app.py

import sys
import os
import gradio as gr
from PIL import Image

"""Generate images using pretrained network pickle."""

import re
from typing import List, Optional, Tuple, Union

import click
import dnnlib
import numpy as np
import PIL.Image
import torch

import legacy

from huggingface_hub import hf_hub_url

#----------------------------------------------------------------------------

def parse_range(s: Union[str, List]) -> List[int]:
    '''Parse a comma separated list of numbers or ranges and return a list of ints.
    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
    '''
    if isinstance(s, list): return s
    ranges = []
    range_re = re.compile(r'^(\d+)-(\d+)$')
    for p in s.split(','):
        m = range_re.match(p)
        if m:
            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
        else:
            ranges.append(int(p))
    return ranges

#----------------------------------------------------------------------------

def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
    '''Parse a floating point 2-vector of syntax 'a,b'.
    Example:
        '0,1' returns (0,1)
    '''
    if isinstance(s, tuple): return s
    parts = s.split(',')
    if len(parts) == 2:
        return (float(parts[0]), float(parts[1]))
    raise ValueError(f'cannot parse 2-vector {s}')

#----------------------------------------------------------------------------

def make_transform(translate: Tuple[float,float], angle: float):
    m = np.eye(3)
    s = np.sin(angle/360.0*np.pi*2)
    c = np.cos(angle/360.0*np.pi*2)
    m[0][0] = c
    m[0][1] = s
    m[0][2] = translate[0]
    m[1][0] = -s
    m[1][1] = c
    m[1][2] = translate[1]
    return m

#----------------------------------------------------------------------------

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')


# models = {
#     'pokemon': 
# }
# base_path = 
# models = dict()
# for i in ["pokemon", "art-paint", "flowers", "landscapes","obama"]:



def generate_images(seeds, name):
    """Generate images using pretrained network pickle.
    Examples:
    \b
    # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, left).
    python gen_images.py --outdir=out --trunc=1 --seeds=2 \\
        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
    \b
    # Generate uncurated images with truncation using the MetFaces-U dataset
    python gen_images.py --outdir=out --trunc=0.7 --seeds=600-605 \\
        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-metfacesu-1024x1024.pkl
    """
    # models
    config_file_url = hf_hub_url("ZJW666/Projected_GAN_CLC", filename=name+".pkl")
    with dnnlib.util.open_url(config_file_url) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device)
    # G = models[name].to(device)
    # Labels.
    label = torch.zeros([1, G.c_dim], device=device)

    # Generate images.
    for seed_idx, seed in enumerate(seeds):
        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device).float()

        # Construct an inverse rotation/translation matrix and pass to the generator.  The
        # generator expects this matrix as an inverse to avoid potentially failing numerical
        # operations in the network.
        if hasattr(G.synthesis, 'input'):
            m = make_transform('0,0', 0)
            m = np.linalg.inv(m)
            G.synthesis.input.transform.copy_(torch.from_numpy(m))

        img = G(z, label, truncation_psi=1, noise_mode='const')
        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
        pilimg = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
    return pilimg


def inference(seedin, name = None):
    print(name)
    listseed = [int(seedin)]
    output = generate_images(listseed, name)
    return output

title = "Projected GAN CLC"
description = "Gradio demo for Projected GANs CLC, Pokemon."

gr.Interface(fn=inference,inputs=[gr.Slider(label="Seed",minimum=0, maximum=5000, step=1, value=0), gr.Radio(["pokemon", "art-paint", "flowers", "landscapes","obama"], label='Dataset', value='art-paint')],outputs=["image"],title=title,description=description
    ).launch()