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 import argparse import os import logging import numpy as np import torch from PIL import Image from tqdm.auto import tqdm import glob import json import cv2 import sys sys.path.append("../") from models.depth_normal_pipeline_clip import DepthNormalEstimationPipeline #from models.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline from models.depth_normal_pipeline_clip_cfg_1 import DepthNormalEstimationPipeline as DepthNormalEstimationPipelineCFG from utils.seed_all import seed_all import matplotlib.pyplot as plt from utils.de_normalized import align_scale_shift from utils.depth2normal import * from diffusers import DiffusionPipeline, DDIMScheduler, AutoencoderKL from models.unet_2d_condition import UNet2DConditionModel from transformers import CLIPTextModel, CLIPTokenizer from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection import torchvision.transforms.functional as TF from torchvision.transforms import InterpolationMode device = torch.device("cuda" if torch.cuda.is_available() else "cpu") #pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT) stable_diffusion_repo_path = '.' vae = AutoencoderKL.from_pretrained(stable_diffusion_repo_path, subfolder='vae') scheduler = DDIMScheduler.from_pretrained(stable_diffusion_repo_path, subfolder='scheduler') sd_image_variations_diffusers_path = '.' image_encoder = CLIPVisionModelWithProjection.from_pretrained(sd_image_variations_diffusers_path, subfolder="image_encoder") feature_extractor = CLIPImageProcessor.from_pretrained(sd_image_variations_diffusers_path, subfolder="feature_extractor") unet = UNet2DConditionModel.from_pretrained('./wocfg/unet_ema') unet_cfg = UNet2DConditionModel.from_pretrained('./cfg/unet_ema') pipe = DepthNormalEstimationPipeline(vae=vae, image_encoder=image_encoder, feature_extractor=feature_extractor, unet=unet, scheduler=scheduler) pipe_cfg = DepthNormalEstimationPipelineCFG(vae=vae, image_encoder=image_encoder, feature_extractor=feature_extractor, unet=unet_cfg, scheduler=scheduler) try: import xformers pipe.enable_xformers_memory_efficient_attention() except: pass # run without xformers pipe = pipe.to(device) pipe_cfg = pipe_cfg.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) # if guidance_scale > 0: # pipe_out = pipe_cfg( # 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, # ) # else: 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 = '''
Generate consistent depth and normal from single image. High quality and rich details.
''' _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, 0 Recommended for no guidance", # minimum=0, # maximum=5, # step=1, # value=0, # ) denoising_steps = gr.Slider( label="Number of denoising steps (More steps, better quality)", minimum=1, maximum=50, step=1, value=10, ) 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)