PyTorch Conference Europe 2026

This session shows how to use torch.compile with the Diffusers library to speed up diffusion models like Flux-1-Dev.

You'll learn practical techniques for both model authors and users. For authors, we cover how to make models compiler-friendly using fullgraph=True. For users, we explain regional compilation (which cuts compile time by 7x while keeping the same runtime gains) and how to avoid recompilations with dynamic=True.

We also cover real-world scenarios: running on memory-constrained GPUs using CPU offloading and quantization, and swapping LoRA adapters without triggering recompilation.

Key takeaways:
- Compiling just the Diffusion Transformer (DiT) delivers ~1.5x speedup on H100
- Regional compilation reduces cold-start compile time from 67s to 9.6s
- NF4 quantization cuts memory from 33GB to 15GB
- Combining quantization + offloading drops memory to 12.2GB
- LoRA hot-swap lets you switch adapters without recompiling

Whether you're building diffusion models or using them, this guide helps you get the best performance with minimal effort.