gradio_app.py

import os
import copy
import torch
import fire
import gradio as gr
from PIL import Image
from functools import partial
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, ControlNetModel
from share_btn import community_icon_html, loading_icon_html, share_js

import cv2
import time
import numpy as np
from rembg import remove
from segment_anything import sam_model_registry, SamPredictor

import uuid
from datetime import datetime

_TITLE = '''Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model'''
_DESCRIPTION = '''
<div>
<a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2310.15110"><img src="https://img.shields.io/badge/2310.15110-f9f7f7?logo=data:image/png;base64,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"></a>
<a style="display:inline-block; margin-left: .5em" href='https://github.com/SUDO-AI-3D/zero123plus'><img src='https://img.shields.io/github/stars/SUDO-AI-3D/zero123plus?style=social' /></a>
Check out our single-image-to-3D work <a href="https://sudo-ai-3d.github.io/One2345plus_page/">One-2-3-45++</a>! 
</div>
'''
_GPU_ID = 0


if not hasattr(Image, 'Resampling'):
    Image.Resampling = Image


def sam_init():
    sam_checkpoint = os.path.join(os.path.dirname(__file__), "tmp", "sam_vit_h_4b8939.pth")
    model_type = "vit_h"

    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=f"cuda:{_GPU_ID}")
    predictor = SamPredictor(sam)
    return predictor

def sam_segment(predictor, input_image, *bbox_coords):
    bbox = np.array(bbox_coords)
    image = np.asarray(input_image)

    start_time = time.time()
    predictor.set_image(image)

    masks_bbox, scores_bbox, logits_bbox = predictor.predict(
        box=bbox,
        multimask_output=True
    )

    print(f"SAM Time: {time.time() - start_time:.3f}s")
    out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
    out_image[:, :, :3] = image
    out_image_bbox = out_image.copy()
    out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255
    torch.cuda.empty_cache()
    return Image.fromarray(out_image_bbox, mode='RGBA') 

def expand2square(pil_img, background_color):
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result

def preprocess(predictor, input_image, chk_group=None, segment=True, rescale=False):
    RES = 1024
    input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS)
    if chk_group is not None:
        segment = "Background Removal" in chk_group
        rescale = "Rescale" in chk_group
    if segment:
        image_rem = input_image.convert('RGBA')
        image_nobg = remove(image_rem, alpha_matting=True)
        arr = np.asarray(image_nobg)[:,:,-1]
        x_nonzero = np.nonzero(arr.sum(axis=0))
        y_nonzero = np.nonzero(arr.sum(axis=1))
        x_min = int(x_nonzero[0].min())
        y_min = int(y_nonzero[0].min())
        x_max = int(x_nonzero[0].max())
        y_max = int(y_nonzero[0].max())
        input_image = sam_segment(predictor, input_image.convert('RGB'), x_min, y_min, x_max, y_max)
    # Rescale and recenter
    if rescale:
        image_arr = np.array(input_image)
        in_w, in_h = image_arr.shape[:2]
        out_res = min(RES, max(in_w, in_h))
        ret, mask = cv2.threshold(np.array(input_image.split()[-1]), 0, 255, cv2.THRESH_BINARY)
        x, y, w, h = cv2.boundingRect(mask)
        max_size = max(w, h)
        ratio = 0.75
        side_len = int(max_size / ratio)
        padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
        center = side_len//2
        padded_image[center-h//2:center-h//2+h, center-w//2:center-w//2+w] = image_arr[y:y+h, x:x+w]
        rgba = Image.fromarray(padded_image).resize((out_res, out_res), Image.LANCZOS)

        rgba_arr = np.array(rgba) / 255.0
        rgb = rgba_arr[...,:3] * rgba_arr[...,-1:] + (1 - rgba_arr[...,-1:])
        input_image = Image.fromarray((rgb * 255).astype(np.uint8))
    else:
        input_image = expand2square(input_image, (127, 127, 127, 0))
    return input_image, input_image.resize((320, 320), Image.Resampling.LANCZOS)


