Add stable diffusion training script for PyTorch/XLA

examples/text_to_image/README_xla.md

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+# Stable Diffusion text-to-image fine-tuning using PyTorch/XLA
+
+The `train_text_to_image_xla.py` script shows how to fine-tune stable diffusion model on TPU devices using PyTorch/XLA.
+
+It has been tested on v4 and v5p TPU versions.
+
+This script implements Distributed Data Parallel using GSPMD feature in XLA compiler
+where we shard the input batches over the TPU devices.
+
+## Create TPU
+
+To create a TPU on Google Cloud first set these environment variables:
+
+```bash
+export TPU_NAME=<tpu-name>
+export PROJECT_ID=<project-id>
+export ZONE=<google-cloud-zone>
+export ACCELERATOR_TYPE=<accelerator type like v5p-8>
+export RUNTIME_VERSION=<runtime version like v2-alpha-tpuv5 for v5p>
+```
+
+Then run the create TPU command:
+```bash
+gcloud alpha compute tpus tpu-vm create ${TPU_NAME} --project ${PROJECT_ID} 
+--zone ${ZONE} --accelerator-type ${ACCELERATOR_TYPE} --version ${RUNTIME_VERSION} 
+--reserved
+```
+
+You can also use other ways to reserve TPUs like GKE or queued resources.
+
+## Setup TPU environment
+
+Install PyTorch and PyTorch/XLA nightly versions:
+```bash
+gcloud compute tpus tpu-vm ssh ${TPU_NAME} \
+--project=${PROJECT_ID} --zone=${ZONE} --worker=all \
+--command='
+pip3 install --pre torch==2.5.0.dev20240905+cpu torchvision==0.20.0.dev20240905+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
+pip install "torch_xla[tpu] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.5.0.dev20240905-cp310-cp310-linux_x86_64.whl" -f https://storage.googleapis.com/libtpu-releases/index.html'
+```
+This script has been tested with the above versions but it expected to work with future versions as well.
+
+Verify that PyTorch and PyTorch/XLA were installed correctly:
+
+```bash
+gcloud compute tpus tpu-vm ssh ${TPU_NAME} \
+--project ${PROJECT_ID} --zone ${ZONE} --worker=all \
+--command='python3 -c "import torch; import torch_xla;"'
+```
+
+Install this fork of huggingface diffusers repo:
+```bash
+gcloud compute tpus tpu-vm ssh ${TPU_NAME} \
+--project=${PROJECT_ID} --zone=${ZONE} --worker=all \
+--command='
+git clone https://github.com/pytorch-tpu/diffusers.git
+cd diffusers
+git checkout main
+cd examples/text_to_image
+pip install -r requirements.txt
+cd ../..
+sudo pip install -e .'
+```
+
+## Run the training job
+
+This script only trains the unet part of the network. The VAE and text encoder
+are fixed.
+
+```bash
+gcloud compute tpus tpu-vm ssh ${TPU_NAME} \
+--project=${PROJECT_ID} --zone=${ZONE} --worker=all \
+--command='
+export XLA_DISABLE_FUNCTIONALIZATION=1 
+export PROFILE_DIR=/tmp/profile  # Update the directory to store profiles if needed.
+export CACHE_DIR=/tmp/xla_cache  # Update the cache to store compiled XLA graphs if needed.
+export DATASET_NAME=lambdalabs/naruto-blip-captions
+export PER_HOST_BATCH_SIZE=16 # This is know to work on TPU v4. Can set this to 64 for TPU v5p.
+export TRAIN_STEPS=50
+export OUTPUT_DIR=/tmp/output/
+python diffusers/examples/text_to_image/train_text_to_image_xla.py \
+    --pretrained_model_name_or_path=stabilityai/stable-diffusion-2-base \
+    --dataset_name=$DATASET_NAME --resolution=512 --center_crop --random_flip \
+    --train_batch_size=$PER_HOST_BATCH_SIZE  --max_train_steps=$TRAIN_STEPS \
+    --learning_rate=1e-06 --mixed_precision=bf16 --profile_duration=80000 \
+    --output_dir=$OUTPUT_DIR --dataloader_num_workers=4 \
+    --loader_prefetch_size=4 --device_prefetch_size=4'
+```
+
+### Environment Envs Explained
+
+*   `XLA_DISABLE_FUNCTIONALIZATION`: To optimize the performance for AdamW optimizer.
+*   `PROFILE_DIR`: Specify where to put the profiling results.
+*   `CACHE_DIR`: Directory to store XLA compiled graphs for persistent caching.
+*   `DATASET_NAME`: Dataset to train the model. 
+*   `PER_HOST_BATCH_SIZE`: Size of the batch to load per CPU host. For e.g. for a v5p-16 with 2 CPU hosts, the global batch size will be 2xPER_HOST_BATCH_SIZE. The input batch is sharded along the batch axis.
+*    `TRAIN_STEPS`: Total number of training steps to run the training for.
+*    `OUTPUT_DIR`: Directory to store the fine-tuned model.
+
+## Run inference using the output model
+
+To run inference using the output, you can simply load the model and pass it
+input prompts:
+
+```python
+import torch
+import os
+import sys
+import  numpy as np
+
+import torch_xla.core.xla_model as xm
+from time import time
+from typing import Tuple
+from diffusers import StableDiffusionPipeline
+
+def main(args):
+    device = xm.xla_device()
+    model_path = <output_dir>
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_path, 
+        torch_dtype=torch.bfloat16
+    )
+    pipe.to(device)
+    prompt = ["A naruto with green eyes and red legs."]
+    image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
+    image.save("naruto.png")
+
+if __name__ == '__main__':
+    main()
+```