Modularize train_text_to_image_lora SD inferencing during and after training in example (#8283)

* Modularized the train_lora file

* Modularized the train_lora file

* Modularized the train_lora file

* Modularized the train_lora file

* Modularized the train_lora file

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

examples/text_to_image/train_text_to_image_lora.py

@@ -52,6 +52,9 @@ from diffusers.utils.import_utils import is_xformers_available
 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")
 
@@ -99,6 +102,48 @@ These are LoRA adaption weights for {base_model}. The weights were fine-tuned on
     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)
+    generator = torch.Generator(device=accelerator.device)
+    if args.seed is not None:
+        generator = generator.manual_seed(args.seed)
+    images = []
+    if torch.backends.mps.is_available():
+        autocast_ctx = nullcontext()
+    else:
+        autocast_ctx = torch.autocast(accelerator.device.type)
+
+    with autocast_ctx:
+        for _ in range(args.num_validation_images):
+            images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])
+
+    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 parse_args():
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
     parser.add_argument(
@@ -414,11 +459,6 @@ def main():
     if torch.backends.mps.is_available():
         accelerator.native_amp = False
 
-    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",
@@ -864,10 +904,6 @@ def main():
 
         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 = DiffusionPipeline.from_pretrained(
                     args.pretrained_model_name_or_path,
@@ -876,38 +912,7 @@ def main():
                     variant=args.variant,
                     torch_dtype=weight_dtype,
                 )
-                pipeline = pipeline.to(accelerator.device)
-                pipeline.set_progress_bar_config(disable=True)
-
-                # run inference
-                generator = torch.Generator(device=accelerator.device)
-                if args.seed is not None:
-                    generator = generator.manual_seed(args.seed)
-                images = []
-                if torch.backends.mps.is_available():
-                    autocast_ctx = nullcontext()
-                else:
-                    autocast_ctx = torch.autocast(accelerator.device.type)
-
-                with autocast_ctx:
-                    for _ in range(args.num_validation_images):
-                        images.append(
-                            pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
-                        )
-
-                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()
@@ -925,21 +930,6 @@ def main():
             safe_serialization=True,
         )
 
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                images=images,
-                base_model=args.pretrained_model_name_or_path,
-                dataset_name=args.dataset_name,
-                repo_folder=args.output_dir,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
         # Final inference
         # Load previous pipeline
         if args.validation_prompt is not None:
@@ -949,40 +939,26 @@ def main():
                 variant=args.variant,
                 torch_dtype=weight_dtype,
             )
-            pipeline = pipeline.to(accelerator.device)
 
             # load attention processors
             pipeline.load_lora_weights(args.output_dir)
 
             # run inference
-            generator = torch.Generator(device=accelerator.device)
-            if args.seed is not None:
-                generator = generator.manual_seed(args.seed)
-            images = []
-            if torch.backends.mps.is_available():
-                autocast_ctx = nullcontext()
-            else:
-                autocast_ctx = torch.autocast(accelerator.device.type)
+            images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True)
 
-            with autocast_ctx:
-                for _ in range(args.num_validation_images):
-                    images.append(
-                        pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
+        if args.push_to_hub:
+            save_model_card(
+                repo_id,
+                images=images,
+                base_model=args.pretrained_model_name_or_path,
+                dataset_name=args.dataset_name,
+                repo_folder=args.output_dir,
             )
-
-            for tracker in accelerator.trackers:
-                if len(images) != 0:
-                    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)
-                                ]
-                            }
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
             )
 
     accelerator.end_training()