i

gradio_app.py

@@ -16,6 +16,7 @@ from einops import repeat, rearrange
 import pytorch_lightning as pl
 from typing import Dict, Optional, Tuple, List
 import gradio as gr
+from typing import Any
 
 proj_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.append(os.path.join(proj_dir))
@@ -232,10 +233,10 @@ if __name__=="__main__":
                     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', 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():

gradio_app_crm.py

@@ -1,279 +0,0 @@
-import spaces
-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
-
-proj_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(os.path.join(proj_dir))
-
-import tempfile
-import craftsman
-from craftsman.systems.base import BaseSystem
-from craftsman.utils.config import ExperimentConfig, load_config
-
-from apps.utils import *
-from apps.mv_models import GenMVImage
-
-_TITLE = '''CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner'''
-_DESCRIPTION = '''
-<div>
-Select or upload a image, then just click 'Generate'. 
-<br>
-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. 
-<br>
-If you found Crafts is helpful, please help to ⭐ the <a href='https://github.com/wyysf-98/CraftsMan/' target='_blank'>Github Repo</a>. Thanks!
-<a style="display:inline-block; margin-left: .5em" href='https://github.com/wyysf-98/CraftsMan/'><img src='https://img.shields.io/github/stars/wyysf-98/CraftsMan?style=social' /></a>
-<br>
-*please note that the model is fliped due to the gradio viewer, please download the obj file and you will get the correct mesh.
-<br>
-*If you have your own multi-view images, you can directly upload it.
-</div>
-'''
-_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 <a href='https://github.com/wjakob/instant-meshes' target='_blank'>Instant Meshes</a> 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 <b>weiyuli.cn@gmail.com</b>.
-"""
-
-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)
-        target_face_count = 1000
-        command = f"{proj_dir}/apps/third_party/InstantMeshes {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"]
-    mvimg_model_config_list = ["CRM", "ImageDream", "Wonder3D"]
-    
-    # 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 <b>seed</b> 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=30, 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])