FINAL-Bench/POCKET-Image-Zimage

🤗 Hugging Face 来源text-to-imageapache-2.03.2B 参数3.2 GBsafetensors✓ 4 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo FINAL-Bench/POCKET-Image-Zimage ./model-folder
需要做种者 →

🆕 POCKET-Qwen3.8-Flash-Next — a 180B model running on a laptop with 8 GB VRAM + 32 GB RAM · 4.17 tok/s measured.

🆕 POCKET-Zimage-CPU — photoreal images in 46 s on a CPU only. No GPU, no CUDA, no Python.

📚 Collections

▶ POCKET Models — this family (on-device, no GPU) Darwin Family · Aether Foundation · VKAE Accelerated

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

Pick your build →

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

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:

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.

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