Spaces:
Build error
Build error
File size: 15,494 Bytes
f251a39 d56163b f251a39 d56163b f251a39 d56163b f251a39 d661b19 df366bf d661b19 3eeba90 d661b19 bd359dc d661b19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 |
import os
import wget
import subprocess
import sys
import torch
if os.getenv('SYSTEM') == 'spaces':
pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
version_str="".join([
f"py3{sys.version_info.minor}_cu",
torch.version.cuda.replace(".",""),
f"_pyt{pyt_version_str}"
])
# subprocess.run(
# 'pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html'.split())
# subprocess.run(
# 'pip install https://download.is.tue.mpg.de/icon/HF/pytorch3d-0.7.0-cp38-cp38-linux_x86_64.whl'.split())
subprocess.run(
f'pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html'.split())
import argparse
import gradio as gr
from functools import partial
from my.config import BaseConf, dispatch_gradio
from run_3DFuse import SJC_3DFuse
import numpy as np
from PIL import Image
from pc_project import point_e
from diffusers import UnCLIPPipeline, DiffusionPipeline
from pc_project import point_e_gradio
import numpy as np
import plotly.graph_objs as go
from my.utils.seed import seed_everything
SHARED_UI_WARNING = f'''### [NOTE] Training may be very slow in this shared UI.
You can duplicate and use it with a paid private GPU.
<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/jyseo/3DFuse?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a>
Alternatively, you can also use the Colab demo on our project page.
<a style="display:inline-block" href="https://ku-cvlab.github.io/3DFuse/"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/Project%20Page-online-brightgreen"></a>
'''
class Intermediate:
def __init__(self):
self.images = None
self.points = None
self.is_generating = False
def gen_3d(model, intermediate, prompt, keyword, seed, ti_step, pt_step) :
intermediate.is_generating = True
images, points = intermediate.images, intermediate.points
if images is None or points is None :
raise gr.Error("Please generate point cloud first")
del model
seed_everything(seed)
model = dispatch_gradio(SJC_3DFuse, prompt, keyword, ti_step, pt_step, seed)
setting = model.dict()
# exp_dir = os.path.join(setting['exp_dir'],keyword)
# initial_images_dir = os.path.join(exp_dir, 'initial_image')
# os.makedirs(initial_images_dir,exist_ok=True)
# for idx,img in enumerate(images) :
# img.save( os.path.join(initial_images_dir, f"instance{idx}.png") )
yield from model.run_gradio(points, images)
intermediate.is_generating = False
def gen_pc_from_prompt(intermediate, num_initial_image, prompt, keyword, type, bg_preprocess, seed) :
seed_everything(seed=seed)
if keyword not in prompt:
raise gr.Error("Prompt should contain keyword!")
elif " " in keyword:
raise gr.Error("Keyword should be one word!")
images = gen_init(num_initial_image=num_initial_image, prompt=prompt,seed=seed, type=type, bg_preprocess=bg_preprocess)
points = point_e_gradio(images[0],'cuda')
intermediate.images = images
intermediate.points = points
coords = np.array(points.coords)
trace = go.Scatter3d(x=coords[:,0], y=coords[:,1], z=coords[:,2], mode='markers', marker=dict(size=2))
layout = go.Layout(
scene=dict(
xaxis=dict(
title="",
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False
),
yaxis=dict(
title="",
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False
),
zaxis=dict(
title="",
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False
),
),
margin=dict(l=0, r=0, b=0, t=0),
showlegend=False
)
fig = go.Figure(data=[trace], layout=layout)
return images[0], fig, gr.update(interactive=True)
def gen_pc_from_image(intermediate, image, prompt, keyword, bg_preprocess, seed) :
seed_everything(seed=seed)
if keyword not in prompt:
raise gr.Error("Prompt should contain keyword!")
elif " " in keyword:
raise gr.Error("Keyword should be one word!")
