Vibeleaderboard
← All Intel
Intel / post

HunyuanImage 3.0-Instruct puts chain-of-thought in front of image editing

Source
x.com
Date
Tencent Hy@TencentHunyuan

Today, we introduce HunyuanImage 3.0-Instruct, a native multimodal model focusing on image-editing by integrating visual understanding with precise image synthesis! 🚀 It understands input images and reasons before generating images. Built on an 80B-parameter MoE architecture (13B activated), it natively unifies deep multimodal comprehension and high-fidelity generation. 🧠 A "Thinking" Model with Native CoT & MixGRPO: The model doesn’t just execute commands, it processes them through a Native Chain-of-Thought (CoT) schema. Enhanced by our self-developed MixGRPO algorithm, it reasons through complex instructions to achieve flawless intent alignment and human-preference consistency. 🎨 Precise Editing & Multi-Image Fusion: The model enables accurate image editing by adding, removing, or modifying elements while keeping non-target areas perfectly intact. It also excels at seamless multi-image fusion, synthesizing complex scenes by extracting and blending elements from multiple sources into a unified, consistent output. 🏆 SOTA Performance: HunyuanImage 3.0-Instruct sets a new benchmark in visual quality and alignment, delivering performance that matches leading proprietary…

Read the full post on X

Context

Tencent's HunyuanImage 3.0-Instruct includes an explicit instruction-processing step before image generation. Its documented reasoning mode (think, then rewrite, then generate) has the model analyze the supplied image and request, expand the intent into structured editing instructions, and rewrite the prompt before producing the result. The documented aim is to turn sparse or vague prompts into detail-rich descriptions. Model documentation.

Terms in this piece · Glossary
  • mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
  • chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.
More from Tencent Hy
Recommended reads
Comments

Checking sign-in…

Loading comments…