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ckpts/README.md

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----
-library_name: craftsman-v1-5
-license: creativeml-openrail-m
-license_name: creativeml-openrail-m
-license_link: https://raw.githubusercontent.com/CompVis/stable-diffusion/refs/heads/main/LICENSE
-pipeline_tag: image-to-3d
-language:
-- en
-- zh
----
-
-## **CraftsMan-v1-5**
-
-<p align="center">
-  <img src="./assets/teaser.png"  height=200>
-</p>
-
-### <div align="center">CraftsMan: High-fidelity Mesh Generation <br> with 3D Native Generation and Interactive Geometry Refiner<div> 
-#####  <p align="center"> [Weiyu Li<sup>*1,2</sup>](https://wyysf-98.github.io/), Jiarui Liu<sup>*1,2</sup>, Hongyu Yan<sup>*1,2</sup>, [Rui Chen<sup>1,2</sup>](https://aruichen.github.io/), [Yixun Liang<sup>2,3</sup>](https://yixunliang.github.io/), [Xuelin Chen<sup>4</sup>](https://xuelin-chen.github.io/), [Ping Tan<sup>1,2</sup>](https://ece.hkust.edu.hk/pingtan), [Xiaoxiao Long<sup>1,2</sup>](https://www.xxlong.site/)</p>
-#####  <p align="center"> <sup>1</sup>HKUST, <sup>2</sup>LightIllusions, <sup>3</sup>HKUST(GZ), <sup>4</sup>Tencent AI Lab</p>
-<div align="center">
-  <a href="https://craftsman3d.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp;
-  <a href="http://algodemo.bj.lightions.top:24926"><img src="https://www.gradio.app/_app/immutable/assets/gradio.CHB5adID.svg" height="25"/></a> &ensp;
-  <a href="https://arxiv.org/pdf/2405.14979"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv&color=red&logo=arxiv"></a> &ensp;
-</div>
-
-# Usage
-
-To use the model, please refer to the [official repository](https://github.com/wyysf-98/CraftsMan) for installation and usage instructions.
-
-```
-
-from craftsman import CraftsManPipeline
-import torch
-
-pipeline = CraftsManPipeline.from_pretrained("./ckpts/craftsman-v1-5", device="cuda:0", torch_dtype=torch.float32) # load from local ckpt
-mesh = pipeline("https://pub-f9073a756ec645d692ce3d171c2e1232.r2.dev/data/werewolf.png").meshes[0]
-mesh.export("werewolf.obj")
-
-```
-
-## 🔥🔥🔥 News!!
-
-* Nov 16, 2024: 💬 We release the CraftsMan-v1-5
-
-
-## 📑 Open-source Plan
-
-- [x] Inference 
-- [x] Checkpoints
-- [x] Training
-- [ ] ComfyUI
-
-## 🎉 **CraftMan-v1-5 Architecture**
-
-<p align="center">
-  <img src="./assets/arch.png"  height=400>
-</p>
-
-
-## Get Started
-
-#### Begin by cloning the repository:
-
-```shell
-git clone https://github.com/wyysf-98/CraftsMan
-cd CraftsMan
-```
-
-#### Installation Guide for Linux
-
-We provide an env_install.sh script file for setting up environment. 
-
-```
-# step 1, create conda env
-conda create -n CraftsMan python=3.10
-conda activate CraftsMan
-
-
-# step 2. install torch realated package
-conda install -c pytorch pytorch=2.3.0 torchvision=0.18.0 cudatoolkit=11.8
-
-# step 3. install other packages
-pip install -r docker/requirements.txt
-```
-<details>
-
-
-#### Using Gradio
-
-We have prepared a gradio demo for you to try out the model. You can run the following command to start the demo.
-
-```shell
-# std 
-python3 gradio.py
-```
-
-Then the demo can be accessed through the output link.
-
-
-## Citation
-
-If you found this repository helpful, please cite our report:
-```bibtex
-@misc{li2024craftsman,
-title         = {CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner}, 
-author        = {Weiyu Li and Jiarui Liu and Rui Chen and Yixun Liang and Xuelin Chen and Ping Tan and Xiaoxiao Long},
-year          = {2024},
-archivePrefix = {arXiv preprint arXiv:2405.14979},
-primaryClass  = {cs.CG}
-}
-```
-
-
-# License
-
-[creativeml-openrail-m](https://raw.githubusercontent.com/CompVis/stable-diffusion/refs/heads/main/LICENSE)

