A 4-bit (NF4) quantized build of 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 (Apache-2.0) · Quantization: bitsandbytes NF4 · By VIDRAFT.

🧩 The POCKET Family — On-device AI by VIDRAFT

Big models, small hardware. No GPU, no cloud.

Models

📦 POCKET-35B-GGUF — flagship, PC / server, no GPU
📦 POCKET-26B-GGUF — compact 26B
🇰🇷 POCKET-KR-GGUF — Korean, Android
🍎 POCKET-KR-MLX — Korean, iPhone / Mac
🌍 POCKET-EN-GGUF — English, phone / PC
🖼️ POCKET-Image-Zimage — 4-bit Z-Image (this repo)

Demos & tools (Spaces)

🎨 POCKET-Image Studio — text-in-image, generate in-page
🖥️ POCKET-35B-CPU — 35B answering on a CPU
🖥️ POCKET-26B-CPU — 26B on a CPU

📚 Full POCKET collection

AI-Joe-git/POCKET-Image-Zimage

🖼️ POCKET-Image-Zimage — 4-bit (NF4) Z-Image for on-device

Usage

🎨 The full POCKET-Image system

🧩 The POCKET Family — On-device AI by VIDRAFT

AI-Joe-git/
POCKET-Image-Zimage