HiDream-ai/HiDream-O1-Image-Dev

HiDream-O1-Image

HiDream-O1-Image is a natively unified image generative foundation model built on a Pixel-level Unified Transformer (UiT) without external VAEs or disjoint text encoders, which natively encodes raw pixels, text, and task-specific conditions in a single shared token space — supporting text-to-image, image editing, and subject-driven personalization at up to 2,048 × 2,048.

Project Updates

• 🤗 May 10, 2026: Try HiDream-O1-Image online on Hugging Face Spaces — 🤗 HiDream-O1-Image and 🤗 HiDream-O1-Image-Dev.
• 📕 May 10, 2026: Our technical report is now available — 📑 HiDream-O1-Image.pdf.
• 🚀 May 8, 2026: We've open-sourced HiDream-O1-Image (8B), including both the undistilled and distilled Dev variants, together with the Reasoning-Driven Prompt Agent.

HiDream-O1-Image (codename: Peanut) debuts at #8 in the Artificial Analysis Text to Image Arena, which is positioned to be the new leading open weights Text to Image model (2026-5-5).

_{Artificial Analysis Text to Image Arena at up to 2,048 × 2,048.}

_{General text-to-image generation at up to 2,048 × 2,048.}

_{Long-text rendering & layout control — accurate, multi-region, multilingual text.}

_{Subject-driven personalization — preserve identity / IP across new scenes.}

Key Features

• 🧬 Pixel-Level Unified Transformer — One end-to-end model on raw pixels, no VAE, no disjoint text encoder.
• 🎨 One Model, Many Tasks — Text-to-image, long-text rendering, instruction editing, subject-driven personalization, and storyboard generation in a single architecture.
• 🧠 Reasoning-Driven Prompt Agent — Built-in "thinking" agent that resolves implicit knowledge, layout, and text rendering before generation.
• 🖼️ Native High Resolution — Direct synthesis up to 2,048 × 2,048 with sharp fine-grained detail.
• ⚡ Exceptional Efficiency and Versatility at 8B Scale — With only 8B parameters, achieves performance parity with or even surpasses larger open-source DiTs and leading closed-source models.

Models

Name	Script	Inference Steps	HuggingFace Repo
HiDream-O1-Image	inference.py	50	🤗 HiDream-O1-Image
HiDream-O1-Image-Dev	inference.py	28	🤗 HiDream-O1-Image-Dev
Prompt Agent	prompt_agent.py	—	🤗 google/gemma-4-31B-it
Web Demo	app.py	—	—

Evaluation

We benchmark HiDream-O1-Image against state-of-the-art open-source and proprietary models on five widely-used evaluation suites covering compositional generation, dense prompt alignment, human preference, complex visual text generation, and long-text rendering. In each table, the best result is highlighted in bold and the second-best is underlined. Click any benchmark below to expand or collapse.

GenEval — compositional generation

Model	#Params	Single-Obj	Two-Obj	Count	Color	Position	Attr	Overall
Nano Banana 2.0	–	1.00	0.96	0.71	0.84	0.86	0.65	0.83
Seedream-4.0	–	1.00	0.92	0.71	0.93	0.78	0.68	0.84
GPT Image 1 [High]	–	0.99	0.92	0.85	0.92	0.75	0.61	0.84
GPT Image 2	–	0.99	0.98	0.85	0.93	0.85	0.77	0.89
PixArt	4.3B + 0.6B	0.98	0.50	0.44	0.80	0.08	0.07	0.48
Show-o	1.3B	0.95	0.52	0.49	0.82	0.11	0.28	0.53
Emu3-Gen	8B	0.98	0.71	0.34	0.81	0.17	0.21	0.54
SD3-Medium	5.5B + 2B	0.98	0.74	0.63	0.67	0.34	0.36	0.62
JanusFlow	1.3B	0.97	0.59	0.45	0.83	0.53	0.42	0.63
FLUX.1 [Dev]	4.8B + 12B	0.98	0.81	0.74	0.79	0.22	0.45	0.66
SD3.5 Large	5.5B + 8.1B	0.98	0.89	0.73	0.83	0.34	0.47	0.71
Janus-Pro-7B	7B	0.99	0.89	0.59	0.90	0.79	0.66	0.80
Z-Image-Turbo	4B + 6B	1.00	0.95	0.77	0.89	0.65	0.68	0.82
FLUX.2 [Dev]	24B + 32B	1.00	0.99	0.79	0.93	0.73	0.78	0.87
Qwen-Image	7B + 20B	0.99	0.92	0.89	0.88	0.76	0.77	0.87
HiDream-O1-Image	8B	1.00	0.99	0.79	0.89	0.93	0.78	0.90
HiDream-O1-Image-Pro	200B+	1.00	0.99	0.85	0.94	0.94	0.79	0.92

