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 ​	The generated image.
 
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+# More Resources
+
+- [Scalar Quantization and Product Quantization - Zilliz blog](https://zilliz.com/learn/scalar-quantization-and-product-quantization): A hands-on dive into scalar quantization (integer quantization) and product quantization with Python.
+- [Supercharged Semantic Similarity Search in Production - Zilliz blog](https://zilliz.com/learn/supercharged-semantic-similarity-search-in-production): Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database.
+- [Optimizing AI: A Guide to Stable Diffusion and Efficient Caching Strategies - Zilliz blog](https://zilliz.com/learn/optimizing-ai-guide-to-stable-diffusion-and-caching-strategies): This blog post will explore various caching strategies for optimizing Stable Diffusion models.
+- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts.
+- [Generative AI for Creative Applications using Storia Lab - Zilliz blog](https://zilliz.com/blog/generative-ai-for-creative-applications-using-storia-lab): This post discusses how Storia AI generates and edits images through simple text prompts or clicks and how we can leverage Storia AI and Milvus to build multimodal RAG.
+- [What Is a Diffusion Model? A Comprehensive Definition](https://zilliz.com/glossary/diffusion-models): A diffusion model applies Gaussian noise to an image and learns to remove the noise in a series of Markov steps. Learn more in this post.
+- [What is a Generative Adversarial Network? An Easy Guide](https://zilliz.com/glossary/generative-adversarial-networks): Just like we classify animal fossils into domains, kingdoms, and phyla, we classify AI networks, too. At the highest level, we classify AI networks as "discriminative" and "generative." A generative neural network is an AI that creates something new. This differs from a discriminative network, which classifies something that already exists into particular buckets. Kind of like we're doing right now, by bucketing generative adversarial networks (GANs) into appropriate classifications.
+So, if you were in a situation where you wanted to use textual tags to create a new visual image, like with Midjourney, you'd use a generative network. However, if you had a giant pile of data that you needed to classify and tag, you'd use a discriminative model.
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