Inverse tone mapping, a technique to restore a high dynamic range (HDR) image from a single low dynamic range (LDR) image, exhibits wide versatility since it may be easily applied to any camera device. Besides, the recent advancement in deep learning has produced great performance improvement in the field of inverse tone mapping. However, it remains a difficult task to accurately restore a wide-range HDR image from a single LDR image. A recent study attempts a spatially adaptive exposure value (EV) condition generated from luminance values to create a pseudo-multi-exposure stack. However, by adopting only luminance values as input, the conditioning method cannot precisely reflect the input image information when generating the EV condition, resulting in the loss of color expression. Moreover, there are some concerns regarding how to apply the EV condition to the image feature. Thus, the key idea of this study is to directly adopt image features in generating EV conditions that are adaptive to both color and brightness. To do this, we design a condition generation network with an encoder-decoder structure and propose a novel multi-exposure stack generation network, which bidirectionally synthesizes the image features and EV-conditioned features. Additionally, to better preserve the feature information in the synthesis of the features, we propose a spatially-adaptive feature transformation block. Our proposed method exhibits outstanding results in restoring the multi-exposure stacks for HDR image synthesis. Furthermore, our method achieves state-of-the-art performance compared to existing methods in multi-exposure stack generation and stack-based HDR restoration.