import functools import os import shutil import sys import git import gradio as gr import numpy as np import torch as torch from PIL import Image from gradio_imageslider import ImageSlider import spaces REPO_URL = "https://github.com/lemonaddie/geowizard.git" CHECKPOINT = "lemonaddie/Geowizard" REPO_DIR = "geowizard" if os.path.isdir(REPO_DIR): shutil.rmtree(REPO_DIR) repo = git.Repo.clone_from(REPO_URL, REPO_DIR) sys.path.append(os.path.join(os.getcwd(), REPO_DIR)) from pipeline.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline device = torch.device("cuda" if torch.cuda.is_available() else "cpu") pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT) try: import xformers pipe.enable_xformers_memory_efficient_attention() except: pass # run without xformers pipe = pipe.to(device) #run_demo_server(pipe) @spaces.GPU def depth_normal(img, denoising_steps, ensemble_size, processing_res, guidance_scale, domain): img = img.resize((processing_res, processing_res), Image.Resampling.LANCZOS) pipe_out = pipe( img, denoising_steps=denoising_steps, ensemble_size=ensemble_size, processing_res=processing_res, batch_size=0, guidance_scale=guidance_scale, domain=domain, show_progress_bar=True, ) depth_colored = pipe_out.depth_colored normal_colored = pipe_out.normal_colored return depth_colored, normal_colored def run_demo(): custom_theme = gr.themes.Soft(primary_hue="blue").set( button_secondary_background_fill="*neutral_100", button_secondary_background_fill_hover="*neutral_200") custom_css = '''#disp_image { text-align: center; /* Horizontally center the content */ }''' with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo: with gr.Row(): with gr.Column(scale=1): gr.Markdown('# ' + _TITLE) gr.Markdown(_DESCRIPTION) with gr.Row(variant='panel'): with gr.Column(scale=1): input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image', tool=None) example_folder = os.path.join(os.path.dirname(__file__), "./files") example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)] gr.Examples( examples=example_fns, inputs=[input_image], # outputs=[input_image], cache_examples=False, label='Examples (click one of the images below to start)', examples_per_page=30 ) with gr.Column(scale=1): with gr.Accordion('Advanced options', open=True): with gr.Row(): domain = gr.Radio( [ ("Outdoor", "outdoor"), ("Indoor", "indoor"), ("Object", "object"), ], label="Data Domain", value="indoor", ) guidance_scale = gr.Slider( label="Classifier Free Guidance Scale", minimum=1, maximum=5, step=1, value=3, ) denoise_steps = gr.Slider( label="Number of denoising steps", minimum=1, maximum=20, step=1, value=10, ) ensemble_size = gr.Slider( label="Ensemble size", minimum=1, maximum=15, step=1, value=1, ) processing_res = gr.Radio( [ ("Native", 0), ("Recommended", 768), ], label="Processing resolution", value=768, ) run_btn = gr.Button('Generate', variant='primary', interactive=True) with gr.Row(): depth = gr.Image(interactive=False, height=384, show_label=False) with gr.Row(): normal = gr.Image(interactive=False, height=384, show_label=False) run_btn.success(fn=partial(depth_normal), inputs=[input_image, denoising_steps, ensemble_size, processing_res, guidance_scale, domain], outputs=[depth, normal] ) demo.queue().launch(share=True, max_threads=80) if __name__ == '__main__': fire.Fire(run_demo)