import gradio as gr import numpy as np import os import subprocess from datetime import datetime os.system('git clone https://huggingface.co/camenduru/GaussianDreamer') os.system('pip install ./gaussiansplatting/submodules/diff-gaussian-rasterization') os.system('pip install ./GaussianDreamer/nerfacc-0.5.3-cp310-cp310-linux_x86_64.whl') os.system('pip install ./gaussiansplatting/submodules/simple-knn') os.system('pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch') os.system('git clone https://github.com/openai/shap-e.git') os.system('pip install -e ./shap-e') os.system('mv ./GaussianDreamer/shapE_finetuned_with_330kdata.pth ./load/shapE_finetuned_with_330kdata.pth') example_inputs = [[ "A fox." ], [ "fries and a hamburger." ], [ "Viking axe, fantasy, weapon, blender, 8k, HD." ], [ "ferrari convertible, trending on artstation, ultra realistic, 4k, HD" ], [ "flamethrower, with fire, scifi, cyberpunk, photorealistic, 8K, HD" ]] example_outputs_1 = [ gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/a_fox.mp4'), autoplay=True), gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/fries_and_a_hamburger.mp4'), autoplay=True), gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/Viking_axe,_fantasy,_weapon,_blender,_8k,_HD.mp4'), autoplay=True), gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/ferrari_convertible,_trending_on_artstation,_ultra_realistic,_4k,_HD.mp4'), autoplay=True), gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/flamethrower,_with_fire,_scifi,_cyberpunk,_photorealistic,_8K,_HD.mp4'), autoplay=True) ] subprocess.run([ f'python shape.py'], shell=True) def main(prompt, iteration,CFG, seed): if [prompt] in example_inputs: return example_outputs_1[example_inputs.index([prompt])] seed = int(seed) iteration = int(iteration) print('==> User Prompt:', prompt) timestamp = datetime.now().strftime("%Y%m%d-%H%M%S") print('==> User Shell:', f'python launch.py --config configs/gaussiandreamer-sd.yaml --train --gpu 0 system.prompt_processor.prompt="{prompt}" seed={seed} system.guidance.guidance_scale={CFG} trainer.max_steps={iteration} use_timestamp=False timestamp="{timestamp}" ',) subprocess.run([ f'python launch.py --config configs/gaussiandreamer-sd.yaml --train --gpu 0 system.prompt_processor.prompt="{prompt}" seed={seed} system.guidance.guidance_scale={CFG} trainer.max_steps={iteration} use_timestamp=False timestamp="{timestamp}" '], shell=True) path= os.path.join("./outputs/gaussiandreamer-sd",f'{prompt.replace(" ","_")}{timestamp}',f"save/it{iteration}-test.mp4") print('==> Save path:', path) return gr.Video(value=path, autoplay=True) with gr.Blocks() as demo: gr.Markdown("#
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models
") gr.Markdown("This live demo allows you to generate high-quality 3D content using text prompts. The outputs are 360° rendered 3d video.
\ It is based on Stable Diffusion 2.1-base. Please check out our Project Page / Paper / Code if you want to learn more about our method!
\ Note that this demo is running on T4, the running time might be longer than the reported 15 minutes (1200 iterations) on RTx 3090.
\ © This Gradio space is developed by Taoran Yi.") gr.Interface(fn=main, inputs=[gr.Textbox(lines=2, value="fries and a hamburger.", label="Your prompt"), gr.Slider(0, 2000, value=1200, label="Number of iteration"), gr.Slider(80, 200, value=100, label="CFG"), gr.Number(value=0, label="Seed")], outputs=["playable_video"], examples=example_inputs, cache_examples=True, concurrency_limit=1) demo.launch()