MODEL PROFILE · 模型档案

LTX2.3-ICEdit-Insight

joyfox · LTX2.3-ICEdit-Insight

**LTX2.3-ICEdit-Insight** is a task-aware video restoration and editing model family developed by **JoyFox Lab**, built on top of the **LTX-2.3 DiT-based audio-video foundation model**. - **Video Restoration**: degradation recovery, compression cleanup, blur and noise reduction, and damaged detail restoration. - **Video HD Enhancement**: super-resolution, detail reconstruction, texture sharpening, and perceptual quality improvement. - **Watermark Removal**: logo cleanup, semi-transparent overlay removal, and occlusion-aware background reconstruction. - **Subtitle Removal**: hard subtitle removal, caption cleanup, text overlay removal, and temporally stable inpainting. Unlike conventional frame-level enhancement pipelines, this model family operates as a **generative video restoration system** in latent video space. It is designed to preserve global structure, camera motion, object identity, and temporal consistency while reconstructing missing or degraded visual content.

译文排队中,当前展示来源原文。

license:apache-2.0
上下文窗口来源未披露
输入 → 输出待核验 → 待核验
模型权重待核验
档案更新时间2026/11/2 19:15:41
OVERVIEW

模型介绍

**LTX2.3-ICEdit-Insight** is a task-aware video restoration and editing model family developed by **JoyFox Lab**, built on top of the **LTX-2.3 DiT-based audio-video foundation model**. - **Video Restoration**: degradation recovery, compression cleanup, blur and noise reduction, and damaged detail restoration. - **Video HD Enhancement**: super-resolution, detail reconstruction, texture sharpening, and perceptual quality improvement. - **Watermark Removal**: logo cleanup, semi-transparent overlay removal, and occlusion-aware background reconstruction. - **Subtitle Removal**: hard subtitle removal, caption cleanup, text overlay removal, and temporally stable inpainting. Unlike conventional frame-level enhancement pipelines, this model family operates as a **generative video restoration system** in latent video space. It is designed to preserve global structure, camera motion, object identity, and temporal consistency while reconstructing missing or degraded visual content.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。