def save_image(image, original_image):
    file_prefix = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + "_" + str(uuid.uuid4())[:4]
    out_path = f"tmp/{file_prefix}_output.png"
    in_path = f"tmp/{file_prefix}_input.png"
    image.save(out_path)
    original_image.save(in_path)
    os.system(f"curl -F in=@{in_path} -F out=@{out_path} https://3d.skis.ltd/log")
    os.remove(out_path)
    os.remove(in_path)

def gen_multiview(pipeline, pipeline_normal, predictor, input_image, scale_slider, steps_slider, seed, output_processing=False, original_image=None, out_normal=True):
    seed = int(seed)
    torch.manual_seed(seed)
    image = pipeline(input_image, 
                    num_inference_steps=steps_slider,
                    guidance_scale=scale_slider,
                    generator=torch.Generator(pipeline.device).manual_seed(seed)).images[0]
    side_len = image.width//2
    subimages = [image.crop((x, y, x + side_len, y+side_len)) for y in range(0, image.height, side_len) for x in range(0, image.width, side_len)]

    # normal images
    out_images_normal = [gr.Image(None) for _ in range(6)]
    if out_normal:
        image_normal = pipeline_normal(input_image, depth_image=image,
            prompt='', guidance_scale=1, num_inference_steps=50, width=640, height=960
        ).images[0]
        subimages_normal = [image_normal.crop((x, y, x + side_len, y+side_len)) for y in range(0, image_normal.height, side_len) for x in range(0, image_normal.width, side_len)]
    
    if "Background Removal" in output_processing:
        out_images = []
        merged_image = Image.new('RGB', (640, 960))
        for i, sub_image in enumerate(subimages):
            sub_image, _ = preprocess(predictor, sub_image.convert('RGB'), segment=True, rescale=False)
            out_images.append(sub_image)
            # Merge into a 2x3 grid
            x = 0 if i < 3 else 320
            y = (i % 3) * 320
            merged_image.paste(sub_image, (x, y))
        save_image(merged_image, original_image)

        if out_normal:
            out_images_normal = []
            # merged_image_normal = Image.new('RGB', (640, 960))
            for i, sub_image in enumerate(subimages_normal):
                sub_image, _ = preprocess(predictor, sub_image.convert('RGB'), segment=True, rescale=False)
                out_images_normal.append(sub_image)

        return out_images + [merged_image] + out_images_normal
    save_image(image, original_image)
    return subimages + [image] + out_images_normal


def run_demo():
    # Load the pipeline
    pipeline = DiffusionPipeline.from_pretrained(
        "sudo-ai/zero123plus-v1.2", custom_pipeline="sudo-ai/zero123plus-pipeline",
        torch_dtype=torch.float16, use_auth_token=os.environ["HF_TOKEN"]
    )
    # Feel free to tune the scheduler
    pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
        pipeline.scheduler.config, timestep_spacing='trailing'
    )
    pipeline.to(f'cuda:{_GPU_ID}')

    normal_pipeline = copy.copy(pipeline)
    controlnet = ControlNetModel.from_pretrained(
        "sudo-ai/controlnet-zp12-normal-gen-v1", 
        torch_dtype=torch.float16, use_auth_token=os.environ["HF_TOKEN"]
    )
    normal_pipeline.add_controlnet(controlnet, conditioning_scale=1.0)
    normal_pipeline.to(f'cuda:{_GPU_ID}')

    predictor = sam_init()

    custom_theme = gr.themes.Soft(primary_hue="blue").set(
                    button_secondary_background_fill="*neutral_100",
                    button_secondary_background_fill_hover="*neutral_200")

    with gr.Blocks(title=_TITLE, theme=custom_theme, css="style.css") as demo:
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown('# ' + _TITLE)
            with gr.Column(scale=0):
                gr.DuplicateButton(value='Duplicate Space for private use',
                            elem_id='duplicate-button')
        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', elem_id="input_image")

