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# Not ready to use yet
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
import rembg
from typing import Any
import json
import os
import json
import argparse
from model import CRM
from inference import generate3d
pipeline = None
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 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 = 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")
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, obj_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).to(args.device)
model.load_state_dict(torch.load(crm_path, map_location = args.device), strict=False)
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.float16
)
with gr.Blocks() as demo:
gr.Markdown("# CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model")
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=50, 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],
)
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 GLB",
interactive=False,
)
gr.Markdown("Note: The GLB model shown here has a darker lighting and enlarged UV seams. Download for correct results.")
output_obj = gr.File(interactive=False, label="Output OBJ")
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()
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