app.py

import spaces
import argparse
import numpy as np
import gradio as gr
from omegaconf import OmegaConf
import torch
from PIL import Image
import PIL
from pipelines import TwoStagePipeline
from huggingface_hub import hf_hub_download
import os
from typing import Any
import json
import os
import json
import argparse

from model import CRM
from inference import generate3d
from dis_bg_remover import remove_background as dis_remove_background

# Configurable ONNX model path (can be set via environment variable)
DIS_ONNX_MODEL_PATH = os.environ.get("DIS_ONNX_MODEL_PATH", "isnet_dis.onnx")

pipeline = None


def expand_to_square(image, bg_color=(0, 0, 0, 0)):
    # expand image to 1:1
    width, height = image.size
    if width == height:
        return image
    new_size = (max(width, height), max(width, height))
    new_image = Image.new("RGBA", new_size, bg_color)
    paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
    new_image.paste(image, paste_position)
    return new_image

def check_input_image(input_image):
    if input_image is None:
        raise gr.Error("No image uploaded!")


def remove_background(
    image: PIL.Image.Image,
    rembg_session: Any = None,
    force: bool = False,
    **rembg_kwargs,
) -> PIL.Image.Image:
    # Check if the ONNX model exists
    if not os.path.exists(DIS_ONNX_MODEL_PATH):
        raise gr.Error(
            f"DIS background remover model file not found at {DIS_ONNX_MODEL_PATH}. "
            "Please download it from https://huggingface.co/stoned0651/isnet_dis.onnx/resolve/main/isnet_dis.onnx "
            "and place it in the project directory or set the DIS_ONNX_MODEL_PATH environment variable."
        )
    extracted_img, mask = dis_remove_background(DIS_ONNX_MODEL_PATH, image)
    return extracted_img

def do_resize_content(original_image: Image, scale_rate):
    # resize image content wile retain the original image size
    if scale_rate != 1:
        # Calculate the new size after rescaling
        new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
        # Resize the image while maintaining the aspect ratio
        resized_image = original_image.resize(new_size)
        # Create a new image with the original size and black background
        padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
        paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
        padded_image.paste(resized_image, paste_position)
        return padded_image
    else:
        return original_image

def add_background(image, bg_color=(255, 255, 255)):
    # given an RGBA image, alpha channel is used as mask to add background color
    background = Image.new("RGBA", image.size, bg_color)
    return Image.alpha_composite(background, image)


def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
    """
    input image is a pil image in RGBA, return RGB image
    """
    print(background_choice)
    if background_choice == "Alpha as mask":
        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
        image = Image.alpha_composite(background, image)
    else:
        image = remove_background(image, force=True)
        if image is None:
            raise gr.Error("Background removal failed. Please check the input image and ensure the model file exists and is valid.")
    image = do_resize_content(image, foreground_ratio)
    image = expand_to_square(image)
    image = add_background(image, backgroud_color)
    return image.convert("RGB")

@spaces.GPU
def gen_image(input_image, seed, scale, step):
    global pipeline, model, args
    pipeline.set_seed(seed)
    rt_dict = pipeline(input_image, scale=scale, step=step)
    stage1_images = rt_dict["stage1_images"]
    stage2_images = rt_dict["stage2_images"]
    np_imgs = np.concatenate(stage1_images, 1)
    np_xyzs = np.concatenate(stage2_images, 1)

    glb_path = generate3d(model, np_imgs, np_xyzs, args.device)
    return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path#, obj_path


parser = argparse.ArgumentParser()
parser.add_argument(
    "--stage1_config",
    type=str,
    default="configs/nf7_v3_SNR_rd_size_stroke.yaml",
    help="config for stage1",
)
parser.add_argument(
    "--stage2_config",
    type=str,
    default="configs/stage2-v2-snr.yaml",
    help="config for stage2",
)

parser.add_argument("--device", type=str, default="cuda")
args = parser.parse_args()

crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
specs = json.load(open("configs/specs_objaverse_total.json"))
model = CRM(specs)
model.load_state_dict(torch.load(crm_path, map_location="cpu"), strict=False)
model = model.to(args.device)

stage1_config = OmegaConf.load(args.stage1_config).config
stage2_config = OmegaConf.load(args.stage2_config).config
stage2_sampler_config = stage2_config.sampler
stage1_sampler_config = stage1_config.sampler

stage1_model_config = stage1_config.models
stage2_model_config = stage2_config.models

xyz_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="ccm-diffusion.pth")
pixel_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth")
stage1_model_config.resume = pixel_path
stage2_model_config.resume = xyz_path

pipeline = TwoStagePipeline(
    stage1_model_config,
    stage2_model_config,
    stage1_sampler_config,
    stage2_sampler_config,
    device=args.device,
    dtype=torch.float32
)

_DESCRIPTION = '''
* Our [official implementation](https://github.com/thu-ml/CRM) uses UV texture instead of vertex color. It has better texture than this online demo.
* Project page of CRM: https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/
* If you find the output unsatisfying, try using different seeds:)
'''

with gr.Blocks() as demo:
    gr.Markdown("# CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model")
    gr.Markdown(_DESCRIPTION)
    with gr.Row():
        with gr.Column():
            with gr.Row():
                image_input = gr.Image(
                    label="Image input",
                    image_mode="RGBA",
                    sources="upload",
                    type="pil",
                )
                processed_image = gr.Image(label="Processed Image", interactive=False, type="pil", image_mode="RGB")
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        background_choice = gr.Radio([
                                "Alpha as mask",
                                "Auto Remove background"
                            ], value="Auto Remove background",
                            label="backgroud choice")
                        # do_remove_background = gr.Checkbox(label=, value=True)
                        # force_remove = gr.Checkbox(label=, value=False)
                    back_groud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=False)
                    foreground_ratio = gr.Slider(
                        label="Foreground Ratio",
                        minimum=0.5,
                        maximum=1.0,
                        value=1.0,
                        step=0.05,
                    )

                with gr.Column():
                    seed = gr.Number(value=1234, label="seed", precision=0)
                    guidance_scale = gr.Number(value=5.5, minimum=3, maximum=10, label="guidance_scale")
                    step = gr.Number(value=30, minimum=30, maximum=100, label="sample steps", precision=0)
            text_button = gr.Button("Generate 3D shape")
            gr.Examples(
                examples=[os.path.join("examples", i) for i in os.listdir("examples")],
                inputs=[image_input],
                examples_per_page = 20,
            )
        with gr.Column():
            image_output = gr.Image(interactive=False, label="Output RGB image")
            xyz_ouput = gr.Image(interactive=False, label="Output CCM image")

            output_model = gr.Model3D(
                label="Output OBJ",
                interactive=False,
            )
            gr.Markdown("Note: Ensure that the input image is correctly pre-processed into a grey background, otherwise the results will be unpredictable.")

    inputs = [
        processed_image,
        seed,
        guidance_scale,
        step,
    ]
    outputs = [
        image_output,
        xyz_ouput,
        output_model,
        # output_obj,
    ]


    text_button.click(fn=check_input_image, inputs=[image_input]).success(
        fn=preprocess_image,
        inputs=[image_input, background_choice, foreground_ratio, back_groud_color],
        outputs=[processed_image],
    ).success(
        fn=gen_image,
        inputs=inputs,
        outputs=outputs,
    )
    demo.queue().launch()