CIVITAI / Workflows

Z-Image Base Upscale Workflow

This ComfyUI workflow is designed for Z-Image Base image upscaling, detail refinement, and high-resolution restoration. It combines a traditional 4x upscale model with Z-Image Base latent refinement, Florence2 automatic captioning, tiled processing, and final image reconstruction. The goal is to turn a lower-resolution or softer image into a cleaner, sharper, and more detailed high-resolution result while keeping the original composition and overall visual identity stable. This is not a simple one-click ESRGAN upscale workflow. It uses a multi-stage enhancement structure. First, the input image is enlarged with a classic upscale model. Then the image is scaled to a target megapixel size. After that, it is divided into tiles, automatically captioned with Florence2, refined through Z-Image Base, decoded with tiled VAE decoding, and finally stitched back into one complete image. This makes the workflow more useful for large images where direct full-frame processing may be unstable or too memory-heavy. The workflow uses z_image_bf16.safetensors as the main Z-Image model, qwen_3_4b.safetensors as the text encoder, and ae.safetensors as the VAE. It also uses 4x_NMKD-Siax_200k.pth as the first-stage upscale model. This gives the workflow a hybrid design: the traditional upscaler provides fast resolution expansion, while Z-Image Base adds AI-driven detail reconstruction, texture polishing, and local refinement. A key part of the workflow is the ImageUpscaleWithModel stage. This step uses the 4x_NMKD-Siax model to enlarge the input image before the Z-Image refinement stage. Traditional upscale models are useful because they preserve the original structure and provide a stable high-resolution base. However, pure traditional upscaling can sometimes look too smooth, too artificial, or lacking in new detail. That is why this workflow continues with a Z-Image refinement pass. After the first upscale, the workflow uses ImageScaleToTotalPixels to bring the image to a target output size. In the included setup, the image is scaled toward a high megapixel target using Lanczos scaling. This gives users a predictable way to control final resolution without manually calculating width and height. It is useful for social media covers, Civitai showcase images, posters, product visuals, portrait enhancement, and high-resolution AI artwork output. The workflow then uses TTP tile