Spaces:
Runtime error
Runtime error
File size: 5,624 Bytes
d758270 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
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") |