---
license: apache-2.0
---

## NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale

[Homepage](https://stepfun.ai/research/en/nextstep-1)  | [GitHub](https://github.com/stepfun-ai/NextStep-1)  | [Paper](https://github.com/stepfun-ai/NextStep-1/blob/main/nextstep_1_tech_report.pdf) 

We introduce **NextStep-1**, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives.
**NextStep-1** achieves state-of-the-art performance for autoregressive models in text-to-image generation tasks, exhibiting strong capabilities in high-fidelity image synthesis.

<div align='center'>
<img src="assets/teaser.jpg" class="interpolation-image" alt="arch." width="100%" />
</div>

## ENV Preparation

To avoid potential errors when loading and running your models, we recommend using the following settings:

```shell
conda create -n nextstep python=3.11 -y
conda activate nextstep

pip install uv # optional

# please check and download requirements.txt in this repo
uv pip install -r requirements.txt

# diffusers==0.34.0
# einops==0.8.1
# gradio==5.42.0
# loguru==0.7.3
# numpy==1.26.4
# omegaconf==2.3.0
# Pillow==11.0.0
# Requests==2.32.4
# safetensors==0.5.3
# tabulate==0.9.0
# torch==2.5.1
# torchvision==0.20.1
# tqdm==4.67.1
# transformers==4.55.0
```

## Usage

```python
import torch
from transformers import AutoTokenizer, AutoModel
from models.gen_pipeline import NextStepPipeline

HF_HUB = "stepfun-ai/NextStep-1-Large"

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True)
model = AutoModel.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True)
pipeline = NextStepPipeline(tokenizer=tokenizer, model=model).to(device="cuda", dtype=torch.bfloat16)

# set prompts
positive_prompt = "masterpiece, film grained, best quality."
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry."
example_prompt = "A realistic photograph of a wall with \"NextStep-1.1 is coming\" prominently displayed"

# generate image from text
IMG_SIZE = 512
image = pipeline.generate_image(
    example_prompt,
    hw=(IMG_SIZE, IMG_SIZE),
    num_images_per_caption=1,
    positive_prompt=positive_prompt,
    negative_prompt=negative_prompt,
    cfg=7.5,
    cfg_img=1.0,
    cfg_schedule="constant",
    use_norm=False,
    num_sampling_steps=28,
    timesteps_shift=1.0,
    seed=3407,
)[0]
image.save("./assets/output.jpg")
```

## Citation

If you find NextStep useful for your research and applications, please consider starring this repository and citing:

```bibtex
@misc{nextstep_1,
    title={NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale},
    author={NextStep Team},
    year={2025},
    url={https://github.com/stepfun-ai/NextStep-1},
}
```