SachinHatikankan/warli-sdxl-dora

Warli Finetuning DoRA

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Model

Base Model	stabilityai/stable-diffusion-xl-base-1.0
VAE	madebyollin/sdxl-vae-fp16-fix
Fine-tuning Method	LoRA (rank 16)
Task	Text-to-image · style transfer
Trigger Word	wrli style
Training Steps	1500
Resolution	768
License	MIT

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Dataset

21 handpicked images of authentic Warli paintings, sourced respectfully and curated for consistency, white-on-brown palette, traditional stick-figure grammar, and varied subjects (dancing, farming, the tarpa dance circle). The set was kept deliberately tight and trained with a single instance prompt to bind the style cleanly to the trigger.

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Training Target

The DoRA is optimised to reproduce:

• White pigment figures on an earthen brown background
• Geometric stick-figure anatomy (triangular torsos, circular heads)
• Dense circular and concentric narrative compositions
• Sun/wheel motifs and decorative borders
• Clean high-contrast line-art

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Usage

Prepend wrli style to your prompt. Short prompts work best — long, detailed prompts can override the style binding (see Limitations).

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipe.load_lora_weights("SachinHatikankan/warli-sdxl-dora")

image = pipe("wrli style, a village festival", guidance_scale=9.0).images[0]
image.save("warli.png")

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Limitations

• Trained with a single instance prompt, so the model has strong style binding but weak subject control. Short prompts keep the trigger dominant; long, semantically heavy prompts (e.g. named landmarks) can override the style and revert toward base-model realism.
• Best suited to generic Warli-friendly subjects (people, animals, villages, activities) rather than specific named objects.
• Optimised for flat line-art and not photorealism. Trained at 768px due to source image resolution constraints.