Warm Hand-Painted Storybook Illustration SDXL LoRA

What it does

This LoRA model transforms your prompts into warm hand-painted gouache illustrations in a charming children's storybook style, featuring visible brushstrokes, soft matte textures, and a cozy muted palette.

How to use

Apply the trigger word 'g0u4ch3' to any prompt for an instant transformation. Ideal strength settings range from 0.6 to 1.0 depending on desired intensity; we recommend starting at 0.85 with EDSampler or LMS and around 25-30 steps.

Best for

  • Cozy scenes like a bakery in the snow
  • Whimsical characters, such as animals in unusual settings
  • Soft landscapes with rolling hills or gentle clouds
  • Magical elements like floating islands or enchanted forests
  • Childhood memories depicted through nostalgic filters

Tips

Use 'g0u4ch3' sparingly to maintain balance between your original prompt and the added illustration style. Experimenting with different samplers can yield unique results.

Example Prompts

g0u4ch3 --ar 1:1 --s 750 --q 0.9 little girl in a meadow at sunset, soft lighting, cozy mood
g0u4ch3 medieval castle by the sea, warm golden hour light, grand architecture, romantic composition
g0u4ch3 whimsical robot toy on a vintage desk, soft shadows, playful mood, close-up perspective
g0u4ch3 girl flying with wings over a forest, vibrant colors, dreamy atmosphere, dynamic action shot
g0u4ch3 enchanted garden scene, dappled sunlight through trees, serene and magical ambiance, wide-angle view
g0u4ch3 ethereal abstract swirls of color, soft pastel palette, gentle curves, soothing and mysterious

Trigger word

Activate with g0u4ch3. Recommended strength: 0.9.

Showcase

showcase 1 showcase 2 showcase 3 showcase 4 showcase 5 showcase 6 showcase 7 showcase 8

Usage with diffusers

from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipe.load_lora_weights("strkyyy/gouache-storybook")
image = pipe("g0u4ch3, your prompt", num_inference_steps=30).images[0]
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