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A diffusion generator learns a reverse-corruption process that can transform a noise representation into a structured sample. The exact target parameterization and sampling rule vary, so every implementation should not be described as literally subtracting one identical noise prediction. Audio supplies several possible representations. DiffWave and WaveGrad study waveform generation, while latent systems perform their generative work in a learned compressed representation. A spectral system may generate a time-frequency representation and then use an associated reconstruction model or vocoder. Those conversion stages affect quality and resource measurements. AudioLDM uses contrastive language-audio pretraining, CLAP, to connect conditioning representations. Its original paper trains the latent diffusion model with audio embeddings and uses text embeddings as conditions during sampling. This is a specific cross-modal design rather than a rule that every audio denoiser uses text cross-attention in the same way. Fast Timing-Conditioned Latent Audio Diffusion supplies another example, with a learned audio latent, text features and explicit timing signals in its stated original Stable Audio system. A model’s capability to generate an audio sample does not establish exact event timing, transcription accuracy, or the rights attached to every output. Fewer-step methods and consistency learning are related acceleration research areas, but reduced iteration count alone does not prove equal quality or lower elapsed time for every audio implementation. Evaluations should state the representation, trained model, conditioning, sampler, decoder, sample rate, duration and hardware when comparing generated sounds.