In-context subject-driven image generation while preserving foreground fidelity
352
stars
32
commits
Python
primary language
Jun 11, 2025
updated
An all-in-one, control framework for unified visual content creation using GenAI.
Generate multi-view, diverse-scene, and task-specific high-resolution images from a single subject image—without fine-tuning.
ZenCtrl is a comprehensive toolkit built to tackle core challenges in image generation:
ZenCtrl is based on OminiControl but enhanced with more fine-grained control, consistent subject preservation, and more improved and ready-to-use models. Our goal is to build an agentic visual generation system that can orchestrate image/video creation from LLM-driven recipes.
All of these tasks can be mixed and layered — ZenCtrl is designed to support real-world visual workflows with agentic task composition.
Before running the Gradio code, please install the requirements and download the weights from our HuggingFace repository:
👉 https://huggingface.co/fotographerai/zenctrl_tools
We matched our original code with the Omnicontrol structure. Our model takes two inputs instead, but we are going to release the original code soon with the LLaMA task driver — so stay tuned. We will also update the tasks for specific verticals (e.g., virtual try-on, ad creatives, etc.).
You can follow the step-by-step setup instructions below:
*** Cloning and setting up ZenCtrl
git clone https://github.com/FotographerAI/ZenCtrl.git
cd ZenCtrl
*** Creating virtual environment
python -m venv venv
call venv\Scripts\activate.bat
*** Installing PyTorch and requirements
pip install torch==2.7.0+cu128 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install --upgrade pip wheel setuptools
pip install -r requirements.txt
*** Downloading model weights
curl --create-dirs -L https://huggingface.co/fotographerai/zenctrl_tools/resolve/main/weights/zen2con_1440_17000/pytorch_lora_weights.safetensors -o weights\zen2con_1440_17000\pytorch_lora_weights.safetensors
*** All set! Launching Gradio app
python app/gradio_app.py
| Type | Name | Base | Resolution | Description | links |
|---|---|---|---|---|---|
| Subject Generation | zen2con_1440_17000 | FLUX.1 | 1024x1024 | Core model for subject-driven gen | link |
| Bg generation + Canny | bg_canny_58000_1024 | FLUX.1 | 1024x1024 | Enhanced background control | link |
| Deblurring Model | deblurr_1024_10000 | OminiControl | 1024x1024 | Quality recovery post-generation | link |
We hope to collaborate closely with the open-source community to make ZenCtrl a powerful and extensible toolkit for visual content creation.
Once the source code is released, we welcome contributions in training, expanding supported use cases, and developing new task-specific modules.
Our vision is to make ZenCtrl the standard framework for agentic, high-quality image and video generation — built together, for everyone.
Python
100.0%
In-context subject-driven image generation while preserving foreground fidelity
352
stars
32
commits
Python
primary language
Jun 11, 2025
updated
An all-in-one, control framework for unified visual content creation using GenAI.
Generate multi-view, diverse-scene, and task-specific high-resolution images from a single subject image—without fine-tuning.
ZenCtrl is a comprehensive toolkit built to tackle core challenges in image generation:
ZenCtrl is based on OminiControl but enhanced with more fine-grained control, consistent subject preservation, and more improved and ready-to-use models. Our goal is to build an agentic visual generation system that can orchestrate image/video creation from LLM-driven recipes.
All of these tasks can be mixed and layered — ZenCtrl is designed to support real-world visual workflows with agentic task composition.
Before running the Gradio code, please install the requirements and download the weights from our HuggingFace repository:
👉 https://huggingface.co/fotographerai/zenctrl_tools
We matched our original code with the Omnicontrol structure. Our model takes two inputs instead, but we are going to release the original code soon with the LLaMA task driver — so stay tuned. We will also update the tasks for specific verticals (e.g., virtual try-on, ad creatives, etc.).
You can follow the step-by-step setup instructions below:
*** Cloning and setting up ZenCtrl
git clone https://github.com/FotographerAI/ZenCtrl.git
cd ZenCtrl
*** Creating virtual environment
python -m venv venv
call venv\Scripts\activate.bat
*** Installing PyTorch and requirements
pip install torch==2.7.0+cu128 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install --upgrade pip wheel setuptools
pip install -r requirements.txt
*** Downloading model weights
curl --create-dirs -L https://huggingface.co/fotographerai/zenctrl_tools/resolve/main/weights/zen2con_1440_17000/pytorch_lora_weights.safetensors -o weights\zen2con_1440_17000\pytorch_lora_weights.safetensors
*** All set! Launching Gradio app
python app/gradio_app.py
| Type | Name | Base | Resolution | Description | links |
|---|---|---|---|---|---|
| Subject Generation | zen2con_1440_17000 | FLUX.1 | 1024x1024 | Core model for subject-driven gen | link |
| Bg generation + Canny | bg_canny_58000_1024 | FLUX.1 | 1024x1024 | Enhanced background control | link |
| Deblurring Model | deblurr_1024_10000 | OminiControl | 1024x1024 | Quality recovery post-generation | link |
We hope to collaborate closely with the open-source community to make ZenCtrl a powerful and extensible toolkit for visual content creation.
Once the source code is released, we welcome contributions in training, expanding supported use cases, and developing new task-specific modules.
Our vision is to make ZenCtrl the standard framework for agentic, high-quality image and video generation — built together, for everyone.
Python
100.0%