README.md

metadata

license: cc-by-nc-sa-4.0
task_categories:
  - image-to-image
language:
  - en
  - zh
  - ja
tags:
  - diffusion
  - artifacts
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': gt
            '1': output
            '2': raw
  splits:
    - name: train
      num_bytes: 86469821780
      num_examples: 60000
    - name: test
      num_bytes: 40709999457
      num_examples: 28908
  download_size: 124732564943
  dataset_size: 127179821237
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

Diffusion-Degradation Restoration Dataset (DDRD)

扩散退化恢复数据集

DDRD is a specialized collection designed to study and mitigate degradation phenomena occurring during diffusion-based image processing, specifically focusing on artifacts introduced during Color Enhancement or Color Transfer tasks.

DDRD 是一个专门用于研究和修复扩散模型处理过程中退化现象的数据集，聚焦于颜色增强或颜色迁移任务中产生的伪影。

1. Overview | 概览

The dataset simulates quality loss in high-speed diffusion inference by utilizing the Flux.1-kontext model, combined with block cache optimizations and distillation acceleration. It captures complex fidelity loss such as color drifts and structural inconsistencies.

该数据集通过 Flux.1-kontext 模型，结合 block cache 优化与蒸馏加速技术，模拟了高速扩散推理过程中的质量损耗。它捕捉了复杂的保真度损失，如颜色漂移和结构不一致性。

注：原图数据来源于 Pexels 的开源数据集，感谢它们对开源的贡献。

2. Dataset Structure | 数据结构

The dataset contains 29,636 triplets (Total: 88,908 images). 数据集包含 29,636 组三元组（共计 88,908 张图片）。

Split	Triplets	Description
Train	20,000	Training set for restoration models
Test	9,636	Benchmark for evaluation

Data Fields

• raw: Simulated input for the diffusion process (Distorted). / 模拟扩散输入图（带退化）。
• gt: Pristine Ground Truth representing the ideal target. / 原始高质量真实目标图（无损）。
• output: Final image from the accelerated diffusion model, containing artifacts. / 经加速模型输出的、带有模拟伪影的图像。

.
├── train/
│   ├── metadata.jsonl  # 20,000 triplets
│   ├── raw/            # Distorted inputs (low-step, block cache drift)
│   ├── gt/             # High-quality ground truth
│   └── output/         # Reference outputs from specific pipelines
└── test/
    ├── metadata.jsonl  # 9,636 triplets
    ...

3. Technical Implementation | 技术细节

DDRD models the fidelity loss from modern generative pipelines: DDRD 模拟了现代生成管线中的保真度损失：

• Model Used | 使用的模型: Flux.1-kontext.
• Acceleration Scheme | 加速方案: Distillation methods are employed to simulate losses generated during low-step inference. / 采用蒸馏方法模拟低步数 (Low-step) 推理产生的损失。
• Optimization Mechanism | 优化机制: Integrated block cache logic to model color drifts and structural inconsistencies occurring during state-managed inference. / 引入 block cache 逻辑，模拟状态管理推理过程中的颜色偏移与结构不一致性。。

4. Usage | 使用方法

from datasets import load_dataset

# Load the dataset | 加载数据集
dataset = load_dataset("ModelMoe/DDRD20K")

# Access a sample | 访问样本
# Keys: 'raw', 'gt', 'output'
sample = dataset["train"][0]
sample["raw"].show()

5. License | 许可协议

This dataset is licensed under CC BY-NC-SA 4.0.

Commercial use is strictly prohibited. This includes: Training commercial models. Integration into paid services or products.

本数据集采用 CC BY-NC-SA 4.0 协议。严禁商业用途，包括但不限于： 训练商业化模型。 集成至付费服务或商业产品中。