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import argparse
import gradio as gr
import os
import shutil
from glob import glob
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from torchvision.utils import make_grid, save_image
from torchvision.io import read_image
import torchvision.transforms.functional as F
from functools import partial
from datetime import datetime
plt.rcParams["savefig.bbox"] = 'tight'
def show(imgs):
if not isinstance(imgs, list):
imgs = [imgs]
fig, axs = plt.subplots(ncols=len(imgs), squeeze=False)
for i, img in enumerate(imgs):
img = F.to_pil_image(img.detach())
axs[0, i].imshow(np.asarray(img))
axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
class Intermediate:
def __init__(self):
self.input_img = None
self.input_img_time = 0
model_ckpts = {"elf": "ffhq-elf.pkl",
"greek_statue": "ffhq-greek_statue.pkl",
"hobbit": "ffhq-hobbit.pkl",
"lego": "ffhq-lego.pkl",
"masquerade": "ffhq-masquerade.pkl",
"neanderthal": "ffhq-neanderthal.pkl",
"orc": "ffhq-orc.pkl",
"pixar": "ffhq-pixar.pkl",
"skeleton": "ffhq-skeleton.pkl",
"stone_golem": "ffhq-stone_golem.pkl",
"super_mario": "ffhq-super_mario.pkl",
"tekken": "ffhq-tekken.pkl",
"yoda": "ffhq-yoda.pkl",
"zombie": "ffhq-zombie.pkl",
"cat_in_Zootopia": "cat-cat_in_Zootopia.pkl",
"fox_in_Zootopia": "cat-fox_in_Zootopia.pkl",
"golden_aluminum_animal": "cat-golden_aluminum_animal.pkl",
}
manip_model_ckpts = {"super_mario": "ffhq-super_mario.pkl",
"lego": "ffhq-lego.pkl",
"neanderthal": "ffhq-neanderthal.pkl",
"orc": "ffhq-orc.pkl",
"pixar": "ffhq-pixar.pkl",
"skeleton": "ffhq-skeleton.pkl",
"stone_golem": "ffhq-stone_golem.pkl",
"tekken": "ffhq-tekken.pkl",
"greek_statue": "ffhq-greek_statue.pkl",
"yoda": "ffhq-yoda.pkl",
"zombie": "ffhq-zombie.pkl",
"elf": "ffhq-elf.pkl",
}
def TextGuidedImageTo3D(intermediate, img, model_name, num_inversion_steps, truncation):
if img != intermediate.input_img:
if os.path.exists('input_imgs_gradio'):
shutil.rmtree('input_imgs_gradio')
os.makedirs('input_imgs_gradio', exist_ok=True)
img.save('input_imgs_gradio/input.png')
intermediate.input_img = img
now = datetime.now()
intermediate.input_img_time = now.strftime('%Y-%m-%d_%H:%M:%S')
all_model_names = manip_model_ckpts.keys()
generator_type = 'ffhq'
if model_name == 'all':
_no_video_models = []
for _model_name in all_model_names:
if not os.path.exists(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/4_manip_result/finetuned___{model_ckpts[_model_name]}__input_inv.mp4'):
print()
_no_video_models.append(_model_name)
model_names_command = ''
for _model_name in _no_video_models:
if not os.path.exists(f'finetuned/{model_ckpts[_model_name]}'):
command = f"""wget https://huggingface.co/gwang-kim/datid3d-finetuned-eg3d-models/resolve/main/finetuned_models/{model_ckpts[_model_name]} -O finetuned/{model_ckpts[_model_name]}
"""
os.system(command)
model_names_command += f"finetuned/{model_ckpts[_model_name]} "
w_pths = sorted(glob(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/3_inversion_result/*.pt'))
if len(w_pths) == 0:
mode = 'manip'
else:
mode = 'manip_from_inv'
if len(_no_video_models) > 0:
command = f"""python datid3d_test.py --mode {mode} \
--indir='input_imgs_gradio' \
--generator_type={generator_type} \
--outdir='test_runs' \
--trunc={truncation} \
--network {model_names_command} \
--num_inv_steps={num_inversion_steps} \
--down_src_eg3d_from_nvidia=False \
--name_tag='_gradio_{intermediate.input_img_time}' \
--shape=False \
--w_frames 60
"""
print(command)
os.system(command)
aligned_img_pth = sorted(glob(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/2_pose_result/*.png'))[0]
aligned_img = Image.open(aligned_img_pth)
result_imgs = []
for _model_name in all_model_names:
img_pth = f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/4_manip_result/finetuned___{model_ckpts[_model_name]}__input_inv.png'
result_imgs.append(read_image(img_pth))
result_grid_pt = make_grid(result_imgs, nrow=1)
result_img = F.to_pil_image(result_grid_pt)
else:
if not os.path.exists(f'finetuned/{model_ckpts[model_name]}'):
command = f"""wget https://huggingface.co/gwang-kim/datid3d-finetuned-eg3d-models/resolve/main/finetuned_models/{model_ckpts[model_name]} -O finetuned/{model_ckpts[model_name]}
"""
os.system(command)