DPG-Bench — dense prompt alignment

Model	#Params	Global	Entity	Attribute	Relation	Other	Overall
GPT Image 1 [High]	–	88.89	88.94	89.84	92.63	90.96	85.15
GPT Image 2	–	87.27	91.91	90.85	91.59	91.58	85.98
Nano Banana 2.0	–	85.17	92.55	91.16	90.45	91.08	86.90
Seedream-4.0	–	87.17	92.41	92.29	93.33	95.48	88.63
SD v1.5	0.12B + 0.86B	74.63	74.23	75.39	73.49	67.81	63.18
PixArt	4.3B + 0.6B	74.97	79.32	78.60	82.57	76.96	71.11
Lumina-Next	2B + 2B	82.82	88.65	86.44	80.53	81.82	74.63
SDXL	0.81B + 2.6B	83.27	82.43	80.91	86.76	80.41	74.65
Hunyuan-DiT	4.8B + 1.5B	84.59	80.59	88.01	74.36	86.41	78.87
Emu3-Gen	8B	85.21	86.68	86.84	90.22	83.15	80.60
DALL-E 3	–	90.97	89.61	88.39	90.58	89.83	83.50
FLUX.1 [Dev]	4.8B + 12B	74.35	90.00	88.96	90.87	88.33	83.84
SD3 Medium	5.5B + 2B	87.90	91.01	88.83	80.70	88.68	84.08
Janus-Pro-7B	7B	86.90	88.90	89.40	89.32	89.48	84.19
Z-Image-Turbo	4B + 6B	91.29	89.59	90.14	92.16	88.68	84.86
HiDream-I1-Full	13.5B + 17B	76.44	90.22	89.48	93.74	91.83	85.89
FLUX.2 [Dev]	24B + 32B	92.20	91.36	93.28	93.52	89.72	87.57
Qwen-Image	7B + 20B	91.32	91.56	92.02	94.31	92.73	88.32
HiDream-O1-Image	8B	95.15	92.32	93.74	92.88	90.25	89.83
HiDream-O1-Image-Pro	200B+	94.97	95.42	92.59	90.82	89.50	90.30

HPSv3 — human preference across 12 categories

Model	#Params	All	Characters	Arts	Design	Architecture	Animals	Natural Scenery	Transportation	Products	Plants	Food	Science	Others
Seedream-4.0	–	9.32	9.83	9.20	8.83	9.95	8.99	9.40	9.58	9.12	9.26	9.75	9.11	9.51
Nano Banana 2.0	–	10.01	10.18	9.18	9.58	10.96	9.71	10.04	10.38	10.36	10.14	10.61	9.14	9.89
GPT Image 2	–	10.21	10.75	9.91	10.15	10.59	10.05	10.29	10.17	10.26	10.07	10.75	10.05	10.00
Z-Image-Turbo	4B + 6B	8.35	8.98	8.29	7.65	9.26	8.51	8.33	8.81	7.83	8.46	8.64	7.93	8.57
FLUX.2 [Dev]	24B + 32B	9.28	10.23	9.56	8.80	9.73	9.43	9.21	9.44	8.93	9.23	9.82	8.67	9.11
Qwen-Image	7B + 20B	9.94	10.91	10.47	9.56	10.22	10.61	9.87	10.10	9.15	9.99	10.08	9.19	9.83
HiDream-O1-Image	8B	10.37	10.59	10.44	10.29	11.02	10.34	10.37	10.54	10.50	10.38	10.85	9.68	10.09
HiDream-O1-Image-Pro	200B+	10.47	10.63	10.51	10.33	11.11	10.08	10.45	10.37	10.75	10.29	11.13	10.09	10.39

CVTG-2K — complex visual text generation (click to expand)

