Alixn/

OmniGen2

News | Quick Start | Usage Tips | Online Demos | Citation | License

🔥 News

• 2025-07-05: Training datasets X2I2 are available.
• 2025-07-03: OmniGen2 now supports TeaCache and TaylorSeer for faster inference, see Usage Tips for details. Thanks @legitnull for great TeaCache-PR and TaylorSeer-PR.
• 2025-07-01: OmniGen2 is supported by ComfyUI official, thanks !!
• 2025-06-30: Training code is available, see fine-tuning for details.
• 2025-06-28: We release OmniContext benchmark. The evaluation codes are in omnicontext.
• 2025-06-24: Technical Report is available.
• 2025-06-23: We’ve updated our code and HF model—OmniGen2 now runs without flash-attn. Users can still install it for optimal performance.
• 2025-06-20: Updated resource requirements, adding CPU offload support for devices with limited VRAM.
• 2025-06-16: Gradio and Jupyter is available. Online Gradio Demo: Demo1; Chat-Demo1; see more demo links in gradio section
• 2025-06-16: We release OmniGen2, a multimodal generation model, model weights can be accessed in huggingface and modelscope.

Introduction

• Visual Understanding: Inherits the robust ability to interpret and analyze image content from its Qwen-VL-2.5 foundation.
• Text-to-Image Generation: Creates high-fidelity and aesthetically pleasing images from textual prompts.
• Instruction-guided Image Editing: Executes complex, instruction-based image modifications with high precision, achieving state-of-the-art performance among open-source models.
• In-context Generation: A versatile capability to process and flexibly combine diverse inputs—including humans, reference objects, and scenes—to produce novel and coherent visual outputs.

Demonstration of OmniGen2's overall capabilities.

Demonstration of OmniGen2's image editing capabilities.

Demonstration of OmniGen2's in-context generation capabilities.

📌 TODO

• Technical report.
• Support CPU offload and improve inference efficiency.
• In-context generation benchmark: OmniContext.
• Integration of diffusers.
• Training datasets.
• Training data construction pipeline.
• ComfyUI Demo (commuity support will be greatly appreciated!).

🚀 Quick Start

🛠️ Environment Setup

✅ Recommended Setup

🌏 For users in Mainland China

🧪 Run Examples

🌐 Gradio Demo

• Online Demo: HF Spaces. Beyond Hugging Face Spaces, we are temporarily allocating additional GPU resources to ensure smooth access to the online demos. If you notice a long queue for a particular link, please try other links:
  Demo1, Demo2, Demo3, Demo4
  Chat-Demo1, Chat-Demo2, Chat-Demo3, Chat-Demo4

• Run Locally:
  bash

  1# for only generating image 2pip install gradio 3python app.py 4# Optional: Share demo with public link (You need to be able to access huggingface) 5python app.py --share 6 7# for generating image or text 8pip install gradio 9python app_chat.py

💡 Usage Tips

• text_guidance_scale: Controls how strictly the output adheres to the text prompt (Classifier-Free Guidance).
• image_guidance_scale: This controls how much the final image should resemble the input reference image.
  • The Trade-off: A higher value makes the output more faithful to the reference image's structure and style, but it might ignore parts of your text prompt. A lower value (~1.5) gives the text prompt more influence.
  • Tip: For image editing task, we recommend to set it between 1.2 and 2.0; for in-context generateion task, a higher image_guidance_scale will maintian more details in input images, and we recommend to set it between 2.5 and 3.0.
• max_pixels: Automatically resizes images when their total pixel count (width × height) exceeds this limit, while maintaining its aspect ratio. This helps manage performance and memory usage.
  • Tip: Default value is 1024*1024. You can reduce this value if you encounter memory issues.
• max_input_image_side_length: Maximum side length for input images.
• negative_prompt: Tell the model what you don't want to see in the image.
  • Example: blurry, low quality, text, watermark
  • Tip: For the best results, try experimenting with different negative prompts. If you're not sure, just use the default negative prompt.
• enable_model_cpu_offload: Reduces VRAM usage by nearly 50% with a negligible impact on speed.
  • This is achieved by offloading the model weights to CPU RAM when they are not in use.
  • See: Model Offloading
• enable_sequential_cpu_offload: Minimizes VRAM usage to less than 3GB, but at the cost of significantly slower performance.
  • This works by offloading the model in submodules and loading them onto the GPU sequentially as needed.
  • See: CPU Offloading
• cfg_range_start, cfg_range_end: Define the timestep range where CFG is applied. Per this paper, reducing cfg_range_end can significantly decrease inference time with a negligible impact on quality.

1. Use High-Quality Images

• Provide clear images, preferably with a resolution greater than 512×512 pixels.
• Small or blurry inputs will result in low-quality outputs.

1. Be Specific with Instructions

• Clearly describe both what to change and how you want it changed.
• For in-context generation tasks, explicitly state which elements should come from which image. For example, instead of "Add bird to desk", say "Add the bird from image 1 onto the desk in image 2."

1. Prioritize English The model currently performs best with English prompts.

💻 Resources Requirement

Inference Efficiency of OmniGen2.

❤️ Citing Us

License