i

apps/mv_models.py

@@ -54,6 +54,8 @@ class GenMVImage(object):
     
     @spaces.GPU
     def gen_image_from_mvdream(self, image, text):
+        sys.path.append(f"{parent_dir}/apps/third_party/mvdream_diffusers")
+        
         from .third_party.mvdream_diffusers.pipeline_mvdream import MVDreamPipeline
         if image is None:
             if "mvdream" in self.pipelines.keys():

gradio_app copy.py

@@ -0,0 +1,279 @@
+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>[email protected]</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])

gradio_app.py

@@ -64,9 +64,26 @@ CraftsMan is under [AGPL-3.0](https://www.gnu.org/licenses/agpl-3.0.en.html), so
 📧 **Contact**
 If you have any questions, feel free to open a discussion or contact us at <b>[email protected]</b>.
 """
+from apps.third_party.CRM.pipelines import TwoStagePipeline
 
 model = None
 cached_dir = None
+stage1_config = OmegaConf.load(f"{parent_dir}/apps/third_party/CRM/configs/nf7_v3_SNR_rd_size_stroke.yaml").config
+stage1_sampler_config = stage1_config.sampler
+stage1_model_config = stage1_config.models
+stage1_model_config.resume = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth", repo_type="model")
+stage1_model_config.config = f"{parent_dir}/apps/third_party/CRM/" + stage1_model_config.config
+crm_pipeline = None
+
+@spaces.GPU
+def gen_mvimg(
+    mvimg_model, text, image, crop_size, seed, guidance_scale, step
+):
+    global crm_pipeline
+    crm_pipeline.set_seed(seed)
+    rt_dict = crm_pipeline(image, scale=guidance_scale, step=step)
+    mv_imgs = rt_dict["stage1_images"]
+    return mv_imgs[5], mv_imgs[3], mv_imgs[2], mv_imgs[0]
 
 @spaces.GPU
 def image2mesh(view_front: np.ndarray, 
@@ -134,7 +151,14 @@ if __name__=="__main__":
     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}")
-    
+
+    crm_pipeline = TwoStagePipeline(
+                        stage1_model_config,
+                        stage1_sampler_config,
+                        device=device,
+                        dtype=torch.float16
+                    )
+
     # for multi-view images generation
     background_choice = OrderedDict({ 
         "Alpha as Mask": "Alpha as Mask",
@@ -250,7 +274,7 @@ if __name__=="__main__":
         outputs = [output_model_obj]
         rmbg = RMBG(device)
         
-        gen_mvimg = GenMVImage(device)
+        # gen_mvimg = GenMVImage(device)
         model = load_model(ckpt_path, config_path, device)
 
         run_btn.click(fn=check_input_image, inputs=[image_input]
@@ -259,7 +283,7 @@ if __name__=="__main__":
                             inputs=[rmbg_type, image_input, crop_size, foreground_ratio, background_choice, backgroud_color],
                             outputs=[image_input]
                     ).success(
-                            fn=gen_mvimg.run,
+                            fn=gen_mvimg,
                             inputs=[mvimg_model, text, image_input, crop_size, seed, mvimg_guidance_scale, mvimg_steps],
                             outputs=[view_front, view_right, view_back, view_left]
                     ).success(
@@ -267,7 +291,7 @@ if __name__=="__main__":
                             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,
+        run_mv_btn.click(fn=gen_mvimg,
                         inputs=[mvimg_model, text, image_input, crop_size, seed, mvimg_guidance_scale, mvimg_steps],
                         outputs=[view_front, view_right, view_back, view_left]
         )