import argparse import os import json import torch import sys import time import importlib import numpy as np from omegaconf import OmegaConf from huggingface_hub import hf_hub_download from collections import OrderedDict import trimesh from einops import repeat, rearrange import pytorch_lightning as pl from typing import Dict, Optional, Tuple, List import gradio as gr from utils import * proj_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(os.path.join(proj_dir)) import tempfile import spaces import craftsman from craftsman.systems.base import BaseSystem from craftsman.utils.config import ExperimentConfig, load_config from mv_models import GenMVImage _TITLE = '''CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner''' _DESCRIPTION = '''
Select or upload a image, then just click 'Generate'.
By mimicking the artist/craftsman modeling workflow, we propose CraftsMan (aka 匠心) that uses 3D Latent Set Diffusion Model that directly generate coarse meshes, then a multi-view normal enhanced image generation model is used to refine the mesh. We provide the coarse 3D diffusion part here.
If you found Crafts is helpful, please help to ⭐ the Github Repo. Thanks!
*please note that the model is fliped due to the gradio viewer, please download the obj file and you will get the correct mesh.
*If you have your own multi-view images, you can directly upload it.
''' _CITE_ = r""" --- 📝 **Citation** If you find our work useful for your research or applications, please cite using this bibtex: ```bibtex @article{craftsman, author = {Weiyu Li and Jiarui Liu and Rui Chen and Yixun Liang and Xuelin Chen and Ping Tan and Xiaoxiao Long}, title = {CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner}, journal = {arxiv:xxx}, year = {2024}, } ``` 🤗 **Acknowledgements** We use Instant Meshes to remesh the generated mesh to a lower face count, thanks to the authors for the great work. 📋 **License** CraftsMan is under [AGPL-3.0](https://www.gnu.org/licenses/agpl-3.0.en.html), so any downstream solution and products (including cloud services) that include CraftsMan code or a trained model (both pretrained or custom trained) inside it should be open-sourced to comply with the AGPL conditions. If you have any questions about the usage of CraftsMan, please contact us first. 📧 **Contact** If you have any questions, feel free to open a discussion or contact us at weiyuli.cn@gmail.com. """ model = None cached_dir = None @spaces.GPU def image2mesh(view_front: np.ndarray, view_right: np.ndarray, view_back: np.ndarray, view_left: np.ndarray, more: bool = False, scheluder_name: str ="DDIMScheduler", guidance_scale: int = 7.5, seed: int = 4, octree_depth: int = 7): sample_inputs = { "mvimages": [[ Image.fromarray(view_front), Image.fromarray(view_right), Image.fromarray(view_back), Image.fromarray(view_left) ]] } global model latents = model.sample( sample_inputs, sample_times=1, guidance_scale=guidance_scale, return_intermediates=False, seed=seed )[0] # decode the latents to mesh box_v = 1.1 mesh_outputs, _ = model.shape_model.extract_geometry( latents, bounds=[-box_v, -box_v, -box_v, box_v, box_v, box_v], octree_depth=octree_depth ) assert len(mesh_outputs) == 1, "Only support single mesh output for gradio demo" mesh = trimesh.Trimesh(mesh_outputs[0][0], mesh_outputs[0][1]) # filepath = f"{cached_dir}/{time.time()}.obj" filepath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name mesh.export(filepath, include_normals=True) if 'Remesh' in more: remeshed_filepath = tempfile.NamedTemporaryFile(suffix=f"_remeshed.obj", delete=False).name print("Remeshing with Instant Meshes...") target_face_count = int(len(mesh.faces)/10) # command = f"{proj_dir}/apps/third_party/InstantMeshes {filepath} -f {target_face_count} -d -S 0 -r 6 -p 6 -o {filepath.replace('.obj', '_remeshed.obj')}" # command = f"{proj_dir}/apps/third_party/InstantMeshes {filepath} -f {target_face_count} -d -S 0 -r 4 -p 4 -o {filepath.replace('.obj', '_remeshed.obj')}" # command = f"{proj_dir}/apps/third_party/InstantMeshes {filepath} -f {target_face_count} -o {filepath.replace('.obj', '_remeshed.obj')}" command = f"{proj_dir}/apps/third_party/quadriflow -i {filepath} -f {target_face_count} -o {remeshed_filepath}" os.system(command) filepath = remeshed_filepath # filepath = filepath.replace('.obj', '_remeshed.obj') return filepath if __name__=="__main__": parser = argparse.ArgumentParser() # parser.add_argument("--model_path", type=str, required=True, help="Path to the object file",) parser.add_argument("--cached_dir", type=str, default="./gradio_cached_dir") parser.add_argument("--device", type=int, default=0) args = parser.parse_args() cached_dir = args.cached_dir os.makedirs(args.cached_dir, exist_ok=True) device = torch.device(f"cuda:{args.device}" if torch.cuda.is_available() else "cpu") print(f"using device: {device}") # for multi-view images generation background_choice = OrderedDict({ "Alpha as Mask": "Alpha as Mask", "Auto Remove Background": "Auto Remove Background", "Original Image": "Original Image", }) mvimg_model_config_list = ["CRM", "ImageDream", "Wonder3D"] # for 