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LatestHiDream.aiE1.1
HiDream.aiImage Editing

HiDream.ai Releases 17B Open Image Editing Model

The new MIT-licensed model, HiDream-E1.1, allows for complex image modifications by following natural language instructions.

Jul 16, 2025
NotableMIT
HiDream-E1.1

Startup HiDream.ai has released HiDream-E1.1, an open-source model designed for instruction-based image editing. The model allows users to modify existing images by providing simple text commands, offering a powerful new tool for developers and creative professionals working with generative AI.

What sets HiDream-E1.1 apart is its scale. At 17 billion parameters, it is a significantly larger model than many open-source alternatives in the image editing space. This substantial size suggests a capacity for understanding more nuanced and complex instructions, potentially leading to more precise and sophisticated edits than smaller models can achieve.

According to the release notes, the model is an evolution of HiDream-I1, the company's foundational text-to-image model. By fine-tuning this base for editing tasks, the new release specializes in modifying images rather than generating them from scratch.

The model is available under a permissive MIT license, encouraging broad adoption for both research and commercial applications. Developers can explore and download the HiDream-E1.1 model on the Hugging Face Hub to begin integrating it into their own projects.

Sources

  • HiDream-ai/HiDream-E1-1

    Hugging Face

    Visit
OlderMing-Lite-Omni 1.5 Brings Any-to-Any Modality to Open SourceinclusionAI · Any-to-Any · last yearNewerQwen Releases Wan 2.2, a 5B Open Video AI ModelQwen · Alibaba · Text → Video · last year

Get the model

Hugging Face

Specs

Parameters17B
Size34.2 GB
PrecisionBF16
ArchitectureHiDreamImageEditingPipeline
LicenseMIT
Downloads93
Likes217

Can you run it?

Runs on a 24 GB GPU at FP8.

  • BF16 (as published)

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    35.2 GB
  • FP8

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    18.1 GB
Your machine
  • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Image Editing

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