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

Seedream Turbo's community ecosystem is growing rapidly. In just the past week, hundreds of new Seedream-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.

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Selection Criteria

We filtered based on:

1. Compatibility - Explicitly supports Seedream 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

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Complete LoRA List

Here's our full curated list, sorted alphabetically:

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Category Highlights

Game Characters

Anime Characters

Style Effects

AI Portraits

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Python Scraper Script

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

#!/usr/bin/env python3
"""
Civitai Seedream LoRA Scraper
Fetches Seedream 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 = "seedream",
    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 = ["seedream", "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 (seedream, zit, illustrious, etc.), automatically deduplicates, and saves to CSV format.

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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 Seedream or Illustrious

Recommended Combinations

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Community Growth

The Seedream Turbo community ecosystem is thriving:

1. LoRA explosion - New Seedream-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!

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Frequently Asked Questions

What is LoRA and how does Seedream Turbo use it?

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

What LoRA formats does Seedream Turbo support?

Seedream 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 Seedream Turbo?

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

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References

• Seedream Turbo Model
• Civitai - Seedream LoRA
• Seedream GitHub Repository

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

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