Adding PyTorch XLA support for sdxl inference

examples/text_to_image/README_sdxl.md

@@ -44,7 +44,7 @@ from accelerate.utils import write_basic_config
 write_basic_config()
 ```
 
-When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. 
+When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
 
 ### Training
 
@@ -73,10 +73,10 @@ accelerate launch train_text_to_image_sdxl.py \
   --push_to_hub
 ```
 
-**Notes**: 
+**Notes**:
 
-*  The `train_text_to_image_sdxl.py` script pre-computes text embeddings and the VAE encodings and keeps them in memory. While for smaller datasets like [`lambdalabs/pokemon-blip-captions`](https://hf.co/datasets/lambdalabs/pokemon-blip-captions), it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. For those purposes, you would want to serialize these pre-computed representations to disk separately and load them during the fine-tuning process. Refer to [this PR](https://github.com/huggingface/diffusers/pull/4505) for a more in-depth discussion. 
-* The training script is compute-intensive and may not run on a consumer GPU like Tesla T4. 
+*  The `train_text_to_image_sdxl.py` script pre-computes text embeddings and the VAE encodings and keeps them in memory. While for smaller datasets like [`lambdalabs/pokemon-blip-captions`](https://hf.co/datasets/lambdalabs/pokemon-blip-captions), it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. For those purposes, you would want to serialize these pre-computed representations to disk separately and load them during the fine-tuning process. Refer to [this PR](https://github.com/huggingface/diffusers/pull/4505) for a more in-depth discussion.
+* The training script is compute-intensive and may not run on a consumer GPU like Tesla T4.
 * The training command shown above performs intermediate quality validation in between the training epochs and logs the results to Weights and Biases. `--report_to`, `--validation_prompt`, and `--validation_epochs` are the relevant CLI arguments here.
 * SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
 
@@ -95,6 +95,35 @@ image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
 image.save("pokemon.png")
 ```
 
+### Inference in Pytorch XLA
+```python
+from diffusers import DiffusionPipeline
+import torch
+import torch_xla.core.xla_model as xm
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+pipe = DiffusionPipeline.from_pretrained(model_id)
+
+device = xm.xla_device()
+pipe.to(device)
+
+prompt = "A pokemon with green eyes and red legs."
+start = time()
+image = pipe(prompt, num_inference_steps=inference_steps).images[0]
+print(f'Compilation time is {time()-start} sec')
+image.save("pokemon.png")
+
+start = time()
+image = pipe(prompt, num_inference_steps=inference_steps).images[0]
+print(f'Inference time is {time()-start} sec after compilation')
+```
+
+Note: There is a warmup step in PyTorch XLA. This takes longer because of
+compilation and optimization. To see the real benefits of Pytorch XLA and
+speedup, we need to call the pipe again on the input with the same length
+as the original prompt to reuse the optimized graph and get the performance
+boost.
+
 ## LoRA training example for Stable Diffusion XL (SDXL)
 
 Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.
@@ -112,7 +141,7 @@ on consumer GPUs like Tesla T4, Tesla V100.
 
 ### Training
 
-First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables and, optionally, the `VAE_NAME` variable. Here, we will use [Stable Diffusion XL 1.0-base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).  
+First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables and, optionally, the `VAE_NAME` variable. Here, we will use [Stable Diffusion XL 1.0-base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
 
 **___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**
 
@@ -122,7 +151,7 @@ export VAE_NAME="madebyollin/sdxl-vae-fp16-fix"
 export DATASET_NAME="lambdalabs/pokemon-blip-captions"
 ```
 
-For this example we want to directly store the trained LoRA embeddings on the Hub, so 
+For this example we want to directly store the trained LoRA embeddings on the Hub, so
 we need to be logged in and add the `--push_to_hub` flag.
 
 ```bash
@@ -149,7 +178,7 @@ accelerate launch train_text_to_image_lora_sdxl.py \
 
 The above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.
 
-**Notes**: 
+**Notes**:
 
 * SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
 
@@ -178,7 +207,7 @@ accelerate launch train_text_to_image_lora_sdxl.py \
 
 ### Inference
 
-Once you have trained a model using above command, the inference can be done simply using the `DiffusionPipeline` after loading the trained LoRA weights.  You 
+Once you have trained a model using above command, the inference can be done simply using the `DiffusionPipeline` after loading the trained LoRA weights.  You
 need to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-pokemon-model-lora-sdxl`.
 
 ```python

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -35,6 +35,7 @@
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     is_invisible_watermark_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -48,6 +49,13 @@
 if is_invisible_watermark_available():
     from .watermark import StableDiffusionXLWatermarker
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -860,7 +868,7 @@ def __call__(
         # 8. Denoising loop
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
 
-        # 7.1 Apply denoising_end
+        # 8.1 Apply denoising_end
         if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
             discrete_timestep_cutoff = int(
                 round(
@@ -908,6 +916,9 @@ def __call__(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -32,6 +32,7 @@
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     is_invisible_watermark_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -45,6 +46,13 @@
 if is_invisible_watermark_available():
     from .watermark import StableDiffusionXLWatermarker
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -1031,6 +1039,9 @@ def denoising_value_valid(dnv):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -34,6 +34,7 @@
 from ...utils import (
     deprecate,
     is_invisible_watermark_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -47,6 +48,13 @@
 if is_invisible_watermark_available():
     from .watermark import StableDiffusionXLWatermarker
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -1355,6 +1363,9 @@ def denoising_value_valid(dnv):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py

@@ -33,6 +33,7 @@
 from ...utils import (
     deprecate,
     is_invisible_watermark_available,
+    is_torch_xla_available,
     logging,
     replace_example_docstring,
 )
@@ -44,6 +45,13 @@
 if is_invisible_watermark_available():
     from .watermark import StableDiffusionXLWatermarker
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -926,6 +934,9 @@ def __call__(
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

src/diffusers/utils/__init__.py

@@ -73,6 +73,7 @@
     is_tensorboard_available,
     is_torch_available,
     is_torch_version,
+    is_torch_xla_available,
     is_torchsde_available,
     is_transformers_available,
     is_transformers_version,

src/diffusers/utils/import_utils.py

@@ -64,6 +64,14 @@
     logger.info("Disabling PyTorch because USE_TORCH is set")
     _torch_available = False
 
+_torch_xla_available = importlib.util.find_spec("torch_xla") is not None
+if _torch_xla_available:
+    try:
+        _torch_xla_version = importlib_metadata.version("torch_xla")
+        logger.info(f"PyTorch XLA version {_torch_xla_version} available.")
+    except ImportError:
+        _torch_xla_available = False
+
 _jax_version = "N/A"
 _flax_version = "N/A"
 if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
@@ -281,6 +289,10 @@ def is_torch_available():
     return _torch_available
 
 
+def is_torch_xla_available():
+    return _torch_xla_available
+
+
 def is_flax_available():
     return _flax_available