---
title: pi-FLUX.2
canonical_url: "https://www.modelscope.cn/models/Lakonik/pi-FLUX.2"
md_url: "https://www.modelscope.cn/models/Lakonik/pi-FLUX.2.md"
repository: Lakonik/pi-FLUX.2
last_updated: 2025-12-13
license: other
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - black-forest-labs/FLUX.2-dev
base_model_relation: finetune
library_name:
  - pytorch
  - safetensors
  - diffusers
frameworks:
  - pytorch
downloads: 60
stars: 0
tags:
  - flux
  - flow-matching
  - distillation
---

# pi-FLUX.2

> pi-FLUX.2 - Lakonik 在 ModelScope 开源的模型。pi-Flow: Policy-Based Flow Models

Lakonik/pi-FLUX.2 是 ModelScope 魔搭社区上的text-to-image-synthesis模型，采用 other 许可，基于 black-forest-labs/FLUX.2-dev 构建。

- **Repository**: Lakonik/pi-FLUX.2
- **License**: other
- **Tasks**: text-to-image-synthesis
- **Base model**: black-forest-labs/FLUX.2-dev
- **Tags**: flux, flow-matching, distillation
- **Downloads**: 60
- **Stars**: 0
- **Last updated**: 2025-12-13

Source: https://www.modelscope.cn/models/Lakonik/pi-FLUX.2

---

# pi-Flow: Policy-Based Flow Models

4-step FLUX.2 models distilled from FLUX.2 dev, using the pi-Flow method proposed in the paper:

**pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation**
<br>
[Hansheng Chen](https://lakonik.github.io/)<sup>1</sup>, 
[Kai Zhang](https://kai-46.github.io/website/)<sup>2</sup>,
[Hao Tan](https://research.adobe.com/person/hao-tan/)<sup>2</sup>,
[Leonidas Guibas](https://geometry.stanford.edu/?member=guibas)<sup>1</sup>,
[Gordon Wetzstein](http://web.stanford.edu/~gordonwz/)<sup>1</sup>, 
[Sai Bi](https://sai-bi.github.io/)<sup>2</sup><br>
<sup>1</sup>Stanford University, <sup>2</sup>Adobe Research
<br>
[[arXiv](https://arxiv.org/abs/2510.14974)] [[Code](https://github.com/Lakonik/piFlow)] [[pi-Qwen Demo🤗](https://huggingface.co/spaces/Lakonik/pi-Qwen)] [[pi-FLUX Demo🤗](https://huggingface.co/spaces/Lakonik/pi-FLUX.1)] [[pi-FLUX.2 Demo🤗](https://huggingface.co/spaces/Lakonik/pi-FLUX.2)]

![teaser](https://cdn-uploads.huggingface.co/production/uploads/638067fcb334960c987fbeda/H0J1LYUcSS5YqOwZqQ0Jb.jpeg)

## Usage

Please first install the [official code repository](https://github.com/Lakonik/piFlow).

We provide diffusers pipelines for easy inference. The following code demonstrates how to sample images from the distilled FLUX.2 models.

### 4-NFE GM-FLUX.2 (GMFlow Policy)
Note: GM-FLUX.2 supports elastic inference. Feel free to set `num_inference_steps` to any value above 4.
```python
import torch
from lakonlab.models.diffusions.schedulers import FlowMapSDEScheduler
from lakonlab.pipelines.pipeline_piflux2 import PiFlux2Pipeline
from diffusers.utils import load_image

pipe = PiFlux2Pipeline.from_pretrained(
    'diffusers/FLUX.2-dev-bnb-4bit',
    torch_dtype=torch.bfloat16)
adapter_name = pipe.load_piflow_adapter(  # you may later call `pipe.set_adapters([adapter_name, ...])` to combine other adapters (e.g., style LoRAs)
    'Lakonik/pi-FLUX.2',
    subfolder='gmflux2_k8_piid_4step',
    target_module_name='transformer')
pipe.scheduler = FlowMapSDEScheduler.from_config(  # use fixed shift=3.2
    pipe.scheduler.config, shift=3.2, use_dynamic_shifting=False, final_step_size_scale=0.5)
pipe = pipe.to('cuda')

# Text-to-image generation example
prompt = "Realistic macro photograph of a hermit crab using a soda can as its shell, partially emerging from the can, captured with sharp detail and natural colors, on a sunlit beach with soft shadows and a shallow depth of field, with blurred ocean waves in the background. The can has the text `BFL Diffusers` on it and it has a color gradient that start with #FF5733 at the top and transitions to #33FF57 at the bottom."
out = pipe(
    prompt=prompt,
    width=1360,
    height=768,
    num_inference_steps=4,
    generator=torch.Generator().manual_seed(42),
).images[0]
out.save('gmflux2_4nfe.png')

# Image editing example
prompt = "Add a hat on top of the cat."
cat_image = load_image("https://huggingface.co/spaces/zerogpu-aoti/FLUX.1-Kontext-Dev-fp8-dynamic/resolve/main/cat.png")
out = pipe(
    prompt=prompt,
    image=[cat_image],  # optional multi-image input
    width=1360,
    height=768,
    num_inference_steps=4,
    generator=torch.Generator().manual_seed(42),
).images[0]
out.save('gmflux2_edit_4nfe.png')
```

## Citation
```
@misc{piflow,
      title={pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation}, 
      author={Hansheng Chen and Kai Zhang and Hao Tan and Leonidas Guibas and Gordon Wetzstein and Sai Bi},
      year={2025},
      eprint={2510.14974},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2510.14974}, 
}
```