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FINAL-Bench/POCKET-Image-Zimage
POCKET-Image-Zimage is a text-to-image model from FINAL-Bench. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as apache-2.0.
π POCKET-Qwen3.8-Flash-Next β a 180B model running on a laptop with 8 GB VRAM + 32 GB RAM Β· 4.17 tok/s measured. [](https://huggingface.co/FINAL-Bench/POCKET-Qwen3.8-Flash-Next-GGUF) []() []() []() <!-- POCKET-FLASHNβ¦
Downloads Β· 30 days
66
25% of all-time downloads
All-time downloads
266
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.safetensors6 GB Β· 100%
How the weights are stored.
U83.2B Β· 100%
From the Hugging Face model README
π POCKET-Qwen3.8-Flash-Next β a 180B model running on a laptop with 8 GB VRAM + 32 GB RAM Β· 4.17 tok/s measured.
π POCKET-Zimage-CPU β photoreal images in 46 s on a CPU only. No GPU, no CUDA, no Python.
π Collections
βΆ POCKET Models β this family (on-device, no GPU) Darwin Family Β· Aether Foundation Β· VKAE Accelerated
A 4-bit (NF4) quantized build of Z-Image (Apache-2.0), packaged by VIDRAFT for low-VRAM, on-device image generation β part of the POCKET line.
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.
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) Β· Quantization: bitsandbytes NF4 Β· By VIDRAFT.
<!-- POCKET-FAMILY -->Big models, small hardware. No GPU, no cloud.
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