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| 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('pip install -q https://huggingface.co./camenduru/CoDeF/resolve/main/tinycudann-1.7-cp310-cp310-linux_x86_64.whl') | |
| 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" | |
| ], [ | |
| "Blue and white porcelain Viking axe" | |
| ], [ | |
| "a DSLR photo of a small saguaro cactus planted in a clay pot" | |
| ], [ | |
| "a zoomed out DSLR photo of an amigurumi motorcycle" | |
| ], [ | |
| "a DSLR photo of a teapot shaped like an elephant head where its snout acts as the spout" | |
| ], [ | |
| "a DSLR photo of a wine bottle and full wine glass on a chessboard" | |
| ], [ | |
| "a panda wearing a necktie and sitting in an office chair" | |
| ], [ | |
| "a spanish galleon sailing on the open sea" | |
| ], [ | |
| "airplane, fighter, steampunk style, ultra realistic, 4k, 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), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/Blue_and_white_porcelain_Viking_axe.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/a_DSLR_photo_of_a_small_saguaro_cactus_planted_in_a_clay_pot.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/a_zoomed_out_DSLR_photo_of_an_amigurumi_motorcycle.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/a_DSLR_photo_of_a_teapot_shaped_like_an_elephant_head_where_its_snout_acts_as_the_spout.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/a_DSLR_photo_of_a_wine_bottle_and_full_wine_glass_on_a_chessboard.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/a_panda_wearing_a_necktie_and_sitting_in_an_office_chair.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/a_spanish_galleon_sailing_on_the_open_sea.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/airplane,_fighter,_steampunk_style,_ultra_realistic,_4k,_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("# <center>GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models</center>") | |
| gr.Markdown("This live demo allows you to generate high-quality 3D content using text prompts. The outputs are 360° rendered 3d video.<br> \ | |
| It is based on Stable Diffusion 2.1-base. Please check out our <strong><a href=https://taoranyi.com/gaussiandreamer/>Project Page</a> / <a href=https://arxiv.org/abs/2310.08529>Paper</a> / <a href=https://github.com/hustvl/GaussianDreamer>Code</a></strong> if you want to learn more about our method!<br> \ | |
| Note that this demo is running on T4, the running time might be longer than the reported 15 minutes (1200 iterations) on RTx 3090.<br> \ | |
| © This Gradio space is developed by <a href=https://taoranyi.com/>Taoran Yi</a>.") | |
| gr.Interface(fn=main, inputs=[gr.Textbox(lines=2, value="fries and a hamburger.", label="Your prompt"), | |
| gr.Slider(0, 2000, value=900, label="Number of iteration (using 1200 in paper)"), | |
| 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() |