Log Unconditional Image Generation Samples to W&B

examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py

@@ -21,7 +21,7 @@
 from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version
+from diffusers.utils import check_min_version, is_tensorboard_available, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
@@ -220,6 +220,7 @@ def parse_args():
         help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
     )
     parser.add_argument("--ddpm_num_steps", type=int, default=1000)
+    parser.add_argument("--ddpm_num_inference_steps", type=int, default=1000)
     parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
     parser.add_argument(
         "--checkpointing_steps",
@@ -271,6 +272,15 @@ def main(args):
         logging_dir=logging_dir,
     )
 
+    if args.logger == "tensorboard":
+        if not is_tensorboard_available():
+            raise ImportError("Make sure to install tensorboard if you want to use it for logging during training.")
+
+    elif args.logger == "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",
@@ -552,7 +562,7 @@ def transform_images(examples):
                     generator=generator,
                     batch_size=args.eval_batch_size,
                     output_type="numpy",
-                    num_inference_steps=args.ddpm_num_steps,
+                    num_inference_steps=args.ddpm_num_inference_steps,
                 ).images
 
                 # denormalize the images and save to tensorboard
@@ -562,6 +572,11 @@ def transform_images(examples):
                     accelerator.get_tracker("tensorboard").add_images(
                         "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
                     )
+                elif args.logger == "wandb":
+                    accelerator.get_tracker("wandb").log(
+                        {"test_samples": [wandb.Image(img) for img in images_processed], "epoch": epoch},
+                        step=global_step,
+                    )
 
             if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
                 # save the model

examples/unconditional_image_generation/train_unconditional.py

@@ -22,7 +22,7 @@
 from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version, is_tensorboard_available
+from diffusers.utils import check_min_version, is_tensorboard_available, is_wandb_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
@@ -280,6 +280,15 @@ def main(args):
         logging_dir=logging_dir,
     )
 
+    if args.logger == "tensorboard":
+        if not is_tensorboard_available():
+            raise ImportError("Make sure to install tensorboard if you want to use it for logging during training.")
+
+    elif args.logger == "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
+
     # `accelerate` 0.16.0 will have better support for customized saving
     if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
         # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
@@ -604,10 +613,15 @@ def transform_images(examples):
                 # denormalize the images and save to tensorboard
                 images_processed = (images * 255).round().astype("uint8")
 
-                if args.logger == "tensorboard" and is_tensorboard_available():
+                if args.logger == "tensorboard":
                     accelerator.get_tracker("tensorboard").add_images(
                         "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
                     )
+                elif args.logger == "wandb":
+                    accelerator.get_tracker("wandb").log(
+                        {"test_samples": [wandb.Image(img) for img in images_processed], "epoch": epoch},
+                        step=global_step,
+                    )
 
             if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
                 # save the model