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license: other
license_name: flux-non-commercial-license-v2.1
license_link: https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B/blob/main/LICENSE.md
language:
- en
base_model:
- microsoft/SciForma-Base
tags:
- text-to-image
- image-to-image
- scientific-diagrams
- methodology-figures
- flux
- diffusers
pipeline_tag: text-to-image
---
# SciForma-9B
**SciForma-9B** is the final M-DPO aligned checkpoint of [SciForma: Structure-Faithful Generation of Scientific Diagrams](https://github.com/microsoft/SciForma), fine-tuned from [SciForma-Base](https://huggingface.co/microsoft/SciForma-Base) via Multi-Dimensional Conjunctive DPO (M-DPO).
| Model | Overall ↑ | Comp. ↑ | Arrow ↑ | Text ↑ |
|-------|-----------|---------|---------|--------|
| **SciForma-9B** | **69.51** | **74.49** | **66.46** | **67.00** |
| GPT-Image-1.5 | 68.96 | 75.50 | 62.50 | 68.20 |
| SciForma-Base | 67.59 | 73.52 | 64.64 | 63.84 |
| FLUX.2-klein-base-9B (no fine-tuning) | 33.87 | 42.80 | 25.20 | 23.60 |
Scores on SciFormaBench-2K (GPT-5.4 judge, split_dims=True).
## Usage
This repository contains only the fine-tuned **transformer** weights. The text encoder, tokenizer, VAE, and scheduler are inherited from the base model.
```python
import torch
from diffusers import Flux2KleinPipeline, Flux2Transformer2DModel
# Load fine-tuned transformer
transformer = Flux2Transformer2DModel.from_pretrained(
"microsoft/SciForma-9B",
subfolder="transformer",
torch_dtype=torch.bfloat16,
)
# Load full pipeline with base model components
pipe = Flux2KleinPipeline.from_pretrained(
"black-forest-labs/FLUX.2-klein-base-9B",
transformer=transformer,
torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()
# Generate
prompt = "The figure illustrates a transformer architecture with encoder and decoder..."
image = pipe(
prompt,
height=576,
width=1008,
num_inference_steps=28,
guidance_scale=4.0,
).images[0]
image.save("output.png")
```
## Training Details
- **Base model**: SciForma-Base (Stage 2 SFT)
- **M-DPO**: Multi-Dimensional Conjunctive DPO with 1v2+global-worst triples (~16.5K groups), targeting Component and Text axes simultaneously
- **Hardware**: 4× B200, batch=1/GPU, GA=3 → eff_batch=12, 10K steps
- **Training data**: [microsoft/SciFormaData-700K](https://huggingface.co/datasets/microsoft/SciFormaData-700K)
## License
Released under the [FLUX Non-Commercial License v2.1](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B/blob/main/LICENSE.md). Non-commercial research use only.
> This FLUX Model is licensed by Black Forest Labs Inc. under the FLUX Non-Commercial License. Copyright Black Forest Labs Inc. This model has been modified by Microsoft Research Asia.
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