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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() | |