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--- |
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license: apache-2.0 |
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--- |
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## NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale |
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[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) |
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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. |
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**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. |
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<div align='center'> |
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<img src="assets/teaser.jpg" class="interpolation-image" alt="arch." width="100%" /> |
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</div> |
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## ENV Preparation |
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To avoid potential errors when loading and running your models, we recommend using the following settings: |
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```shell |
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conda create -n nextstep python=3.11 -y |
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conda activate nextstep |
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pip install uv # optional |
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# please check and download requirements.txt in this repo |
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uv pip install -r requirements.txt |
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# diffusers==0.34.0 |
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# einops==0.8.1 |
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# gradio==5.42.0 |
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# loguru==0.7.3 |
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# numpy==1.26.4 |
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# omegaconf==2.3.0 |
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# Pillow==11.0.0 |
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# Requests==2.32.4 |
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# safetensors==0.5.3 |
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# tabulate==0.9.0 |
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# torch==2.5.1 |
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# torchvision==0.20.1 |
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# tqdm==4.67.1 |
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# transformers==4.55.0 |
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``` |
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## Usage |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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from models.gen_pipeline import NextStepPipeline |
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HF_HUB = "stepfun-ai/NextStep-1-Large" |
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# load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True) |
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model = AutoModel.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True) |
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pipeline = NextStepPipeline(tokenizer=tokenizer, model=model).to(device="cuda", dtype=torch.bfloat16) |
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# set prompts |
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positive_prompt = "masterpiece, film grained, best quality." |
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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." |
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example_prompt = "A realistic photograph of a wall with \"NextStep-1.1 is coming\" prominently displayed" |
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# generate image from text |
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IMG_SIZE = 512 |
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image = pipeline.generate_image( |
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example_prompt, |
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hw=(IMG_SIZE, IMG_SIZE), |
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num_images_per_caption=1, |
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positive_prompt=positive_prompt, |
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negative_prompt=negative_prompt, |
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cfg=7.5, |
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cfg_img=1.0, |
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cfg_schedule="constant", |
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use_norm=False, |
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num_sampling_steps=28, |
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timesteps_shift=1.0, |
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seed=3407, |
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)[0] |
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image.save("./assets/output.jpg") |
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``` |
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## Citation |
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If you find NextStep useful for your research and applications, please consider starring this repository and citing: |
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```bibtex |
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@misc{nextstep_1, |
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title={NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale}, |
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author={NextStep Team}, |
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year={2025}, |
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url={https://github.com/stepfun-ai/NextStep-1}, |
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} |
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``` |
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