Delete app1.py

app1.py

@@ -1,173 +0,0 @@
-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
-
-import fire
-
-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 */
-    }'''
-
-    _TITLE = '''GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image'''
-    _DESCRIPTION = '''
-    <div>
-    Generate consistent depth and normal from single image. High quality and rich details.
-    <a style="display:inline-block; margin-left: .5em" href='https://github.com/fuxiao0719/GeoWizard/'><img src='https://img.shields.io/github/stars/fuxiao0719/GeoWizard?style=social' /></a>
-    </div>
-    '''
-    _GPU_ID = 0
-
-    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')
-
-                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.Column():
-                        
-                        domain = gr.Radio(
-                         [
-                             ("Outdoor", "outdoor"),
-                             ("Indoor", "indoor"),
-                             ("Object", "object"),
-                         ],
-                         label="Data Type (Must Select One matches your image)",
-                         value="indoor",
-                     )
-                        guidance_scale = gr.Slider(
-                         label="Classifier Free Guidance Scale",
-                         minimum=1,
-                         maximum=5,
-                         step=1,
-                         value=3,
-                     )
-                        denoising_steps = gr.Slider(
-                         label="Number of denoising steps (More stepes, better quality)",
-                         minimum=1,
-                         maximum=50,
-                         step=1,
-                         value=20,
-                     )
-                        ensemble_size = gr.Slider(
-                         label="Ensemble size (1 will be enough. More steps, higher accuracy)",
-                         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():
-            with gr.Column():
-                depth = gr.Image(interactive=False, show_label=False)
-            with gr.Column():
-                normal = gr.Image(interactive=False, show_label=False)
-
-
-        run_btn.click(fn=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)
-