IC Edit HD Video Restoration & Enhancement Workflow

This workflow is designed for IC Edit-style high-definition video restoration and enhancement, built on an LTX 2.3 video upscale / repair pipeline. Its main purpose is to take an existing low-quality or compressed video, preserve the original composition and motion, and rebuild the final output with cleaner details, reduced artifacts, better texture, and a more stable high-definition look.

Unlike a simple video upscaler or sharpening filter, this workflow is closer to a generative restoration pipeline. It does not only enlarge the frame or add artificial sharpness. Instead, it uses LTX 2.3, IC LoRA video upscale models, video frame extraction, prompt-guided enhancement, latent reconstruction, tiled VAE decoding, and final video recombination to improve the clip while keeping the original structure intact. This makes it useful when the goal is not to create a new video from scratch, but to repair and polish an existing result.

The workflow uses ltx-2.3-22b-dev-dare-ties-distilled-1.1 as the core model route, with LTXVAudioVAELoader, CheckpointLoaderSimple, LTXAVTextEncoderLoader, LTXVConditioning, LTXAddVideoICLoRAGuide, LTXVImgToVideoConditionOnly, VAEEncodeForInpaint, SamplerCustomAdvanced, VAEDecodeTiled, VHS_LoadVideo, and VHS_VideoCombine. It also loads IC LoRA upscale models such as ltx2.3-ic-video-upscale-general and ltx2.3-video-upscale-v2, which shows that the workflow is specifically tuned for video quality restoration rather than normal image-to-video generation.

A key part of this workflow is the positive enhancement prompt. The workflow asks the model to enhance the input video to clean high-definition quality, remove compression artifacts, noise, blur, jagged edges, color blocks, motion smearing, local dirt, and frame flickering, while reconstructing clearer facial details, skin texture, hair strands, clothing fabric, and background structure. At the same time, it explicitly preserves the original composition, character identity, motion rhythm, camera movement, lighting atmosphere, and color style. This is the correct direction for repair work: improve quality, but do not rewrite the video.

The negative prompt is also practical. It suppresses blur, oversaturation, pixelation, low resolution, grain, distortion, noise, compression artifacts, JPEG artifacts, glitches, watermark, text, logo, signature, copyright marks, subtitles, distorted sound, saturated sound, and overly loud audio artifacts. This makes the workflow suitable for cleaning AI-generated clips, compressed social-media videos, rough preview renders, low-bitrate outputs, and unstable video drafts.

The workflow also preserves audio and frame timing through VideoHelperSuite loading and combining logic. This matters because many repair workflows only process frames and lose the original audio or timing structure. Here, the video frames and audio route can be recombined into a final MP4, making the result more useful for direct publishing.

This workflow is ideal for creators who want to improve AI video outputs before posting them on YouTube, Bilibili, RunningHub, Civitai, TikTok, or other platforms. It can be used for AI video cleanup, HD restoration, compression artifact removal, facial detail recovery, texture enhancement, frame stability improvement, and final “publish-ready” polishing. If you want to see how IC Edit HD repair, LTX 2.3 video upscale LoRAs, prompt-guided restoration, tiled decoding, and final video export work together, watch the full tutorial from the YouTube link above.

⚙️ Try the Workflow Online

👉 Workflow: the link above to run the workflow directly online and view the generation results in real time.

If the results meet your expectations, you can also deploy it locally for further customization.

🎁 Fan Benefits: Register now to get 1000 points, plus 100 daily login points — enjoy 4090-level performance and 48 GB of powerful compute!

📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.

📺 Bilibili Video: will continue updating model resources on Quark Drive:

👉 resources are mainly prepared for local users, making creation and learning more convenient.

⚙️ 在线体验工作流

👉 工作流： 粉丝福利： 注册即送 1000 积分，每日登录 100 积分，畅玩 4090 体验 48 G 超级性能！

📺 Bilibili 更新（中国大陆及南亚太地区）

如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。

📺 B站视频： 夸克网盘 持续更新模型资源：

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