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 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: Any = 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=True) 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) # Read the GLB file and encode it in base64 with open(glb_path, 'rb') as f: glb_bytes = f.read() encoded_glb = 'data:model/gltf-binary;base64,' + base64.b64encode(glb_bytes).decode('utf-8') # Return images and the encoded GLB data return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), encoded_glb 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()