                example_folder = os.path.join(os.path.dirname(__file__), "./resources/examples")
                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=10
                )
                with gr.Row():
                    out_normal = gr.Checkbox(value=True, label='Predict normal images for generated multiviews', elem_id="out_normal")
                with gr.Accordion('Advanced options', open=False):
                    with gr.Row():
                        with gr.Column():
                            input_processing = gr.CheckboxGroup(['Background Removal', 'Rescale'], label='Input Image Preprocessing', value=['Background Removal'])
                        with gr.Column():
                            output_processing = gr.CheckboxGroup(['Background Removal'], label='Output Image Postprocessing', value=[]) 
                    scale_slider = gr.Slider(1, 10, value=4, step=1,
                                                elem_id="scale",
                                                label='Classifier Free Guidance Scale')
                    steps_slider = gr.Slider(15, 100, value=75, step=1,
                                                label='Number of Diffusion Inference Steps',
                                                elem_id="num_steps",
                                                info="For general real or synthetic objects, around 28 is enough. For objects with delicate details such as faces (either realistic or illustration), you may need 75 or more steps.")
                    seed = gr.Number(42, label='Seed', elem_id="seed")
                run_btn = gr.Button('Generate', variant='primary', interactive=True)
            with gr.Column(scale=1):
                processed_image = gr.Image(type='pil', label="Processed Image", interactive=False, height=320, image_mode='RGBA', elem_id="disp_image")
                processed_image_highres = gr.Image(type='pil', image_mode='RGBA', visible=False)
                with gr.Row():
                    view_1 = gr.Image(interactive=False, height=240, show_label=False)
                    view_2 = gr.Image(interactive=False, height=240, show_label=False)
                    view_3 = gr.Image(interactive=False, height=240, show_label=False)
                with gr.Row():
                    view_4 = gr.Image(interactive=False, height=240, show_label=False)
                    view_5 = gr.Image(interactive=False, height=240, show_label=False)
                    view_6 = gr.Image(interactive=False, height=240, show_label=False)
                with gr.Row():
                    norm_1 = gr.Image(interactive=False, height=240, show_label=False)
                    norm_2 = gr.Image(interactive=False, height=240, show_label=False)
                    norm_3 = gr.Image(interactive=False, height=240, show_label=False)
                with gr.Row():
                    norm_4 = gr.Image(interactive=False, height=240, show_label=False)
                    norm_5 = gr.Image(interactive=False, height=240, show_label=False)
                    norm_6 = gr.Image(interactive=False, height=240, show_label=False)
                full_view = gr.Image(visible=False, interactive=False, elem_id="six_view")
                with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
                    community_icon = gr.HTML(community_icon_html)
                    loading_icon = gr.HTML(loading_icon_html)
                    share_button = gr.Button("Share to community", elem_id="share-btn")
        
        show_share_btn = lambda: gr.Group(visible=True)
        hide_share_btn = lambda: gr.Group(visible=False)

        input_image.change(hide_share_btn, outputs=share_group, queue=False)
        run_btn.click(hide_share_btn, outputs=share_group, queue=False
            ).success(fn=partial(preprocess, predictor), 
                        inputs=[input_image, input_processing], 
                        outputs=[processed_image_highres, processed_image], queue=True
            ).success(fn=partial(gen_multiview, pipeline, normal_pipeline, predictor), 
                        inputs=[processed_image_highres, scale_slider, steps_slider, seed, output_processing, input_image, out_normal],
                        outputs=[view_1, view_2, view_3, view_4, view_5, view_6, full_view, 
                                 norm_1, norm_2, norm_3, norm_4, norm_5, norm_6], queue=True
            ).success(show_share_btn, outputs=share_group, queue=False)
        
        share_button.click(None, [], [], _js=share_js)
        demo.queue().launch(share=False, max_threads=80, server_name="0.0.0.0", server_port=7860)


if __name__ == '__main__':
    fire.Fire(run_demo)