# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # NVIDIA CORPORATION and its licensors retain all intellectual property # and proprietary rights in and to this software, related documentation # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. """Generate images using pretrained network pickle.""" import os import re from typing import List, Optional import torchvision from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize import click import dnnlib import numpy as np import PIL.Image import torch from torch import linalg as LA import clip from PIL import Image import legacy import torch.nn.functional as F import cv2 import matplotlib.pyplot as plt from torch_utils import misc from torch_utils import persistence from torch_utils.ops import conv2d_resample from torch_utils.ops import upfirdn2d from torch_utils.ops import bias_act from torch_utils.ops import fma import random import math import time import id_loss def block_forward(self, x, img, ws, shapes, force_fp32=False, fused_modconv=None, **layer_kwargs): misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) w_iter = iter(ws.unbind(dim=1)) dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format if fused_modconv is None: with misc.suppress_tracer_warnings(): # this value will be treated as a constant fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1) # Input. if self.in_channels == 0: x = self.const.to(dtype=dtype, memory_format=memory_format) x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) else: misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) x = x.to(dtype=dtype, memory_format=memory_format) # Main layers. if self.in_channels == 0: x = self.conv1(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs) elif self.architecture == 'resnet': y = self.skip(x, gain=np.sqrt(0.5)) x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) x = y.add_(x) else: x = self.conv0(x, next(w_iter)[...,:shapes[0]], fused_modconv=fused_modconv, **layer_kwargs) x = self.conv1(x, next(w_iter)[...,:shapes[1]], fused_modconv=fused_modconv, **layer_kwargs) # ToRGB. if img is not None: misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) img = upfirdn2d.upsample2d(img, self.resample_filter) if self.is_last or self.architecture == 'skip': y = self.torgb(x, next(w_iter)[...,:shapes[2]], fused_modconv=fused_modconv) y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) img = img.add_(y) if img is not None else y assert x.dtype == dtype assert img is None or img.dtype == torch.float32 return x, img def unravel_index(index, shape): out = [] for dim in reversed(shape): out.append(index % dim) index = index // dim return tuple(reversed(out)) #---------------------------------------------------------------------------- def num_range(s: str) -> List[int]: '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.''' range_re = re.compile(r'^(\d+)-(\d+)$') m = range_re.match(s) if m: return list(range(int(m.group(1)), int(m.group(2))+1)) vals = s.split(',') return [int(x) for x in vals] #---------------------------------------------------------------------------- @click.command() @click.pass_context @click.option('--network', 'network_pkl', help='Network pickle filename', required=True) @click.option('--seeds', type=num_range, help='List of random seeds') @click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True) @click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)') @click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True) @click.option('--projected-w', help='Projection result file', type=str, metavar='FILE') @click.option('--projected_s', help='Projection result file', type=str, metavar='FILE') @click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR') @click.option('--resolution', help='Resolution of output images', type=int, required=True) @click.option('--batch_size', help='Batch Size', type=int, required=True) @click.option('--identity_power', help='How much change occurs on the face', type=str, required=True) def generate_images( ctx: click.Context, network_pkl: str, seeds: Optional[List[int]], truncation_psi: float, noise_mode: str, outdir: str, class_idx: Optional[int], projected_w: Optional[str], projected_s: Optional[str], resolution: int, batch_size: int, identity_power: str ): """Generate images using pretrained network