Modularize train_text_to_image_lora_sdxl inferencing during and after training in example (#8335)

* Modularized the train_lora_sdxl file

* Modularized the train_lora_sdxl file

* Modularized the train_lora_sdxl file

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

examples/text_to_image/train_text_to_image_lora_sdxl.py

@@ -64,6 +64,9 @@ from diffusers.utils.import_utils import is_torch_npu_available, is_xformers_ava
 from diffusers.utils.torch_utils import is_compiled_module
 
 
+if is_wandb_available():
+    import wandb
+
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.29.0.dev0")
 
@@ -119,6 +122,47 @@ Special VAE used for training: {vae_path}.
     model_card.save(os.path.join(repo_folder, "README.md"))
 
 
+def log_validation(
+    pipeline,
+    args,
+    accelerator,
+    epoch,
+    is_final_validation=False,
+):
+    logger.info(
+        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+        f" {args.validation_prompt}."
+    )
+    pipeline = pipeline.to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=True)
+
+    # run inference
+    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
+    pipeline_args = {"prompt": args.validation_prompt}
+    if torch.backends.mps.is_available():
+        autocast_ctx = nullcontext()
+    else:
+        autocast_ctx = torch.autocast(accelerator.device.type)
+
+    with autocast_ctx:
+        images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
+
+    for tracker in accelerator.trackers:
+        phase_name = "test" if is_final_validation else "validation"
+        if tracker.name == "tensorboard":
+            np_images = np.stack([np.asarray(img) for img in images])
+            tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
+        if tracker.name == "wandb":
+            tracker.log(
+                {
+                    phase_name: [
+                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
+                    ]
+                }
+            )
+    return images
+
+
 def import_model_class_from_model_name_or_path(
     pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
 ):
@@ -523,11 +567,6 @@ def main(args):
         kwargs_handlers=[kwargs],
     )
 
-    if args.report_to == "wandb":
-        if not is_wandb_available():
-            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-        import wandb
-
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -1196,10 +1235,6 @@ def main(args):
 
         if accelerator.is_main_process:
             if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
-                logger.info(
-                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-                    f" {args.validation_prompt}."
-                )
                 # create pipeline
                 pipeline = StableDiffusionXLPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
@@ -1212,36 +1247,7 @@ def main(args):
                     torch_dtype=weight_dtype,
                 )
 
-                pipeline = pipeline.to(accelerator.device)
-                pipeline.set_progress_bar_config(disable=True)
-
-                # run inference
-                generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
-                pipeline_args = {"prompt": args.validation_prompt}
-                if torch.backends.mps.is_available():
-                    autocast_ctx = nullcontext()
-                else:
-                    autocast_ctx = torch.autocast(accelerator.device.type)
-
-                with autocast_ctx:
-                    images = [
-                        pipeline(**pipeline_args, generator=generator).images[0]
-                        for _ in range(args.num_validation_images)
-                    ]
-
-                for tracker in accelerator.trackers:
-                    if tracker.name == "tensorboard":
-                        np_images = np.stack([np.asarray(img) for img in images])
-                        tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
-                    if tracker.name == "wandb":
-                        tracker.log(
-                            {
-                                "validation": [
-                                    wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
-                                    for i, image in enumerate(images)
-                                ]
-                            }
-                        )
+                images = log_validation(pipeline, args, accelerator, epoch)
 
                 del pipeline
                 torch.cuda.empty_cache()
@@ -1288,33 +1294,13 @@ def main(args):
             variant=args.variant,
             torch_dtype=weight_dtype,
         )
-        pipeline = pipeline.to(accelerator.device)
 
         # load attention processors
         pipeline.load_lora_weights(args.output_dir)
 
         # run inference
-        images = []
         if args.validation_prompt and args.num_validation_images > 0:
-            generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
-            images = [
-                pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
-                for _ in range(args.num_validation_images)
-            ]
-
-            for tracker in accelerator.trackers:
-                if tracker.name == "tensorboard":
-                    np_images = np.stack([np.asarray(img) for img in images])
-                    tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
-                if tracker.name == "wandb":
-                    tracker.log(
-                        {
-                            "test": [
-                                wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
-                                for i, image in enumerate(images)
-                            ]
-                        }
-                    )
+            images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True)
 
         if args.push_to_hub:
             save_model_card(