CIVITAI / Workflows

Z-Image-i2L Image to LoRA Fast Training & Testing Workflow

This ComfyUI workflow is designed for Z-Image-i2L, also known as Image to LoRA. The main purpose of this workflow is to let creators quickly train a lightweight LoRA from a small group of reference images, save the generated LoRA, and immediately test it inside the same ComfyUI graph with Z-Image Base. Unlike a traditional LoRA training workflow that requires dataset preparation, caption files, training scripts, optimizer settings, command-line configuration, and manual model loading, this workflow is designed as a fast and practical image-to-LoRA pipeline. The user only needs to provide several reference images, run the i2L generation node, save the LoRA, and then test the newly generated LoRA through a normal Z-Image generation route. The workflow is built around the RunningHub Z-Image-i2L node system. It uses RunningHub_ZImageI2L_Loader to load the Image-to-LoRA pipeline, RunningHub_ZImageI2L_LoraGenerator to generate a LoRA from the uploaded training images, and RunningHub_ZImageI2L_Saver to save the generated LoRA file. This makes the workflow much more convenient for creators who want to quickly capture a character style, object style, visual identity, costume concept, creature design, or artistic direction from a few images. The training input section uses multiple LoadImage nodes and ImageBatchMulti. In the uploaded setup, the workflow accepts six image inputs and combines them into one image batch. These images become the training references for the Image-to-LoRA generator. This is useful because a single image may not be enough to define a stable visual concept. Multiple images help the i2L pipeline understand the repeated features across the references, such as face shape, clothing style, color theme, character identity, object design, or general aesthetic. The ImageBatchMulti node is important because it merges the reference images into one training batch. The workflow is configured with an input count of 6, which means users can provide a small set of images without preparing a full dataset folder manually. This is suitable for quick creator testing, lightweight character adaptation, concept extraction, and rapid LoRA prototyping. The i2L generation stage uses RunningHub_ZImageI2L_LoraGenerator. This node receives the loaded ZImageI2LPipeline and the batched training images, then generates a LoRA name and LoRA path. In the included setup, t