Add model card

Model card for the Sprint3D student weights: files and hashes, use through the code repository, CC BY-NC 4.0 licence, citation.

README.md

@@ -0,0 +1,80 @@
+---
+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: *Sprint3D: Accelerating End-to-End 3D Asset Generation through Pipeline Optimization* (arXiv link to follow)
+
+## 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
+@article{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},
+  journal = {arXiv preprint},
+  year    = {2026}
+}
+```