CIVITAI / Workflows

Z-Image Base + Turbo Segmented Rendering Workflow

This ComfyUI workflow is designed for segmented rendering with Z-Image Base and Z-Image Turbo. Instead of using only one model for the entire generation process, this workflow splits the sampling process into different stages and lets Z-Image Base and Z-Image Turbo handle different parts of the render. The goal is to combine the stronger global structure and composition ability of Z-Image Base with the faster, sharper, and more detail-oriented finishing behavior of Z-Image Turbo. The core idea is simple: use Z-Image Base to build the main image foundation during the earlier high-noise stage, then hand the latent result to Z-Image Turbo for the later low-noise stage. This makes the workflow useful when a single-model workflow is not stable enough, or when Turbo alone is fast but not always strong enough for complex composition, and Base alone is more stable but slower or less efficient for final iteration. By separating the render into stages, the workflow gives creators more control over composition, detail, speed, and final polish. The workflow is built around two Z-Image models. The first model route uses z_image_bf16.safetensors as the Base model. This route is responsible for the main structure, subject placement, scene logic, atmosphere, and broad visual composition. The second model route uses z_image_turbo_bf16.safetensors as the Turbo model. This route is used for continuation, refinement, and detail strengthening after the Base model has already established the image direction. The workflow uses qwen_3_4b.safetensors as the text encoder and ae.safetensors as the VAE. The prompt is encoded through CLIPTextEncode, then passed into CFGGuider. The sampling process is handled through RandomNoise, BasicScheduler, SplitSigmas, DetailDaemonSamplerNode, SamplerEulerAncestral, and SamplerCustomAdvanced. This structure gives the workflow a more technical and controllable sampling chain than a normal KSampler-only setup. A key part of this workflow is SplitSigmas. The workflow generates a sigma schedule, then splits it into a high-sigma section and a low-sigma section. The high-sigma section represents the earlier generation stage, where the model is still deciding major image structure and composition. The low-sigma section represents the later refinement stage, where the image is already formed and the model mainly improves detail, texture, edge quality,