AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation

AnyStory is a unified approach for personalized subject generation. It not only achieves high-fidelity personalization for single subjects, but also for multiple subjects, without sacrificing subject fidelity.

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News

• [2025/05/01] 🚀 We release the code and demo for the FLUX.1-dev version of AnyStory.

Usage

importtorchfromPILimportImagefromhuggingface_hubimporthf_hub_downloadfromanystory.generateimportAnyStoryFluxPipelineanystory_path=hf_hub_download(repo_id="Junjie96/AnyStory", filename="anystory_flux.bin")
story_pipe=AnyStoryFluxPipeline(
hf_flux_pipeline_path="black-forest-labs/FLUX.1-dev",
hf_flux_redux_path="black-forest-labs/FLUX.1-Redux-dev",
anystory_path=anystory_path,
device="cuda",
torch_dtype=torch.bfloat16
)
# you can add lora here# story_pipe.flux_pipeline.load_lora_weights(lora_path, adapter_name="...")# single-subjectsubject_image=Image.open("assets/examples/1.webp").convert("RGB")
subject_mask=Image.open("assets/examples/1_mask.webp").convert("L")
prompt="Cartoon style. A sheep is riding a skateboard and gliding through the city," \
" holding a wooden sign that says \"hello\"."image=story_pipe.generate(prompt=prompt, images=[subject_image], masks=[subject_mask], seed=2025,
num_inference_steps=25, height=512, width=512,
guidance_scale=3.5)
image.save("output_1.png")
# multi-subjectsubject_image_1=Image.open("assets/examples/6_1.webp").convert("RGB")
subject_mask_1=Image.open("assets/examples/6_1_mask.webp").convert("L")
subject_image_2=Image.open("assets/examples/6_2.webp").convert("RGB")
subject_mask_2=Image.open("assets/examples/6_2_mask.webp").convert("L")
prompt="Two men are sitting by a wooden table, which is laden with delicious food and a pot of wine. " \
"One of the men holds a wine glass, drinking heartily with a bold expression; " \
"the other smiles as he pours wine for his companion, both of them engaged in cheerful conversation. " \
"In the background is an ancient pavilion surrounded by emerald bamboo groves, with sunlight filtering " \
"through the leaves to cast dappled shadows."image=story_pipe.generate(prompt=prompt,
images=[subject_image_1, subject_image_2],
masks=[subject_mask_1, subject_mask_2],
seed=2025,
enable_router=True, ref_start_at=0.09,
num_inference_steps=25, height=512, width=512,
guidance_scale=3.5)
image.save("output_2.png")

Storyboard generation

importjsonfromstoryboardimportStoryboardPipelinestoryboard_pipe=StoryboardPipeline()
storyboard_pipe.new_story()
script_dict=json.load(open("assets/scripts/013420.json"))
print(script_dict)
results=storyboard_pipe(script_dict, style_name="Comic book")
forkey, resultinresults.items():
result.save(f"output_1_{key}.png")
# 狮子王辛巴成长storyboard_pipe.new_story()
script_dict=json.load(open("assets/scripts/014933.json"))
print(script_dict)
results=storyboard_pipe(script_dict, style_name="Japanese Anime")
forkey, resultinresults.items():
result.save(f"output_2_{key}.png")

Example output:

Applications

Intelligent creation of AI story pictures with Qwen Agent (please refer to storyboard.py)

AI Animation Video Production with Wan Image-to-Video

(please refer to assets/example_videos)

Acknowledgements

This code is built on diffusers and OminiControl. Highly appreciate their great work!

Cite

@article{he2025anystory,
title={AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation},
author={He, Junjie and Tuo, Yuxiang and Chen, Binghui and Zhong, Chongyang and Geng, Yifeng and Bo, Liefeng},
journal={arXiv preprint arXiv:2501.09503},
year={2025}
}