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README.md
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---
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title: "SD Dataset Automaker:
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emoji: 🎴💆📦
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colorFrom: indigo
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colorTo: pink
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sdk: gradio
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sdk_version: 6.0.1
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app_file: app.py
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pinned: false
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license: mit
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---
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# 🎴 SD Dataset Automaker
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Anime character dataset generator for LoRA/fine-tuning.
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## ⚡ Quick Start
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1. Find show on [fancaps.net](https://fancaps.net) → copy URL
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2. Upload 3-5 reference faces of target character
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3. Run → download ZIP with images + captions
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## 🎛️ Parameters
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| Param | Default | Description |
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|-------|---------|-------------|
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| URL | — | `fancaps.net/.../showimages.php?ID-Name` |
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| Refs | — | 3-5 face images of target character |
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| Character | auto | Output name & tag (optional) |
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| Max Downloads | 200 | Frames to scrape |
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| Similarity | 32 | Lower = stricter (25-30 strict, 35+ loose) |
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| Trigger Word | — | Prepends to all captions |
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## 🔌 MCP Server
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[](https://huggingface.co/docs/hub/spaces-mcp-servers)
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**SSE:** `https://nekochu-sd-dataset-automaker.hf.space/gradio_api/mcp/sse`
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<details>
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<summary>Claude Desktop config</summary>
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```json
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{
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"mcpServers": {
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"sd-dataset-automaker": {
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"command": "npx",
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"args": ["mcp-remote", "https://nekochu-sd-dataset-automaker.hf.space/gradio_api/mcp/sse"]
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}
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}
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}
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```
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</details>
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<details>
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<summary>Python API (Gallery)</summary>
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```python
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from gradio_client import Client, handle_file
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client = Client("Nekochu/SD-Dataset-Automaker")
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zip_path, log = client.predict(
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q="https://fancaps.net/anime/showimages.php?3092-Cowboy_Bebop",
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c="spike_spiegel",
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imgs=[
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{"image": handle_file("https://huggingface.co/spaces/Nekochu/SD-Dataset-Automaker/resolve/main/from_url_spike_spiegel1.jpg"), "caption": None},
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{"image": handle_file("https://huggingface.co/spaces/Nekochu/SD-Dataset-Automaker/resolve/main/from_url_spike_spiegel2.jpg"), "caption": None},
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],
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files=None, mi=200, th=32, at="", an=True, mo=False, tv=False,
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api_name="/process"
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)
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print(f"ZIP: {zip_path}")
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```
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</details>
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<details>
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<summary>Python API (File - MCP compatible)</summary>
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```python
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from gradio_client import Client, handle_file
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client = Client("Nekochu/SD-Dataset-Automaker")
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zip_path, log = client.predict(
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q="https://fancaps.net/anime/showimages.php?3092-Cowboy_Bebop",
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c="spike_spiegel",
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imgs=None, # Skip Gallery
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files=[ # Use File input (simpler schema, MCP-friendly)
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handle_file("spike_ref1.jpg"),
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handle_file("spike_ref2.jpg"),
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],
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mi=200, th=32, at="", an=True, mo=False, tv=False,
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api_name="/process"
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)
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print(f"ZIP: {zip_path}")
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```
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</details>
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---
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*CPU runtime ~5-10 min* | Based on [Maximax67/LoRA-Dataset-Automaker](https://github.com/Maximax67/LoRA-Dataset-Automaker)
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---
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title: "SD Dataset Automaker: Scrap image → Face similarity → +Tag"
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emoji: 🎴💆📦
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colorFrom: indigo
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colorTo: pink
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sdk: gradio
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sdk_version: 6.0.1
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app_file: app.py
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pinned: false
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license: mit
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---
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+
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# 🎴 SD Dataset Automaker
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+
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| 15 |
+
Anime character dataset generator for LoRA/fine-tuning.
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+
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+
## ⚡ Quick Start
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+
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+
1. Find show on [fancaps.net](https://fancaps.net) → copy URL
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| 20 |
+
2. Upload 3-5 reference faces of target character
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3. Run → download ZIP with images + captions
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| 22 |
+
|
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+
## 🎛️ Parameters
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| 24 |
+
|
| 25 |
+
| Param | Default | Description |
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+
|-------|---------|-------------|
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| 27 |
+
| URL | — | `fancaps.net/.../showimages.php?ID-Name` |
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| 28 |
+
| Refs | — | 3-5 face images of target character |
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| 29 |
+
| Character | auto | Output name & tag (optional) |
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| 30 |
+
| Max Downloads | 200 | Frames to scrape |
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| 31 |
+
| Similarity | 32 | Lower = stricter (25-30 strict, 35+ loose) |
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| 32 |
+
| Trigger Word | — | Prepends to all captions |
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| 33 |
+
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+
## 🔌 MCP Server
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+
|
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+
[](https://huggingface.co/docs/hub/spaces-mcp-servers)
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+
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**SSE:** `https://nekochu-sd-dataset-automaker.hf.space/gradio_api/mcp/sse`
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+
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<details>
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<summary>Claude Desktop config</summary>
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+
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```json
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{
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"mcpServers": {
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"sd-dataset-automaker": {
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"command": "npx",
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"args": ["mcp-remote", "https://nekochu-sd-dataset-automaker.hf.space/gradio_api/mcp/sse"]
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}
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}
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}
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```
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</details>
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+
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+
<details>
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+
<summary>Python API (Gallery)</summary>
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+
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+
```python
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from gradio_client import Client, handle_file
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+
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client = Client("Nekochu/SD-Dataset-Automaker")
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zip_path, log = client.predict(
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q="https://fancaps.net/anime/showimages.php?3092-Cowboy_Bebop",
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c="spike_spiegel",
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imgs=[
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{"image": handle_file("https://huggingface.co/spaces/Nekochu/SD-Dataset-Automaker/resolve/main/from_url_spike_spiegel1.jpg"), "caption": None},
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{"image": handle_file("https://huggingface.co/spaces/Nekochu/SD-Dataset-Automaker/resolve/main/from_url_spike_spiegel2.jpg"), "caption": None},
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],
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files=None, mi=200, th=32, at="", an=True, mo=False, tv=False,
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api_name="/process"
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)
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print(f"ZIP: {zip_path}")
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```
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</details>
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+
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+
<details>
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+
<summary>Python API (File - MCP compatible)</summary>
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+
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+
```python
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from gradio_client import Client, handle_file
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+
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client = Client("Nekochu/SD-Dataset-Automaker")
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+
zip_path, log = client.predict(
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q="https://fancaps.net/anime/showimages.php?3092-Cowboy_Bebop",
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+
c="spike_spiegel",
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imgs=None, # Skip Gallery
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files=[ # Use File input (simpler schema, MCP-friendly)
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handle_file("spike_ref1.jpg"),
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handle_file("spike_ref2.jpg"),
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],
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mi=200, th=32, at="", an=True, mo=False, tv=False,
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api_name="/process"
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)
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print(f"ZIP: {zip_path}")
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```
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</details>
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+
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+
---
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+
*CPU runtime ~5-10 min* | Based on [Maximax67/LoRA-Dataset-Automaker](https://github.com/Maximax67/LoRA-Dataset-Automaker)
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