# HswAI2026/JuZhou-V1

Repository: https://github.com/HswAI2026/JuZhou-V1
Canonical: https://ross.abutalabs.com/products/juzhou-v1
License Family: other
Last push: 2026-07-03T12:03:21+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 90, release rhythm 35, longevity 7
- inputs: {"age_days": 105, "days_push": 61, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1097, forks 194 (observed 2026-08-28T04:03:34.663477+00:00)

## What it is
JuZhou 1.0 is an ultra-lightweight 0.387B-parameter text-to-image foundation model designed for fully offline, on-device execution on mobile hardware. It was trained entirely on China-developed Sugon K100 AI accelerators and features native Chinese semantic alignment from 9M curated Chinese image-text pairs.

## Use cases
- generate images from text prompts offline on a phone
- run a lightweight text-to-image model on mobile devices
- generate images from Chinese text prompts without translation
- privacy-preserving local image generation without cloud APIs
- deploy a compact diffusion model on edge hardware
- compare small T2I model quality against SDXL and SD3

## When to choose
- you need offline, on-device image generation on mobile
- your prompts are in Chinese and you want native semantic alignment
- you need a small model that fits edge hardware constraints
- privacy rules out sending prompts to a cloud image API

## When to avoid
- you need the highest possible image fidelity from large frontier models
- you require English-first prompt understanding
- you need fine-grained control like inpainting or ControlNet workflows
- you depend on NVIDIA CUDA tooling for training or inference

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, image-processing, llm-inference
- domain: artificial-intelligence, image-processing, mobile-development
- platform: cross-platform
- tags: text-to-image, diffusion-model, on-device-inference, chinese-language, edge-ai, stable-diffusion, offline, android, mobile

## Member repositories
- HswAI2026/JuZhou-V1 (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:34.663477+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:46:21.589562+00:00, confidence not recorded.
  - readme: https://github.com/HswAI2026/JuZhou-V1 (fetched 2026-08-28T04:03:34.663477+00:00, sha 260881848917)
- Data as of 2026-08-30T08:39:29.467469+00:00.