HiDream-I1
A solid pick for self-hosting and full data control.
Overview
HiDream-I1 is a 17-billion-parameter, MIT-licensed image generation model built to run on local hardware. It outperforms leading open models such as Flux.1-dev, SD3-Medium and CogView4 on the GenEval, DPG and HPSv2.1 benchmarks, both for prompt fidelity and perceived quality. The catch: its text encoder relies on Meta's Llama 3.1 8B, governed by a separate community licence, which complicates any claim of a fully uniform open-source stack. Weights and VAE can run entirely on-premise, with no cloud API dependency.
Skill profile
Not disclosed
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- you have GDPR constraints
Ideal use cases
- In-house marketing illustration
- Product visual prototyping
- Cloud-free creative assets
- Custom image generation pipelines
Access & availability
Key specifications
Privacy
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 9.7 GB | 13 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
| Q8 · balanced | 18.2 GB | 22 GB | 32GB Mac / RTX 3090-4090 |
| FP16 · max quality | 34 GB | 39 GB | 64GB Mac / dual 24GB GPUs |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve HiDream-ai/HiDream-I1-Full \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |