Make text-to-image SDXL LoRA Training Script torch.compile compatible (#6556)

make compile compatible

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -54,6 +54,7 @@ from diffusers.optimization import get_scheduler
 from diffusers.training_utils import cast_training_params, compute_snr
 from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
+from diffusers.utils.torch_utils import is_compiled_module
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
@@ -460,13 +461,12 @@ def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
             text_input_ids = text_input_ids_list[i]
 
         prompt_embeds = text_encoder(
-            text_input_ids.to(text_encoder.device),
-            output_hidden_states=True,
+            text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False
         )
 
         # We are only ALWAYS interested in the pooled output of the final text encoder
         pooled_prompt_embeds = prompt_embeds[0]
-        prompt_embeds = prompt_embeds.hidden_states[-2]
+        prompt_embeds = prompt_embeds[-1][-2]
         bs_embed, seq_len, _ = prompt_embeds.shape
         prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
         prompt_embeds_list.append(prompt_embeds)
@@ -637,6 +637,11 @@ def main(args):
             # only upcast trainable parameters (LoRA) into fp32
             cast_training_params(models, dtype=torch.float32)
 
+    def unwrap_model(model):
+        model = accelerator.unwrap_model(model)
+        model = model._orig_mod if is_compiled_module(model) else model
+        return model
+
     # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
     def save_model_hook(models, weights, output_dir):
         if accelerator.is_main_process:
@@ -647,13 +652,13 @@ def main(args):
             text_encoder_two_lora_layers_to_save = None
 
             for model in models:
-                if isinstance(model, type(accelerator.unwrap_model(unet))):
+                if isinstance(model, type(unwrap_model(unet))):
                     unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))
-                elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
+                elif isinstance(model, type(unwrap_model(text_encoder_one))):
                     text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(
                         get_peft_model_state_dict(model)
                     )
-                elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
+                elif isinstance(model, type(unwrap_model(text_encoder_two))):
                     text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(
                         get_peft_model_state_dict(model)
                     )
@@ -678,11 +683,11 @@ def main(args):
         while len(models) > 0:
             model = models.pop()
 
-            if isinstance(model, type(accelerator.unwrap_model(unet))):
+            if isinstance(model, type(unwrap_model(unet))):
                 unet_ = model
-            elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
+            elif isinstance(model, type(unwrap_model(text_encoder_one))):
                 text_encoder_one_ = model
-            elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
+            elif isinstance(model, type(unwrap_model(text_encoder_two))):
                 text_encoder_two_ = model
             else:
                 raise ValueError(f"unexpected save model: {model.__class__}")
@@ -1031,8 +1036,12 @@ def main(args):
                 )
                 unet_added_conditions.update({"text_embeds": pooled_prompt_embeds})
                 model_pred = unet(
-                    noisy_model_input, timesteps, prompt_embeds, added_cond_kwargs=unet_added_conditions
-                ).sample
+                    noisy_model_input,
+                    timesteps,
+                    prompt_embeds,
+                    added_cond_kwargs=unet_added_conditions,
+                    return_dict=False,
+                )[0]
 
                 # Get the target for loss depending on the prediction type
                 if args.prediction_type is not None:
@@ -1125,9 +1134,9 @@ def main(args):
                 pipeline = StableDiffusionXLPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
                     vae=vae,
-                    text_encoder=accelerator.unwrap_model(text_encoder_one),
-                    text_encoder_2=accelerator.unwrap_model(text_encoder_two),
-                    unet=accelerator.unwrap_model(unet),
+                    text_encoder=unwrap_model(text_encoder_one),
+                    text_encoder_2=unwrap_model(text_encoder_two),
+                    unet=unwrap_model(unet),
                     revision=args.revision,
                     variant=args.variant,
                     torch_dtype=weight_dtype,
@@ -1166,12 +1175,12 @@ def main(args):
     # Save the lora layers
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
-        unet = accelerator.unwrap_model(unet)
+        unet = unwrap_model(unet)
         unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))
 
         if args.train_text_encoder:
-            text_encoder_one = accelerator.unwrap_model(text_encoder_one)
-            text_encoder_two = accelerator.unwrap_model(text_encoder_two)
+            text_encoder_one = unwrap_model(text_encoder_one)
+            text_encoder_two = unwrap_model(text_encoder_two)
 
             text_encoder_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_one))
             text_encoder_2_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(text_encoder_two))