TODDLERS,2.0 : Stellar feedback and observables across diverse initial mass functions, binary populations, and cloud environments

Modeling the feedback-driven evolution of star-forming regions and their multiwavelength emission is central to interpreting observations of galaxies across cosmic time. The framework couples 1D shell dynamics with photoionization to produce UV-to-millimeter observables. The original framework, however, assumed instantaneous star formation, uniform cloud density, a fixed initial mass function (IMF) with single-star evolution, and fixed dust grain properties. TODDLERS Cloudy We present , which removes these restrictions and extends the framework to cover a broader range of stellar populations, birth-cloud physics, and dust properties. TODDLERS,2.0 For stellar feedback and input spectra, we integrate (arbitrary IMFs, upper mass limits up to 500,M_⊙) and pySTARBURST99 BPASS (binary evolution, upper mass limits up to 300,M_⊙), alongside stochastic IMF sampling for low-mass clusters (M_* łesssim 10^4,M_⊙) and a constant star formation rate mode. The 1D evolution includes nonuniform cloud density profiles and dynamic cloud density evolution driven by escaping ionizing radiation. The Cloudy post-processing is extended with modified grain size distributions and diffuse ionized gas. Cloud density profile and star formation mode jointly control the fragmentation timescale (the onset of momentum-driven expansion) and shell extent: centrally concentrated profiles fragment earlier, and constant star formation delays fragmentation relative to instantaneous bursts. A top-heavy IMF drives stronger feedback and earlier fragmentation than a standard Kroupa IMF. Dynamic cloud density evolution adds a feedback channel that is most consequential at low metallicity, where the unswept cloud density can drop by three orders of magnitude. At low cluster masses, stochastic sampling introduces order-of-magnitude variance in the feedback, confirming the breakdown of the fully sampled IMF assumption. TODDLERS,2.0 provides the flexibility to model diverse stellar populations, cloud structures, and star formation modes within a single framework. It can be used as a standalone tool for individual star-forming regions or as a sub-grid emission model in galaxy-scale simulations.