Krea 2 cleanup in ComfyUI: one shared recipe across three input and output setups

Three Krea 2 cleanup workflows in ComfyUI follow the same skeleton: reverse-prompting the input image, rebuilding it through three depth-structure passes with one shared LoRA, then upscaling with 4x-UltraSharp. The variants differ only in I/O, covering a single image, a local folder loop, and a RunningHub ZIP batch. All three use the same Krea 2 Turbo model, text encoder and VAE, with 8-step KSampler passes and a console that selects render stages, the final long edge, and the reverse-prompt route. The single-image graph is presented as the clearest example, and the author suggests a 2048-pixel long edge as the best value for cost, though this is guidance rather than a measured benchmark. The source also notes that the in-graph label on the batch variant is outdated, so the node graph should be trusted over the note text.