[Paint by Example] Better default for image width (#1587)

src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py

@@ -442,14 +442,7 @@ class PaintByExamplePipeline(DiffusionPipeline):
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.
         """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs
-        self.check_inputs(example_image, height, width, callback_steps)
-
-        # 2. Define call parameters
+        # 1. Define call parameters
         if isinstance(image, PIL.Image.Image):
             batch_size = 1
         elif isinstance(image, list):
@@ -462,14 +455,18 @@ class PaintByExamplePipeline(DiffusionPipeline):
         # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
 
-        # 3. Encode input image
+        # 2. Preprocess mask and image
+        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
+        width, height = masked_image.shape[-2:]
+
+        # 3. Check inputs
+        self.check_inputs(example_image, height, width, callback_steps)
+
+        # 4. Encode input image
         image_embeddings = self._encode_image(
             example_image, device, num_images_per_prompt, do_classifier_free_guidance
         )
 
-        # 4. Preprocess mask and image
-        mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
-
         # 5. set timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps = self.scheduler.timesteps