SeeSee21 / Z-Anime

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

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🖼️ Preview Gallery

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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 :

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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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⚡ Z-Anime Distill-8-Step

The sweet spot of the family.

Distilled from Z-Anime Base, this version delivers strong anime results in just 8 steps while keeping most of the quality.

Recommended Settings

steps: 8
cfg: 1.0   # max ~1.5
sampler: euler_ancestral
scheduler: beta
negative_prompt: limited effect

CFG Guide

• Best at CFG 1.0
• Small increases to 1.3–1.5 are possible
• Do not go above 1.5 — artifacts may appear

Negative prompts have only limited effect at this distillation level. If your workflow includes ConditioningZeroOut , prefer that instead of a large negative prompt.

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🚀 Z-Anime Distill-4-Step

The fastest Z-Anime variant.

Built for maximum throughput — ideal for rapid prototyping, quick batch generation, and speed-focused workflows.

Recommended Settings

steps: 4
cfg: 1.0   # max ~1.5
sampler: euler_ancestral
scheduler: beta
negative_prompt: limited effect

Tips for 4-Step

• Stay at CFG 1.0 for the most stable results
• Put the most important visual details early in the prompt
• An optional upscaler such as hires fix or SeedVR2 can help recover fine detail

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📐 Resolution Guide

Supported range: approximately 512 × 512 to 2048 × 2048 , any aspect ratio.
All main variants are designed to run on 8GB VRAM .

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💡 Prompting Guide

Natural language works best — not tag lists.

✅ Good

A young anime girl with long silver hair and golden eyes, wearing a traditional shrine maiden outfit with white haori and red hakama. She stands in a sunlit bamboo forest, cherry blossoms falling softly around her. Warm afternoon light filtering through the trees, detailed fabric shading, expressive face, calm serene expression, high quality anime illustration with fine line work.

❌ Avoid

anime girl, silver hair, shrine maiden, bamboo, cherry blossom, warm light

Character Portraits

Detailed anime portrait of [character], soft rim lighting, expressive eyes with detailed reflections, fine hair strands, clean linework, professional anime illustration quality.

Action Scenes

Dynamic anime [scene], dramatic angle, motion energy, speed lines, particle effects, cinematic composition, detailed shading, high quality anime art.

Backgrounds & Landscapes

Anime [location] at [time of day], [lighting], [atmosphere], beautiful background art, wallpaper quality, highly detailed environment.

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🔧 Installation

Step 1 — Download the version you want

Choose between:

• Standard / Distill models in BF16 or FP8 (+ VAE + Text Encoder)
• GGUF variants for low VRAM / CPU / AMD-friendly inference (+ VAE + Text Encoder)
• AIO variants for single-file convenience (no extra VAE / Text Encoder needed)

Step 2 — Place the files

Standard BF16 / FP8 models

ComfyUI/models/diffusion_models/
├── z-anime-base-bf16.safetensors
├── z-anime-base-fp8.safetensors
├── z-anime-distill-8step-bf16.safetensors
├── z-anime-distill-8step-fp8.safetensors
├── z-anime-distill-4step-bf16.safetensors
└── z-anime-distill-4step-fp8.safetensors

GGUF variants

ComfyUI/models/unet/
├── z-anime-base-q8_0.gguf
└── z-anime-base-q4_k_s.gguf

Text Encoder

Two text encoders are included — pick one :

ComfyUI/models/clip/
└── qwen_3_4b-bf16.safetensors          # default (Z-Image standard, BF16)
   or
└── qwen_3_4b-fp8.safetensors           # default (Z-Image standard, FP8)
   or
└── qwen_3_4b-engineer-v4-bf16.safetensors   # alternative (Engineer V4, BF16)
   or
└── qwen_3_4b-engineer-v4-fp8.safetensors    # alternative (Engineer V4, FP8)

• Default ( qwen_3_4b-* ) — the standard Z-Image text encoder, repackaged as a single .safetensors file (BF16 + FP8). This is what the model was trained against.
• Engineer V4 ( qwen_3_4b-engineer-v4-* ) — an alternative full fine-tune of the Z-Image text encoder by BennyDaBall , drop-in compatible. Often produces more varied outputs from the same seed. See Credits below for the original repo.

VAE

ComfyUI/models/vae/
└── ae.safetensors

AIO variants

For the AIO versions, you only need the single checkpoint file — no extra VAE or Text Encoder required:

ComfyUI/models/checkpoints/
├── z-anime-base-aio-bf16.safetensors
├── z-anime-base-aio-fp8.safetensors
├── z-anime-distill-8step-aio-bf16.safetensors
├── z-anime-distill-8step-aio-fp8.safetensors
├── z-anime-distill-4step-aio-bf16.safetensors
└── z-anime-distill-4step-aio-fp8.safetensors

Step 3 — Load in ComfyUI

For standard BF16 / FP8 versions

Use:

• Load Diffusion Model for the model file
• CLIP Loader for the text encoder
• VAE Loader for the VAE

For GGUF versions

• Load the GGUF model from the models/unet/ folder
• Use the same CLIP and VAE files as above

For AIO versions

Use a standard Checkpoint Loader — no extra CLIP or VAE loading required.

