Z-Image Turbo LoRA Collection 2025: 80+ Community Favorites | Z Image LoRA Guide

Z-Image Turbo's community ecosystem is growing rapidly. In just the past week, hundreds of new Z-Image-optimized LoRAs appeared on Civitai.

We curated 80+ high-quality LoRAs covering:

• Game characters (Overwatch, Baldur's Gate, Zelda, etc.)
• Anime characters (Naruto, One Piece, Frieren, etc.)
• Style effects (pixel art, 80s movie, cyberpunk, etc.)
• AI portraits (Asian faces, European styles, AI influencers, etc.)

Note: All LoRAs are from the Civitai community. Please respect each LoRA's usage license. LoRAs with NSFW level >10 are marked in the table.

---

Selection Criteria

We filtered based on:

1. Compatibility - Explicitly supports Z-Image Turbo or SDXL/Illustrious
2. Quality - High-quality preview images, positive community feedback
3. Diversity - Covers different categories, avoids duplicates
4. Freshness - Primarily recent releases

---

Complete LoRA List

Here's our full curated list, sorted alphabetically:

---

Category Highlights

Game Characters

Anime Characters

Style Effects

AI Portraits

---

Python Scraper Script

We're sharing our Python script for batch-collecting Z-Image LoRA information from Civitai:

#!/usr/bin/env python3
"""
Civitai Z-Image LoRA Scraper
Fetches Z-Image Turbo related LoRA information from Civitai

Usage:
1. pip install requests
2. python civitai_lora_scraper.py
3. Results saved to loras.csv
"""

import requests
import csv
import time
from typing import Optional

CIVITAI_API = "https://civitai.com/api/v1"

def search_loras(
    query: str = "z-image",
    limit: int = 100,
    sort: str = "Newest"
) -> list:
    """Search for LoRA models on Civitai"""

    params = {
        "query": query,
        "types": "LORA",
        "sort": sort,
        "limit": limit,
        "nsfw": "true",  # Include all content
    }

    all_models = []
    url = f"{CIVITAI_API}/models"

    while url and len(all_models) < limit:
        try:
            response = requests.get(url, params=params if url == f"{CIVITAI_API}/models" else None)
            response.raise_for_status()
            data = response.json()

            models = data.get("items", [])
            all_models.extend(models)

            # Get next page
            metadata = data.get("metadata", {})
            url = metadata.get("nextPage")

            print(f"Fetched {len(all_models)} models...")
            time.sleep(0.5)  # Rate limiting

        except Exception as e:
            print(f"Request error: {e}")
            break

    return all_models[:limit]


def extract_lora_info(model: dict) -> Optional[dict]:
    """Extract LoRA information from model data"""

    try:
        # Get latest version
        versions = model.get("modelVersions", [])
        if not versions:
            return None

        latest = versions[0]

        # Get file info
        files = latest.get("files", [])
        safetensors_file = next(
            (f for f in files if f.get("name", "").endswith(".safetensors")),
            files[0] if files else {}
        )

        # Get preview image
        images = latest.get("images", [])
        preview_url = images[0].get("url") if images else ""
        nsfw_level = images[0].get("nsfwLevel", 0) if images else 0

        # Get trigger words
        trigger_words = latest.get("trainedWords", [])
        trigger = ", ".join(trigger_words) if trigger_words else ""

        return {
            "name": model.get("name", ""),
            "version_id": latest.get("id", ""),
            "filename": safetensors_file.get("name", ""),
            "trigger": trigger,
            "civitai_url": f"https://civitai.com/models/{model.get('id')}?modelVersionId={latest.get('id')}",
            "preview_url": preview_url,
            "nsfw_level": nsfw_level
        }

    except Exception as e:
        print(f"Parse error: {e}")
        return None


def save_to_csv(loras: list, filename: str = "loras.csv"):
    """Save to CSV file"""

    if not loras:
        print("No data to save")
        return

    fieldnames = ["name", "version_id", "filename", "trigger", "civitai_url", "preview_url", "nsfw_level"]

    with open(filename, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
        writer.writerows(loras)

    print(f"Saved {len(loras)} LoRAs to {filename}")


def main():
    # Search multiple keywords
    keywords = ["z-image", "zit", "z image turbo", "illustrious"]
    all_loras = []
    seen_ids = set()

    for keyword in keywords:
        print(f"\nSearching: {keyword}")
        models = search_loras(query=keyword, limit=50)

        for model in models:
            info = extract_lora_info(model)
            if info and info["version_id"] not in seen_ids:
                all_loras.append(info)
                seen_ids.add(info["version_id"])

    # Sort by name
    all_loras.sort(key=lambda x: x["name"].lower())

    # Save results
    save_to_csv(all_loras)

    print(f"\nTotal: {len(all_loras)} LoRAs")


if __name__ == "__main__":
    main()

Script Features

1. Install dependencies: pip install requests
2. Run script: python civitai_lora_scraper.py
3. Output file: loras.csv

The script searches multiple keywords (z-image, zit, illustrious, etc.), automatically deduplicates, and saves to CSV format.

---

Usage Tips

Basic Usage

# Loading LoRA in ComfyUI
LoRA weight: 0.8
Trigger words: [see table above]

Best Practices

1. Start with low weights - Begin at 0.5, gradually increase to 0.8-1.0
2. Use trigger words - Some LoRAs require specific triggers to activate
3. Mix and match - One character LoRA + one style LoRA often produces better results
4. Check compatibility - Prioritize LoRAs labeled for Z-Image or Illustrious

Recommended Combinations

---

Community Growth

The Z-Image Turbo community ecosystem is thriving:

1. LoRA explosion - New Z-Image-optimized LoRAs appear daily
2. Quality improvement - From simple characters to complex styles, coverage expands
3. Cross-platform sharing - Quality content across Civitai, LiblibAI, and other platforms
4. Technical iteration - Community-shared training parameters and techniques mature rapidly

We'll continue updating this list. Share your favorite LoRA discoveries in the comments!

---

Frequently Asked Questions

What is LoRA and how does Z-Image Turbo use it?

LoRA (Low-Rank Adaptation) is a lightweight fine-tuning technique that adds new styles or characters without modifying the base model. Z-Image Turbo fully supports LoRA - just specify the LoRA weights during generation.

What LoRA formats does Z-Image Turbo support?

Z-Image Turbo supports standard .safetensors format LoRAs, with high compatibility for Stable Diffusion XL and Illustrious series LoRAs.

How do I know if a LoRA works with Z-Image Turbo?

Check if the LoRA description mentions SDXL, Illustrious, or Z-Image support. All LoRAs in this article have been community-verified for Z-Image Turbo.

What weight should I set for LoRAs?

Start with 0.7-0.8 and adjust based on results. Character LoRAs typically work best at 0.8-1.0, while style LoRAs work well at 0.5-0.7.

Can I use multiple LoRAs at once?

Yes, but keep total weight under 1.5. For example, using a character LoRA (0.8) with a style LoRA (0.5) usually produces great results.

---

References

• Z-Image Turbo Model
• Civitai - Z-Image LoRA
• Z-Image GitHub Repository

---

Try these LoRAs with Z-Image Turbo - free, no login required.

---

Keep Reading

• Z-Image Turbo vs Flux: 2025 Showdown — Complete comparison of speed, quality, and ecosystem
• Best Sampler for Z-Image Turbo — Complete sampler selection guide
• Z-Image Prompt Engineering — Prompt writing techniques