---
license: other
license_name: minimax-h3-community-license
license_link: https://huggingface.co/Enderfga/SolarRenderer/blob/main/LICENSE
base_model: MiniMaxAI/MiniMax-H3
pipeline_tag: video-to-video
tags:
- video-generation
- video-to-video
- structure-conditioned
- lora
---
SolarRenderer: Few-Step Generative Reference Rendering
🌐 Project Page •
🎮 Online Demo •
📄 Paper •
💻 Code •
🤗 Data
**SolarRenderer** turns a structure video (grey box, semantic map, skeletons, depth, body mesh, or a sketch it never
saw in training) into a finished video, with an optional first frame and a prompt deciding the look. These are its
LoRAs on **MiniMax-H3** (Ref2VA transformer): a control LoRA and a four-step LoRA, each in a full-clip version and a
block-causal streaming version. Together they render a clip in **four steps**.
## Method
## Files
| File | What it is |
|---|---|
| `solarrenderer_control_lora.safetensors` | control LoRA, rank 384: the structure video enters H3's reference pathway, frame-aligned |
| `solarrenderer_4step_lora.safetensors` | four-step LoRA, rank 384: fine-tuned from [DMAD](https://huggingface.co/ZhengmingYu/DMAD) by structure-guided distribution matching |
| `solarrenderer_causal_control_lora.safetensors` | causal control LoRA, rank 384: the control LoRA trained further with block-causal attention. Each block of up to 5 latent frames sees the prompt, the first frame, the structure of the last 6 blocks and the 5 blocks rendered before it, never a later block |
| `solarrenderer_causal_4step_lora.safetensors` | causal four-step LoRA, rank 384: the four-step LoRA post-trained, with the causal control LoRA frozen, on its own block-by-block rollouts by distribution matching with structure guidance (w = 1.5) toward the full-clip control model |
## Quick Start
```bash
git clone https://github.com/Enderfga/SolarRenderer && cd SolarRenderer
pip install -e . && pip install flash-attn==2.7.4.post1 --no-build-isolation
python scripts/prepare_h3.py --dir ./MiniMax-H3
hf download Enderfga/SolarRenderer --local-dir ./weights
python scripts/render.py --h3 ./MiniMax-H3 \
--control weights/solarrenderer_control_lora.safetensors \
--fewstep weights/solarrenderer_4step_lora.safetensors \
--structure structure.mp4 --first first.png --prompt prompt.txt --out render.mp4
```
Output: 1344×768 (or 768×1344), 24 fps, 158 frames; about 3.5 minutes of sampling on one GPU. The prompt follows one
six-part form: see the [prompt guide](https://github.com/Enderfga/SolarRenderer/blob/main/docs/prompts.md).
**Streaming.** The two causal LoRAs render the video block by block, four steps per block; each block can be shown as
soon as it is done. Same inputs, one flag:
```bash
python scripts/render.py --h3 ./MiniMax-H3 --causal \
--control weights/solarrenderer_causal_control_lora.safetensors \
--fewstep weights/solarrenderer_causal_4step_lora.safetensors \
--structure structure.mp4 --first first.png --prompt prompt.txt --out render.mp4
```
## Citation
```bibtex
@misc{fang2026solarrenderer,
title = {SolarRenderer: Few-Step Generative Reference Rendering},
author = {Fang, Guian and Qian, Shengju and Lu, Dongyue and
Huang, Junchao and Jiang, Lutao and Wang, Xin and
Song, Yiren and Jiang, Li and Shou, Mike Zheng},
year = {2026},
eprint = {2610.28630},
archivePrefix = {arXiv}
}
```
## License
The weights are LoRA adapters on MiniMax-H3 and are distributed under the MiniMax H3 Community License Agreement (see
LICENSE and NOTICE), including its territorial restrictions (it excludes the European Union, the United Kingdom, the
Republic of Korea and the United States) and its Acceptable Use Policy. The four-step LoRAs descend from the
public DMAD four-step LoRA and are released under the same license. The code is released under the Apache License 2.0.