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Browse files- app.py +66 -0
- requirements.txt +1 -0
app.py
CHANGED
@@ -4,6 +4,72 @@ import torch
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import matplotlib.pyplot as plt
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import torchvision
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from networks_fastgan import MyGenerator
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def image_generation(model, number_of_images=1):
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G = MyGenerator.from_pretrained("Cropinky/projected_gan_impressionism")
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import matplotlib.pyplot as plt
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import torchvision
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from networks_fastgan import MyGenerator
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import click
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import PIL
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@click.command()
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@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', default = 10-15, required=True)
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@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
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@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
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@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
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@click.option('--translate', help='Translate XY-coordinate (e.g. \'0.3,1\')', type=parse_vec2, default='0,0', show_default=True, metavar='VEC2')
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@click.option('--rotate', help='Rotation angle in degrees', type=float, default=0, show_default=True, metavar='ANGLE')
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@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
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def generate_images(
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seeds: List[int],
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truncation_psi: float,
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noise_mode: str,
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outdir: str,
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translate: Tuple[float,float],
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rotate: float,
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class_idx: Optional[int]
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):
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"""Generate images using pretrained network pickle.
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Examples:
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\b
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# Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
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python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
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--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
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\b
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# Generate uncurated images with truncation using the MetFaces-U dataset
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python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
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--network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
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"""
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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G = MyGenerator.from_pretrained("Cropinky/projected_gan_impressionism")
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print("network loaded")
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# Labels.
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label = torch.zeros([1, G.c_dim], device=device)
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if G.c_dim != 0:
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if class_idx is None:
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raise click.ClickException('Must specify class label with --class when using a conditional network')
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label[:, class_idx] = 1
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else:
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if class_idx is not None:
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print ('warn: --class=lbl ignored when running on an unconditional network')
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# Generate images.
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for seed_idx, seed in enumerate(seeds):
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print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
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z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device).float()
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# Construct an inverse rotation/translation matrix and pass to the generator. The
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# generator expects this matrix as an inverse to avoid potentially failing numerical
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# operations in the network.
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if hasattr(G.synthesis, 'input'):
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m = make_transform(translate, rotate)
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m = np.linalg.inv(m)
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G.synthesis.input.transform.copy_(torch.from_numpy(m))
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img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
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img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
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PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
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def image_generation(model, number_of_images=1):
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G = MyGenerator.from_pretrained("Cropinky/projected_gan_impressionism")
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requirements.txt
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@@ -2,3 +2,4 @@ gradio
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torchvision
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matplotlib
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torch
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torchvision
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matplotlib
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torch
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dnnlib
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