pickle. Examples: \b # Generate curated MetFaces images without truncation (Fig.10 left) python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \\ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl \b # Generate uncurated MetFaces images with truncation (Fig.12 upper left) python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl \b # Generate class conditional CIFAR-10 images (Fig.17 left, Car) python generate.py --outdir=out --seeds=0-35 --class=1 \\ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/cifar10.pkl \b # Render an image from projected W python generate.py --outdir=out --projected_w=projected_w.npz \\ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl """ print('Loading networks from "%s"...' % network_pkl) device = torch.device('cuda') with dnnlib.util.open_url(network_pkl) as f: G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore os.makedirs(outdir, exist_ok=True) # Synthesize the result of a W projection. if projected_w is not None: if seeds is not None: print ('warn: --seeds is ignored when using --projected-w') print(f'Generating images from projected W "{projected_w}"') ws = np.load(projected_w)['w'] ws = torch.tensor(ws, device=device) # pylint: disable=not-callable assert ws.shape[1:] == (G.num_ws, G.w_dim) for idx, w in enumerate(ws): img = G.synthesis(w.unsqueeze(0), noise_mode=noise_mode) img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/proj{idx:02d}.png') return if seeds is None: ctx.fail('--seeds option is required when not using --projected-w') # Labels. label = torch.zeros([1, G.c_dim], device=device).requires_grad_() if G.c_dim != 0: if class_idx is None: ctx.fail('Must specify class label with --class when using a conditional network') label[:, class_idx] = 1 else: if class_idx is not None: print ('warn: --class=lbl ignored when running on an unconditional network') model, preprocess = clip.load("ViT-B/32", device=device) text_prompts_file = open("text_prompts.txt") text_prompts = text_prompts_file.read().split("\n") text_prompts_file.close() text = clip.tokenize(text_prompts).to(device) text_features = model.encode_text(text) # Generate images. for i in G.parameters(): i.requires_grad = True mean = torch.as_tensor((0.48145466, 0.4578275, 0.40821073), dtype=torch.float, device=device) std = torch.as_tensor((0.26862954, 0.26130258, 0.27577711), dtype=torch.float, device=device) if mean.ndim == 1: mean = mean.view(-1, 1, 1) if std.ndim == 1: std = std.view(-1, 1, 1) transf = Compose([ Resize(224, interpolation=Image.BICUBIC), CenterCrop(224), ]) styles_array = [] print("seeds:", seeds) t1 = time.time() for seed_idx, seed in enumerate(seeds): if seed==seeds[-1]: print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds))) z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device) ws = G.mapping(z, label, truncation_psi=truncation_psi) block_ws = [] with torch.autograd.profiler.record_function('split_ws'): misc.assert_shape(ws, [None, G.synthesis.num_ws, G.synthesis.w_dim]) ws = ws.to(torch.float32) w_idx = 0 for res in G.synthesis.block_resolutions: block = getattr(G.synthesis, f'b{res}') block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) w_idx += block.num_conv styles = torch.zeros(1,26,512, device=device) styles_idx = 0 temp_shapes = [] for res, cur_ws in zip(G.synthesis.block_resolutions, block_ws): block = getattr(G.synthesis, f'b{res}') if res == 4: temp_shape = (block.conv1.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0]) styles[0,:1,:] = block.conv1.affine(cur_ws[0,:1,:]) styles[0,1:2,:] = block.torgb.affine(cur_ws[0,1:2,:]) if seed_idx==(len(seeds)-1): block.conv1.affine = torch.nn.Identity() block.torgb.affine = torch.nn.Identity() styles_idx += 2 else: temp_shape = (block.conv0.affine.weight.shape[0], block.conv1.affine.weight.shape[0], block.torgb.affine.weight.shape[0]) styles[0,styles_idx:styles_idx+1,:temp_shape[0]] = block.conv0.affine(cur_ws[0,:1,:]) styles[0,styles_idx+1:styles_idx+2,:temp_shape[1]] = block.conv1.affine(cur_ws[0,1:2,:]) styles[0,styles_idx+2:styles_idx+3,:temp_shape[2]] = block.torgb.affine(cur_ws[0,2:3,:]) if seed_idx==(len(seeds)-1): block.conv0.affine = torch.nn.Identity() block.conv1.affine = torch.nn.Identity() block.torgb.affine = torch.nn.Identity() styles_idx += 3 temp_shapes.append(temp_shape) styles = styles.detach() styles_array.append(styles) resolution_dict = {256: 6, 512: 7, 1024: 8} identity_coefficient_dict = {"high": 2,"medium": 0.5, "low": 0.1, "none": 0} identity_coefficient = identity_coefficient_dict[identity_power] styles_wanted_direction = torch.zeros(1,26,512, device=device) styles_wanted_direction_grad_el2 = torch.zeros(1,26,512, device=device) styles_wanted_direction.requires_grad_() global id_loss id_loss = id_loss.IDLoss("a").to(device).eval() temp_photos = [] grads = [] for i in range(math.ceil(len(seeds)/batch_size)): #print(i*batch_size, "processed", time.time()-t1) styles = torch.vstack(styles_array[i*batch_size:(i+1)*batch_size]).to(device) seed = seeds[i] styles_idx = 0 x2 = img2 = None for k , (res, cur_ws) in enumerate(zip(G.synthesis.block_resolutions, block_ws)): block = getattr(G.synthesis, f'b{res}') if k>resolution_dict[resolution]: continue if res == 4: x2, img2 = block_forward(block, x2, img2, styles[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode) styles_idx += 2 else: x2, img2 = block_forward(block, x2, img2, styles[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode) styles_idx += 3 img2_cpu = img2.detach().cpu().numpy() temp_photos.append(img2_cpu) if i>3: continue styles2 = styles + styles_wanted_direction styles_idx = 0 x = img = None for k , (res, cur_ws) in enumerate(zip(G.synthesis.block_resolutions, block_ws)): block = getattr(G.synthesis, f'b{res}') if k>resolution_dict[resolution]: continue if res == 4: x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode) styles_idx += 2 else: x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode) styles_idx += 3 identity_loss, _ = id_loss(img, img2) identity_loss *= identity_coefficient img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255) img = (transf(img.permute(0, 3, 1, 2))/255).sub_(mean).div_(std) image_features = model.encode_image(img) cos_sim = -1*F.cosine_similarity(image_features, (text_features[0]).unsqueeze(0)) (identity_loss + cos_sim.sum()).backward(retain_graph=True) #t1 = time.time() for text_counter in range(len(text_prompts)): text_prompt = text_prompts[text_counter] print(text_prompt) styles_wanted_direction.grad.data.zero_() styles_wanted_direction_grad_el2 = torch.zeros(1,26,512, device=device) with torch.no_grad(): styles_wanted_direction *= 0 for i in range(math.ceil(len(seeds)/batch_size)): print(i*batch_size, "processed", time.time()-t1) styles = torch.vstack(styles_array[i*batch_size:(i+1)*batch_size]).to(device) seed = seeds[i] img2 = torch.tensor(temp_photos[i]).to(device) styles2 = styles + styles_wanted_direction styles_idx = 0 x = img = None for k , (res, cur_ws) in enumerate(zip(G.synthesis.block_resolutions, block_ws)): block = getattr(G.synthesis, f'b{res}') if k>resolution_dict[resolution]: continue if res == 4: x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+2, :], temp_shapes[k], noise_mode=noise_mode) styles_idx += 2 else: x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode) styles_idx += 3 identity_loss, _ = id_loss(img, img2) identity_loss *= identity_coefficient img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255) img = (transf(img.permute(0, 3, 1, 2))/255).sub_(mean).div_(std) image_features = model.encode_image(img) cos_sim = -1*F.cosine_similarity(image_features, (text_features[text_counter]).unsqueeze(0)) (identity_loss + cos_sim.sum()).backward(retain_graph=True) styles_wanted_direction.grad[:, [0, 1, 4, 7, 10, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25], :] = 0 if i%2==1: styles_wanted_direction.data = styles_wanted_direction - styles_wanted_direction.grad*5 grads.append(styles_wanted_direction.grad.clone()) styles_wanted_direction.grad.data.zero_() if i>3: styles_wanted_direction_grad_el2[grads[-2]*grads[-1]<0] += 1 styles_wanted_direction_cpu = styles_wanted_direction.detach() styles_wanted_direction_cpu[styles_wanted_direction_grad_el2>(len(seeds)/batch_size)/4] = 0 np.savez(f'{outdir}/direction_'+text_prompt.replace(" ", "_")+'.npz', s=styles_wanted_direction_cpu.cpu().numpy()) print("time passed:", time.time()-t1) #---------------------------------------------------------------------------- if __name__ == "__main__": generate_images() # pylint: disable=no-value-for-parameter #----------------------------------------------------------------------------