FINAL-Bench/POCKET-Image-Zimage
🖼️ 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
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.
<!-- POCKET-FAMILY -->
🧩 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
<!-- /POCKET-FAMILY -->