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📦 Custom Nodes

• rgthree-comfy
• ComfyUI-Lora-Manager
• ComfyUI-GGUF (only for the GGUF variants)
• ComfyUI-SeedVR2_VideoUpscaler (optional, only for SeedVR2 upscale)

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🐍 Using the Diffusers Folder

For Python users, the full Diffusers-format folder is included under diffusers/ and can be loaded directly with the subfolder argument:

import torch
from diffusers import ZImagePipeline

pipe = ZImagePipeline.from_pretrained(
    "SeeSee21/Z-Anime",
    subfolder="diffusers",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(
    prompt="A young anime girl with long silver hair and golden eyes, "
           "shrine maiden outfit, sunlit bamboo forest, cherry blossoms, "
           "professional anime illustration, fine line work.",
    num_inference_steps=40,
    guidance_scale=4.0,
).images[0]

image.save("z-anime-output.png")

This format is also a clean starting point for further fine-tuning (LoRA or full fine-tune) with frameworks like OneTrainer , diffusers , or kohya-ss .

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🧩 Official Workflow

A ready-to-use ComfyUI workflow that supports all variants (Base / Distill-8 / Distill-4, BF16 / FP8 / GGUF / AIO) is included in workflows/Z-Anime-Workflow-v1.json .

It includes:

• 📦 Model switch (Diffusion / GGUF / AIO loaders — toggle one at a time)
• 📖 Optional LoRA loader
• ✍️ Positive + Negative prompt nodes (with default anime negative)
• 📐 Resolution presets
• 🎨 Generate + 🔼 Optional 1.5× upscale with side-by-side compare
• 📚 Built-in MarkdownNote guide with settings per variant

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📁 Repository Structure

Z-Anime/
├── README.md
├── config.json
│
├── diffusion_models/
│   ├── z-anime-base-bf16.safetensors
│   ├── z-anime-base-fp8.safetensors
│   ├── z-anime-distill-8step-bf16.safetensors
│   ├── z-anime-distill-8step-fp8.safetensors
│   ├── z-anime-distill-4step-bf16.safetensors
│   └── z-anime-distill-4step-fp8.safetensors
│
├── gguf/
│   ├── z-anime-base-q8_0.gguf
│   └── z-anime-base-q4_k_s.gguf
│
├── aio/
│   ├── z-anime-base-aio-bf16.safetensors
│   ├── z-anime-base-aio-fp8.safetensors
│   ├── z-anime-distill-8step-aio-bf16.safetensors
│   ├── z-anime-distill-8step-aio-fp8.safetensors
│   ├── z-anime-distill-4step-aio-bf16.safetensors
│   └── z-anime-distill-4step-aio-fp8.safetensors
│
├── text_encoder/
│   ├── qwen_3_4b-bf16.safetensors                  # default
│   ├── qwen_3_4b-fp8.safetensors                   # default
│   ├── qwen_3_4b-engineer-v4-bf16.safetensors      # alternative (BennyDaBall)
│   └── qwen_3_4b-engineer-v4-fp8.safetensors       # alternative (BennyDaBall)
│
├── vae/
│   └── ae.safetensors
│
├── diffusers/
│   ├── model_index.json
│   ├── scheduler/
│   ├── tokenizer/
│   ├── text_encoder/
│   ├── transformer/   (sharded safetensors + index)
│   └── vae/
│
├── images/
│   ├── cover.png
│   ├── workflow-cover.png
│   ├── workflow-overview.png
│   ├── 1.png
│   ├── 2.png
│   ├── 3.png
│   ├── 4.png
│   ├── 5.png
│   ├── 6.png
│   ├── 7.png
│   ├── 8.png
│   └── 9.png
└── workflows/
    └── Z-Anime-Workflow-v1.json

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📈 Version History

v1.0 — Initial Release

• Z-Anime Base released in BF16 & FP8
• Z-Anime Distill-8-Step released in BF16 & FP8
• Z-Anime Distill-4-Step released in BF16 & FP8
• GGUF variants added
  • 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 added — Base, Distill-4-Step and Distill-8-Step (each in BF16 & FP8)
• VAE ( ae.safetensors ) and Text Encoder ( qwen_3_4b.safetensors ) included
• Optimized for euler_ancestral , euler + beta , and simple practical use across the family

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🔗 Links

• CivitAI Page: civitai.red/models/2483351
• Base Model: Tongyi-MAI/Z-Image
• Author: SeeSee21 on Hugging Face

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🙏 Credits

• Base Architecture: Tongyi Lab (Alibaba) — Z-Image
• Fine-Tune: SeeSee21
• License: Apache 2.0
• Architecture: S3-DiT (Single-Stream Diffusion Transformer, 6B parameters)
• Base Model: Tongyi-MAI/Z-Image
• Engineer V4 Text Encoder: BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 — full fine-tune with SMART training, included as alternative text encoder

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❤️ Notes

Z-Anime is an experimental anime-focused model family built to explore what a full fine-tune on Z-Image Base can achieve in this space.

It is already strong for anime aesthetics, character work, and fast iteration, and future versions will continue to improve diversity, character handling, prompting flexibility, and overall quality.

Z-Anime — anime at its finest, powered by Z-Image Base. 🎌