In the ever-evolving landscape of artificial intelligence, access to high-quality data is paramount. But what if we could create our own data, tailored to specific needs? That's the promise of Auto Cherry-Picker (ACP), a novel framework that leverages language and diffusion models to not just generate images, but to cultivate entire training datasets enriched with descriptions and layouts. Imagine training an AI model not on static, pre-existing data, but on a dynamic, expanding dataset that grows and evolves with the task at hand. ACP makes this possible by employing large language models (LLMs) to create detailed scene graphs, which include attributes, relationships, captions, and spatial layouts, all based on object combinations from real data. These scene graphs then guide diffusion models to generate corresponding images, producing a rich cross-modality training set. The core of ACP's power lies in its Composite Layout and Image Score (CLIS). CLIS acts as a quality control mechanism, meticulously evaluating the reasonableness of generated layouts and the visual quality of images, including their alignment with scene graphs. This careful curation ensures that the generated data enhances model performance, especially in challenging situations like imbalanced datasets or the long-tailed distribution problems that plague many machine learning projects. ACP's impact extends beyond just generating pretty pictures. It addresses the critical bottleneck of data scarcity in AI, allowing for the scaling up of training samples without the cost and time associated with manual annotation. Experiments with various downstream tasks, including object detection, instance segmentation, and visual question answering, have shown impressive performance gains. ACP-generated datasets boost the abilities of state-of-the-art models, enabling them to reason about visual information with greater accuracy. The road ahead for ACP is paved with exciting possibilities. Future research will focus on refining the generation process, making it even more efficient and capable of utilizing lower-quality data. This breakthrough technology has the potential to redefine how we train AI models, creating a future where synthetic, yet high-quality, data empowers the next generation of intelligent systems.