---
license: apache-2.0
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- diffusers
- lora
- scientific-machine-learning
- physics-simulation
- visual-reasoning
---

# VR-LoRA: N-Body Physics Simulator (SD-v1.5)

This repository contains a Visual Reasoning LoRA (VR-LoRA) fine-tuned on `runwayml/stable-diffusion-v1-5` to predict the temporal evolution of a 3-body gravitational system.

This model is the primary output of the paper: **"Visual Reasoning Transfer: Leveraging Pretrained Visual Models for Physical and Temporal Prediction"** (link to paper coming soon).

## Model Description

The model does not generate images. Instead, it acts as a dynamics engine in latent space. When given a latent representation of a physical state (encoded as a "spatial field image"), it predicts the latent representation of the next physical state.

This LoRA was trained for 15,000 steps on a synthetic dataset of 10,000 N-body trajectories.

*   **Research Project Repository:** [https://github.com/sandner-art/SC-Visual-Reasoning](https://github.com/sandner-art/SC-Visual-Reasoning)
*   **Training Dataset:** [huggingface.co/datasets/sandner/n-body-trajectories-for-vrlora](https://huggingface.co/datasets/sandner/n-body-trajectories-for-vrlora) (Replace with your link)

## How to Use

This LoRA is designed to be used with the evaluation script found in the main research repository. It is not intended for standard text-to-image generation.

```python
# See the evaluate_vr_lora.py script in the main GitHub repo for a full example.
from diffusers import StableDiffusionPipeline

base_model = "runwayml/stable-diffusion-v1-5"
lora_model = "sandner/vr-lora-physics-sd15" # Replace with your repo name

pipeline = StableDiffusionPipeline.from_pretrained(base_model)
pipeline.load_lora_weights(lora_model)
pipeline.to("cuda")

# Now the `pipeline.unet` component is ready for physics simulation.
unet = pipeline.unet
# ...
```

## Training Procedure

The model was trained using the `train_vr_lora.py` script from the project repository. Key hyperparameters:

*   **Learning Rate:** 1e-4
*   **Batch Size:** 32
*   **Max Steps:** 15,000
*   **Optimizer:** AdamW
*   **Scheduler:** Cosine

## Citing this Work

If you use this model in your research, please cite our paper (BibTeX entry coming soon).