3D latent set diffusion # for 3D latent set diffusion ckpt_path = hf_hub_download(repo_id="wyysf/CraftsMan", filename="image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/model.ckpt", repo_type="model") config_path = hf_hub_download(repo_id="wyysf/CraftsMan", filename="image-to-shape-diffusion/clip-mvrgb-modln-l256-e64-ne8-nd16-nl6/config.yaml", repo_type="model") scheluder_dict = OrderedDict({ "DDIMScheduler": 'diffusers.schedulers.DDIMScheduler', # "DPMSolverMultistepScheduler": 'diffusers.schedulers.DPMSolverMultistepScheduler', # not support yet # "UniPCMultistepScheduler": 'diffusers.schedulers.UniPCMultistepScheduler', # not support yet }) # main GUI custom_theme = gr.themes.Soft(primary_hue="blue").set( button_secondary_background_fill="*neutral_100", button_secondary_background_fill_hover="*neutral_200") custom_css = '''#disp_image { text-align: center; /* Horizontally center the content */ }''' with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo: with gr.Row(): with gr.Column(scale=1): gr.Markdown('# ' + _TITLE) gr.Markdown(_DESCRIPTION) with gr.Row(): with gr.Column(scale=2): with gr.Row(): image_input = gr.Image( label="Image Input", image_mode="RGBA", sources="upload", type="pil", ) with gr.Row(): text = gr.Textbox(label="Prompt (Optional, only works for mvdream)", visible=False) with gr.Row(): gr.Markdown('''Try a different seed if the result is unsatisfying. Good Luck :)''') with gr.Row(): seed = gr.Number(42, label='Seed', show_label=True) more = gr.CheckboxGroup(["Remesh", "Symmetry(TBD)"], label="More", show_label=False) # remesh = gr.Checkbox(value=False, label='Remesh') # symmetry = gr.Checkbox(value=False, label='Symmetry(TBD)', interactive=False) run_btn = gr.Button('Generate', variant='primary', interactive=True) with gr.Row(): gr.Examples( examples=[os.path.join("./apps/examples", i) for i in os.listdir("./apps/examples")], inputs=[image_input], examples_per_page=8 ) with gr.Column(scale=4): with gr.Row(): output_model_obj = gr.Model3D( label="Output Model (OBJ Format)", camera_position=(90.0, 90.0, 3.5), interactive=False, ) with gr.Row(): view_front = gr.Image(label="Front", interactive=True, show_label=True) view_right = gr.Image(label="Right", interactive=True, show_label=True) view_back = gr.Image(label="Back", interactive=True, show_label=True) view_left = gr.Image(label="Left", interactive=True, show_label=True) with gr.Accordion('Advanced options', open=False): with gr.Row(equal_height=True): run_mv_btn = gr.Button('Only Generate 2D', interactive=True) run_3d_btn = gr.Button('Only Generate 3D', interactive=True) with gr.Accordion('Advanced options (2D)', open=False): with gr.Row(): crop_size = gr.Number(224, label='Crop size') mvimg_model = gr.Dropdown(value="CRM", label="MV Image Model", choices=mvimg_model_config_list) with gr.Row(): foreground_ratio = gr.Slider( label="Foreground Ratio", minimum=0.5, maximum=1.0, value=1.0, step=0.05, ) with gr.Row(): background_choice = gr.Dropdown(label="Backgroud Choice", value="Auto Remove Background",choices=list(background_choice.keys())) rmbg_type = gr.Dropdown(label="Backgroud Remove Type", value="rembg",choices=['sam', "rembg"]) backgroud_color = gr.ColorPicker(label="Background Color", value="#FFFFFF", interactive=True) with gr.Row(): mvimg_guidance_scale = gr.Number(value=3.5, minimum=3, maximum=10, label="2D Guidance Scale") mvimg_steps = gr.Number(value=50, minimum=20, maximum=100, label="2D Sample Steps", precision=0) with gr.Accordion('Advanced options (3D)', open=False): with gr.Row(): guidance_scale = gr.Number(label="3D Guidance Scale", value=7.5, minimum=3.0, maximum=10.0) steps = gr.Number(value=50, minimum=20, maximum=100, label="3D Sample Steps", precision=0) with gr.Row(): scheduler = gr.Dropdown(label="scheluder", value="DDIMScheduler",choices=list(scheluder_dict.keys())) octree_depth = gr.Slider(label="Octree Depth", value=7, minimum=4, maximum=8, step=1) gr.Markdown(_CITE_) outputs = [output_model_obj] rmbg = RMBG(device) gen_mvimg = GenMVImage(device) model = load_model(ckpt_path, config_path, device) run_btn.click(fn=check_input_image, inputs=[image_input] ).success( fn=rmbg.run, inputs=[rmbg_type, image_input, crop_size, foreground_ratio, background_choice, backgroud_color], outputs=[image_input] ).success( fn=gen_mvimg.run, inputs=[mvimg_model, text, image_input, crop_size, seed, mvimg_guidance_scale, mvimg_steps], outputs=[view_front, view_right, view_back, view_left] ).success( fn=image2mesh, inputs=[view_front, view_right, view_back, view_left, more, scheduler, guidance_scale, seed, octree_depth], outputs=outputs, api_name="generate_img2obj") run_mv_btn.click(fn=gen_mvimg.run, inputs=[mvimg_model, text, image_input, crop_size, seed, mvimg_guidance_scale, mvimg_steps], outputs=[view_front, view_right, view_back, view_left] ) run_3d_btn.click(fn=image2mesh, inputs=[view_front, view_right, view_back, view_left, more, scheduler, guidance_scale, seed, octree_depth], outputs=outputs, api_name="generate_img2obj") demo.queue().launch(share=True, allowed_paths=[args.cached_dir])