Model	#Params	2 regions	3 regions	4 regions	5 regions	Average	NED	CLIP Score
Nano Banana 2.0	–	0.7465	0.7720	0.8067	0.7980	0.7875	0.8945	0.7212
GPT Image 1 [High]	–	0.8779	0.8659	0.8731	0.8218	0.8569	0.9478	0.7982
Seedream-4.0	–	0.8980	0.8949	0.9044	0.9015	0.9003	0.9511	0.8033
GPT Image 2	–	0.8904	0.8887	0.9101	0.9044	0.9003	0.9515	0.7798
TextDiffuser-2	0.12B + 0.9B	0.5322	0.3255	0.1787	0.0809	0.2326	0.4353	0.6765
RAG-Diffusion	4.8B + 12B	0.4388	0.3316	0.2116	0.1910	0.2648	0.4498	0.7797
AnyText	0.123B + 1.2B	0.0513	0.1739	0.1948	0.2249	0.1804	0.4675	0.7432
3DIS	0.81B + 2.6B	0.4495	0.3959	0.3880	0.3303	0.3813	0.6505	0.7767
FLUX.1 [Dev]	4.8B + 12B	0.6089	0.5531	0.4661	0.4316	0.4965	0.6879	0.7401
SD3.5 Large	5.5B + 8.1B	0.7293	0.6825	0.6574	0.5940	0.6548	0.8470	0.7797
TextCrafter	7B + 20B	0.7628	0.7628	0.7406	0.6977	0.7370	0.8679	0.7868
Qwen-Image	7B + 20B	0.8370	0.8364	0.8313	0.8158	0.8288	0.9116	0.8017
Z-Image-Turbo	4B + 6B	0.8872	0.8662	0.8628	0.8347	0.8585	0.9281	0.8048
FLUX.2 [Dev]	24B + 32B	0.9261	0.8897	0.8995	0.8732	0.8926	0.9475	0.8104
HiDream-O1-Image	8B	0.9085	0.9159	0.9216	0.9015	0.9128	0.9561	0.8076
HiDream-O1-Image-Pro	200B+	0.9133	0.9221	0.9365	0.9175	0.9222	0.9628	0.8349

LongText-Bench — long-text rendering, EN & ZH (click to expand)

Model	#Params	LongText-Bench-EN	LongText-Bench-ZH
Seedream-4.0	–	0.936	0.946
GPT Image 1 [High]	–	0.956	0.619
GPT Image 2	–	0.960	0.961
Nano Banana 2.0	–	0.980	0.965
Janus-Pro-7B	7B	0.019	0.006
BLIP3-o	7B + 1.4B	0.021	0.018
Kolors 2.0	–	0.258	0.329
BAGEL	7B + 7B	0.373	0.310
OmniGen2	3B + 4B	0.561	0.059
X-Omni	7B	0.900	0.814
HiDream-I1-Full	13.5B + 17B	0.543	0.024
FLUX.1 [Dev]	4.8B + 12B	0.607	0.005
Z-Image-Turbo	4B + 6B	0.917	0.926
FLUX.2 [Dev]	24B + 32B	0.963	0.757
Qwen-Image	7B + 20B	0.943	0.946
HiDream-O1-Image	8B	0.979	0.978
HiDream-O1-Image-Pro	200B+	0.982	0.980

Installation

1. Clone this repository:

git clone https://github.com/HiDream-ai/HiDream-O1-Image.git
cd HiDream-O1-Image

2. Install the required dependencies:

pip install -r requirements.txt

Note on flash-attn. We highly recommend installing flash-attn for optimized attention computation. If you do not (or cannot) install flash-attn, you must edit models/pipeline.py line 291 and change "use_flash_attn": True to "use_flash_attn": False — otherwise inference will fail to import the kernel.

Reasoning-Driven Prompt Agent

HiDream-O1-Image ships with a Reasoning-Driven Prompt Agent (prompt_agent.py) that explicitly reasons through layout, subject attributes, physical logic, and text-rendering details, then rewrites a raw user instruction into a self-contained English prompt. It supports two backends — pick one with --backend.

The agent prints a JSON object with three fields: prompt (rewritten English prompt), reasoning, and resolved_knowledge. Feed the prompt field into inference.py for best results on intricate, reasoning-heavy requests.

Option A — Local Backend (Gemma-4-31B-it)

1. Download the Gemma weights (requires accepting the Gemma license on HuggingFace):

huggingface-cli download google/gemma-4-31B-it --local-dir /path/to/gemma-4-31B-it

2. Run the refiner locally:

python prompt_agent.py \
    --backend local \
    --model_id /path/to/gemma-4-31B-it \
    --prompt "李白的静夜思写在古墙上"

Option B — External OpenAI-Compatible API

Use any OpenAI-compatible endpoint (OpenAI, Azure, vLLM, SGLang, DeepSeek, etc.) by providing --base_url, --api_key, and --model_name:

python prompt_agent.py \
    --backend api \
    --base_url https://api.openai.com/v1 \
    --api_key $OPENAI_API_KEY \
    --model_name deepseek-v4-pro \
    --prompt "李白的静夜思写在古墙上"

Usage

A CUDA-capable GPU is required for inference. The examples below use the undistilled model (--model_type full); see the last subsection for running the same tasks with the distilled model (--model_type dev).

