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| # 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 math | |
| import legacy | |
| import clip | |
| import dnnlib | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision.transforms import Compose, Resize, CenterCrop | |
| from PIL import Image | |
| from torch_utils import misc | |
| from torch_utils.ops import upfirdn2d | |
| import id_loss | |
| from copy import deepcopy | |
| 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 find_direction( | |
| GIn, | |
| text_prompt: str, | |
| truncation_psi: float = 0.7, | |
| noise_mode: str = "const", | |
| resolution: int = 256, | |
| identity_power: float = 0.5, | |
| ): | |
| G = deepcopy(GIn) | |
| seeds=np.random.randint(0, 1000, 128) | |
| batch_size=1 | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| # Labels | |
| class_idx=None | |
| label = torch.zeros([1, G.c_dim], device=device).requires_grad_() | |
| if G.c_dim != 0: | |
| label[:, class_idx] = 1 | |
| model, preprocess = clip.load("ViT-B/32", device=device) | |
| text = clip.tokenize([text_prompt]).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 = [] | |
| 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} | |
| id_coeff = identity_power | |
| styles_direction = torch.zeros(1, 26, 512, device=device) | |
| styles_direction_grad_el2 = torch.zeros(1, 26, 512, device=device) | |
| styles_direction.requires_grad_() | |
| global id_loss2 | |
| #id_loss = id_loss.IDLoss("a").to(device).eval() | |
| id_loss2 = id_loss.IDLoss("a").to(device).eval() | |
| temp_photos = [] | |
| grads = [] | |
| for i in range(math.ceil(len(seeds) / batch_size)): | |
| 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, force_fp32=True) | |
| styles_idx += 2 | |
| else: | |
| x2, img2 = block_forward(block, x2, img2, styles[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True) | |
| styles_idx += 3 | |
| img2_cpu = img2.detach().cpu().numpy() | |
| temp_photos.append(img2_cpu) | |
| if i > 3: | |
| continue | |
| styles2 = styles + styles_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, force_fp32=True) | |
| styles_idx += 2 | |
| else: | |
| x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True) | |
| styles_idx += 3 | |
| identity_loss, _ = id_loss2(img, img2) | |
| identity_loss *= id_coeff | |
| 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) | |
| styles_direction.grad[:, list(range(26)), :] = 0 | |
| with torch.no_grad(): | |
| styles_direction *= 0 | |
| for i in range(math.ceil(len(seeds) / batch_size)): | |
| seed = seeds[i] | |
| styles = torch.vstack(styles_array[i*batch_size:(i+1)*batch_size]).to(device) | |
| img2 = torch.tensor(temp_photos[i]).to(device) | |
| styles2 = styles + styles_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, force_fp32=True) | |
| styles_idx += 2 | |
| else: | |
| x, img = block_forward(block, x, img, styles2[:, styles_idx:styles_idx+3, :], temp_shapes[k], noise_mode=noise_mode, force_fp32=True) | |
| styles_idx += 3 | |
| identity_loss, _ = id_loss2(img, img2) | |
| identity_loss *= id_coeff | |
| 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) | |
| styles_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_direction.data = (styles_direction - styles_direction.grad * 5) | |
| grads.append(styles_direction.grad.clone()) | |
| styles_direction.grad.data.zero_() | |
| if i > 3: | |
| styles_direction_grad_el2[grads[-2] * grads[-1] < 0] += 1 | |
| styles_direction = styles_direction.detach() | |
| styles_direction[styles_direction_grad_el2 > (len(seeds) / batch_size) / 4] = 0 | |
| return styles_direction.cpu().numpy() | |