Fashion image generation attracts increasing attentions with wide applications in fashion design, virtual try-on, cosmetic industry, etc. Editing clues such as segmentation masks, keypoints and sketches are usually taken to guide the desired transformation of a reference image. However, spatial manipulation of the reference image remains a challenge, especially facing large-scale deformations and multiple editing requirements. In this paper, we propose a general model for multiple fashion editing tasks such as facial editing, pose transformation and clothes design based on user-defined editing instructions like semantic segmentation masks, keypoints, and sketches. With diverse editing requirements and various deformation scales, it is hard to learn the corresponding relationship between the editing clue and reference image with a uniform framework. Accordingly, we design a feature flow estimation network, which can adaptively adjust the feature flow according to the editing clue and the reference image, and generate a coarsely aligned image. Then we propose an image generative network to enrich the texture details of the transformed reference image. Experiments on three tasks verify the effectiveness of the proposed method and the adaptability to multiple tasks.