README.md

metadata

title: Indian Art Generator
emoji: 🎨
colorFrom: pink
colorTo: red
sdk: gradio
sdk_version: 4.42.0
python_version: '3.12'
app_file: app.py
pinned: false

Indian Art LoRA API

Generate Indian traditional art (Madhubani, Warli, Gond, Pattachitra, Tanjore, etc.) using SD 1.5 + LoRA.

---

Features

• Built-in negative prompts to reduce Western/modern bias
• Supports LoRA (local or HF Hub)
• Simple API + Gradio UI

---

Quick Start (Local)

git clone https://github.com/yourusername/INDI_ART.git
cd INDI_ART

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

pip install -r requirements.txt

Configure LoRA

.env:

LORA_PATH=your-username/indian-art-lora

or local:

LORA_PATH=./models/lora.safetensors

Run

python app.py

• UI: http://localhost:7860
• API: http://localhost:7860/api/predict/generate_api

---

API Usage

Python

from gradio_client import Client

client = Client("https://<space>.hf.space")

result = client.predict(
    "peacock with geometric feathers",
    "madhubani",
    api_name="/generate_api"
)

cURL

curl -X POST https://<space>.hf.space/api/predict/generate_api \
-H "Content-Type: application/json" \
-d '{"data": ["warli village scene", "warli"]}'

---

Configuration

Variable	Description	Default
LORA_PATH	LoRA weights (HF Hub ID or local path)	None
BASE_MODEL	Base model repo	Realistic_Vision_V5.1_noVAE
DEFAULT_STEPS	Inference steps	30
DEFAULT_GUIDANCE	CFG scale	7.5
DEFAULT_LORA_SCALE	LoRA strength	0.8
DEVICE	cuda or cpu	cuda
CACHE_DIR	Model cache directory	/tmp

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Notes

• Uses SD 1.5 compatible LoRA (not SDXL)
• Keep resolution ≤ 768×768 on CPU spaces
• CPU spaces: expect 3–8 min per generation at 30 steps

---

Structure

src/
  config.py
  model.py
  utils.py
app.py
requirements.txt
Dockerfile

---

License

MIT