ckpts/craftsman-v1-5

@@ -1 +0,0 @@
-Subproject commit 9a5e9189c2dfab20cf838885dd6acaf99b41844e

server.py

@@ -1,98 +0,0 @@
-import argparse
-import base64
-import os
-from datetime import datetime
-import traceback
-import torch
-import trimesh
-from craftsman import CraftsManPipeline
-
-CURRENT_DIR = f'/tmp/native3d_server/{os.getpid()}'
-os.makedirs(CURRENT_DIR, exist_ok=True)
-
-def parse_parameters():
-    parser = argparse.ArgumentParser("native3d")
-    parser.add_argument('--host', default="0.0.0.0", type=str)
-    parser.add_argument('--port', default=80, type=int)
-    return parser.parse_args()
-    
-# -------------------- fastapi --------------------
-from typing import Optional
-from pydantic import BaseModel, Field
-
-class Native3DRequestV1(BaseModel):
-    image_path: str  # input image path
-    mesh_path: str  # output mesh path, support glb or obj in clean dir
-
-class Native3DResponseV1(BaseModel):
-    pass
-
-class Native3DRequestV2(BaseModel):
-    image_bytes: str  # input image bytes(base64)
-    mesh_type: str  # output mesh type, support glb or obj
-
-class Native3DResponseV2(BaseModel):
-    mesh_bytes: str  # output mesh bytes(base64)
-
-if __name__=="__main__":
-    parse_args = parse_parameters()
-
-    # prepare models
-    pipeline = CraftsManPipeline.from_pretrained("ckpts/craftsman-v1-5", device="cuda:0", torch_dtype=torch.float32)
-
-    # -------------------- fastapi --------------------
-    from fastapi import FastAPI, Request
-    import requests
-    app = FastAPI()
-
-    @app.post("/native3d_v1", response_model=Native3DResponseV1)
-    async def native3d(request: Request, image_to_mesh_request: Native3DRequestV1):
-        try:
-            print(f"image_to_mesh_request = {image_to_mesh_request}")
-            mesh = pipeline(image_to_mesh_request.image_path).meshes[0]
-            os.makedirs(os.path.dirname(os.path.abspath(image_to_mesh_request.mesh_path)), exist_ok=True)
-            mesh.export(image_to_mesh_request.mesh_path)
-        except Exception as e:
-            traceback.print_exc()
-            print(f"generate_model error: {e}")
-        return Native3DResponseV1()
-
-    @app.post("/native3d_v2", response_model=Native3DResponseV2)
-    async def native3d(request: Request, image_to_mesh_request: Native3DRequestV2):
-        try:
-            # print(f"image_to_mesh_request = {image_to_mesh_request}")
-            mesh_type = image_to_mesh_request.mesh_type
-            assert mesh_type in ['obj', 'glb']
-            task_id = datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f') + '-' + 'native3d'
-            current_dir = os.path.join(CURRENT_DIR, task_id)
-            os.makedirs(current_dir, exist_ok=True)
-            image_path = os.path.join(current_dir, 'input_image.png')
-            with open(image_path, 'wb') as f:
-                f.write(base64.b64decode(image_to_mesh_request.image_bytes))
-            mesh_path = os.path.join(current_dir, f'output_mesh.{mesh_type}')
-            import time
-            start = time.time()
-            # mesh = pipeline(image_path, mc_depth=8, num_inference_steps=25).meshes[0]
-            # mesh = pipeline(image_path, mc_depth=7, num_inference_steps=25).meshes[0]
-            mesh = pipeline(image_path, mc_depth=7, num_inference_steps=50).meshes[0]
-            print(f"Time: {time.time() - start}s")
-            os.makedirs(os.path.dirname(os.path.abspath(mesh_path)), exist_ok=True)
-            mesh.visual = trimesh.visual.TextureVisuals(
-                material=trimesh.visual.material.PBRMaterial(
-                    baseColorFactor=(255, 255, 255), main_color=(255, 255, 255), metallicFactor=0.05, roughnessFactor=1.0
-                )
-            )
-            mesh.export(mesh_path)
-            with open(mesh_path, 'rb') as f:
-                mesh_bytes = f.read()
-        except Exception as e:
-            traceback.print_exc()
-            print(f"generate_model error: {e}")
-        return Native3DResponseV2(mesh_bytes=base64.b64encode(mesh_bytes).decode('utf-8'))
-
-    @app.get("/health")
-    async def health():
-        return {"status": "OK"}
-
-    import uvicorn
-    uvicorn.run(app, host=parse_args.host, port=parse_args.port)