Hadimeeee/mongle-lora-v3-32bit

Mongle 32-bit Pixel Art LoRA

SDXL DreamBooth LoRA for converting stuffed animal photos into modern 32-bit style pixel art characters.

This repository is intended to be used as a HuggingFace Hub package for RunPod. HuggingFace stores the LoRA weights and pipeline code; inference runs on a RunPod GPU server.

input image
  -> rembg background removal
  -> OpenCV Canny edge extraction
  -> SDXL ControlNet Img2Img
  -> Mongle 32-bit LoRA
  -> output image

No color quantization or pixelation post-processing is applied in this version.

Model Details

Item	Value
Base model	stabilityai/stable-diffusion-xl-base-1.0
Training method	DreamBooth LoRA
LoRA rank	32
Training steps	2,000
Learning rate	1e-4
Dataset	243 images after copyright-risk exclusion
Style	modern 32-bit pixel art, chibi proportions, soft shading

Runtime Components

Component	Role
rembg	removes photo background
OpenCV Canny	extracts silhouette edges
diffusers/controlnet-canny-sdxl-1.0	preserves input shape
stabilityai/stable-diffusion-xl-base-1.0	base image generation model
this LoRA	applies Mongle 32-bit pixel art style

RunPod Usage

Install dependencies:

pip install -r requirements.txt

Set cache paths to /workspace:

source setup_runpod_env.sh

RunPod serverless entrypoint:

python runpod_handler.py

The handler expects a base64-encoded image:

{
  "input": {
    "image": "<base64 png or jpeg>",
    "num_inference_steps": 50,
    "guidance_scale": 7.5,
    "controlnet_conditioning_scale": 0.8,
    "strength": 0.6
  }
}

The response returns a base64-encoded PNG:

{
  "image": "<base64 png>",
  "rembg_ok": true
}

Local/RunPod Python Example

from huggingface_hub import snapshot_download
from PIL import Image
import sys

repo_dir = snapshot_download("Hadimeeee/mongle-lora-v3-32bit")
sys.path.insert(0, repo_dir)

from pipeline import load_pipeline

pipe = load_pipeline(repo_dir)
image = Image.open("your_image.jpg").convert("RGB")
result = pipe(image)["image"]
result.save("mongle_32bit_result.png")

Parameter Grid Testing

Use test_grid.py on RunPod to compare prompts and generation settings. This script does not apply color quantization or pixelation post-processing.

python test_grid.py \
  --input image \
  --output outputs/grid_test \
  --strengths 0.45,0.55,0.65,0.75 \
  --controlnet-scales 0.6,0.8,1.0 \
  --guidance-scales 7.5 \
  --steps 30 \
  --prompt-presets reference \
  --limit 0

By default, only *_grid.png comparison files are saved. Add --save-individual if each generated image should also be saved.

Recommended first pass:

Parameter	Values
strength	0.45, 0.55, 0.65, 0.75
controlnet_conditioning_scale	0.6, 0.8, 1.0
guidance_scale	7.5
steps	30 for search, 50 for final candidates

Notes

The LoRA file does not include SDXL, ControlNet, or rembg. Those components are loaded at inference time by the pipeline code.