DIFFUSEMIX is a novel data augmentation technique that leverages diffusion models to generate diverse and label-preserving augmented images. The method combines a natural image with a generated image using conditional prompts and then blends a fractal image to enhance structural diversity and avoid overfitting. This approach improves generalization, adversarial robustness, and performance on tasks such as general classification, fine-grained classification, and data scarcity. DIFFUSEMIX outperforms existing state-of-the-art methods on seven datasets, demonstrating superior performance in various tasks. The method involves three key steps: generation, concatenation, and fractal blending. Generation uses a diffusion model with conditional prompts to create a generated image. Concatenation combines a portion of the original image with the generated image to form a hybrid image. Fractal blending then adds a random fractal image to the hybrid image to enhance structural diversity. The method is effective in preserving key semantics while introducing diversity, leading to better performance and robustness. DIFFUSEMIX is compatible with a wide range of datasets and can be integrated into various existing architectures. The approach addresses limitations of existing methods by avoiding label ambiguity and ensuring the availability of original data alongside generated images. The method also reduces the risk of overfitting by introducing structural diversity through fractal blending. Experimental results show that DIFFUSEMIX achieves significant performance gains compared to existing methods, demonstrating its effectiveness in enhancing model performance and robustness.DIFFUSEMIX is a novel data augmentation technique that leverages diffusion models to generate diverse and label-preserving augmented images. The method combines a natural image with a generated image using conditional prompts and then blends a fractal image to enhance structural diversity and avoid overfitting. This approach improves generalization, adversarial robustness, and performance on tasks such as general classification, fine-grained classification, and data scarcity. DIFFUSEMIX outperforms existing state-of-the-art methods on seven datasets, demonstrating superior performance in various tasks. The method involves three key steps: generation, concatenation, and fractal blending. Generation uses a diffusion model with conditional prompts to create a generated image. Concatenation combines a portion of the original image with the generated image to form a hybrid image. Fractal blending then adds a random fractal image to the hybrid image to enhance structural diversity. The method is effective in preserving key semantics while introducing diversity, leading to better performance and robustness. DIFFUSEMIX is compatible with a wide range of datasets and can be integrated into various existing architectures. The approach addresses limitations of existing methods by avoiding label ambiguity and ensuring the availability of original data alongside generated images. The method also reduces the risk of overfitting by introducing structural diversity through fractal blending. Experimental results show that DIFFUSEMIX achieves significant performance gains compared to existing methods, demonstrating its effectiveness in enhancing model performance and robustness.