# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr # # Copyright 2023 LucidDreamer Authors # # Computer Vision Lab, SNU, its affiliates and licensors retain all intellectual # property and proprietary rights in and to this material, related # documentation and any modifications thereto. Any use, reproduction, # disclosure or distribution of this material and related documentation # without an express license agreement from the Computer Vision Lab, SNU or # its affiliates is strictly prohibited. # # For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com. import os import time import pathlib import shlex import subprocess import gradio as gr from huggingface_hub import snapshot_download root = pathlib.Path(__file__).parent example_root = os.path.join(root, 'examples') ckpt_root = os.path.join(root, 'stablediffusion') d = example_root if not os.path.exists(d): snapshot_download(repo_id="ironjr/LucidDreamerDemo", repo_type="model", local_dir=d) d = os.path.join(ckpt_root, 'Blazing Drive V11m') if not os.path.exists(d): snapshot_download(repo_id="ironjr/BlazingDriveV11m", repo_type="model", local_dir=d) d = os.path.join(ckpt_root, 'RealCartoon-Pixar V5') if not os.path.exists(d): snapshot_download(repo_id="ironjr/RealCartoon-PixarV5", repo_type="model", local_dir=d) d = os.path.join(ckpt_root, 'Realistic Vision V5.1') if not os.path.exists(d): snapshot_download(repo_id="ironjr/RealisticVisionV5-1", repo_type="model", local_dir=d) d = os.path.join(ckpt_root, 'SD1-5') if not os.path.exists(d): snapshot_download(repo_id="runwayml/stable-diffusion-inpainting", repo_type="model", local_dir=d) try: import simple_knn except ModuleNotFoundError: # subprocess.run(shlex.split('python setup.py install'), cwd=os.path.join(root, 'submodules', 'simple-knn')) subprocess.run(shlex.split(f'pip install {root}/dist/simple_knn-0.0.0-cp39-cp39-linux_x86_64.whl')) try: import depth_diff_gaussian_rasterization_min except ModuleNotFoundError: # subprocess.run(shlex.split('python setup.py install'), cwd=os.path.join(root, 'submodules', 'depth-diff-gaussian-rasterization-min')) subprocess.run(shlex.split(f'pip install {root}/dist/depth_diff_gaussian_rasterization_min-0.0.0-cp39-cp39-linux_x86_64.whl')) from luciddreamer import LucidDreamer css = """ #run-button { background: coral; color: white; } """ ld = LucidDreamer() with gr.Blocks(css=css) as demo: gr.HTML( """

LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes

If you like our project, please visit our Github, too! ✨✨✨ More features are waiting!

  Project Page    
""" ) with gr.Row(): result_gallery = gr.Video(label='RGB Video', show_label=True) result_depth = gr.Video(label='Depth Video', show_label=True) result_ply_file = gr.File(label='Gaussian splatting PLY', show_label=True) with gr.Row(): input_image = gr.Image( label='Image prompt', sources='upload', type='pil', ) with gr.Column(): model_name = gr.Radio( label='SD checkpoint', choices=['SD1.5 (default)', 'Blazing Drive V11m', 'Realistic Vision V5.1', 'RealCartoon-Pixar V5',], value='SD1.5 (default)' ) prompt = gr.Textbox( label='Text prompt', value='A cozy livingroom', ) n_prompt = gr.Textbox( label='Negative prompt', value='photo frame, frame, boarder, simple color, inconsistent, humans, people', ) gen_camerapath = gr.Radio( label='Camera trajectory for generation (STEP 1)', choices=['lookaround', 'lookdown', 'rotate360'], value='lookaround', ) with gr.Row(): seed = gr.Slider( label='Seed', minimum=1, maximum=2147483647, step=1, randomize=True, ) diff_steps = gr.Slider( label='SD inpainting steps', minimum=1, maximum=50, step=1, value=30, ) render_camerapath = gr.Radio( label='Camera trajectory for rendering (STEP 2)', choices=['back_and_forth', 'llff', 'headbanging'], value='llff', ) with gr.Column(): run_button = gr.Button(value='Run! (it may take a while)', elem_id='run-button') gr.HTML( """

