++: Instruction-Based Image Creation and Editing
via Context-Aware Content Filling

Chaojie Mao · Jingfeng Zhang · Yulin Pan · Zeyinzi Jiang · Zhen Han
· Yu Liu · Jingren Zhou
Tongyi Lab, Alibaba Group

📚 Introduction

The original intention behind the design of ACE++ was to unify reference image generation, local editing, and controllable generation into a single framework, and to enable one model to adapt to a wider range of tasks. A more versatile model is often capable of handling more complex tasks. We have already released three LoRA models, focusing on portraits, objects, and regional editing, with the expectation that each would demonstrate strong adaptability within their respective domains. Undoubtedly, this presents certain challenges.

We are currently training a fully fine-tuned model, which has now entered the final stage of quality tuning. We are confident it will be released soon. This model will support a broader range of capabilities and is expected to empower community developers to build even more interesting applications.

📢 News

• [2025.01.06] Release the code and models of ACE++.
• [2025.01.07] Release the demo on HuggingFace.
• [2025.01.16] Release the training code for lora.
• [2025.02.15] Collection of workflows in Comfyui.
• [2025.02.15] Release the config for fully fine-tuning.
• [] [ToDo] Release a unified fft model for ACE++, support more image to image tasks.

🔥 Comfyui Workflows in community

We are deeply grateful to the community developers for building many fascinating applications based on the ACE++ series of models. During this process, we have received valuable feedback, particularly regarding artifacts in generated images and the stability of the results. In response to these issues, many developers have proposed creative solutions, which have greatly inspired us, and we pay tribute to them. At the same time, we will take these concerns into account in our further optimization efforts, carefully evaluating and testing before releasing new models.

In the table below, we have briefly listed some workflows for everyone to use.

Workflow	Author	Example
【飞翔鲸】王炸！FLUX+ACE++换头	飞翔鲸
最新ACE++ Redux 万物迁移 AI模特换装 UP 楚门的AI世界	楚门的AI世界
Ace Plus+Fill+Redux稳定无抽卡换装工作流	T8star-Aix
Ace Plus+Fill+Redux稳定少抽卡标志工作流	T8star-Aix
Ace Plus+Fill+Redux稳定无抽卡换脸工作流	T8star-Aix
ace++ face swap in different styles	jax
fllux ace++ subject without reference image	leeguandong
Scepter-ACE++ More convenient replacement of everything	HaoBeen

Additionally, many bloggers have published tutorials on how to use it, which are listed in the table below.

Tutorial

Best Faceswapper I've Seen. ACE++ in ComfyUI.

ACE ++ In ComfyUI All-round Creator & Editor - More Than Just A Faceswap AI

Ai绘画进阶140-咦？大家用的都不对？！Ace Plus工作流正确搭建方式及逻辑，参数详解，Flux Fill，Redux联用-T8 Comfyui教程

ace++：告别 Lora 训练，无需pulid，轻松打造专属角色！ | No Lora Training, Easily Create Exclusive Characters!

Ace++ and Flux Fill: Advanced Face Swapping Made Easy in ComfyUI | No Lora Training, Easily Create Exclusive Characters!

ComfyUI - ACE Plus Subject Portrait Lora

🤗AI一致性技术新突破！ACE++技术一致性comfyui工作流🍋‍

ComfyUI 第55集 人像换脸 FLUX的FILL模型+ACE LORA

换装变脸贴logo，无所不能的Ace_Plus lora

🔥 ACE Models

ACE++ provides a comprehensive toolkit for image editing and generation to support various applications. We encourage developers to choose the appropriate model based on their own scenarios and to fine-tune their models using data from their specific scenarios to achieve more stable results.

ACE++ Portrait

Portrait-consistent generation to maintain the consistency of the portrait.

Tuning Method	Input	Output	Instruction	Models
LoRA + ACE Data			"Maintain the facial features. A girl is wearing a neat police uniform and sporting a badge. She is smiling with a friendly and confident demeanor. The background is blurred, featuring a cartoon logo."

