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from __future__ import annotations |
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import pickle |
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import sys |
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import gradio as gr |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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from huggingface_hub import hf_hub_download |
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sys.path.insert(0, "stylegan3") |
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TITLE = "StyleGAN3 Anime Face Generation" |
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MODEL_REPO = "hysts/stylegan3-anime-face-exp002-model" |
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MODEL_FILE_NAME = "009000.pkl" |
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def make_transform(translate: tuple[float, float], angle: float) -> np.ndarray: |
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mat = np.eye(3) |
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sin = np.sin(angle / 360 * np.pi * 2) |
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cos = np.cos(angle / 360 * np.pi * 2) |
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mat[0][0] = cos |
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mat[0][1] = sin |
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mat[0][2] = translate[0] |
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mat[1][0] = -sin |
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mat[1][1] = cos |
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mat[1][2] = translate[1] |
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return mat |
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def load_model(device: torch.device) -> nn.Module: |
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path = hf_hub_download(MODEL_REPO, MODEL_FILE_NAME) |
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with open(path, "rb") as f: |
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model = pickle.load(f) |
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model.eval() |
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model.to(device) |
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with torch.inference_mode(): |
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z = torch.zeros((1, 512)).to(device) |
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c = torch.zeros(0).to(device) |
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model(z, c) |
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return model |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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model = load_model(device) |
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def generate_z(seed: int, device: torch.device) -> torch.Tensor: |
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return torch.from_numpy(np.random.RandomState(seed).randn(1, 512)).to(device) |
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@torch.inference_mode() |
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def generate_image(seed: int, truncation_psi: float, tx: float, ty: float, angle: float) -> np.ndarray: |
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seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max)) |
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z = generate_z(seed, device) |
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c = torch.zeros(0).to(device) |
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mat = make_transform((tx, ty), angle) |
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mat = np.linalg.inv(mat) |
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model.synthesis.input.transform.copy_(torch.from_numpy(mat)) |
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out = model(z, c, truncation_psi=truncation_psi) |
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out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) |
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return out[0].cpu().numpy() |
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demo = gr.Interface( |
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fn=generate_image, |
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inputs=[ |
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gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.uint32).max, step=1, value=3407851645), |
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gr.Slider(label="Truncation psi", minimum=0, maximum=2, step=0.05, value=0.7), |
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gr.Slider(label="Translate X", minimum=-1, maximum=1, step=0.05, value=0), |
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gr.Slider(label="Translate Y", minimum=-1, maximum=1, step=0.05, value=0), |
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gr.Slider(label="Angle", minimum=-180, maximum=180, step=5, value=0), |
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], |
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outputs=gr.Image(label="Output"), |
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title=TITLE, |
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css="style.css", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20, api_open=False).launch(show_api=False) |
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