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3. Compute similarity between images and text

inputs = processor(text=texts, images=images, return_tensors=“pt”, padding=True) outputs = model(**inputs) logits_per_image = outputs.logits_per_image # Higher = better match probs = logits_per_image.softmax(dim=1) # Probabilities for each text-image pair print(“Probabilities:”, probs)

How does this function?

1. **Load CLIP and processor** from Hugging Face, using `AutoModel` and `AutoProcessor` for compatibility with future models.
2. **Inputs:** Provide images and text descriptions.
3. **Processing:** Model computes how well each image matches each description.
4. **Output:** Probabilities demonstrate the best match.

You can also explore newer multimodal models like BLIP-2, LLaVA, or ImageBind using the same Hugging Face APIs.

## Generative and Diffusion Models: The Next Wave

Text-to-image diffusion models have rapidly advanced. While **Stable Diffusion** remains popular, recent open-source models like **SDXL**, **Stable Diffusion 3**, and **PixArt-α** deliver higher resolution, improved speed. more flexible generation. These are available via Hugging Face's `diffusers` library, with a consistent `API` for experimentation and deployment.

## SGLang and Modern AI Pipelines: From Prototype to Production

Building a demo is one thing—serving real users is another. **SGLang** (Serving Graph Language) and Hugging Face's latest deployment tools empower you chain together models and data flows for production. You can combine text, vision, audio. multimodal models in a single, robust workflow. SGLang lets you design these pipelines visually or in code, then deploy them at scale. (See Article 15 for more on deployment.)

## Key Takeaways

- Transformers now power vision, audio, and multimodal AI, not just text.
- Hugging Face makes advanced models easy to use, fine-tune, and deploy—with APIs that future-proof your code.
- Modern vision (DeiT, Swin, state-space models) and audio (Whisper, SeamlessM4T) transformers set new standards for **efficiency** and accuracy.
- Multimodal models (like CLIP, BLIP-2, LLaVA, ImageBind) connect text, images, audio, and more for smarter search and creative tools.
- SGLang and Hugging Face deployment tools bridge the gap from prototype to scalable, production-ready AI pipelines.

## Reinforce What You've Learned

- **ViT, DeiT, Swin:** For image understanding and classification.
- **Wav2Vec 2.0, Whisper, SeamlessM4T:** For speech recognition and audio analytics.
- **Diffusion Models (SDXL, Stable Diffusion 3, PixArt-α):** For generative AI—creating images from text.
- **CLIP, BLIP-2, LLaVA, ImageBind:** For connecting and understanding text, images, and more.
- **SGLang:** For deploying and scaling these capabilities in real systems.

## What's Next?

You're now ready to apply transformers across vision, audio, and multimodal domains using the latest models and best practices. In the next articles, you'll learn to customize pipelines, fine-tune models for your data. deploy them at scale with modern Hugging Face tools. Need a refresher on transformer basics? See Article 4. For advanced deployment, check Article 15.

Keep experimenting—each new modality and model family opens new possibilities!

## Summary

This chapter took you on a guided tour of transformers beyond language, revealing how these models are transforming vision, audio, and multimodal AI. By exploring ViT, Wav2Vec, diffusion models. multimodal systems like CLIP and BLIP, you gained both conceptual understanding and practical skills. With tools like Hugging Face and SGLang, you're equipped to build, deploy, and scale advanced AI systems that see, hear, and create—unlocking new possibilities across industries.

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