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README.md
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---
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license: mit
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: control_images
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list: image
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- name: control_mask
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dtype: image
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- name: target_image
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dtype: image
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- name: prompt
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dtype: string
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splits:
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- name: train
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num_bytes: 8458819
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num_examples: 20
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- name: test
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num_bytes: 2807087
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num_examples: 2
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download_size: 11241583
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dataset_size: 11265906
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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---
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license: mit
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: control_images
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list: image
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- name: control_mask
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dtype: image
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- name: target_image
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dtype: image
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- name: prompt
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dtype: string
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splits:
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- name: train
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num_bytes: 8458819
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num_examples: 20
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- name: test
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num_bytes: 2807087
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num_examples: 2
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download_size: 11241583
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dataset_size: 11265906
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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task_categories:
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- image-to-image
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language:
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- en
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tags:
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- face_seg
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pretty_name: tsien
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size_categories:
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- n<1K
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---
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This is the dataset used for FluxKontext or Qwen-Image-Edit training with the task image-text2image.
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This is a good adtaset that can very fine the effect of finetuning, where the base model is not good at
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Refer this for the lora finetune model [**qwen-image-edit-lora-face-segmentation**](https://huggingface.co/TsienDragon/qwen-image-edit-lora-face-segmentation)
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Usage of the dataset
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```
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dd = load_editing_dataset("TsienDragon/face_segmentation_20")
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sample = dd["test"][0]
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```
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Example of the data structure
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```bash
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DATASET_ROOT/train
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├── control_images
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│ ├── 060002_4_028450_FEMALE_30.png
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│ ├── 060002_4_028450_FEMALE_30_1.png
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│ ├── 060002_4_028450_FEMALE_30_2.jpg
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│ ├── 060002_4_028450_FEMALE_30_mask.png
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│ ├── 060003_4_028451_FEMALE_65.png
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│ ├── 060003_4_028451_FEMALE_65_1.png
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│ └── 060003_4_028451_FEMALE_65_mask.png
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└── training_images
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├── 060002_4_028450_FEMALE_30.txt
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├── 060002_4_028450_FEMALE_30.png
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├── 060003_4_028451_FEMALE_65.txt
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└── 060003_4_028451_FEMALE_65.png
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```
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