if not os.path.exists(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/4_manip_result/finetuned___{model_ckpts[model_name]}__input_inv.mp4'):
w_pths = sorted(glob(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/3_inversion_result/*.pt'))
if len(w_pths) == 0:
mode = 'manip'
else:
mode = 'manip_from_inv'
command = f"""python datid3d_test.py --mode {mode} \
--indir='input_imgs_gradio' \
--generator_type={generator_type} \
--outdir='test_runs' \
--trunc={truncation} \
--network finetuned/{model_ckpts[model_name]} \
--num_inv_steps={num_inversion_steps} \
--down_src_eg3d_from_nvidia=0 \
--name_tag='_gradio_{intermediate.input_img_time}' \
--shape=False
--w_frames 60"""
print(command)
os.system(command)
aligned_img_pth = sorted(glob(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/2_pose_result/*.png'))[0]
aligned_img = Image.open(aligned_img_pth)
result_img_pth = sorted(glob(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/4_manip_result/*{model_ckpts[model_name]}*.png'))[0]
result_img = Image.open(result_img_pth)
if model_name=='all':
result_video_pth = f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/4_manip_result/finetuned___ffhq-all__input_inv.mp4'
if os.path.exists(result_video_pth):
os.remove(result_video_pth)
command = 'ffmpeg '
for _model_name in all_model_names:
command += f'-i test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/4_manip_result/finetuned___ffhq-{_model_name}.pkl__input_inv.mp4 '
# command += '-filter_complex "[0:v]scale=2*iw:-1[v0];[1:v]scale=2*iw:-1[v1];[2:v]scale=2*iw:-1[v2];[3:v]scale=2*iw:-1[v3];[4:v]scale=2*iw:-1[v4];[5:v]scale=2*iw:-1[v5];[6:v]scale=2*iw:-1[v6];[7:v]scale=2*iw:-1[v7];[8:v]scale=2*iw:-1[v8];[9:v]scale=2*iw:-1[v9];[10:v]scale=2*iw:-1[v10];[11:v]scale=2*iw:-1[v11];[v0][v1][v2][v3][v4][v5][v6][v7][v8][v9][v10][v11]xstack=inputs=12:layout=0_0|w0_0|w0+w1_0|w0+w1+w2_0|0_h0|w4_h0|w4+w5_h0|w4+w5+w6_h0|0_h0+h4|w8_h0+h4|w8+w9_h0+h4|w8+w9+w10_h0+h4" '
command += '-filter_complex "[v0][v1][v2][v3][v4][v5][v6][v7][v8][v9][v10][v11]concat=n=12:v=1:a=0[output]"'
command += f" -vcodec libx264 {result_video_pth}"
print()
print(command)
os.system(command)
else:
result_video_pth = sorted(glob(f'test_runs/manip_3D_recon_gradio_{intermediate.input_img_time}/4_manip_result/*{model_ckpts[model_name]}*.mp4'))[0]
return aligned_img, result_img, result_video_pth
def SampleImage(model_name, num_samples, truncation, seed):
seed_list = np.random.RandomState(seed).choice(np.arange(10000), num_samples).tolist()
seeds = ''
for seed in seed_list:
seeds += f'{seed},'
seeds = seeds[:-1]
if model_name in ["fox_in_Zootopia", "cat_in_Zootopia", "golden_aluminum_animal"]:
generator_type = 'cat'
else:
generator_type = 'ffhq'
if not os.path.exists(f'finetuned/{model_ckpts[model_name]}'):
command = f"""wget https://huggingface.co/gwang-kim/datid3d-finetuned-eg3d-models/resolve/main/finetuned_models/{model_ckpts[model_name]} -O finetuned/{model_ckpts[model_name]}
"""
os.system(command)
command = f"""python datid3d_test.py --mode image \
--generator_type={generator_type} \
--outdir='test_runs' \
--seeds={seeds} \
--trunc={truncation} \
--network=finetuned/{model_ckpts[model_name]} \
--shape=False"""
print(command)
os.system(command)
result_img_pths = sorted(glob(f'test_runs/image/*{model_ckpts[model_name]}*.png'))
result_imgs = []
for img_pth in result_img_pths:
result_imgs.append(read_image(img_pth))
result_grid_pt = make_grid(result_imgs, nrow=1)
result_grid_pil = F.to_pil_image(result_grid_pt)
return result_grid_pil
def SampleVideo(model_name, grid_height, truncation, seed):
seed_list = np.random.RandomState(seed).choice(np.arange(10000), grid_height**2).tolist()
seeds = ''
for seed in seed_list:
seeds += f'{seed},'
seeds = seeds[:-1]
if model_name in ["fox_in_Zootopia", "cat_in_Zootopia", "golden_aluminum_animal"]:
generator_type = 'cat'
else:
generator_type = 'ffhq'
if not os.path.exists(f'finetuned/{model_ckpts[model_name]}'):
command = f"""wget https://huggingface.co/gwang-kim/datid3d-finetuned-eg3d-models/resolve/main/finetuned_models/{model_ckpts[model_name]} -O finetuned/{model_ckpts[model_name]}
"""
os.system(command)
command = f"""python datid3d_test.py --mode video \
--generator_type={generator_type} \
--outdir='test_runs' \
--seeds={seeds} \
--trunc={truncation} \
--grid={grid_height}x{grid_height} \
--network=finetuned/{model_ckpts[model_name]} \
--shape=False"""