if bg_preprocess:
import cv2
from carvekit.api.high import HiInterface
interface = HiInterface(object_type="object",
batch_size_seg=5,
batch_size_matting=1,
device='cuda' if torch.cuda.is_available() else 'cpu',
seg_mask_size=640, # Use 640 for Tracer B7 and 320 for U2Net
matting_mask_size=2048,
trimap_prob_threshold=231,
trimap_dilation=30,
trimap_erosion_iters=5,
fp16=False)
img_without_background = interface([image])
mask = np.array(img_without_background[0]) > 127
image = np.array(image)
image[~mask] = [255., 255., 255.]
image = Image.fromarray(np.array(image))
points = point_e_gradio(image,'cuda')
intermediate.images = [image]
intermediate.points = points
coords = np.array(points.coords)
trace = go.Scatter3d(x=coords[:,0], y=coords[:,1], z=coords[:,2], mode='markers', marker=dict(size=2))
layout = go.Layout(
scene=dict(
xaxis=dict(
title="",
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False
),
yaxis=dict(
title="",
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False
),
zaxis=dict(
title="",
showgrid=False,
zeroline=False,
showline=False,
ticks='',
showticklabels=False
),
),
margin=dict(l=0, r=0, b=0, t=0),
showlegend=False
)
fig = go.Figure(data=[trace], layout=layout)
return image, fig, gr.update(interactive=True)
def gen_init(num_initial_image, prompt,seed,type="Karlo", bg_preprocess=False):
pipe = UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16) if type=="Karlo (Recommended)" \
else DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to('cuda')
view_prompt=["front view of ","overhead view of ","side view of ", "back view of "]
if bg_preprocess:
import cv2
from carvekit.api.high import HiInterface
interface = HiInterface(object_type="object",
batch_size_seg=5,
batch_size_matting=1,
device='cuda' if torch.cuda.is_available() else 'cpu',
seg_mask_size=640, # Use 640 for Tracer B7 and 320 for U2Net
matting_mask_size=2048,
trimap_prob_threshold=231,
trimap_dilation=30,
trimap_erosion_iters=5,
fp16=False)
images = []
generator = torch.Generator(device='cuda').manual_seed(seed)
for i in range(num_initial_image):
t=", white background" if bg_preprocess else ", white background"
if i==0:
prompt_ = f"{view_prompt[i%4]}{prompt}{t}"
else:
prompt_ = f"{view_prompt[i%4]}{prompt}"
image = pipe(prompt_, generator=generator).images[0]
if bg_preprocess:
# motivated by NeuralLift-360 (removing bg)
# NOTE: This option was added during the code orgranization process.
# The results reported in the paper were obtained with [bg_preprocess: False] setting.
img_without_background = interface([image])
mask = np.array(img_without_background[0]) > 127
image = np.array(image)
image[~mask] = [255., 255., 255.]
image = Image.fromarray(np.array(image))
images.append(image)
return images
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--share', action='store_true', help="public url")
args = parser.parse_args()
weights_dir = './weights'
if not os.path.exists(weights_dir):
os.makedirs(weights_dir)
weights_path = os.path.join(weights_dir, '3DFuse_sparse_depth_injector.ckpt')
# 파일이 존재하지 않으면 wget으로 다운로드하여 저장
if not os.path.isfile(weights_path):
url = 'https://huggingface.co/jyseo/3DFuse_weights/resolve/main/models/3DFuse_sparse_depth_injector.ckpt'
wget.download(url, weights_path)
print(f'{weights_path} downloaded.')
else:
print(f'{weights_path} already exists.')
model = None
intermediate = Intermediate()
demo = gr.Blocks(title="3DFuse Interactive Demo")
with demo:
with gr.Box():
gr.Markdown(SHARED_UI_WARNING)
gr.Markdown("# 3DFuse Interactive Demo")
gr.Markdown("### Official Implementation of the paper \"Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation\"")
gr.Markdown("Enter your own prompt and enjoy! With this demo, you can preview the point cloud before 3D generation and determine the desired shape.")
