Reprinted from Hugging Face Tutorial, Author: Heihe001

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🎌 Z-Anime | Full Anime Fine-Tune on Z-Image Base

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✨ What is Z-Anime?

Z-Anime is a full fine-tune of Alibaba's Z-Image Base architecture — not a LoRA merge, but a fully trained anime-focused model family built from the ground up.

Built on the S3-DiT (Single-Stream Diffusion Transformer, 6B parameters), Z-Anime inherits the strong foundation of Z-Image Base: rich diversity, strong controllability, full negative prompt support, and a high ceiling for fine-tuning — now adapted for anime-style generation.

This repository contains the full Z-Anime family:

Variant	Focus	Best For
🎌 Z-Anime Base	Highest quality	Final renders, full control
⚡ Z-Anime Distill-8-Step	Speed + quality balance	Everyday generation
🚀 Z-Anime Distill-4-Step	Maximum speed	Fast iteration, batches
📦 GGUF Variants	Lower memory usage	Low VRAM / CPU / AMD-friendly workflows
📦 AIO Variants	Single-file convenience	Easy ComfyUI setup
🐍 Diffusers Folder	from_pretrained() ready	Python pipelines, further fine-tuning

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🎯 Key Features

• ✅ Full fine-tune on Z-Image Base — not a LoRA merge
• ✅ Rich anime aesthetics with strong style diversity
• ✅ Natural language prompting — works best with descriptive prompts, not tag lists
• ✅ High diversity across characters, poses, compositions, and layouts
• ✅ LoRA training ready — strong base for further fine-tuning
• ✅ Partially NSFW capable
• ✅ 8GB VRAM compatible
• ✅ GGUF variants available
• ✅ AIO variants available (Base, 4-Step, 8-Step)

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🗺️ Z-Anime Roadmap

✅ Released

🎌 Z-Anime Base

Full fine-tune on Z-Image Base — BF16 & FP8

⚡ Z-Anime Distill-8-Step

BF16 & FP8 — fast anime generation in 8 steps, CFG 1.0

🚀 Z-Anime Distill-4-Step

BF16 & FP8 — ultra-fast anime generation in 4 steps, CFG 1.0

📦 GGUF Variants

Available for low VRAM, CPU inference, and AMD-friendly workflows.

• Z-Anime-Base-Q8_0 — Q8_0 quantization (~6.73 GB)
• Z-Anime-Base-Q4_K_S — Q4_K_S quantization (~4.2 GB)

📦 AIO Variants

All-in-one checkpoints with image model + VAE + Text Encoder integrated in a single file.
Available for Base, Distill-4-Step and Distill-8-Step — each in BF16 & FP8.

🧩 VAE & Text Encoder

The required VAE (ae.safetensors) and Text Encoder (qwen_3_4b.safetensors) are also included in this repository for users running the standard (non-AIO) variants.

🐍 Diffusers Folder

The full Diffusers-format folder (diffusers/) is included — drop-in compatible with ZImagePipeline.from_pretrained() for Python users who want to run inference outside ComfyUI or use Z-Anime as a starting point for further fine-tuning.

More updates coming — follow to stay notified! 🎌

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📦 Versions Overview

🟢 BF16 (~12GB)

Maximum precision. BFloat16 format with minimal quality compromise. Best for final renders, careful work, and LoRA training.

🟡 FP8 (~6GB)

Recommended for most users. Smaller files, faster downloads, and excellent quality with only minor tradeoffs compared to BF16.

🔵 GGUF

Optimized for lightweight inference setups, especially useful for low VRAM, CPU inference, or alternative backends.

🟣 AIO

All-in-one checkpoints with image model + Text Encoder + VAE integrated into a single file for the easiest setup. Available for Base, Distill-4-Step and Distill-8-Step.

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🎌 Z-Anime Base

The foundation of the Z-Anime family.

A full fine-tune with the highest quality ceiling, the widest creative range, and full negative prompt support.

Recommended Settings

steps: 28-50
cfg: 3.0-5.0   # up to 9.0 possible
sampler: euler_ancestral
scheduler: beta
negative_prompt: strongly recommended

CFG Guide

• 3.0–5.0 → sweet spot for balanced quality and creativity
• 5.0–7.0 → tighter prompt adherence
• 7.0–9.0 → maximum control, but watch for oversaturation
• Above 9.0 → not recommended

Negative prompts have full effect on Z-Anime Base and are highly recommended.

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