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| import gradio as gr | |
| import numpy as np | |
| import os | |
| try: | |
| from train import * | |
| print('==> simple-knn & diff-gaussian-rasterization already installed!') | |
| except: | |
| print('==> simple-knn & diff-gaussian-rasterization are NOT installed!') | |
| # https://github.com/pytorch/extension-cpp/issues/71 | |
| os.environ["TORCH_CUDA_ARCH_LIST"] = "3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX" | |
| print('==> TORCH_CUDA_ARCH_LIST =', os.environ.get('TORCH_CUDA_ARCH_LIST')) | |
| os.system("python -m pip install git+https://github.com/YixunLiang/simple-knn.git") | |
| print('==> simple-knn installed!') | |
| os.system("python -m pip install git+https://github.com/YixunLiang/diff-gaussian-rasterization.git") | |
| print('==> diff-gaussian-rasterization installed!') | |
| 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_1 = [ | |
| 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) | |
| ] | |
| example_outputs_2 = [ | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/boots_pro.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/Donut_pro.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/durian_pro.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/pillow_huskies_pro.mp4'), autoplay=True), | |
| gr.Video(value=os.path.join(os.path.dirname(__file__), 'example/wooden_car_pro.mp4'), autoplay=True) | |
| ] | |
| def main(prompt, init_prompt, negative_prompt, num_iter, CFG, seed): | |
| if [prompt, init_prompt] in example_inputs: | |
| return example_outputs_1[example_inputs.index([prompt, init_prompt])], example_outputs_2[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) | |
| print('==> User Prompt:', gp.text) | |
| lp.workspace = 'gradio_demo' | |
| video_path, pro_video_path = start_training(args, lp, op, pp, gcp, gp) | |
| return gr.Video(value=video_path, autoplay=True), gr.Video(value=pro_video_path, autoplay=True) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# <center>LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching</center>") | |
| gr.Markdown("This live demo allows you to generate high-quality 3D content using text prompts. The outputs are 360° rendered 3d gaussian video and training progress visualization.<br> \ | |
| It is based on Stable Diffusion 2.1. Please check out our <strong><a href=https://github.com/EnVision-Research/LucidDreamer>Project Page</a> / <a href=https://arxiv.org/abs/2311.11284>Paper</a> / <a href=https://github.com/EnVision-Research/LucidDreamer>Code</a></strong> if you want to learn more about our method!<br> \ | |
| Note that this demo is running on A10G, the running time might be longer than the reported 35 minutes (5000 iterations) on A100.<br> \ | |
| © This Gradio space was developed by Haodong LI.") | |
| gr.Interface(fn=main, inputs=[gr.Textbox(lines=2, value="A portrait of IRONMAN, white hair, head, photorealistic, 8K, HDR.", label="Your prompt"), | |
| gr.Textbox(lines=1, value="a man head.", label="Point-E init prompt (optional)"), | |
| gr.Textbox(lines=2, value="unrealistic, blurry, low quality, out of focus, ugly, low contrast, dull, low-resolution.", label="Negative prompt (optional)"), | |
| gr.Slider(1000, 5000, value=3000, label="Number of iterations"), | |
| gr.Slider(7.5, 100, value=7.5, label="CFG"), | |
| gr.Number(value=0, label="Seed")], | |
| outputs=["playable_video", "playable_video"], | |
| examples=example_inputs, | |
| cache_examples=True, | |
| concurrency_limit=1) | |
| demo.launch() | |