🚀 Z-Image AIO Collection

⚡ Base & Turbo • All-in-One • Bilingual Text • Qwen3-4B

⚠️ IMPORTANT: Requires ComfyUI v0.11.0+

📥 Download ComfyUI

✨ What is Z-Image AIO?

Z-Image AIO is an All-in-One repackage of Alibaba Tongyi Lab's 6B parameter image generation models.

Everything integrated:

• ✅ VAE already built-in

• ✅ Qwen3-4B Text Encoder integrated

• ✅ Just download and generate!

🎯 Available Versions

🔥 Z-Image-Turbo-AIO (8 Steps • CFG 1.0)

Ultra-fast generation for production & daily use

⚫ NVFP4-AIO (7.8 GB) 🆕

🎯 ONLY for NVIDIA Blackwell GPUs (RTX 50xx)!
⚡ Maximum speed optimized
💾 Smallest file size
🚀 FP4 precision - blazing fast

Perfect for: RTX 5070, 5080, 5090 owners who want maximum speed

🟡 FP8-AIO (10 GB) ⭐ RECOMMENDED

✅ Best balance of size & quality
✅ Works on 8GB VRAM
✅ Fast downloads
✅ Ideal for most users

Perfect for: Daily use, testing, RTX 3060/4060/4070

🔵 FP16-AIO (20 GB)

💾 Same file size as BF16
🔄 ComfyUI auto-casts to BF16 for compute
⚠️ Does NOT enable FP16 compute mode
📦 Alternative download option

Note: Z-Image does not support FP16 compute - activation values exceed FP16's max range, causing NaN/black images. Weights are cast to BF16 during inference regardless of file format.

Perfect for: Alternative to BF16 download (identical inference behavior)

🌟 BF16-AIO (20 GB) ⭐ RECOMMENDED FOR FULL PRECISION

✅ BFloat16 full precision
✅ Absolute best quality
✅ Professional projects
✅ Also works on 8GB VRAM

Perfect for: Professional work, maximum quality

🎨 Z-Image-Base-AIO (28-50 Steps • CFG 3-5)

Full creative control for pros & LoRA training

🟡 FP8-AIO (10 GB)

✅ Efficient for daily use
✅ Full CFG control
✅ Negative prompts supported
✅ 8GB VRAM compatible

Perfect for: Daily work with full control

🔵 FP16-AIO (20 GB)

💾 Same file size as BF16
🔄 ComfyUI auto-casts to BF16 for compute
⚠️ Does NOT enable FP16 compute mode
📦 Alternative download option

Note: See technical explanation in FAQ below.

Perfect for: Alternative to BF16 download (identical inference behavior)

🌟 BF16-AIO (20 GB) ⭐ RECOMMENDED FOR FULL PRECISION

✅ Maximum quality
✅ Ideal for LoRA training
✅ Professional projects
✅ Highest precision

Perfect for: LoRA training, professional work

🆚 Turbo vs Base - When to Use?

⚡ Use TURBO when:

⚡ Speed is priority → 8 steps = 3-10 seconds
📸 Production workflows → Consistent high quality
💾 Quick iterations → Rapid prototyping
🎯 Simple prompts → Less complex scenes

🎨 Use BASE when:

🎨 Creative exploration → Higher diversity
🔧 LoRA/ControlNet dev → Undistilled foundation
📝 Complex prompting → Full CFG control
🚫 Negative prompts needed → Remove unwanted elements

⚙️ Recommended Settings

⚡ Turbo Settings (incl. NVFP4)

📊 Steps: 8
🎚️ CFG: 1.0 (don't change!)
🎲 Sampler: res_multistep OR euler_ancestral
📈 Scheduler: simple OR beta
📐 Resolution: 1920×1088 (recommended)
🚫 Negative Prompt: ❌ Not used!

🎨 Base Settings

📊 Steps: 28-50
🎚️ CFG: 3.0-5.0 (start with 4.0)
🎲 Sampler: euler ⭐ OR dpmpp_2m
📈 Scheduler: normal ⭐ OR karras
📐 Resolution: 512×512 to 2048×2048
🚫 Negative Prompt: ✅ Fully supported!

📊 Quick Overview

Turbo Versions

⚫ NVFP4  │ 7.8 GB  │ RTX 50xx only  │ Max Speed 🆕
🟡 FP8   │ 10 GB   │ 8GB VRAM       │ Recommended ⭐
🔵 FP16  │ 20 GB   │ → BF16 compute │ See FAQ ⚠️
🌟 BF16  │ 20 GB   │ 8GB VRAM       │ Max Quality ⭐

Base Versions

🟡 FP8   │ 10 GB   │ 8GB VRAM       │ Efficient
🔵 FP16  │ 20 GB   │ → BF16 compute │ See FAQ ⚠️
🌟 BF16  │ 20 GB   │ 8GB VRAM       │ LoRA Training ⭐

💡 Prompting Guide

✅ Good Example:

Professional food photography of artisan breakfast plate. 
Golden poached eggs on sourdough toast, crispy bacon, fresh 
avocado slices. Morning sunlight creating warm glow. Shallow 
depth of field, magazine-quality presentation.

