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app.py
CHANGED
@@ -10,7 +10,7 @@ import sys
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import imageio
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import numpy as np
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sys.path.append('./code')
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sys.path.append('/home/user/app/code')
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@@ -236,11 +236,6 @@ def run_main_app(semantic_concept, word, script, prompt_suffix, font_name, num_s
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# compose image with white background
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img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=device) * (1 - img[:, :, 3:4])
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img = img[:, :, :3]
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if step % skip == 0:
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img_tensor = img.detach().cpu()
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img_tensor = torch.nn.functional.interpolate(img_tensor, size=(200, 200, 3), mode='bicubic', align_corners=False)
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gif_frames += [img_tensor.numpy()]
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filename = os.path.join(cfg.experiment_dir, "video-svg", f"iter{step:04d}.svg")
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check_and_create_dir(filename)
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@@ -249,6 +244,13 @@ def run_main_app(semantic_concept, word, script, prompt_suffix, font_name, num_s
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yield gr.update(visible=True),gr.update(value=filename, label=f'iters: {step} / {num_iter}', visible=True),gr.update(visible=False),gr.update(value=cfg.caption,visible=True),gr.update(value=cfg.seed,visible=True)
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x = img.unsqueeze(0).permute(0, 3, 1, 2) # HWC -> NCHW
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x = x.repeat(cfg.batch_size, 1, 1, 1)
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x_aug = data_augs.forward(x)
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import imageio
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import numpy as np
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# sys.path.append('./code')
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sys.path.append('/home/user/app/code')
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# compose image with white background
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img = img[:, :, 3:4] * img[:, :, :3] + torch.ones(img.shape[0], img.shape[1], 3, device=device) * (1 - img[:, :, 3:4])
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img = img[:, :, :3]
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filename = os.path.join(cfg.experiment_dir, "video-svg", f"iter{step:04d}.svg")
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check_and_create_dir(filename)
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yield gr.update(visible=True),gr.update(value=filename, label=f'iters: {step} / {num_iter}', visible=True),gr.update(visible=False),gr.update(value=cfg.caption,visible=True),gr.update(value=cfg.seed,visible=True)
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x = img.unsqueeze(0).permute(0, 3, 1, 2) # HWC -> NCHW
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if step % skip == 0:
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img_tensor = x.detach().cpu()
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img_tensor = torch.nn.functional.interpolate(img_tensor, size=(200, 200), mode='bicubic', align_corners=False)
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img_tensor = img_tensor.permute(0, 3, 2, 1).squeeze(0)
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gif_frames += [img_tensor.numpy()]
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x = x.repeat(cfg.batch_size, 1, 1, 1)
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x_aug = data_augs.forward(x)
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