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metadata
annotations_creators:
- crowdsourced
language:
- en
license: mit
task_categories:
- text-to-image
- image-to-image
tags:
- minecraft
- skins
- pixel-art
- texture
- diffusion
size_categories:
- n<1K
dataset_info:
features:
- name: image
dtype: image
- name: username
dtype: string
- name: uuid
dtype: string
- name: texture_id
dtype: string
- name: texture_url
dtype: string
- name: model
dtype: string
- name: caption
dtype: string
- name: tags
list: string
- name: quality
dtype: int32
splits:
- name: train
num_bytes: 265554
num_examples: 101
download_size: 257876
dataset_size: 265554
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
minecraft-skins
A curated, high-quality dataset of Minecraft character skins with descriptive captions and tags for training generative AI diffusion models (such as Stable Diffusion 1.5, SDXL, and FLUX LoRAs).
Dataset Summary
- Total Skins: 101
- Image Resolution: 64x64 RGBA (standard Minecraft 1.8+ dual-layer UV layout)
- Model Variants: Classic (4px arms) and Slim (3px arms)
- Metadata Fields:
image,username,uuid,caption,tags,model,quality
Quick Start / Usage
from datasets import load_dataset
dataset = load_dataset("honvert/minecraft-skins", split="train")
# Access first sample
sample = dataset[0]
image = sample["image"] # PIL.Image (64x64 RGBA)
caption = sample["caption"] # Text prompt
tags = sample["tags"] # List of keywords
print(f"Skin: {sample['username']} | Caption: {caption}")
image.save("sample_skin.png")
Schema
| Field | Type | Description |
|---|---|---|
image |
Image |
64x64 RGBA Minecraft skin texture map |
username |
string |
Player nickname / source handle |
uuid |
string |
Mojang player UUID |
caption |
string |
Natural language spatial description of the skin |
tags |
list[string] |
Keywords (e.g. ["medieval", "armor", "knight"]) |
model |
string |
classic (4px arms) or slim (3px arms) |
quality |
int32 |
Curated aesthetic rating (1 to 5 stars) |
texture_id |
string |
Mojang texture CDN identifier |