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from __future__ import annotations |
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import functools |
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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, "StyleGAN-Human") |
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TITLE = "StyleGAN-Human (Interpolation)" |
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DESCRIPTION = "https://github.com/stylegan-human/StyleGAN-Human" |
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def load_model(file_name: str, device: torch.device) -> nn.Module: |
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path = hf_hub_download("public-data/StyleGAN-Human", f"models/{file_name}") |
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with open(path, "rb") as f: |
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model = pickle.load(f)["G_ema"] |
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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, model.z_dim)).to(device) |
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label = torch.zeros([1, model.c_dim], device=device) |
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model(z, label, force_fp32=True) |
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return model |
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def generate_z(z_dim: int, seed: int, device: torch.device) -> torch.Tensor: |
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return torch.from_numpy(np.random.RandomState(seed).randn(1, z_dim)).to(device).float() |
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@torch.inference_mode() |
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def generate_interpolated_images( |
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seed0: int, psi0: float, seed1: int, psi1: float, num_intermediate: int, model: nn.Module, device: torch.device |
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) -> list[np.ndarray]: |
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seed0 = int(np.clip(seed0, 0, np.iinfo(np.uint32).max)) |
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seed1 = int(np.clip(seed1, 0, np.iinfo(np.uint32).max)) |
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z0 = generate_z(model.z_dim, seed0, device) |
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z1 = generate_z(model.z_dim, seed1, device) |
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vec = z1 - z0 |
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dvec = vec / (num_intermediate + 1) |
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zs = [z0 + dvec * i for i in range(num_intermediate + 2)] |
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dpsi = (psi1 - psi0) / (num_intermediate + 1) |
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psis = [psi0 + dpsi * i for i in range(num_intermediate + 2)] |
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label = torch.zeros([1, model.c_dim], device=device) |
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res = [] |
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for z, psi in zip(zs, psis): |
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out = model(z, label, truncation_psi=psi, force_fp32=True) |
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out = (out.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) |
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out = out[0].cpu().numpy() |
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res.append(out) |
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return res |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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model = load_model("stylegan_human_v2_1024.pkl", device) |
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fn = functools.partial(generate_interpolated_images, model=model, device=device) |
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gr.Interface( |
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fn=fn, |
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inputs=[ |
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gr.Slider(label="Seed 1", minimum=0, maximum=100000, step=1, value=0, randomize=True), |
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gr.Slider(label="Truncation psi 1", minimum=0, maximum=2, step=0.05, value=0.7), |
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gr.Slider(label="Seed 2", minimum=0, maximum=100000, step=1, value=1, randomize=True), |
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gr.Slider(label="Truncation psi 2", minimum=0, maximum=2, step=0.05, value=0.7), |
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gr.Slider(label="Number of Intermediate Frames", minimum=0, maximum=21, step=1, value=7), |
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], |
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outputs=gr.Gallery(label="Output Images", type="numpy"), |
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title=TITLE, |
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description=DESCRIPTION, |
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).queue(max_size=10).launch() |
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