print(command)
os.system(command)
result_video_pth = sorted(glob(f'test_runs/video/*{model_ckpts[model_name]}*.mp4'))[0]
return result_video_pth
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--share', action='store_true', help="public url")
args = parser.parse_args()
demo = gr.Blocks(title="DATID-3D Interactive Demo")
os.makedirs('finetuned', exist_ok=True)
intermediate = Intermediate()
with demo:
gr.Markdown("# DATID-3D Interactive Demo")
gr.Markdown(
"### Demo of the CVPR 2023 paper \"DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model\"")
with gr.Tab("Text-guided Manipulated 3D reconstruction"):
gr.Markdown("Text-guided Image-to-3D Translation")
with gr.Row():
with gr.Column(scale=1, variant='panel'):
t_image_input = gr.Image(source='upload', type="pil", interactive=True)
t_model_name = gr.Radio(["super_mario", "lego", "neanderthal", "orc",
"pixar", "skeleton", "stone_golem","tekken",
"greek_statue", "yoda", "zombie", "elf", "all"],
label="Model fine-tuned through DATID-3D",
value="super_mario", interactive=True)
with gr.Accordion("Advanced Options", open=False):
t_truncation = gr.Slider(label="Truncation psi", minimum=0, maximum=1.0, step=0.01, randomize=False, value=0.8)
t_num_inversion_steps = gr.Slider(200, 1000, value=200, step=1, label='Number of steps for the invresion')
with gr.Row():
t_button_gen_result = gr.Button("Generate Result", variant='primary')
# t_button_gen_video = gr.Button("Generate Video", variant='primary')
# t_button_gen_image = gr.Button("Generate Image", variant='secondary')
with gr.Row():
t_align_image_result = gr.Image(label="Alignment result", interactive=False)
with gr.Column(scale=1, variant='panel'):
with gr.Row():
t_video_result = gr.Video(label="Video result", interactive=False)
with gr.Row():
t_image_result = gr.Image(label="Image result", interactive=False)
with gr.Tab("Sample Images"):
with gr.Row():
with gr.Column(scale=1, variant='panel'):
i_model_name = gr.Radio(
["elf", "greek_statue", "hobbit", "lego", "masquerade", "neanderthal", "orc", "pixar",
"skeleton", "stone_golem", "super_mario", "tekken", "yoda", "zombie", "fox_in_Zootopia",
"cat_in_Zootopia", "golden_aluminum_animal"],
label="Model fine-tuned through DATID-3D",
value="super_mario", interactive=True)
i_num_samples = gr.Slider(0, 20, value=4, step=1, label='Number of samples')
i_seed = gr.Slider(label="Seed", minimum=0, maximum=1000000000, step=1, value=1235)
with gr.Accordion("Advanced Options", open=False):
i_truncation = gr.Slider(label="Truncation psi", minimum=0, maximum=1.0, step=0.01, randomize=False, value=0.8)
with gr.Row():
i_button_gen_image = gr.Button("Generate Image", variant='primary')
with gr.Column(scale=1, variant='panel'):
with gr.Row():
i_image_result = gr.Image(label="Image result", interactive=False)
with gr.Tab("Sample Videos"):
with gr.Row():
with gr.Column(scale=1, variant='panel'):
v_model_name = gr.Radio(
["elf", "greek_statue", "hobbit", "lego", "masquerade", "neanderthal", "orc", "pixar",
"skeleton", "stone_golem", "super_mario", "tekken", "yoda", "zombie", "fox_in_Zootopia",
"cat_in_Zootopia", "golden_aluminum_animal"],
label="Model fine-tuned through DATID-3D",
value="super_mario", interactive=True)
v_grid_height = gr.Slider(0, 5, value=2, step=1,label='Height of the grid')
v_seed = gr.Slider(label="Seed", minimum=0, maximum=1000000000, step=1, value=1235)
with gr.Accordion("Advanced Options", open=False):
v_truncation = gr.Slider(label="Truncation psi", minimum=0, maximum=1.0, step=0.01, randomize=False,
value=0.8)
with gr.Row():
v_button_gen_video = gr.Button("Generate Video", variant='primary')
with gr.Column(scale=1, variant='panel'):
with gr.Row():
v_video_result = gr.Video(label="Video result", interactive=False)
# functions
t_button_gen_result.click(fn=partial(TextGuidedImageTo3D, intermediate),
inputs=[t_image_input, t_model_name, t_num_inversion_steps, t_truncation],
outputs=[t_align_image_result, t_image_result, t_video_result])
i_button_gen_image.click(fn=SampleImage,
inputs=[i_model_name, i_num_samples, i_truncation, i_seed],
outputs=[i_image_result])
v_button_gen_video.click(fn=SampleVideo,
inputs=[i_model_name, v_grid_height, v_truncation, v_seed],
outputs=[v_video_result])
demo.queue(concurrency_count=1)
demo.launch(share=args.share)
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