Text-to-Image
Diffusers
Safetensors
English
Flux2KleinPipeline
scientific-diagrams
methodology-figures
flux
Instructions to use LoYuXrqw/SciForma-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LoYuXrqw/SciForma-Base with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LoYuXrqw/SciForma-Base", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| 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: | |
| - black-forest-labs/FLUX.2-klein-base-9B | |
| tags: | |
| - text-to-image | |
| - scientific-diagrams | |
| - methodology-figures | |
| - flux | |
| - diffusers | |
| pipeline_tag: text-to-image | |
| # SciForma-Base | |
| **SciForma-Base** is the Stage 1+2 SFT checkpoint of [SciForma: Structure-Faithful Generation of Scientific Diagrams](https://github.com/microsoft/SciForma), fine-tuned from [FLUX.2-klein-base-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B). | |
| | Model | Overall ↑ | Comp. ↑ | Arrow ↑ | Text ↑ | | |
| |-------|-----------|---------|---------|--------| | |
| | SciForma-9B | 69.51 | 74.49 | 66.46 | 67.00 | | |
| | **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-Base", | |
| 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**: FLUX.2-klein-base-9B (9.08B parameters) | |
| - **Stage 1**: Full fine-tuning on 661K arXiv methodology diagrams at 768px (200K steps, 8× B200) | |
| - **Stage 2**: Joint generation + editing fine-tuning on 244K high-quality 1024px diagrams + 70K editing triplets (120K steps, 8× B200) | |
| - **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. | |