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++.
[2025.02.15] Release the config for fully fine-tuning.
[2025.03.03] Release a unified fft model for ACE++, support more image to image tasks.
🔥The unified fft model for ACE++
Fully finetuning a composite model with ACE’s data to support various editing and reference generation tasks through an instructive approach.
We found that there are conflicts between the repainting task and the editing task during the experimental process. This is because the edited image is concatenated with noise in the channel dimension, whereas the repainting task modifies the region using zero pixel values in the VAE's latent space. The editing task uses RGB pixel values in the modified region through the VAE's latent space, which is similar to the distribution of the non-modified part of the repainting task, making it a challenge for the model to distinguish between the two tasks.
To address this issue, we introduced 64 additional channels in the channel dimension to differentiate between these two tasks. In these channels, we place the latent representation of the pixel space from the edited image, while keeping other channels consistent with the repainting task. This approach significantly enhances the model's adaptability to different tasks.
One issue with this approach is that it changes the input channel number of the FLUX-Fill-Dev model from 384 to 448. The specific configuration can be referenced in the configuration file.
Examples
Input Reference Image
Input Edit Image
Input Edit Mask
Output
Instruction
Function
"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."
"Character ID Consistency Generation"
"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."
"Subject Consistency Generation"
"The item is put on the table."
"Subject Consistency Editing"
"The logo is printed on the headphones."
"Subject Consistency Editing"
"The woman dresses this skirt."
"Try On"
"{image}, the man faces the camera."
"Face swap"
"{image} features a close-up of a young, furry tiger cub on a rock. The tiger, which appears to be quite young, has distinctive orange, black, and white striped fur, typical of tigers. The cub's eyes have a bright and curious expression, and its ears are perked up, indicating alertness. The cub seems to be in the act of climbing or resting on the rock. The background is a blurred grassland with trees, but the focus is on the cub, which is vividly colored while the rest of the image is in grayscale, drawing attention to the tiger's details. The photo captures a moment in the wild, depicting the charming and tenacious nature of this young tiger, as well as its typical interaction with the environment."
"Super-resolution"
"a blue hand"
"Regional Editing"
"Mechanical hands like a robot"
"Regional Editing"
"{image} Beautiful female portrait, Robot with smooth White transparent carbon shell, rococo detailing, Natural lighting, Highly detailed, Cinematic, 4K."
"Recolorizing"
"{image} Beautiful female portrait, Robot with smooth White transparent carbon shell, rococo detailing, Natural lighting, Highly detailed, Cinematic, 4K."
"Depth Guided Generation"
"{image} Beautiful female portrait, Robot with smooth White transparent carbon shell, rococo detailing, Natural lighting, Highly detailed, Cinematic, 4K."
"Contour Guided Generation"
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.
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."
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."
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.
.
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)
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:
bash
1exportFLUX_FILL_PATH="hf://black-forest-labs/FLUX.1-Fill-dev"2exportPORTRAIT_MODEL_PATH="ms://iic/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors"3exportSUBJECT_MODEL_PATH="ms://iic/ACE_Plus@subject/comfyui_subject_lora16.safetensors"4exportLOCAL_MODEL_PATH="ms://iic/ACE_Plus@local_editing/comfyui_local_lora16.safetensors"5# Use the model from huggingface6# export PORTRAIT_MODEL_PATH="hf://ali-vilab/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors" 7# export SUBJECT_MODEL_PATH="hf://ali-vilab/ACE_Plus@subject/comfyui_subject_lora16.safetensors" 8# export LOCAL_MODEL_PATH="hf://ali-vilab/ACE_Plus@local_editing/comfyui_local_lora16.safetensors" 9python 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.
angular2html
1"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.
2"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.
3"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.
4"target_image": represents the generated target image and cannot be empty.
5"prompt": represents the prompt for the generation task.
6"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.
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.
bash
1exportFLUX_FILL_PATH="hf://black-forest-labs/FLUX.1-Fill-dev"2exportPORTRAIT_MODEL_PATH="ms://iic/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors"3exportSUBJECT_MODEL_PATH="ms://iic/ACE_Plus@subject/comfyui_subject_lora16.safetensors"4exportLOCAL_MODEL_PATH="ms://iic/ACE_Plus@local_editing/comfyui_local_lora16.safetensors"5# Use the model from huggingface6# export PORTRAIT_MODEL_PATH="hf://ali-vilab/ACE_Plus@portrait/comfyui_portrait_lora64.safetensors" 7# export SUBJECT_MODEL_PATH="hf://ali-vilab/ACE_Plus@subject/comfyui_subject_lora16.safetensors" 8# export LOCAL_MODEL_PATH="hf://ali-vilab/ACE_Plus@local_editing/comfyui_local_lora16.safetensors" 9python 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:
bibtex
1@article{mao2025ace++,
2 title={ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling},
3 author={Mao, Chaojie and Zhang, Jingfeng and Pan, Yulin and Jiang, Zeyinzi and Han, Zhen and Liu, Yu and Zhou, Jingren},
4 journal={arXiv preprint arXiv:2501.02487},
5 year={2025}
6}
bibtex
1@article{han2024ace,
2 title={ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer},
3 author={Han, Zhen and Jiang, Zeyinzi and Pan, Yulin and Zhang, Jingfeng and Mao, Chaojie and Xie, Chenwei and Liu, Yu and Zhou, Jingren},
4 journal={arXiv preprint arXiv:2410.00086},
5 year={2024}
6}
