SDXL text-to-image torch compatible (#6550)

* torch compatible

* code quality fix

* ruff style

* ruff format

Author: gzguevara

examples/text_to_image/train_text_to_image_sdxl.py

@@ -44,16 +44,12 @@
 from transformers import AutoTokenizer, PretrainedConfig
 
 import diffusers
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    StableDiffusionXLPipeline,
-    UNet2DConditionModel,
-)
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel, compute_snr
 from diffusers.utils import check_min_version, 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.
@@ -508,11 +504,12 @@ def encode_prompt(batch, text_encoders, tokenizers, proportion_empty_prompts, ca
             prompt_embeds = text_encoder(
                 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)
@@ -955,6 +952,12 @@ def collate_fn(examples):
     if accelerator.is_main_process:
         accelerator.init_trackers("text2image-fine-tune-sdxl", config=vars(args))
 
+    # Function for unwraping if torch.compile() was used in accelerate.
+    def unwrap_model(model):
+        model = accelerator.unwrap_model(model)
+        model = model._orig_mod if is_compiled_module(model) else model
+        return model
+
     # Train!
     total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
 
@@ -1054,8 +1057,12 @@ def compute_time_ids(original_size, crops_coords_top_left):
                 pooled_prompt_embeds = batch["pooled_prompt_embeds"].to(accelerator.device)
                 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:
@@ -1206,7 +1213,7 @@ def compute_time_ids(original_size, crops_coords_top_left):
 
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
-        unet = accelerator.unwrap_model(unet)
+        unet = unwrap_model(unet)
         if args.use_ema:
             ema_unet.copy_to(unet.parameters())