Despite the impressive progress of multimodal generative models, generating sound solely from text poses challenges in ensuring comprehensive scene depiction and temporal alignment.Meanwhile, video-to-audio generation limits the flexibility to prioritize sound synthesis for specific objects within the scene.To tackle these challenges, we propose a novel video-and-text-to-audio generation method, called \ours, where video serves as a conditional control for a text-to-audio generation model.Especially, our method estimates the structural information of sound (namely, energy) from the video while receiving key content cues from a user prompt.We employ a well-performing text-to-audio model to consolidate the video control, which is much more efficient for training multimodal diffusion models with massive triplet-paired (audio-video-text) data.In addition, by separating the generative components of audio, it becomes a more flexible system that allows users to freely adjust the energy, surrounding environment, and primary sound source according to their preferences.Experimental results demonstrate that our method shows superiority in terms of quality, controllability, and training efficiency.Our demo is available at https://naver-ai.github.io/rewas.