# HiDream-ai/HiDream-O1-Image

Repository: https://github.com/HiDream-ai/HiDream-O1-Image
Canonical: https://ross.abutalabs.com/products/hidream-o1-image
Language: Python
License: MIT
License Family: permissive
Last push: 2026-06-22T07:50:59+00:00

## Health v2 (maintenance only)
Score: 53/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 88, release rhythm 35, longevity 8
- inputs: {"age_days": 117, "days_push": 72, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1510, forks 38 (observed 2026-08-28T04:04:55.882948+00:00)

## What it is
HiDream-O1-Image is an open-weights 8B image generation foundation model built on a Pixel-level Unified Transformer (UiT) that natively encodes raw pixels, text, and task conditions in a single token space. It supports text-to-image generation, image editing, and subject-driven personalization at up to 2048x2048 resolution, with PyTorch inference pipelines and Hugging Face integrations.

## Use cases
- generate images from text prompts
- edit existing images with natural language instructions
- create images featuring a specific subject or character in new scenes
- render long multilingual text accurately inside generated images
- run a state-of-the-art open-weights text-to-image model locally
- control layout and skeleton conditioning for image generation

## When to choose
- you need high-quality open-weights text-to-image generation up to 2K resolution
- you want unified text-to-image, editing, and subject personalization in one model
- you need accurate text rendering and layout control in generated images
- you want to self-host image generation without external VAEs or disjoint text encoders

## When to avoid
- you need a lightweight model for CPU-only or low-VRAM devices
- you only need simple image manipulation like cropping or filtering
- you require a managed cloud API rather than self-hosted inference
- you depend on PyTorch 2.9.x, which has known compatibility issues

## Facets
- artifact type: library
- maturity: active
- function: image-processing, machine-learning, deep-learning, llm-inference
- domain: artificial-intelligence, image-processing, deep-learning, machine-learning
- platform: python, cross-platform
- tags: text-to-image, image-editing, diffusion-model, transformer, personalization, image-generation, open-weights, huggingface, gpu, linux

## Member repositories
- HiDream-ai/HiDream-O1-Image (main) score 53

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:55.882948+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 2026-08-30T04:32:24.410916+00:00, confidence not recorded.
  - readme: https://github.com/HiDream-ai/HiDream-O1-Image (fetched 2026-08-28T04:04:55.882948+00:00, sha 43eda6063f70)
- Data as of 2026-08-30T08:39:29.467469+00:00.