import sys import os import gradio as gr from PIL import Image os.system("git clone https://github.com/autonomousvision/projected_gan.git") sys.path.append("projected_gan") """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') config_file_url = hf_hub_url("autonomousvision/Projected_GAN_Pokemon", filename="pokemon.pkl") with dnnlib.util.open_url(config_file_url) as f: G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore def generate_images(seeds): """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 """ # 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): listseed = [int(seedin)] output = generate_images(listseed) return output title = "Example: Pokemon GAN" description = "Gradio demo for Pokemon GAN. To use it, provide a seed, or click one of the examples to load them. Read more at the links below." article = "

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" gr.Interface(inference,gr.inputs.Slider(label="Seed",minimum=0, maximum=5000, step=1, default=0),"pil",title=title,description=description,article=article, allow_screenshot=False, allow_flagging="never", live=True, examples=[ [0],[1],[10],[20],[30],[42],[50],[60],[77],[102] ]).launch(enable_queue=True,cache_examples=True)