strkyyy/anime-ghibli-soft

Soft Watercolor Anime SDXL LoRA

What it does

Transform your images into hand-painted watercolor anime art reminiscent of classic Japanese animated film studios, featuring lush natural backgrounds, soft warm light, gentle characters, atmospheric depth, painterly clouds, and whimsical cozy charm.

How to use

Add the trigger word 'gh1bl1st' to your prompt. For optimal results, set the strength between 0.6 and 1.0 with a recommended default of 0.85 using the Euler A sampler at around 25-30 steps.

Best for

• Scenes of children playing in natural settings
• Quaint village or rural landscapes
• Cozy indoor scenes like kitchens or libraries
• Whimsical fantasy elements such as dragons and fairies
• Gentle, atmospheric moments like sunsets or early mornings

Tips

1. Adjust the strength based on how much watercolor effect you want.
2. Try different samplers to see which one brings out the softness of this style best.

Example Prompts

gh1bl1st A cozy anime café scene with soft warm light filtering through windows, creating a gentle atmosphere.

gh1bl1st Portrait of an anime girl with long hair, standing in front of lush natural backgrounds, looking into the distance thoughtfully.

gh1bl1st Whimsical object arrangement on a desk, with painterly clouds and atmospheric depth, capturing a serene moment.

gh1bl1st A peaceful riverbank scene where an anime boy is sitting by a stream, surrounded by soft watercolor reflections.

gh1bl1st A mood-driven piece showcasing gentle characters under soft warm light, evoking feelings of nostalgia and coziness.

gh1bl1st Abstract composition with painterly clouds and atmospheric depth, creating a dreamy and whimsical ambiance.

Trigger word

Activate with gh1bl1st. Recommended strength: 0.9.

Showcase

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/anime-ghibli-soft")
image = pipe("gh1bl1st, your prompt", num_inference_steps=30).images[0]