❌ Bad Example:

breakfast, eggs, bacon, toast, food, morning, plate

📝 Tips

DO:

• ✅ Use natural language

• ✅ Be detailed (100-300 words)

• ✅ Describe lighting & mood

• ✅ Specify camera angle

• ✅ English OR Chinese (or both!)

DON'T:

• ❌ Tag-style prompts (tag1, tag2, tag3)

• ❌ Very short prompts (under 50 words)

• ❌ Negative prompts with Turbo

🌐 Bilingual Text Rendering

English:

Neon sign reading "OPEN 24/7" in bright blue letters 
above entrance. Modern sans-serif font, glowing effect.

中文:

Traditional tea house entrance with sign reading 
"古韵茶坊" in elegant gold Chinese calligraphy.

Both:

Modern cafe with bilingual sign. "Morning Brew" in 
white script above, "晨曦咖啡" in Chinese below.

📥 Installation

Step 1: Download

Choose your version based on:

• GPU: RTX 50xx → NVFP4 possible

• VRAM: 8GB → FP8 recommended

• Purpose: LoRA Training → Base BF16

Step 2: Place File

ComfyUI/models/checkpoints/
└── Z-Image-Turbo-FP8-AIO.safetensors

Step 3: Load & Generate

1. Open ComfyUI (v0.11.0+!)

2. Use "Load Checkpoint" node

3. Select your AIO version

4. Generate!

No separate VAE or Text Encoder needed!

🙏 Credits

Original Model

👨‍💻 Developer: Tongyi Lab (Alibaba Group)
🏗️ Architecture: Single-Stream DiT (6B parameters)
📜 License: Apache 2.0

Links

🔗 Z-Image Base: https://huggingface.co/Tongyi-MAI/Z-Image

🔗 Z-Image Turbo: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo

🧠 Text Encoder: https://huggingface.co/Qwen/Qwen3-4B

📈 Version History

v2.2 - FP16 Clarification

📝 Updated FP16 descriptions for technical accuracy
⚠️ Clarified: FP16 weights ≠ FP16 compute
🔄 FP16 files are cast to BF16 during inference

v2.1 - NVFP4 Release 🆕

➕ Z-Image-Turbo-NVFP4-AIO (7.8GB)
⚡ Optimized for NVIDIA Blackwell (RTX 50xx)
🚀 Maximum speed generation

v2.0 - Base AIO Release

➕ Z-Image-Base-BF16-AIO
➕ Z-Image-Base-FP16-AIO
➕ Z-Image-Base-FP8-AIO
🔄 ComfyUI v0.11.0+ support
📝 Qwen3-4B Text Encoder

v1.1 - FP16 Added

➕ Z-Image-Turbo-FP16-AIO
🔧 Wider GPU compatibility

v1.0 - Initial Release

✅ Z-Image-Turbo-FP8-AIO
✅ Z-Image-Turbo-BF16-AIO
✅ Integrated VAE + Text Encoder

❓ FAQ

Q: Which version should I choose?

RTX 50xx + Speed → NVFP4 🆕
Most users       → Turbo FP8 ⭐
Full precision   → BF16 ⭐
LoRA Training    → Base BF16

Q: Turbo or Base?

Fast & simple    → Turbo ⚡
Full control     → Base 🎨

Q: Will NVFP4 work on my RTX 4090?

❌ No! NVFP4 is only for RTX 50xx (Blackwell architecture).

Use FP8 instead for RTX 40xx and older.

Q: Do I need separate VAE/Text Encoder?

❌ No! Everything is already integrated.

Just Load Checkpoint and go!

Q: Works on 8GB VRAM?

✅ Yes! All versions work on 8GB VRAM.

(NVFP4 requires RTX 50xx regardless of VRAM)

⚠️ Q: What about FP16 for older GPUs (RTX 2000/3000)?

Important technical clarification:

Z-Image does NOT support FP16 compute type. Here's why:

📊 Technical reason:
- FP16 max value: ~65,504
- BF16 max value: ~3.39e+38 (same as FP32)
- Z-Image's activation values exceed FP16's range
- Result: Overflow → NaN → Black images

What actually happens:

• ComfyUI automatically casts weights to BF16 for computation

• You can see this in logs: "model weight dtype X, manual cast: torch.bfloat16"

• "Weight dtype" (file format) ≠ "Compute dtype" (actual calculation)

For RTX 20xx users (no native BF16):

• BF16 is emulated via FP32 = slower but works

• There is no way to run Z-Image in true FP16 compute

• FP8 with CPU offload may be a better option for limited VRAM

TL;DR: FP16 and BF16 files behave identically during inference. Choose based on download preference, not GPU compatibility.

🚀 Get Started Now!

Download → Load Checkpoint → Generate!

Recommended versions:

• 🟡 FP8 for most users (best size/quality balance)

• 🌟 BF16 for maximum quality

• ⚫ NVFP4 for RTX 50xx speed

All versions work on 8GB VRAM

Happy generating! 🎨