diff --git a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
index 8486641da2c4..f6585f819928 100644
--- a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
+++ b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md
@@ -23,6 +23,7 @@ The abstract of the paper is the following:
 - Stable Diffusion XL works especially well with images between 768 and 1024.
 - Stable Diffusion XL can pass a different prompt for each of the text encoders it was trained on as shown below. We can even pass different parts of the same prompt to the text encoders.
 - Stable Diffusion XL output image can be improved by making use of a refiner as shown below.
+- One can make use of `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` to influence the generation process.
 
 ### Available checkpoints:
 
@@ -74,6 +75,37 @@ prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
 image = pipe(prompt=prompt).images[0]
 ```
 
+You can additionally pass negative conditions about an image's size and position to avoid undesirable cropping behavior in the generated image, and improve image resolution. Let's take an example:
+
+```python
+from diffusers import StableDiffusionXLPipeline
+import torch
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+pipe.to("cuda")
+
+prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
+image = pipe(
+    prompt=prompt,
+    negative_original_size=(512, 512),
+    negative_crops_coords_top_left=(0, 0),
+    negative_target_size=(1024, 1024),
+).images[0]
+```
+
+Here is a comparative example that shows the influence of using three `negative_original_size`s of
+(128, 128), (256, 256), and (512, 512) respectively:
+
+![](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/negative_conditions.png)
+
+<Tip>
+
+One can use these negative conditions in the other SDXL pipelines ([Image-To-Image](#image-to-image), [Inpainting](#inpainting), [ControlNet](../controlnet_sdxl.md)) too!
+
+</Tip>
+
 ### Image-to-image 
 
 You can use SDXL as follows for *image-to-image*:
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
index 9e23337a0338..9a8d5edc7e07 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
@@ -789,6 +789,9 @@ def __call__(
         original_size: Tuple[int, int] = None,
         crops_coords_top_left: Tuple[int, int] = (0, 0),
         target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -895,6 +898,22 @@ def __call__(
                 For most cases, `target_size` should be set to the desired height and width of the generated image. If
                 not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
                 section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+
         Examples:
 
         Returns:
@@ -1058,10 +1077,20 @@ def __call__(
             original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
         )
 
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
         if do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
 
         prompt_embeds = prompt_embeds.to(device)
         add_text_embeds = add_text_embeds.to(device)
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
index 1686b1e0bb28..11e575d68269 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
@@ -589,6 +589,9 @@ def __call__(
         original_size: Optional[Tuple[int, int]] = None,
         crops_coords_top_left: Tuple[int, int] = (0, 0),
         target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -688,6 +691,21 @@ def __call__(
                 For most cases, `target_size` should be set to the desired height and width of the generated image. If
                 not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
                 section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
 
         Examples:
 
@@ -783,11 +801,20 @@ def __call__(
         add_time_ids = self._get_add_time_ids(
             original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
         )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
 
         if do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
 
         prompt_embeds = prompt_embeds.to(device)
         add_text_embeds = add_text_embeds.to(device)
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
index 03341c6e9044..ff51f8765e4a 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
@@ -601,14 +601,25 @@ def prepare_latents(
         return latents
 
     def _get_add_time_ids(
-        self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
+        self,
+        original_size,
+        crops_coords_top_left,
+        target_size,
+        aesthetic_score,
+        negative_aesthetic_score,
+        negative_original_size,
+        negative_crops_coords_top_left,
+        negative_target_size,
+        dtype,
     ):
         if self.config.requires_aesthetics_score:
             add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
-            add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))
+            add_neg_time_ids = list(
+                negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)
+            )
         else:
             add_time_ids = list(original_size + crops_coords_top_left + target_size)
-            add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)
 
         passed_add_embed_dim = (
             self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
@@ -690,6 +701,9 @@ def __call__(
         original_size: Tuple[int, int] = None,
         crops_coords_top_left: Tuple[int, int] = (0, 0),
         target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
         aesthetic_score: float = 6.0,
         negative_aesthetic_score: float = 2.5,
     ):
@@ -804,6 +818,21 @@ def __call__(
                 For most cases, `target_size` should be set to the desired height and width of the generated image. If
                 not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
                 section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
             aesthetic_score (`float`, *optional*, defaults to 6.0):
                 Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
                 Part of SDXL's micro-conditioning as explained in section 2.2 of
@@ -908,6 +937,11 @@ def denoising_value_valid(dnv):
         target_size = target_size or (height, width)
 
         # 8. Prepare added time ids & embeddings
+        if negative_original_size is None:
+            negative_original_size = original_size
+        if negative_target_size is None:
+            negative_target_size = target_size
+
         add_text_embeds = pooled_prompt_embeds
         add_time_ids, add_neg_time_ids = self._get_add_time_ids(
             original_size,
@@ -915,6 +949,9 @@ def denoising_value_valid(dnv):
             target_size,
             aesthetic_score,
             negative_aesthetic_score,
+            negative_original_size,
+            negative_crops_coords_top_left,
+            negative_target_size,
             dtype=prompt_embeds.dtype,
         )
         add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
index 2885bd2223dd..eecbdc7e669e 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
@@ -813,14 +813,25 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
     def _get_add_time_ids(
-        self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
+        self,
+        original_size,
+        crops_coords_top_left,
+        target_size,
+        aesthetic_score,
+        negative_aesthetic_score,
+        negative_original_size,
+        negative_crops_coords_top_left,
+        negative_target_size,
+        dtype,
     ):
