diff --git a/examples/text_to_image/train_text_to_image.py b/examples/text_to_image/train_text_to_image.py
index 90dd06d33c5e..a2ea8f17dd74 100644
--- a/examples/text_to_image/train_text_to_image.py
+++ b/examples/text_to_image/train_text_to_image.py
@@ -171,7 +171,13 @@ def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight
             autocast_ctx = torch.autocast(accelerator.device.type)
 
         with autocast_ctx:
-            image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
+            image = pipeline(
+                args.validation_prompts[i],
+                height=args.resolution,
+                width=args.resolution,
+                num_inference_steps=20,
+                generator=generator,
+            ).images[0]
 
         images.append(image)
 
@@ -1150,7 +1156,13 @@ def unwrap_model(model):
 
             for i in range(len(args.validation_prompts)):
                 with torch.autocast("cuda"):
-                    image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
+                    image = pipeline(
+                        args.validation_prompts[i],
+                        height=args.resolution,
+                        width=args.resolution,
+                        num_inference_steps=20,
+                        generator=generator,
+                    ).images[0]
                 images.append(image)
 
         if args.push_to_hub:
diff --git a/examples/text_to_image/train_text_to_image_lora.py b/examples/text_to_image/train_text_to_image_lora.py
index 310a50ac4e9a..9f10ba0586eb 100644
--- a/examples/text_to_image/train_text_to_image_lora.py
+++ b/examples/text_to_image/train_text_to_image_lora.py
@@ -131,7 +131,15 @@ def log_validation(
 
     with autocast_ctx:
         for _ in range(args.num_validation_images):
-            images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])
+            images.append(
+                pipeline(
+                    args.validation_prompt,
+                    height=args.resolution,
+                    width=args.resolution,
+                    num_inference_steps=30,
+                    generator=generator,
+                ).images[0]
+            )
 
     for tracker in accelerator.trackers:
         phase_name = "test" if is_final_validation else "validation"
diff --git a/examples/text_to_image/train_text_to_image_lora_sdxl.py b/examples/text_to_image/train_text_to_image_lora_sdxl.py
index 88f5c3cede6e..900e6ea3d611 100644
--- a/examples/text_to_image/train_text_to_image_lora_sdxl.py
+++ b/examples/text_to_image/train_text_to_image_lora_sdxl.py
@@ -138,7 +138,11 @@ def log_validation(
 
     # run inference
     generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
-    pipeline_args = {"prompt": args.validation_prompt}
+    pipeline_args = {
+        "prompt": args.validation_prompt,
+        "height": args.resolution,
+        "width": args.resolution,
+    }
     if torch.backends.mps.is_available():
         autocast_ctx = nullcontext()
     else:
diff --git a/examples/text_to_image/train_text_to_image_sdxl.py b/examples/text_to_image/train_text_to_image_sdxl.py
index 4eafa8f28a19..5fc0d323a7a1 100644
--- a/examples/text_to_image/train_text_to_image_sdxl.py
+++ b/examples/text_to_image/train_text_to_image_sdxl.py
@@ -1258,7 +1258,11 @@ def compute_time_ids(original_size, crops_coords_top_left):
                     if args.seed is not None
                     else None
                 )
-                pipeline_args = {"prompt": args.validation_prompt}
+                pipeline_args = {
+                    "prompt": args.validation_prompt,
+                    "height": args.resolution,
+                    "width": args.resolution,
+                }
 
                 with autocast_ctx:
                     images = [
@@ -1327,7 +1331,13 @@ def compute_time_ids(original_size, crops_coords_top_left):
 
             with autocast_ctx:
                 images = [
-                    pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
+                    pipeline(
+                        args.validation_prompt,
+                        height=args.resolution,
+                        width=args.resolution,
+                        num_inference_steps=25,
+                        generator=generator,
+                    ).images[0]
                     for _ in range(args.num_validation_images)
                 ]