1. Text-to-Image Generation

Generate an image from a text prompt:

python inference.py \
    --model_path /path/to/HiDream-O1-Image \
    --prompt "medium shot, eye-level, front view. A woman is seated in an ornate bedroom, illuminated by candlelight, with a calm and composed expression. The subject is a young woman with fair skin, light brown hair styled in an updo with loose tendrils framing her face, and blue eyes. She wears a cream-colored satin robe with delicate floral embroidery and lace trim along the neckline. Her ears are adorned with pearl drop earrings. She is seated on a bed with a dark, intricately carved wooden headboard. To her left, a wooden nightstand holds three lit white candles and a candelabra with multiple lit candles in the background. The bed is covered with patterned pillows and a dark, textured blanket. The walls are paneled with dark wood and feature a large, ornate tapestry with muted earth tones. The lighting creates soft highlights on her face and robe, with warm shadows cast across the room." \
    --output_image results/t2i.png \
    --height 2048 \
    --width 2048

2. Instruction-Based Image Editing

Provide a single reference image and an editing instruction:

python inference.py \
    --model_path /path/to/HiDream-O1-Image \
    --prompt "remove the earphones" \
    --ref_images assets/edit/test.jpg \
    --output_image results/edit.png \
    --keep_original_aspect

3. Multi-Reference Subject-Driven Personalization

Provide two or more reference images that define the subject(s), and a prompt that places them in a new scene:

python inference.py \
    --model_path /path/to/HiDream-O1-Image \
    --prompt "A young boy with blonde hair stands on steps wearing light blue jeans, a white t-shirt with logo, and blue and white sneakers. He wears a brown cord necklace with beads, a black wristwatch with digital display, and carries a yellow fanny pack with white zipper. In his hand is a red boxing glove with white top, a teal plastic toy car, and a plastic toy figure of Captain America. He wears a straw hat with cream band. Natural light illuminates the scene." \
    --ref_images assets/IP/1.jpg assets/IP/2.jpg assets/IP/3.jpg assets/IP/4.jpg assets/IP/5.jpg assets/IP/6.jpg assets/IP/7.jpg assets/IP/8.jpg assets/IP/9.jpg assets/IP/10.jpg \
    --output_image results/subject.png

4. Running with the Dev Model

All three tasks above can be run with the Dev model by switching --model_path to the Dev checkpoint and setting --model_type dev. For example:

python inference.py \
    --model_path /path/to/HiDream-O1-Image-Dev \
    --prompt "A dog holds a sign that says \"HiDream-O1-Image release.\"" \
    --output_image results/t2i_dev.png \
    --model_type dev

Command Line Arguments

• --model_path: Path to the complete HuggingFace model directory (undistilled or distilled).
• --prompt: Text prompt for the generation or editing task.
• --ref_images: Paths to one or more reference images (optional; space-separated).
• --output_image: Path to save the generated image (default: output.png).
• --height / --width: Output image dimensions (default: 2048 × 2048; values snap to valid resolutions internally).
• --model_type: full or dev (default: full). Selects the inference recipe:
  • full: 50 steps, guidance scale 5.0, shift 3.0, default scheduler.
  • dev: 28 steps, guidance scale 0.0, shift 1.0, flash scheduler with predefined timesteps.
• --seed: Random seed (default: 32).
• --guidance_scale: Guidance scale (default: 5.0). Only effective when --model_type is full.
• --noise_scale_start, --noise_scale_end: Control the scale of the noise injected by the scheduler at each denoising step; the per-step scale linearly interpolates from noise_scale_start (first step) to noise_scale_end (last step). See models/pipeline.py:262 and models/pipeline.py:273. Defaults: 7.5, 7.5.
• --noise_clip_std: Per-step clipping threshold (in units of the injected noise's standard deviation) applied to the noise added during scheduler stepping. See models/flash_scheduler.py:348-350. Default: 2.5.
• --keep_original_aspect: When exactly one reference image is provided, resize it with max_size=2048 and use its dimensions for the target image (preserves the reference's aspect ratio) if True.

Web Demo

app.py is a self-contained Flask web application that exposes all generation modes. It also integrates the Reasoning-Driven Prompt Agent.

Starting the server

python app.py \
    --model_path /path/to/HiDream-O1-Image \
    --host 0.0.0.0 \
    --port 7860

Then open http://localhost:7860 in your browser.

Command-line arguments

Argument	Default	Description
--model_path	$HIDREAM_MODEL_PATH	Path to the checkpoint directory (HiDream-O1-Image or HiDream-O1-Image-Dev).
--model_type	full	full (50-step) or dev (28-step).
--host	0.0.0.0	Bind address for the Flask server.
--port	7860	Port for the Flask server.

All four arguments can also be set via environment variables (see .env.example): HIDREAM_MODEL_PATH, HIDREAM_MODEL_TYPE, HIDREAM_HOST, and HIDREAM_PORT.

Prompt Agent in the UI

The sidebar contains a Prompt Agent panel that calls the same Reasoning-Driven Prompt Agent used by prompt_agent.py. Select either the OpenAI-compatible API backend (any endpoint, key, and model name) or the Local · Gemma backend (set HIDREAM_AGENT_MODEL in .env or the environment to point to your local Gemma-4-31B-it weights).

License

The code in this repository and the HiDream-O1-Image models are licensed under MIT License.