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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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**FireRed-Image-Edit** is a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios. - **Strong Editing Performance**: FireRed-Image-Edit delivers leading open-source results with accurate instruction following, high image quality, and consistent visual coherence. - **Native Editing Capability**: Built directly from text-to-image foundation model and endowed with editing capabilities. - **Text Style Preservation**: Maintains text styles with high fidelity, achieving performance comparable to closed-source solutions. - **Photo Restoration**: High-quality old photo restoration and enhancement. - **Multi-Image Editing**: Flexible editing of multiple images such as virtual try-on. - 2026.02.14: We released FireRed-Image-Edit-1.0 model weights. Check more details in the Model Zoo section. - 2026.02.10: We released the Technical Report of FireRed-Image-Edit-1.0.

Image editing
Input Image→ Output Image
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We introduce FireRed-Image-Edit-1.1, an upgrade to our general-purpose image editing foundation model. Building upon the capabilities presented in the FireRed-Image-Edit-1.0 Technical Report, version 1.1 significantly enhances identity consistency, multi-image conditioning, and domain-specialized editing performance, bringing the model closer to real-world creative production needs. FireRed-Image-Edit establishes a new state-of-the-art among open-source models on Imgedit, Gedit, and RedEdit, while surpassing our closed-source competitors in specific dimensions—a distinction further corroborated by human evaluations highlighting its superior prompt following and visual consistency.

Image editing
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**FireRed-Image-Edit-1.1** is a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios.

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FireRed-OCR

FireRedTeam

Figure 1: Performance comparison on the OmniDocBench v1.5 benchmark. FireRed-OCR achieves state-of-the-art performance among end-to-end solutions, ranking first with a score above 92%. **FireRed-OCR** is a systematic framework designed to specialize general Large Vision-Language Models (LVLMs) into high-performance, pixel-precise structural document parsing experts. General VLMs frequently suffer from **"Structural Hallucination"** (e.g., disordered rows, invented formulas) when processing complex documents. FireRed-OCR addresses this by shifting the paradigm from "impressionist" text generation to "structural engineering," achieving State-of-the-Art (SOTA) results on authoritative benchmarks like OmniDocBench v1.5.

Image captioning
Input Image→ Output Text
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FireRedLID

FireRedTeam

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Audio processing
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FireRedPunc

FireRedTeam

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Speech recognition
Input Audio→ Output Text
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FireRedVAD

FireRedTeam

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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