The existing target-dependent scalable image compression network can control the target of the compressed images between the human visual system and the deep learning based classification task. However, in its RNN based structure controls the bit-rate through the number of iterations, where each iteration generates a fixed size of the bit stream. Therefore, a large number of iterations are required at the high BPP, and fine-grained image quality control is not supported at the low BPP. In this paper, we propose a novel RNN-based image compression model that can schedule the channel size per iteration, to reduce the number of iterations at the high BPP and fine-grained bit-rate control at the low BPP. To further enhance the efficiency, multiple network models for various channel sizes are combined into a single model using the slimmable network architecture. The experimental results show that the proposed method achieves comparable performance to the existing method with finer BPP adjustment, increases parameters by only 0.15% and reduces the average amount of computation by 40.4%.