Enable dreambooth lora finetune example on other devices (#10602)

* enable dreambooth_lora on other devices
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* enable xpu
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* check cuda device before empty cache
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix comment
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* import free_memory
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

---------
Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

examples/dreambooth/train_dreambooth_lora.py

@@ -54,7 +54,11 @@ from diffusers import (
 )
 from diffusers.loaders import StableDiffusionLoraLoaderMixin
 from diffusers.optimization import get_scheduler
-from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params
+from diffusers.training_utils import (
+    _set_state_dict_into_text_encoder,
+    cast_training_params,
+    free_memory,
+)
 from diffusers.utils import (
     check_min_version,
     convert_state_dict_to_diffusers,
@@ -151,14 +155,14 @@ def log_validation(
     if args.validation_images is None:
         images = []
         for _ in range(args.num_validation_images):
-            with torch.cuda.amp.autocast():
+            with torch.amp.autocast(accelerator.device.type):
                 image = pipeline(**pipeline_args, generator=generator).images[0]
                 images.append(image)
     else:
         images = []
         for image in args.validation_images:
             image = Image.open(image)
-            with torch.cuda.amp.autocast():
+            with torch.amp.autocast(accelerator.device.type):
                 image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
             images.append(image)
 
@@ -177,7 +181,7 @@ def log_validation(
             )
 
     del pipeline
-    torch.cuda.empty_cache()
+    free_memory()
 
     return images
 
@@ -793,7 +797,7 @@ def main(args):
         cur_class_images = len(list(class_images_dir.iterdir()))
 
         if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
+            torch_dtype = torch.float16 if accelerator.device.type in ("cuda", "xpu") else torch.float32
             if args.prior_generation_precision == "fp32":
                 torch_dtype = torch.float32
             elif args.prior_generation_precision == "fp16":
@@ -829,8 +833,7 @@ def main(args):
                     image.save(image_filename)
 
             del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
+            free_memory()
 
     # Handle the repository creation
     if accelerator.is_main_process:
@@ -1085,7 +1088,7 @@ def main(args):
         tokenizer = None
 
         gc.collect()
-        torch.cuda.empty_cache()
+        free_memory()
     else:
         pre_computed_encoder_hidden_states = None
         validation_prompt_encoder_hidden_states = None

src/diffusers/training_utils.py

@@ -299,6 +299,8 @@ def free_memory():
         torch.mps.empty_cache()
     elif is_torch_npu_available():
         torch_npu.npu.empty_cache()
+    elif hasattr(torch, "xpu") and torch.xpu.is_available():
+        torch.xpu.empty_cache()
 
 
 # Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14