Instructions to use Abiray/HiDream-O1-Image-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abiray/HiDream-O1-Image-MXFP8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Abiray/HiDream-O1-Image-MXFP8", dtype="auto") - Notebooks
- Google Colab
- Kaggle
HiDream-O1-Image (MXFP8)
Repository Notice: This repository hosts the MXFP8 (
hidream_o1_image_mxfp8.safetensors) quantized weights of the base 8B HiDream-O1-Image model.About MXFP8 (Microscaling Formats): Unlike standard globally scaled 8-bit quantization, MXFP8 uses block-level scaling. This preserves significantly more dynamic range, keeping the image fidelity and text-adherence nearly identical to the uncompressed 16-bit model while still maintaining a reduced file size (~8.92 GB). Hardware Note: Full hardware acceleration for MXFP8 is highly architecture-dependent (e.g., native on Hopper architectures like the H100).
For the official, uncompressed weights and source code, visit the original HiDream-O1-Image repository.
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 โ supporting text-to-image, image editing, and subject-driven personalization at up to 2,048 ร 2,048.
Key Features
- ๐งฌ Pixel-Level Unified Transformer โ One end-to-end model on raw pixels, no VAE, no disjoint text encoder.
- ๐จ One Model, Many Tasks โ Text-to-image, long-text rendering, instruction editing, subject-driven personalization, and storyboard generation in a single architecture.
- ๐ง Reasoning-Driven Prompt Agent โ Built-in "thinking" agent that resolves implicit knowledge, layout, and text rendering before generation.
- ๐ผ๏ธ Native High Resolution โ Direct synthesis up to 2,048 ร 2,048 with sharp fine-grained detail.
- โก Exceptional Efficiency and Versatility at 8B Scale โ With only 8B parameters, achieves performance parity with or even surpasses larger open-source DiTs and leading closed-source models.
Usage & Installation
To use these MXFP8 weights, you will need the original HiDream inference pipeline.
1. Install the Original Repository
Clone the official repository and install dependencies:
git clone [https://github.com/HiDream-ai/HiDream-O1-Image.git](https://github.com/HiDream-ai/HiDream-O1-Image.git)
cd HiDream-O1-Image
pip install -r requirements.txt
Model tree for Abiray/HiDream-O1-Image-MXFP8
Base model
HiDream-ai/HiDream-O1-Image