README.md

GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation, NeurIPS 2024

Phillip Y. Lee*, Taehoon Yoon*, Minhyuk Sung (* equal contribution)

| Website | Paper | arXiv |

🚀 Introduction

This repository contains the official implementation of GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation.

GrounDiT is a training-free method for spatial grounding in text-to-image generation, using Diffusion Transformers (DiT) to generate precise, controllable images based on user-specified bounding boxes. More results can be viewed on our project page.

We introduce a novel training-free spatial grounding technique for text-to-image generation using Diffusion Transformers (DiT). Spatial grounding with bounding boxes has gained attention for its simplicity and versatility, allowing for enhanced user control in image generation. However, prior training-free approaches often rely on updating the noisy image during the reverse diffusion process via backprop- agation from custom loss functions, which frequently struggle to provide precise control over individual bounding boxes. In this work, we leverage the flexibility of the Transformer architecture, demonstrating that DiT can generate noisy patches corresponding to each bounding box, fully encoding the target object and allowing for fine-grained control over each region. Our approach builds on an intriguing property of DiT, which we refer to as semantic sharing. Due to semantic sharing, when a smaller patch is jointly denoised alongside a generatable-size image, the two become "semantic clones". Each patch is denoised in its own branch of the gen- eration process and then transplanted into the corresponding region of the original noisy image at each timestep, resulting in robust spatial grounding for each bound- ing box. In our experiments on the HRS and DrawBench benchmarks, we achieve state-of-the-art performance compared to previous training-free spatial grounding approaches.

🛠️ Environment

1. Download PyTorch with CUDA version 11.8. (Any PyTorch version with ≥ 2.0.0 would be fine.)

2. Install other dependencies via pip install -r requirements.txt

🔥 Inference

Jupyter Notebook demo is available at groundit_demo.ipynb.

Or you can generate image via following command

python main.py 

Additional Arguments

Argument	Description
--save_dir	Directory where the results will be saved.
--model_version	Model version to use. Options: 512 or 1024.
--input_config_path	Path to the input configuration file.
--gpu_id	GPU ID to use for inference Default: 0
--seed	Random seed.
--num_inference_steps	Number of inference steps to perform. Default: 50
--groundit_gamma	Apply GrounDiT for the initial γ% steps. Default: 0.5

📝 Input Data Format

You can find the example of input data format in the config.json file.

Detailed explanation of the input data format

{
    "0": {
        "prompt": "a wide view picture of an antique living room with a chair, table, fireplace, and a bed",
        "phrases": ["chair", "table", "fireplace", "bed"],
        "bboxes": [[[0.0, 0.4, 0.15, 1.0]], [[0.25, 0.6, 0.45, 1.0]], [[0.475, 0.1, 0.65, 0.9]], [[0.7, 0.5, 1.0, 1.0]]],
        "height": 288,
        "width": 896
    }
}

Fields

1. prompt

   • Type: str
   • Description: The input text describing the image to be generated.
   • Example: "a wide view picture of an antique living room with a chair, table, fireplace, and a bed"

2. phrases

   • Type: list[str]
   • Description: A list of object descriptions (phrase) that you want to position in the image.
   • IMPORTANT: Each phrase must be presented inside the prompt.
   • Notes:
     • Each phrase can contain multiple words (e.g., brown bear).
   • Example: ["chair", "table", "fireplace", "bed"]

3. bboxes

   • Type: list[list[list[float]]]
   • Description: A list containing bounding box coordinates for each phrase.
   • IMPORTANT: The order of bounding boxes list must match the order of phrases.
   • Notes:
     • Each phrase can have multiple bounding boxes.
     • Bounding boxes follow the format [ul_x, ul_y, lr_x, lr_y], where:
       • ul_x: x-coordinate of the upper-left corner (0 to 1).
       • ul_y: y-coordinate of the upper-left corner (0 to 1).
       • lr_x: x-coordinate of the lower-right corner (0 to 1).
       • lr_y: y-coordinate of the lower-right corner (0 to 1).
   • Example:

     "bboxes": [
         [[0.0, 0.4, 0.15, 1.0]],    // Bounding box for "chair"
         [[0.25, 0.6, 0.45, 1.0]],   // Bounding box for "table"
         [[0.475, 0.1, 0.65, 0.9]],  // Bounding box for "fireplace"
         [[0.7, 0.5, 1.0, 1.0]]      // Bounding box for "bed"
     ]

4. height and width

   • Type: int
   • Description: The dimensions of the generated image in pixels.
   • Notes:
     • Use either height and width or aspect_ratio. At least one should be present.
     • Specify both height and width for exact resolution.
     • Values that deviate significantly from the generatable resolutions may result in implausible images.
   • Example:

     "height": 288,
     "width": 896

5. aspect_ratio

   • Type: float
   • Description: The aspect ratio of the image (width / height).
   • Notes:
     • Use either height and width or aspect_ratio. At least one should be present.
     • Recommended range: [0.25, 4.0]
     • Extreme values may result in unrealistic images.

---

Guidelines for Resolution

• You can consult reasonable resolution values in the ASPECT_RATIO_512_BIN or ASPECT_RATIO_1024_BIN dictionaries, depending on your specified model_version, inside the /groundit/pipeline_groundit.py file.

• For the details of generatable resolution, please check Appendix D in our paper.

🙏 Acknowledgements

This code is heavily based on the diffusers library, and the official code for PixArt-α and R&B. We sincerely thank the authors for open-sourcing their code.

🎓 Citation

@inproceedings{lee2024groundit,
  title={GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation},
  author={Lee, Phillip Y. and Yoon, Taehoon and Sung, Minhyuk},
  booktitle={Advances in Neural Information Processing Systems},
  year={2024}
}