BlueFox3D retrained characters (4-stage L4 pack)
This is not a replacement for Hydrilla/BlueFox3D.
Default BlueFox3D inference still uses that stock repo.
This pack is four 1536-cascade denoisers for a small character-like Objaverse-LVIS subset:
| Slot | Source | Retrained here? |
|---|---|---|
| SS-64 | last-4-block finetune from stock SS-64 | Yes (1000 steps) |
| Shape-512 | copy of Hydrilla/BlueFox3D-character-shape512 | No (not rerun) |
| Shape-1024 | last-4-block finetune from stock shape-1024 | Yes (1000 steps) |
| Tex-1024 | last-4-block finetune from stock tex-1024 | Yes (1000 steps) |
| SS / shape / tex VAEs | Hydrilla/BlueFox3D | No |
This is not a full retrain of BlueFox3D. Same 149 meshes, last 4 of 30 blocks + out_layer, 8-bit Adam, 1000 steps per new stage. Do not call it a quality win until a hold-out compare says so.
What was trained
| Item | Value |
|---|---|
| Trainable | last 4 transformer blocks + out_layer |
| Steps | 1000 per new stage (50-step VRAM smoke first) |
| Batch | 1 / GPU, max_tokens 2048 on 1024/tex |
| GPU | 1× NVIDIA L4 24 GB (sequential; DDP does not pool VRAM) |
| Optimizer | AdamW 8-bit, lr 1e-5, no EMA |
| Shape-512 | reused previous character finetune |
Dataset
Objaverse-LVIS only. We did not download the ~8.9 TB allenai/objaverse snapshot.
LVIS keys (no class named character): person, teddy bear, doll, figurine, statue, mannequin, puppet, action figure, toy soldier, snowman, scarecrow.
| Split | Count | Used? |
|---|---|---|
| Train GLBs | 150 downloaded, 149 dual-grid / SS / shape | Yes |
| PBR / tex | 120 (shader dump failed on the rest) | Tex-1024 only |
| Hold-out GLBs | 20 | Never |
Use
python inference.py --image assets/images/21_img.png --output ./pack.glb \
--seed 42 --low_vram --resolution 1536 --preset baseline \
--model_path Hydrilla/BlueFox3D-retrained-characters
--low_vram is required on 24 GB GPUs (~18–22 GB). Full 1536 without it needs ~40 GB+.
Stock (unchanged):
python inference.py --image assets/images/21_img.png --output ./stock.glb \
--seed 42 --low_vram --resolution 1536 --preset baseline \
--model_path Hydrilla/BlueFox3D
Files
pipeline.json— SS-64 / shape-512 / shape-1024 / tex-1024 from this repo; decoders fromHydrilla/BlueFox3Dckpts/ss_flow_img_dit_1_3B_64_bf16.safetensorsckpts/slat_flow_img2shape_dit_1_3B_512_bf16.safetensors(copied, not retrained)ckpts/slat_flow_img2shape_dit_1_3B_1024_bf16.safetensorsckpts/slat_flow_imgshape2tex_dit_1_3B_1024_bf16.safetensors
Code
Training and convert scripts live in HydrillaAI/Bluefox3D.
License / citation
MIT, same as BlueFox3D. Upstream: Pixal3D / TRELLIS.2.
@article{li2026pixal3d,
title={Pixal3D: Pixel-Aligned 3D Generation from Images},
author={Li, Dong-Yang and Zhao, Wang and Chen, Yuxin and Hu, Wenbo and Guo, Meng-Hao and Zhang, Fang-Lue and Shan, Ying and Hu, Shi-Min},
journal={arXiv preprint arXiv:2605.10922},
year={2026}
}