| --- |
| license: cc-by-nc-4.0 |
| base_model: TencentARC/Pixal3D |
| pipeline_tag: image-to-3d |
| tags: |
| - image-to-3d |
| - 3d-generation |
| - mesh |
| - lora |
| - distillation |
| - pixal3d |
| --- |
| |
| # Sprint3D student weights |
|
|
| Sprint3D is a fast image-to-3D asset pipeline built on the publicly released |
| [Pixal3D](https://github.com/TencentARC/Pixal3D) pipeline. It rebuilds the mesh post-processing on |
| the GPU and replaces the four rectified flows of Pixal3D by distilled *students*, cutting the median |
| time per asset from 133 s to 4.05 s on one H200 GPU at the same export settings (33×). |
|
|
| This repository holds only the students: four LoRA adapters (EMA weights) on top of the Pixal3D flow |
| models, plus `students.json`, which records for each student its base flow model, the LoRA layout and |
| the sampling schedule. The Pixal3D base model, the DINOv3 encoder and the other upstream models are |
| not redistributed here; the code fetches them from their original repositories at pinned revisions. |
|
|
| - Code: https://github.com/AlayaLab/sprint3d |
| - Project page: https://alaya-lab.github.io/sprint3d/ |
| - Technical report: [arXiv:2611.16701](https://arxiv.org/abs/2611.16701), *Sprint3D: Accelerating End-to-End 3D Asset Generation through Pipeline Optimization* |
|
|
| ## Files |
|
|
| | File | Student | Steps | Size | sha256 | |
| |---|---|---|---|---| |
| | `structure_student.safetensors` | sparse structure flow (`ss_flow_img_dit_1_3B_64_bf16`) | 4 | 1,274,085,008 B | `a938130271c96eaaf67643bb428aa2aa8e53033b09608ea3eedc5531d9b97396` | |
| | `coarse_shape_student.safetensors` | coarse shape flow (`slat_flow_img2shape_dit_1_3B_512_bf16`) | 1 | 1,305,542,328 B | `59be306522816b4d1defa525cfecf3f9666a161902b245fee387cd7a34fb9a66` | |
| | `fine_shape_student.safetensors` | fine shape flow (`slat_flow_img2shape_dit_1_3B_1024_bf16`) | 1 | 1,305,542,328 B | `6beb37c82f5f618f84d9bc988564d41e3971ff85c42ef06217e580186e7dd583` | |
| | `texture_student.safetensors` | texture flow (`slat_flow_imgshape2tex_dit_1_3B_1024_bf16`) | 2 | 1,305,542,328 B | `ecc4ae096358120cde9ef2a6bbdff8bfee5c04e5f3bc49db84183d0f7d37887c` | |
| | `students.json` | metadata: base model revision, LoRA layout, schedules, hashes | | 6,362 B | `310db80728d8f0ff5c95ae73ce43bb3c544a0560d7812ba339e099683cd311e5` | |
|
|
| Each adapter has rank 256 (alpha 256) on the attention and MLP linears of all 30 transformer blocks |
| (240 adapters, 480 tensors), stored in float32. The students run 4 + 1 + 1 + 2 = 8 network |
| evaluations in place of the 76 of the released schedules. Base model: `TencentARC/Pixal3D` at |
| revision `0b31f9160aa400719af409098bff7936a932f726` (MIT). |
|
|
| ## Use |
|
|
| The intended way to use these files is through the code repository, which downloads every weight at |
| its pinned revision and verifies sizes and sha256 values: |
|
|
| ```bash |
| git clone --recursive https://github.com/AlayaLab/sprint3d.git |
| cd sprint3d |
| python scripts/download_weights.py # students + upstream models, about 32.6 GB |
| python scripts/verify_weights.py |
| ``` |
|
|
| `students.json` documents the key convention (`backbone.model.<path>.lora_A.weight` / |
| `lora_B.weight`, update `(alpha / rank) * lora_B @ lora_A`) and the fold rule, so the adapters can |
| also be applied to the Pixal3D flow models without this code. |
|
|
| ## License |
|
|
| The student weights are released under **CC BY-NC 4.0**. They were trained on subsets of the |
| TRELLIS-500K object lists; parts of that data (HSSD and ABO under CC BY-NC, 3D-FUTURE for research |
| use only) do not allow commercial use, and the weights inherit that restriction. The code is |
| Apache-2.0; the Pixal3D base model is MIT and is fetched separately. Sprint3D is not affiliated |
| with, endorsed by or reviewed by Tencent or the authors of Pixal3D. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{kuno2026sprint3d, |
| title = {Sprint3D: Accelerating End-to-End 3D Asset Generation through Pipeline Optimization}, |
| author = {Kuno, Sho and Yu, Ruihan and Yu, Yonghao and Lin, Guixu and Fu, Lian and Niu, Muyao and |
| Tsai, Yu-Ju and Huang, Zheng-Hui and Huang, Yi-Chuan and Yang, Siqi and Lan, Fengbo and |
| Wu, Erwin and Zhang, Kaipeng and Wang, Zhixiang}, |
| year = {2026}, |
| eprint = {2611.16701}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CV} |
| } |
| ``` |
|
|
