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license: apache-2.0
base_model:
- Tongyi-MAI/Z-Image
pipeline_tag: text-to-image
library_name: diffusers
language:
- en
- ko
tags:
- text-to-image
- image-generation
- quantized
- bitsandbytes
- nf4
- 4-bit
- on-device
- korean
- pocket
- vidraft
---
# 🖼️ POCKET-Image-Zimage — 4-bit (NF4) Z-Image for on-device
A **4-bit (NF4) quantized build of [Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image)** (Apache-2.0),
packaged by VIDRAFT for **low-VRAM, on-device** image generation — part of the POCKET line.
- 📦 **~6 GB on disk** (transformer + text encoder in NF4, VAE in fp16)
- ⚡ **Runs from ~8.6 GB VRAM** (≈**4.5 GB** with CPU offload) — vs **23.3 GB** for bf16
- 🎯 ~2.7–5× smaller footprint, quality on par with the bf16 base
## Usage
```python
import torch
from diffusers import ZImagePipeline # or ZImageImg2ImgPipeline / ZImageInpaintPipeline
pipe = ZImagePipeline.from_pretrained(
"FINAL-Bench/POCKET-Image-Zimage", torch_dtype=torch.bfloat16
).to("cuda")
img = pipe("a serene mountain lake at sunrise, photorealistic", num_inference_steps=20).images[0]
img.save("out.png")
```
Requires **`bitsandbytes`** (CUDA). Measured reload + generate peak: ~10.9 GB VRAM.
For Apple Silicon / CPU, an `optimum-quanto` int8 build (~13.4 GB) is the portable option.
## 🎨 The full POCKET-Image system
This repo hosts the **quantized base model** only. The headline **character-perfect Korean &
multilingual text** feature is delivered by the POCKET-Image *pipeline*, not by these weights alone.
Try the full system here:
- **🎨 Studio (generate here):** https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio
Base model: [Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image) (Apache-2.0) · Quantization: bitsandbytes NF4 · By VIDRAFT.
<!-- POCKET-FAMILY -->
---
## 🧩 The POCKET Family — On-device AI by VIDRAFT
*Big models, small hardware. No GPU, no cloud.*
**Models**
- 📦 [POCKET-35B-GGUF](https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF) — flagship, PC / server, no GPU
- 📦 [POCKET-26B-GGUF](https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF) — compact 26B
- 🇰🇷 [POCKET-KR-GGUF](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) — Korean, Android
- 🍎 [POCKET-KR-MLX](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) — Korean, iPhone / Mac
- 🌍 [POCKET-EN-GGUF](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF) — English, phone / PC
- 🖼️ [POCKET-Image-Zimage](https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage) — 4-bit Z-Image (this repo)
**Demos & tools (Spaces)**
- 🎨 [POCKET-Image Studio](https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio) — text-in-image, generate in-page
- 🖥️ [POCKET-35B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU) — 35B answering on a CPU
- 🖥️ [POCKET-26B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU) — 26B on a CPU
📚 [Full POCKET collection](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6)
<!-- /POCKET-FAMILY -->
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