# gr.Markdown("Enter your own prompt and enjoy! With this demo, you can preview the point cloud before 3D generation and determine the desired shape.")
with gr.Row():
with gr.Column(scale=1., variant='panel'):
with gr.Tab("Text to 3D"):
prompt_input = gr.Textbox(label="Prompt", value="a comfortable bed", interactive=True)
word_input = gr.Textbox(label="Keyword for initialization (should be a noun included in the prompt)", value="bed", interactive=True)
semantic_model_choice = gr.Radio(["Karlo (Recommended)","Stable Diffusion"], label="Backbone for initial image generation", value="Karlo (Recommended)", interactive=True)
seed = gr.Slider(label="Seed", minimum=0, maximum=2100000000, step=1, randomize=True)
preprocess_choice = gr.Checkbox(True, label="Preprocess intially-generated image by removing background", interactive=True)
with gr.Accordion("Advanced Options", open=False):
opt_step = gr.Slider(0, 1000, value=500, step=1, label='Number of text embedding optimization step')
pivot_step = gr.Slider(0, 1000, value=500, step=1, label='Number of pivotal tuning step for LoRA')
with gr.Row():
button_gen_pc = gr.Button("1. Generate Point Cloud", interactive=True, variant='secondary')
button_gen_3d = gr.Button("2. Generate 3D", interactive=False, variant='primary')
with gr.Tab("Image to 3D"):
image_input = gr.Image(source='upload', type="pil", interactive=True)
prompt_input_2 = gr.Textbox(label="Prompt", value="a dog", interactive=True)
word_input_2 = gr.Textbox(label="Keyword for initialization (should be a noun included in the prompt)", value="dog", interactive=True)
seed_2 = gr.Slider(label="Seed", minimum=0, maximum=2100000000, step=1, randomize=True)
preprocess_choice_2 = gr.Checkbox(True, label="Preprocess intially-generated image by removing background", interactive=False)
with gr.Accordion("Advanced Options", open=False):
opt_step_2 = gr.Slider(0, 1000, value=500, step=1, label='Number of text embedding optimization step')
pivot_step_2 = gr.Slider(0, 1000, value=500, step=1, label='Number of pivotal tuning step for LoRA')
with gr.Row():
button_gen_pc_2 = gr.Button("1. Generate Point Cloud", interactive=True, variant='secondary')
button_gen_3d_2 = gr.Button("2. Generate 3D", interactive=False, variant='primary')
gr.Markdown("Note: A photo showing the entire object in a front view is recommended. Also, our framework is not designed with the goal of single shot reconstruction, so it may be difficult to reflect the details of the given image.")
with gr.Row(scale=1.):
with gr.Column(scale=1.):
pc_plot = gr.Plot(label="Inferred point cloud")
with gr.Column(scale=1.):
init_output = gr.Image(label='Generated initial image', interactive=False)
# init_output.style(grid=1)
with gr.Column(scale=1., variant='panel'):
with gr.Row():
with gr.Column(scale=1.):
intermediate_output = gr.Image(label="Intermediate Output", interactive=False)
with gr.Column(scale=1.):
logs = gr.Textbox(label="logs", lines=8, max_lines=8, interactive=False)
with gr.Row():
video_result = gr.Video(label="Video result for generated 3D", interactive=False)
gr.Markdown("Note: Keyword is used for Textual Inversion. Please choose one important noun included in the prompt. This demo may be slower than directly running run_3DFuse.py.")
# functions
button_gen_pc.click(fn=partial(gen_pc_from_prompt,intermediate,4), inputs=[prompt_input, word_input, semantic_model_choice, \
preprocess_choice, seed], outputs=[init_output, pc_plot, button_gen_3d])
button_gen_3d.click(fn=partial(gen_3d,model,intermediate), inputs=[prompt_input, word_input, seed, opt_step, pivot_step], \
outputs=[intermediate_output,logs,video_result])
button_gen_pc_2.click(fn=partial(gen_pc_from_image,intermediate), inputs=[image_input, prompt_input_2, word_input_2, \
preprocess_choice_2, seed_2], outputs=[init_output, pc_plot, button_gen_3d_2])
button_gen_3d_2.click(fn=partial(gen_3d,model,intermediate), inputs=[prompt_input_2, word_input_2, seed_2, opt_step_2, pivot_step_2], \
outputs=[intermediate_output,logs,video_result])
demo.queue(concurrency_count=1)
demo.launch(share=args.share)
|