vaporwave-aesthetic

Vaporwave SDXL LoRA Style - Retro Gradients & Glitch Artifacts

What it does

This LoRA model captures the essence of vaporwave aesthetics, characterized by its neon pink and cyan gradients, chrome and glass surfaces, retro grids, palm trees, Roman busts, and glitch effects reminiscent of VHS artifacts. It creates a dreamy nostalgic mood that evokes memories of the 1980s-90s era.

How to use

To apply this style, include the trigger word 'v4porw4ve' in your prompt. A suggested strength range is between 0.6 and 1.0; a default value of 0.85 usually works well. Use EMA sampler with steps around 30 for optimal results.

Best for

• Adding neon pink and cyan gradients to scenes
• Enhancing retro arcade machines or other vintage electronics
• Applying glitch effects to classic marble statues
• Creating dreamy cityscapes under a pink sunset
• Incorporating palm trees in futuristic beach settings

Tips

For best results, pair this LoRA with images that already have a nostalgic or retro feel. Adjust the strength based on how prominent you want the vaporwave elements to be.

Example Prompts

v4porw4ve neon pink palm tree silhouette against chrome grid background, soft sunset light

v4porw4ve retro computer monitor with glitch effect, close-up shot showing VHS artifacts, futuristic mood

v4porw4ve serene beach scene at dusk, roman busts scattered along the shore, dreamy nostalgia

v4porw4ve abstract composition of chrome glass reflections, neon cyan lines forming a grid pattern, vibrant atmosphere

v4porw4ve woman in vintage swimsuit dancing under rainbow sky with glitched palm trees, happy and nostalgic mood

v4porw4ve futuristic cityscape at night with retro neon signs, soft rain adds moodiness to the scene

Trigger word

Activate with v4porw4ve. 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/vaporwave-aesthetic")
image = pipe("v4porw4ve, your prompt", num_inference_steps=30).images[0]