Datasets:

limingcv
/

MultiGen-20M_train

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 1145721433301.688
      num_examples: 2810604
  download_size: 5473812704
  dataset_size: 1145721433301.688
language:
  - en
task_categories:
  - text-to-image
license: cc-by-nc-4.0
tags:
  - diffusion
  - controlnet
  - image-generation
size_categories:
  - 1M<n<10M

Dataset Card for "MultiGen-20M_train"

This dataset is constructed from UniControl and is a large-scale dataset for conditional text-to-image generation. It is notably used for training and evaluation in:

The paper ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback. Its GitHub repository: https://github.com/liming-ai/ControlNet_Plus_Plus
The paper Heeding the Inner Voice: Aligning ControlNet Training via Intermediate Features Feedback (InnerControl).
- Project page: https://controlgenai.github.io/InnerControl/
- Code: https://github.com/control/InnerControl

The dataset consists of various control types like LineArt, Hed, and Depth, and is designed to improve spatial control over generated images in text-to-image diffusion models.

Abstract of "Heeding the Inner Voice":

Despite significant progress in text-to-image diffusion models, achieving precise spatial control over generated outputs remains challenging. ControlNet addresses this by introducing an auxiliary conditioning module, while ControlNet++ further refines alignment through a cycle consistency loss applied only to the final denoising steps. However, this approach neglects intermediate generation stages, limiting its effectiveness. We propose InnerControl, a training strategy that enforces spatial consistency across all diffusion steps. Our method trains lightweight convolutional probes to reconstruct input control signals (e.g., edges, depth) from intermediate UNet features at every denoising step. These probes efficiently extract signals even from highly noisy latents, enabling pseudo ground truth controls for training. By minimizing the discrepancy between predicted and target conditions throughout the entire diffusion process, our alignment loss improves both control fidelity and generation quality. Combined with established techniques like ControlNet++, InnerControl achieves state-of-the-art performance across diverse conditioning methods (e.g., edges, depth).

Data structure

Folder ./data/data_part0 contains an example subset and folder ./data/full_data contains the full data currently, 1.04M images in total. Under each .zip package, data is arranged as below:

- package_idx
--- package_idx.json # metadata
--- images
----- 00001.png
----- 00002.png
...

Each sample is a 2x2 image grid at the resolution of 1024x1024, we count the data sample as ONE sample, allowing for more space and diversity for randomly choosing different sample within the 4 images at training time.

metadata.json contains the metadata for each sample, where we show an example as below:

# Metadata structure
[
    {
      "idx": ..., # index
      "image_path": "", # path to the image
      "features": {
            "attributes": [{"attribute type": "..."}, {"attribute type": "..."}], # attribute types and the specific description in this sample.
            "subject": "..." # subject name
        },
      "prompt": "...", # the prompt used for image generation
    },
  ...
]

# An example
[
    {
      "idx": 0,
      "image_path": "0/00000.png",
      "features": {
            "attributes": [
                {
                    "lighting": "1@hard light, highlighting texture, sharp shadow"
                },
                {
                    "color": "30@soft lavender, mint green, pale peach, and baby blue"
                }
            ],
            "subject": "introspective sports portrait"
        },
       "prompt": "soft lavender, mint green, pale peach, and baby blue, hard light, highlighting texture, sharp shadow, introspective sports portrait",
    },
  ...
]

Code and supporting files

Attributes and Subjects

./code/attributes_and_subjects.json contains the attribute and subject dictionaries.

Range-sensitive filtering

./code/range_sensitive_filter.json contains our meta data for the filter, and ./code/data_filter.py converts it into a format that can be used in the dataloader.

Data Loader

./code/dataloader.py provides an example in loading the data into image pairs with the filter and balanced resampling adopted.