CIVITAI / Workflows

Anima Base Image-to-Image + ControlNet Face & Hand Repair Workflow

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