import gradio as gr
import numpy as np
from train import *
example_inputs = [[
"A DSLR photo of a Rugged, vintage-inspired hiking boots with a weathered leather finish, best quality, 4K, HD.",
"Rugged, vintage-inspired hiking boots with a weathered leather finish."
], [
"a DSLR photo of a Cream Cheese Donut.",
"a Donut."
], [
"A durian, 8k, HDR.",
"A durian"
], [
"A pillow with huskies printed on it",
"A pillow"
], [
"A DSLR photo of a wooden car, super detailed, best quality, 4K, HD.",
"a wooden car."
]]
example_outputs = [
gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/boots.mp4'), autoplay=True),
gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/Donut.mp4'), autoplay=True),
gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/durian.mp4'), autoplay=True),
gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/pillow_huskies.mp4'), autoplay=True),
gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/wooden_car.mp4'), autoplay=True)
]
def main(prompt, init_prompt, negative_prompt, num_iter, CFG, seed):
if [prompt, init_prompt] in example_inputs:
return example_outputs[example_inputs.index([prompt, init_prompt])]
args, lp, op, pp, gcp, gp = args_parser(default_opt=os.path.join(os.path.dirname(__file__), 'configs/white_hair_ironman.yaml'))
gp.text = prompt
gp.negative = negative_prompt
if len(init_prompt) > 1:
gcp.init_shape = 'pointe'
gcp.init_prompt = init_prompt
else:
gcp.init_shape = 'sphere'
gcp.init_prompt = '.'
op.iterations = num_iter
gp.guidance_scale = CFG
gp.noise_seed = int(seed)
lp.workspace = 'gradio_demo'
video_path = start_training(args, lp, op, pp, gcp, gp)
return gr.Video(value=video_path, autoplay=True)
with gr.Blocks() as demo:
gr.Markdown("# LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching")
gr.Markdown("Yixun Liang*, Xin Yang*, Jiantao Lin, Haodong Li, Xiaogang Xu, Yingcong Chen**")
gr.Markdown("*: Equal contribution. **: Corresponding author.")
gr.Markdown("We present a text-to-3D generation framework, named the *LucidDreamer*, to distill high-fidelity textures and shapes from pretrained 2D diffusion models.")
gr.Markdown("CLICK for the full abstract
The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise, they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS, that it brings inconsistent and low-quality updating direction for the 3D model, causing the over-smoothing effect. To address this, we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore, we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.