This workflow is designed for Anima Base image-to-image generation with ControlNet-style structure guidance, face refinement, and hand repair. Its main purpose is to take an existing image as the visual reference, regenerate it through Anima Base, preserve the original composition and style direction, and then automatically improve the most error-prone areas: the face, eyes, hands, and fingers.
Unlike a basic image-to-image workflow, this setup is not only a simple restyle or redraw process. It combines Anima Base generation, image scaling, latent encoding, prompt-guided reconstruction, optional ControlNet / LLLite-style guidance, face detection, SAM-assisted refinement, hand detection, hand segmentation, FaceDetailer repair, and final preview / export logic. This makes it more suitable for creators who want a complete anime image polishing pipeline instead of a one-pass redraw.
The workflow uses anima_baseV10.safetensors as the main Anima Base model route, qwen_3_06b_base.safetensors as the text encoder, and qwen_image_vae.safetensors as the VAE. It also includes image_scale_pixel_v2, VAEEncode, VAEDecode, CLIPTextEncode, NAGuidance, AnimaLLLiteApply, AIO_Preprocessor, FaceDetailer, SAMLoader, UltralyticsDetectorProvider, and multiple preview nodes. The structure shows that this workflow is built for controlled regeneration plus automatic detail correction.
The image-to-image section first receives the input image, scales it to a suitable working resolution, encodes it into latent space, and uses the prompt to guide the Anima Base redraw. This helps preserve the main layout while giving the model enough freedom to improve detail, color, lighting, anime rendering quality, and character styling. The positive prompt route defines the target anime key visual style, while the negative prompt suppresses common failures such as low quality, blurry faces, bad anatomy, extra fingers, malformed hands, duplicated characters, cropped bodies, text, and watermark artifacts.
A key part of this workflow is the ControlNet-style guidance section. The workflow includes a depth preprocessor route and Anima LLLite application logic. This can help preserve the structural relationship of the input image, such as pose, depth, silhouette, body placement, and large composition. In image-to-image generation, this kind of control is important because a pure prompt-based redraw can easily change the subject too much. The control route helps keep the image more stable while still allowing the Anima model to enhance style and quality.
The face repair section uses face detection and SAM-based refinement to locate the face area and run a focused detail pass. This is useful because anime image generation often produces a good full-body composition but leaves the face slightly soft, distorted, asymmetrical, or lacking detail. By isolating the face area, the workflow can improve eyes, facial clarity, expression, and local rendering quality without forcing the whole image to regenerate.
The hand repair section uses dedicated hand detection and segmentation models, including a hand bounding-box detector and a hand detail segmentation route. This is one of the most practical parts of the workflow because hands are still one of the most common failure points in AI image generation. The hand detail pass is designed to identify the hand region, crop it, refine it, and paste the improved result back into the full image. This helps reduce extra fingers, fused fingers, broken hands, malformed palms, and weak hand detail.
This workflow is especially useful for anime creators who already have a promising base image but want a cleaner publish-ready version. It can be used for image-to-image restyling, anime character enhancement, composition-preserving redraw, ControlNet-style structure retention, automatic face repair, automatic hand repair, Civitai preview images, RunningHub demos, and social media cover artwork.
If you want to see how Anima Base, image-to-image generation, ControlNet-style guidance, face detection, hand detection, SAM refinement, and final detail repair work together in one practical pipeline, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.cn/post/2055116648939565057?inviteCode=rh-v1111
Open 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.
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📺 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: https://www.bilibili.com/video/BV1ci5q65EGQ/
I will continue updating model resources on Quark Drive:
👉 https://pan.quark.cn/s/20c6f6f8d87b
These resources are mainly prepared for local users, making creation and learning more convenient.
⚙️ 在线体验工作流
👉 工作流: https://www.runninghub.cn/post/2055116648939565057?inviteCode=rh-v1111
打开上方链接即可直接运行该工作流,实时查看生成效果。
如果觉得效果理想,你也可以在本地进行自定义部署。
🎁 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: https://www.bilibili.com/video/BV1ci5q65EGQ/
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。
