CIVITAI / Workflows

Anima Preview2 Image-to-Image Workflow

This workflow is designed for Anima Preview2 image-to-image generation, giving creators a clean and efficient way to transform an input image into a more polished anime-style result while still preserving the original composition and core visual structure. Unlike a pure text-to-image workflow, this setup begins with a source image, analyzes it, converts it into latent space, and then regenerates it through Anima Preview2 with prompt guidance. This makes it especially useful for style transfer, anime enhancement, visual cleanup, character refinement, and turning an existing image into a more cinematic anime illustration. The workflow uses anima-preview2.safetensors as the main generation model, qwen_3_06b_base.safetensors as the text encoder, and qwen_image_vae.safetensors as the VAE. It starts by loading a source image, then rescales it with image_scale_pixel_v2 so the input is normalized to a model-friendly pixel target while keeping alignment stable. After that, the image is encoded into latent space with VAEEncode, which becomes the structural starting point for the generation stage. This design makes the workflow ideal for users who want to preserve the general framing, subject position, and image layout rather than generating from scratch. One of the useful features in this workflow is the WD14 tagger support. The loaded image is automatically analyzed to produce tag-style prompt information, which can then be combined with the manual positive prompt. In the provided setup, the base prompt includes a beach-sunset singing-girl concept, showing how this workflow can mix image-derived tags with user-written prompt direction. This is practical for image-to-image generation because it reduces prompt-writing difficulty and helps the model better understand the existing content of the input image. The negative prompt is focused on common quality issues, including low quality, blur, bad anatomy, bad hands, extra fingers, fused fingers, deformed faces, text, watermark, logo, and JPEG artifacts. This helps keep the result clean during regeneration, especially when the source image already contains imperfect details or when the user wants a sharper anime-style finish. The main generation stage uses a KSampler with 40 steps, CFG around 3, DPM++ 2M SDE GPU sampling, SGM Uniform scheduling, and denoise around 0.75. This is important because the denoise value make