         if self.config.requires_aesthetics_score:
             add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
-            add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))
+            add_neg_time_ids = list(
+                negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)
+            )
         else:
             add_time_ids = list(original_size + crops_coords_top_left + target_size)
-            add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)
 
         passed_add_embed_dim = (
             self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
@@ -905,6 +916,9 @@ def __call__(
         original_size: Tuple[int, int] = None,
         crops_coords_top_left: Tuple[int, int] = (0, 0),
         target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
         aesthetic_score: float = 6.0,
         negative_aesthetic_score: float = 2.5,
     ):
@@ -1025,6 +1039,21 @@ def __call__(
                 For most cases, `target_size` should be set to the desired height and width of the generated image. If
                 not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
                 section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
             aesthetic_score (`float`, *optional*, defaults to 6.0):
                 Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
                 Part of SDXL's micro-conditioning as explained in section 2.2 of
@@ -1199,6 +1228,11 @@ def denoising_value_valid(dnv):
         target_size = target_size or (height, width)
 
         # 10. Prepare added time ids & embeddings
+        if negative_original_size is None:
+            negative_original_size = original_size
+        if negative_target_size is None:
+            negative_target_size = target_size
+
         add_text_embeds = pooled_prompt_embeds
         add_time_ids, add_neg_time_ids = self._get_add_time_ids(
             original_size,
@@ -1206,6 +1240,9 @@ def denoising_value_valid(dnv):
             target_size,
             aesthetic_score,
             negative_aesthetic_score,
+            negative_original_size,
+            negative_crops_coords_top_left,
+            negative_target_size,
             dtype=prompt_embeds.dtype,
         )
         add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
diff --git a/tests/pipelines/controlnet/test_controlnet_sdxl.py b/tests/pipelines/controlnet/test_controlnet_sdxl.py
index 7906467e3918..2496b9c48ab8 100644
--- a/tests/pipelines/controlnet/test_controlnet_sdxl.py
+++ b/tests/pipelines/controlnet/test_controlnet_sdxl.py
@@ -160,7 +160,7 @@ def get_dummy_inputs(self, device, seed=0):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "image": image,
         }
 
@@ -680,6 +680,25 @@ def test_xformers_attention_forwardGenerator_pass(self):
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    def test_negative_conditions(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(torch_device)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        image = pipe(**inputs).images
+        image_slice_without_neg_cond = image[0, -3:, -3:, -1]
+
+        image = pipe(
+            **inputs,
+            negative_original_size=(512, 512),
+            negative_crops_coords_top_left=(0, 0),
+            negative_target_size=(1024, 1024),
+        ).images
+        image_slice_with_neg_cond = image[0, -3:, -3:, -1]
+
+        self.assertTrue(np.abs(image_slice_without_neg_cond - image_slice_with_neg_cond).max() > 1e-2)
+
 
 @slow
 @require_torch_gpu
diff --git a/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py b/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py
index e5d8328e1230..909f759ff745 100644
--- a/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py
+++ b/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py
@@ -129,7 +129,7 @@ def get_dummy_inputs(self, device, seed=0):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 5.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -691,6 +691,27 @@ def test_stable_diffusion_xl_multi_prompts(self):
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
 
+    def test_stable_diffusion_xl_negative_conditions(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionXLPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice_with_no_neg_cond = image[0, -3:, -3:, -1]
+
+        image = sd_pipe(
+            **inputs,
+            negative_original_size=(512, 512),
+            negative_crops_coords_top_left=(0, 0),
+            negative_target_size=(1024, 1024),
+        ).images
+        image_slice_with_neg_cond = image[0, -3:, -3:, -1]
+
+        self.assertTrue(np.abs(image_slice_with_no_neg_cond - image_slice_with_neg_cond).max() > 1e-2)
+
     def test_stable_diffusion_xl_save_from_pretrained(self):
         pipes = []
         components = self.get_dummy_components()
diff --git a/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py b/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py
index 1e879151ac2f..04cbb09f5196 100644
--- a/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py
+++ b/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py
@@ -138,7 +138,7 @@ def get_dummy_inputs(self, device, seed=0):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 5.0,
-            "output_type": "numpy",
+            "output_type": "np",
             "strength": 0.8,
         }
         return inputs
@@ -315,3 +315,31 @@ def test_stable_diffusion_xl_multi_prompts(self):
 
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
+
+    def test_stable_diffusion_xl_img2img_negative_conditions(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+
+        sd_pipe = self.pipeline_class(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]
+
+        image = sd_pipe(
+            **inputs,
+            negative_original_size=(512, 512),
+            negative_crops_coords_top_left=(
+                0,
+                0,
+            ),
+            negative_target_size=(1024, 1024),
+        ).images
+        image_slice_with_neg_conditions = image[0, -3:, -3:, -1]
+
+        assert (
+            np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
+            > 1e-4
+        )
diff --git a/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py b/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py
index 05ce3f11973e..a4c378adbb23 100644
--- a/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py
+++ b/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_inpaint.py
@@ -139,7 +139,7 @@ def get_dummy_inputs(self, device, seed=0):
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
-            "output_type": "numpy",
+            "output_type": "np",
         }
         return inputs
 
@@ -471,3 +471,30 @@ def test_stable_diffusion_xl_multi_prompts(self):
 
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
+
+    def test_stable_diffusion_xl_img2img_negative_conditions(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = self.pipeline_class(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        image = sd_pipe(**inputs).images
+        image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]
+
+        image = sd_pipe(
+            **inputs,
+            negative_original_size=(512, 512),
+            negative_crops_coords_top_left=(
+                0,
+                0,
+            ),
+            negative_target_size=(1024, 1024),
+        ).images
+        image_slice_with_neg_conditions = image[0, -3:, -3:, -1]
+
+        assert (
+            np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
+            > 1e-4
+        )