...or you can run in two steps

(hint: press STEP 2 if you have already baked Gaussians in STEP 1).
""" ) with gr.Row(): gaussian_button = gr.Button(value='STEP 1: Generate Gaussians') render_button = gr.Button(value='STEP 2: Render A Video') gr.HTML( """
...or you can just watch a quick preload we have baked already.
""" ) example_name = gr.Radio( label='Quick load', choices=['girl', 'ruin', 'doge', 'christmas', 'elf', 'animelake', 'fantasy', 'kitchen', 'DON\'T'], value='DON\'T', ) ips = [example_name, input_image, prompt, n_prompt, gen_camerapath, seed, diff_steps, render_camerapath, model_name] run_button.click(fn=ld.run, inputs=ips[1:] + ips[:1], outputs=[result_ply_file, result_gallery, result_depth]) gaussian_button.click(fn=ld.create, inputs=ips[1:-1] + ips[:1], outputs=[result_ply_file]) render_button.click(fn=ld.render_video, inputs=ips[-2:-1] + ips[:1], outputs=[result_gallery, result_depth]) gr.Examples( examples=[ [ 'girl', 'examples/girl.jpg', 'dark messy room, noir style, indoors, bottle, shoe soles, jacket, cup, window, blurry, black footwear, depth of field, box, couch, table, gun, chair, foreshortening', 'photo frame, frame, boarder, simple color, inconsistent', 'lookaround', 10, 25, 'back_and_forth', 'Blazing Drive V11m', ], [ 'ruin', 'examples/ruin.png', 'Postapocalyptic city in desert', 'photo frame, frame, boarder, simple color, inconsistent, humans, people', 'lookaround', 4, 25, 'back_and_forth', 'Blazing Drive V11m', ], [ 'doge', 'examples/doge.png', 'a cozy livingroom', 'photo frame, frame, boarder, simple color, inconsistent, humans, people', 'lookaround', 10, 25, 'back_and_forth', 'Realistic Vision V5.1', ], [ 'christmas', 'examples/christmas.png', 'Cozy livingroom in christmas', 'photo frame, frame, boarder, simple color, inconsistent, humans, people', 'lookaround', 3, 25, 'back_and_forth', 'Realistic Vision V5.1', ], [ 'elf', 'examples/elf.png', 'serene deep forest', 'photo frame, frame, boarder, simple color, inconsistent, humans, people', 'lookaround', 10, 25, 'back_and_forth', 'RealCartoon-Pixar V5', ], [ 'animelake', 'examples/Image015_animelakehouse.jpg', 'anime style, animation, best quality, a boat on lake, trees and rocks near the lake. a house and port in front of a house', 'photo frame, frame, boarder, simple color, inconsistent', 'lookdown', 1, 50, 'back_and_forth', 'SD1.5 (default)', ], [ 'fantasy', 'examples/Image003_fantasy.jpg', 'A vibrant, colorful floating community city, clouds above a beautiful, enchanted landscape filled with whimsical flora, enchanted forest landscape, Magical and dreamy woodland with vibrant green foliage and sparkling flowers, Landscape with twisted trees and vines, natural lighting and dark shadows, unique fantastical elements like floating islands and floating orbs, Highly detailed vegetation and foliage, deep contrast and color vibrancy,', # texture and intricate details in a floating element', 'photo frame, frame, boarder, simple color, inconsistent', 'lookaround', 30, 50, 'back_and_forth', 'SD1.5 (default)', ], [ 'kitchen', 'examples/image020.png', 'High-resolution photography kitchen design, wooden floor, small windows opening onto the garden, Bauhaus furniture and decoration, high ceiling, beige blue salmon pastel palette, interior design magazine, cozy atmosphere; 8k, intricate detail, photorealistic, realistic light, wide angle, kinfolk photography, A+D architecture, Kitchen Sink, Basket of fruits and vegetables, a bottle of ', #drinking water, walls painted magazine style photo, looking towards a sink under a window, with a door on the left of the sink with a 25 cm distance from the kitchen, the kitchen is an L shaped starting from the right corner, on the far right a fridge nest to it a stove, next the dishwasher then the sink, a smokey grey kitchen with modern touches, taupe walls, a taup ceiling with spotlights inside the ceiling with 90 cm distance, wooden parquet floor', 'photo frame, frame, boarder, simple color, inconsistent', 'rotate360', 1, 50, 'headbanging', 'SD1.5 (default)', ], ], inputs=ips, outputs=[result_ply_file, result_gallery, result_depth], fn=ld.run, cache_examples=False, ) gr.HTML( """

Acknowledgement and Disclaimer

We deeply thank br_d, 7whitefire7, and SG161222 for their awesome Stable Diffusion models. We also appreciate ai_pictures21 and recatm for the beautiful illustrations used in the examples. Please note that the authors of this work do not own the model checkpoints and the illustrations in this demo. LucidDreamer algorithm cannot be used for commercial purpose. Please contact the authors for permission requests.

""" ) if __name__ == '__main__': demo.queue(max_size=20).launch()