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Hi, thanks for adding this. how can I perform Style-mixing with two/more Uploaded Images? here's code that I tried.
def style_mixing_example(network_pkl, src_dlatents, dst_dlatents, truncation_psi, col_styles, minibatch_size=4): print('Loading networks from "%s"...' % network_pkl) _G, _D, Gs = pretrained_networks.load_networks(network_pkl) w_avg = Gs.get_var('dlatent_avg') # [component] Gs_syn_kwargs = dnnlib.EasyDict() Gs_syn_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) Gs_syn_kwargs.randomize_noise = False Gs_syn_kwargs.minibatch_size = minibatch_size print('Generating W vectors...') ''' all_seeds = list(set(row_seeds + col_seeds)) all_z = np.stack([np.random.RandomState(seed).randn(*Gs.input_shape[1:]) for seed in all_seeds]) # [minibatch, component] all_w = Gs.components.mapping.run(all_z, None) # [minibatch, layer, component] all_w = np.load('latent_representations/2_01.npy') all_w = w_avg + (all_w - w_avg) * truncation_psi # [minibatch, layer, component] w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))} # [layer, component] #image generation all_images = Gs.components.synthesis.run(all_w, **Gs_syn_kwargs) # [minibatch, height, width, channel] image_dict = {(seed, seed): image for seed, image in zip(all_seeds, list(all_images))} ''' # these 2 lines are from stylegan-encoder! src_images = Gs.components.synthesis.run(src_dlatents, **Gs_syn_kwargs) # [minibatch, height, width, channel] dst_images = Gs.components.synthesis.run(dst_dlatents,**Gs_syn_kwargs ) print('Generating style-mixed images...') for row_seed in row_seeds: for col_seed in col_seeds: w = w_dict[row_seed].copy() w[col_styles] = w_dict[col_seed][col_styles] image = Gs.components.synthesis.run(w[np.newaxis], **Gs_syn_kwargs)[0] image_dict[(row_seed, col_seed)] = image print('Saving images...') for (row_seed, col_seed), image in image_dict.items(): PIL.Image.fromarray(image, 'RGB').save(dnnlib.make_run_dir_path('%d-%d.png' % (row_seed, col_seed))) print('Saving image grid...') _N, _C, H, W = Gs.output_shape canvas = PIL.Image.new('RGB', (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), 'black') for row_idx, row_seed in enumerate([None] + row_seeds): for col_idx, col_seed in enumerate([None] + col_seeds): if row_seed is None and col_seed is None: continue key = (row_seed, col_seed) if row_seed is None: key = (col_seed, col_seed) if col_seed is None: key = (row_seed, row_seed) canvas.paste(PIL.Image.fromarray(image_dict[key], 'RGB'), (W * col_idx, H * row_idx)) canvas.save(dnnlib.make_run_dir_path('grid.png')) tflib.init_tf() synthesis_kwargs = dict(output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True), minibatch_size=1) _Gs_cache = dict() style_mixing_example(network_pkl, src_dlatents=2_01.reshape((1, 18, 512)), dst_dlatents=01.reshape((1, 18, 512)), 1, col_styles=[range(6,14)], minibatch_size=4)
Greetings!
The text was updated successfully, but these errors were encountered:
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Hi,
thanks for adding this. how can I perform Style-mixing with two/more Uploaded Images? here's code that I tried.
Greetings!
The text was updated successfully, but these errors were encountered: