This workflow is the image-to-image companion to the Anima Aesthetic 1.1 face and hand repair setup. It is useful when you already have an image and want to redraw or improve it while keeping the main composition. The active value is the combination of image-guided generation and local detail correction. The source name mentions CN, but the inspected graph shows the LLLite control node is bypassed, so this Civitai copy keeps the public claim focused on the parts that are active: image-to-image transformation, face detection, hand detection, and detailer-based cleanup.

The graph includes Anima model loading paths, Qwen Image CLIP, Qwen Image HDR VAE, and a 30-step sampling route with denoise around 0.84 to 0.85. It also contains FaceDetailer nodes and detector providers for face and hand regions, including face YOLO and hand segmentation/detection models. That makes it a practical workflow for improving existing character renders, fixing facial softness, cleaning small hand defects, or making a generated concept more presentable without restarting from pure text-to-image. Because the control branch is bypassed, users who want active ControlNet-style guidance should enable and test that branch deliberately before publishing results as ControlNet-controlled.

Main features:

- Image-to-image Anima Aesthetic 1.1 workflow
- Active face detailer route
- Active hand detection and hand refinement route
- Qwen Image CLIP text encoding
- Qwen Image HDR VAE path
- 30-step redraw sampling route observed
- Denoise around 0.84 to 0.85 for strong image transformation
- Useful for improving existing anime character renders
- Helps clean faces, eyes, hands, and local details
- Multiple model paths are present in the graph
- LLLite/CN node is bypassed and not advertised as active
- Good for controlled redraws before final polishing

Suggested workflow:

Load a source image that already has the composition you want. Keep the prompt close to the original subject at first, then add style or costume changes gradually. If the image changes too much, lower denoise; if the result stays too close to the source, increase it slightly. Use the detailer passes after the redraw is stable, not as a replacement for a clear source image.

⚙️ RunningHub Workflow

Try the workflow online right now — no installation required.
👉 Workflow: https://www.runninghub.ai/post/2078374948430950401?inviteCode=rh-v1111

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

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📺 Bilibili Updates (Mainland China & Asia-Pacific)

If you’re in the Asia-Pacific region, you can watch the video below to see the workflow demonstration and creative breakdown.
📺 Bilibili Video: https://www.bilibili.com/video/BV1xJKB6vEfp/

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⚙️打开下方链接即可在线体验，无需安装。
👉 工作流： https://www.runninghub.ai/post/2078374948430950401?inviteCode=rh-v1111
如果觉得效果理想，你也可以在本地进行自定义部署。

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📺 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： https://www.bilibili.com/video/BV1xJKB6vEfp/

我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。