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run.py

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+import torch
+from libs.base_utils import do_resize_content
+from imagedream.ldm.util import (
+    instantiate_from_config,
+    get_obj_from_str,
+)
+from omegaconf import OmegaConf
+from PIL import Image
+import numpy as np
+from inference import generate3d
+from huggingface_hub import hf_hub_download
+import json
+import argparse
+import shutil
+from model import CRM
+import PIL
+import rembg
+import os
+from pipelines import TwoStagePipeline
+
+rembg_session = rembg.new_session()
+
+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 remove_background(
+    image: PIL.Image.Image,
+    rembg_session = None,
+    force: bool = False,
+    **rembg_kwargs,
+) -> PIL.Image.Image:
+    do_remove = True
+    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+        # explain why current do not rm bg
+        print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
+        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
+        image = Image.alpha_composite(background, image)
+        do_remove = False
+    do_remove = do_remove or force
+    if do_remove:
+        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+    return image
+
+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, rembg_session, force_remove=True)
+    image = do_resize_content(image, foreground_ratio)
+    image = expand_to_square(image)
+    image = add_background(image, backgroud_color)
+    return image.convert("RGB")
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--inputdir",
+        type=str,
+        default="examples/kunkun.webp",
+        help="dir for input image",
+    )
+    parser.add_argument(
+        "--scale",
+        type=float,
+        default=5.0,
+    )
+    parser.add_argument(
+        "--step",
+        type=int,
+        default=50,
+    )
+    parser.add_argument(
+        "--bg_choice",
+        type=str,
+        default="Auto Remove background",
+        help="[Auto Remove background] or [Alpha as mask]",
+    )
+    parser.add_argument(
+        "--outdir",
+        type=str,
+        default="out/",
+    )    
+    args = parser.parse_args()
+    
+
+    img = Image.open(args.inputdir)
+    img = preprocess_image(img, args.bg_choice, 1.0, (127, 127, 127))
+    os.makedirs(args.outdir, exist_ok=True)
+    img.save(args.outdir+"preprocessed_image.png")
+
+    crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
+    specs = json.load(open("configs/specs_objaverse_total.json"))
+    model = CRM(specs).to("cuda")
+    model.load_state_dict(torch.load(crm_path, map_location = "cuda"), strict=False)
+
+    stage1_config = OmegaConf.load("configs/nf7_v3_SNR_rd_size_stroke.yaml").config
+    stage2_config = OmegaConf.load("configs/stage2-v2-snr.yaml").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,
+    )
+
+    rt_dict = pipeline(img, scale=args.scale, step=args.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)
+    Image.fromarray(np_imgs).save(args.outdir+"pixel_images.png")
+    Image.fromarray(np_xyzs).save(args.outdir+"xyz_images.png")
+
+    glb_path, obj_path = generate3d(model, np_imgs, np_xyzs, "cuda")
+    shutil.copy(obj_path, args.outdir+"output3d.zip")