CIVITAI / Workflows

Anima Preview2 Tiled Upscale Workflow

This workflow is designed for Anima Preview2 tiled upscaling and detail enhancement. Its main purpose is to take an existing image, enlarge it to a higher usable resolution, then refine the enlarged result tile by tile with Anima Preview2 so the final image looks cleaner, sharper, and more detailed without relying on simple interpolation alone. It is especially useful for creators who already have a good base image but want a higher-quality final version for publishing, preview display, cover images, or showcase output. The workflow uses anima-preview2.safetensors as the main refinement model, qwen_3_06b_base.safetensors as the text encoder, and qwen_image_vae.safetensors as the VAE. It also includes a LoRA loader in the graph, showing that the upscaling route can be combined with an additional style or detail bias when needed. The overall design is not just “make the image bigger.” Instead, it builds a multi-stage pipeline: upscale first, split into tiles, describe the tiles, refine them in latent space, decode them, and then reassemble the final image. The process starts with a source image loaded into the workflow. That image is first enlarged through a traditional upscale model using 4x_NMKD-Siax_200k. After that, the result is further normalized with ImageScaleToTotalPixels, targeting a larger working size while keeping the image manageable. This gives the workflow a stronger high-resolution base before diffusion refinement begins. A major feature of this workflow is tiled processing. The enlarged image is divided into tiles with TTP_Image_Tile_Batch, and the tile layout is controlled through TTP_Tile_image_size. This is important because very large images can be difficult to refine in a single pass, especially when you want detail recovery without destroying the whole composition. By splitting the image into tiles, the workflow can enhance local detail more effectively while still reconstructing the whole image afterward through TTP_Image_Assy. Another useful feature is automatic tile captioning. The workflow uses WD14Tagger to analyze the image tiles and generate prompt-like tag information. Those generated tags are passed through ShowText and then into the positive CLIPTextEncode route. This means the workflow does not depend entirely on a manually written prompt. Instead, it can derive a descriptive prompt from the source content itself, which h