examples/research_projects/intel_opts/README.md

## Diffusers examples with Intel optimizations

**This research project is not actively maintained by the diffusers team. For any questions or comments, please make sure to tag @hshen14 .**

This aims to provide diffusers examples with Intel optimizations such as Bfloat16 for training/fine-tuning acceleration and 8-bit integer (INT8) for inference acceleration on Intel platforms.

## Accelerating the fine-tuning for textual inversion

We accelereate the fine-tuning for textual inversion with Intel Extension for PyTorch. The [examples](textual_inversion) enable both single node and multi-node distributed training with Bfloat16 support on Intel Xeon Scalable Processor.

## Accelerating the inference for Stable Diffusion using Bfloat16

We start the inference acceleration with Bfloat16 using Intel Extension for PyTorch. The [script](inference_bf16.py) is generally designed to support standard Stable Diffusion models with Bfloat16 support.
```bash
pip install diffusers transformers accelerate scipy safetensors

export KMP_BLOCKTIME=1
export KMP_SETTINGS=1
export KMP_AFFINITY=granularity=fine,compact,1,0

# Intel OpenMP
export OMP_NUM_THREADS=< Cores to use >
export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libiomp5.so
# Jemalloc is a recommended malloc implementation that emphasizes fragmentation avoidance and scalable concurrency support.
export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libjemalloc.so
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:9000000000"

# Launch with default DDIM
numactl --membind <node N> -C <cpu list> python python inference_bf16.py
# Launch with DPMSolverMultistepScheduler
numactl --membind <node N> -C <cpu list> python python inference_bf16.py --dpm

```

## Accelerating the inference for Stable Diffusion using INT8

Coming soon ...