This repository contains LoRA (Low-Rank Adaptation) weights designed to infuse images with a gritty, neon-drenched Cyberpunk aesthetic. These weights are fine-tuned specifically for the Z-Image-Turbo base model, allowing for high-quality, futuristic generations in very few steps.
π Model Details
- Developed by: srmahapatra95
- Model Type: LoRA (Image-to-Image)
- Base Model: Tongyi-MAI/Z-Image-Turbo
- Training Dataset: CyberVerse (Kaggle)
- License: MIT
π¨ Style Characteristics
- Neon Dominance: Enhances cyan, magenta, and electric blue lighting.
- Atmospheric Effects: Adds "wet look" textures, rain-slicked streets, and cinematic fog.
- Futuristic Detail: Increases complexity in mechanical parts, cybernetics, and urban architecture.
π Usage Instructions
1. Using with Diffusers
Since this is a LoRA for a Turbo model, you can achieve great results in just 1β4 steps.
import torch
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
# Load the base Turbo model
pipe = AutoPipelineForImage2Image.from_pretrained(
"Tongyi-MAI/Z-Image-Turbo",
torch_dtype=torch.float16,
variant="fp16"
).to("cuda")
# Load this Cyberpunk LoRA
pipe.load_lora_weights("srmahapatra95/Cyberpunk-Lora-Weights")
# Initial Image
init_image = load_image("[https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png)")
# Prompt
prompt = "cyberpunk style, futuristic city, neon lights, high tech, rain, 8k"
# Generate (Keep steps low for Turbo models)
image = pipe(prompt, image=init_image, strength=0.6, num_inference_steps=4).images[0]
image.save("cyberpunk_output.png")
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Base model
Tongyi-MAI/Z-Image-Turbo