Models' scepter_path:

• ModelScope: ms://iic/ACE_Plus@portrait/xxxx.safetensors
• HuggingFace: hf://ali-vilab/ACE_Plus@portrait/xxxx.safetensors

ACE++ Subject

Subject-driven image generation task to maintain the consistency of a specific subject in different scenes.

Tuning Method	Input	Output	Instruction	Models
LoRA + ACE Data			"Display the logo in a minimalist style printed in white on a matte black ceramic coffee mug, alongside a steaming cup of coffee on a cozy cafe table."

Models' scepter_path:

• ModelScope: ms://iic/ACE_Plus@subject/xxxx.safetensors
• HuggingFace: hf://ali-vilab/ACE_Plus@subject/xxxx.safetensors

ACE++ LocalEditing

Redrawing the mask area of images while maintaining the original structural information of the edited area.

Tuning Method	Input	Output	Instruction	Models
LoRA + ACE Data			"By referencing the mask, restore a partial image from the doodle {image} that aligns with the textual explanation: "1 white old owl"."

Models' scepter_path:

• ModelScope: ms://iic/ACE_Plus@local_editing/xxxx.safetensors
• HuggingFace: hf://ali-vilab/ACE_Plus@local_editing/xxxx.safetensors

ACE++ Fully [Coming soon]

Fully finetuning a composite model with ACE’s data to support various editing and reference generation tasks through an instructive approach.

🔥 Applications

The ACE++ model supports a wide range of downstream tasks through simple adaptations. Here are some examples, and we look forward to seeing the community explore even more exciting applications utilizing the ACE++ model.

Application	ACE++ Model	Examples
Try On	ACE++ Subject					"The woman dresses this skirt."
Logo Paste	ACE++ Subject					"The logo is printed on the headphones."
Photo Editing	ACE++ Subject					"The item is put on the ground."
Movie Poster Editor	ACE++ Portrait					"The man is facing the camera and is smiling."

⚙️️ Installation

Download the code using the following command:

git clone https://github.com/ali-vilab/ACE_plus.git

Install the necessary packages with pip:

cd ACE_plus
pip install -r requirements.txt

ACE++ depends on FLUX.1-Fill-dev as its base model, which you can download from . In order to run the inference code or Gradio demo normally, we have defined the relevant environment variables to specify the location of the model. For model preparation, we provide three methods for downloading the model. The summary of relevant settings is as follows.

Model Downloading Method	Clone to Local Path	Automatic Downloading during Runtime (Setting the Environment Variables using scepter_path in ACE Models)
Environment Variables Setting	export FLUX_FILL_PATH="path/to/FLUX.1-Fill-dev" export PORTRAIT_MODEL_PATH="path/to/ACE++ PORTRAIT PATH" export SUBJECT_MODEL_PATH="path/to/ACE++ SUBJECT PATH" export LOCAL_MODEL_PATH="path/to/ACE++ LOCAL EDITING PATH"	export FLUX_FILL_PATH="hf://black-forest-labs/FLUX.1-Fill-dev" export PORTRAIT_MODEL_PATH="${scepter_path}" export SUBJECT_MODEL_PATH="${scepter_path}" export LOCAL_MODEL_PATH="${scepter_path}"

🚀 Inference

Under the condition that the environment variables defined in Installation, users can run examples and test your own samples by executing infer.py. The relevant commands are as follows:

export FLUX_FILL_PATH="hf://black-forest-labs/FLUX.1-Fill-dev"
export PORTRAIT_MODEL_PATH="ms://iic/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors"                                                                                                                                      
export SUBJECT_MODEL_PATH="ms://iic/ACE_Plus@subject/comfyui_subject_lora16.safetensors"                                                                                                                                         
export LOCAL_MODEL_PATH="ms://iic/ACE_Plus@local_editing/comfyui_local_lora16.safetensors" 
# Use the model from huggingface
# export PORTRAIT_MODEL_PATH="hf://ali-vilab/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors"        
# export SUBJECT_MODEL_PATH="hf://ali-vilab/ACE_Plus@subject/comfyui_subject_lora16.safetensors"        
# export LOCAL_MODEL_PATH="hf://ali-vilab/ACE_Plus@local_editing/comfyui_local_lora16.safetensors" 
python infer.py

🚀 Train

We provide training code that allows users to train on their own data. Reference the data in 'data/train.csv' and 'data/eval.csv' to construct the training data and test data, respectively. We use '#;#' to separate fields. The required fields include the following six, with their explanations as follows.

