This is the
FP8 mixed-precision
quantization of
HiDream-O1-Image
for use with
ComfyUI
. By quantizing to 8-bit floats, the model fits comfortably within ~10 GB of VRAM — making it accessible on 12 GB GPUs (RTX 3080/4070/4080, etc.) with minimal quality trade-off.
This is the recommended variant for GPUs with less than 16 GB VRAM. Tested on 12 GB cards at 2048 × 2048 resolution.
What is FP8 Mixed?
Weights are stored in
float8_e4m3fn
format. Sensitive layers (norms, embeddings, output heads) retain higher precision to preserve stability, hence "mixed." On CUDA-capable GPUs with Hopper or Ada Lovelace architecture (RTX 40xx, H100), FP8 compute is hardware-accelerated. On older GPUs, weights are dequantized on-the-fly — still saving VRAM, with a small speed penalty.
Quick Start — ComfyUI
1. Install the Custom Node
cd ComfyUI/custom_nodes
git clone https://github.com/Saganaki22/HiDream_O1-ComfyUI
pip install -r HiDream_O1-ComfyUI/requirements.txt
Or install via
ComfyUI Manager
by searching for
HiDream O1
.
Open ComfyUI and use the workflow provided in the custom node repository. Point the model loader to
HiDream-O1-Image-fp8
.
About HiDream-O1-Image
HiDream-O1-Image is a natively unified image generative foundation model built on a
Pixel-level Unified Transformer (UiT)
— no external VAEs, no disjoint text encoders. It encodes raw pixels, text, and task-specific conditions in a single shared token space, supporting:
At only 9B parameters it matches or exceeds much larger open-source DiTs and leading closed-source models. It debuted at
#8 in the Artificial Analysis Text to Image Arena
(2026-05-05).
Key Features
🧬
Pixel-Level Unified Transformer
— end-to-end on raw pixels, no VAE, no disjoint text encoder
🎨
One Model, Many Tasks
— T2I, editing, personalization, storyboard generation
🧠
Reasoning-Driven Prompt Agent
— built-in "thinking" agent that resolves layout and rendering before generation
🖼️
Native High Resolution
— direct synthesis up to 2,048 × 2,048
⚡
9B Parameters
— performance parity with models many times larger
💾
FP8 Quantized
— ~half the VRAM of full-precision variants, minimal quality loss
GenEval
(compositional generation) — HiDream-O1-Image scores
0.90
overall at 9B params, second only to the 200B+ Pro variant and ahead of GPT Image 2 (0.89).
DPG-Bench
(dense prompt alignment) — Overall score
89.83
, ranking second behind the Pro variant.
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