batch_size] with torch.no_grad(): cond = netG.text_encoder(text) bs = len(text) t0 = time.time() x_t_1 = torch.randn(bs, cfg['num_channels'], cfg['image_size'], cfg['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(generator=netG, x_init=x_t_1, cond=cond) fake_sample = to_range_0_1(fake_sample) 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 args.compute_image_reward: for k, img in enumerate(fake_sample): img = img.cpu().numpy().transpose(1,2,0) img = img * 255 img = img.astype(np.uint8) text_k = text[k] score = image_reward.score(text_k, img) image_rewards.append(score) if i % 10 == 0: print('evaluating batch ', i, time.time() - t0) #break 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 if args.compute_image_reward: reward = np.mean(image_rewards) results['image_reward'] = reward results.update(vars(args)) with open(dest, "w") as fd: json.dump(results, fd) print(results) else: # just generate some samples 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: 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 = netG.text_encoder(texts) x_t_1 = torch.randn(len(texts), cfg['num_channels'], cfg['image_size'] * args.scale_factor_h, cfg['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": (cfg['image_size'], cfg['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(generator=netG, x_init=x_t_1, cond=cond) print(time.time() - t0) fake_sample = to_range_0_1(fake_sample) torchvision.utils.save_image(fake_sample, 'samples.jpg') if __name__ == '__main__': parser = argparse.ArgumentParser('ddgan parameters') parser.add_argument('--name', type=str, default="", help="model config name") parser.add_argument('--batch-size', type=int, default=16) parser.add_argument('--seed', type=int, default=1024, help='seed used for initialization') # by default, we just generate samples and save them to samples.jpg # for evaluation, one or several of the following should be set to True 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('--compute-image-reward', action='store_true', default=False, help='whether or not compute CLIP score') # clip model for clip evaluation parser.add_argument('--clip-model', type=str,default="ViT-L/14") # nb images to use for FID evaluation parser.add_argument('--nb-images-for-fid', type=int, default=0) # eval name to use when saving the evaluation results parser.add_argument('--eval-name', type=str,default="") # epoch to use for evaluation, if -1, iterate over all epochs (for evaluation) parser.add_argument('--epoch-id', type=int,default=-1) parser.add_argument('--guidance-scale', type=float,default=0) parser.add_argument('--dynamic-thresholding-quantile', type=float,default=0) # either a text, or a .txt file, where each line is a prompt 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('--cond-text', type=str,default="a chair in the form of an avocado") args = parser.parse_args() sample_and_test(args)