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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( |
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1, z_dim)).to(device).float() |
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@torch.inference_mode() |
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def generate_interpolated_images(seed0: int, psi0: float, seed1: int, |
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psi1: float, num_intermediate: int, |
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model: nn.Module, |
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device: torch.device) -> 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( |
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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, |
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model=model, |
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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', |
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minimum=0, |
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maximum=100000, |
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step=1, |
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value=0, |
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randomize=True), |
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gr.Slider(label='Truncation psi 1', |
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minimum=0, |
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maximum=2, |
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step=0.05, |
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value=0.7), |
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gr.Slider(label='Seed 2', |
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minimum=0, |
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maximum=100000, |
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step=1, |
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value=1, |
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randomize=True), |
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gr.Slider(label='Truncation psi 2', |
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minimum=0, |
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maximum=2, |
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step=0.05, |
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value=0.7), |
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gr.Slider(label='Number of Intermediate Frames', |
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minimum=0, |
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maximum=21, |
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step=1, |
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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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