"edit_image": represents the input image for the editing task. If it is not an editing task but a reference generation, this field can be left empty.
"edit_mask": represents the input image mask for the editing task, used to specify the editing area. If it is not an editing task but rather for reference generation, this field can be left empty.
"ref_image": represents the input image for the reference image generation task; if it is a pure editing task, this field can be left empty.
"target_image": represents the generated target image and cannot be empty.
"prompt": represents the prompt for the generation task.
"data_type": represents the type of data, which can be 'portrait', 'subject', or 'local'. This field is not used in training phase.

All parameters related to training are stored in 'train_config/ace_plus_lora.yaml'. To run the training code, execute the following command.

export FLUX_FILL_PATH="hf://black-forest-labs/FLUX.1-Fill-dev"
python run_train.py  --cfg train_config/ace_plus_lora.yaml

The models trained by ACE++ can be found in ./examples/exp_example/xxxx/checkpoints/xxxx/0_SwiftLoRA/comfyui_model.safetensors.

💻 Demo

We have built a GUI demo based on Gradio to help users better utilize the ACE++ model. Just execute the following command.

export FLUX_FILL_PATH="hf://black-forest-labs/FLUX.1-Fill-dev"
export PORTRAIT_MODEL_PATH="ms://iic/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors"                                                                                                                                      
export SUBJECT_MODEL_PATH="ms://iic/ACE_Plus@subject/comfyui_subject_lora16.safetensors"                                                                                                                                         
export LOCAL_MODEL_PATH="ms://iic/ACE_Plus@local_editing/comfyui_local_lora16.safetensors" 
# Use the model from huggingface
# export PORTRAIT_MODEL_PATH="hf://ali-vilab/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors"        
# export SUBJECT_MODEL_PATH="hf://ali-vilab/ACE_Plus@subject/comfyui_subject_lora16.safetensors"        
# export LOCAL_MODEL_PATH="hf://ali-vilab/ACE_Plus@local_editing/comfyui_local_lora16.safetensors" 
python demo.py

📚 Limitations

• For certain tasks, such as deleting and adding objects, there are flaws in instruction following. For adding and replacing objects, we recommend trying the repainting method of the local editing model to achieve this.
• The generated results may contain artifacts, especially when it comes to the generation of hands, which still exhibit distortions.
• The current version of ACE++ is still in the development stage. We are working on improving the model's performance and adding more features.

📝 Citation

ACE++ is a post-training model based on the FLUX.1-dev series from black-forest-labs. Please adhere to its open-source license. The test materials used in ACE++ come from the internet and are intended for academic research and communication purposes. If the original creators feel uncomfortable, please contact us to have them removed.

If you use this model in your research, please cite the works of FLUX.1-dev and the following papers:

@article{mao2025ace++,
  title={ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling},
  author={Mao, Chaojie and Zhang, Jingfeng and Pan, Yulin and Jiang, Zeyinzi and Han, Zhen and Liu, Yu and Zhou, Jingren},
  journal={arXiv preprint arXiv:2501.02487},
  year={2025}
}

@article{han2024ace,
  title={ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer},
  author={Han, Zhen and Jiang, Zeyinzi and Pan, Yulin and Zhang, Jingfeng and Mao, Chaojie and Xie, Chenwei and Liu, Yu and Zhou, Jingren},
  journal={arXiv preprint arXiv:2410.00086},
  year={2024}
}