Although natural language instructions offer an intuitive way to guide automated image editing, deep-learning models often struggle to achieve high-quality results, largely due to challenges in creating large, high-quality training datasets. Previous work has typically relied on text-to-image (T2I) generative models to produce pairs of original and edited images that simulate the input/output of an instruction-guided image-editing model. However, these image pairs often fail to align with the specified edit instructions due to the limitations of T2I models, which negatively impacts models trained on such datasets. To address this, we present Instruct-CLIP, a self-supervised method that learns the semantic changes between original and edited images to refine and better align the instructions in existing datasets. Furthermore, we adapt Instruct-CLIP to handle noisy latent images and diffusion timesteps so that it can be used to train latent diffusion models (LDMs) and efficiently enforce alignment between the edit instruction and the image changes in latent space at any step of the diffusion pipeline. We use Instruct-CLIP to correct the InstructPix2Pix (IP2P) dataset and get over 60K refined samples we then use to fine-tune the IP2P model, guided by our novel Instruct-CLIP-based loss function. The resulting model produces better edits that are more aligned with the given instructions, and visibly and quantitatively outperforms state-of-the-art approaches. Our code and dataset will be released upon acceptance of the paper.