diff --git a/docs/source/en/api/pipelines/hunyuandit.md b/docs/source/en/api/pipelines/hunyuandit.md
index 9ac5d90fedbf..250533837ed0 100644
--- a/docs/source/en/api/pipelines/hunyuandit.md
+++ b/docs/source/en/api/pipelines/hunyuandit.md
@@ -34,6 +34,12 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.m
 
 </Tip>
 
+<Tip>
+
+You can further improve generation quality by passing the generated image from [`HungyuanDiTPipeline`] to the [SDXL refiner](../../using-diffusers/sdxl#base-to-refiner-model) model.
+
+</Tip>
+
 ## Optimization
 
 You can optimize the pipeline's runtime and memory consumption with torch.compile and feed-forward chunking. To learn about other optimization methods, check out the [Speed up inference](../../optimization/fp16) and [Reduce memory usage](../../optimization/memory) guides.
diff --git a/docs/source/en/api/pipelines/pixart_sigma.md b/docs/source/en/api/pipelines/pixart_sigma.md
index 2bf69f1ecc6d..592ba0f374be 100644
--- a/docs/source/en/api/pipelines/pixart_sigma.md
+++ b/docs/source/en/api/pipelines/pixart_sigma.md
@@ -37,6 +37,12 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers)
 
 </Tip>
 
+<Tip>
+
+You can further improve generation quality by passing the generated image from [`PixArtSigmaPipeline`] to the [SDXL refiner](../../using-diffusers/sdxl#base-to-refiner-model) model.
+
+</Tip>
+
 ## Inference with under 8GB GPU VRAM
 
 Run the [`PixArtSigmaPipeline`] with under 8GB GPU VRAM by loading the text encoder in 8-bit precision. Let's walk through a full-fledged example.
diff --git a/docs/source/en/using-diffusers/sdxl.md b/docs/source/en/using-diffusers/sdxl.md
index 6b9ab7f475e6..9938d561052b 100644
--- a/docs/source/en/using-diffusers/sdxl.md
+++ b/docs/source/en/using-diffusers/sdxl.md
@@ -285,6 +285,12 @@ refiner = DiffusionPipeline.from_pretrained(
 ).to("cuda")
 ```
 
+<Tip warning={true}>
+
+You can use SDXL refiner with a different base model. For example, you can use the [Hunyuan-DiT](../../api/pipelines/hunyuandit) or [PixArt-Sigma](../../api/pipelines/pixart_sigma) pipelines to generate images with better prompt adherence. Once you have generated an image, you can pass it to the SDXL refiner model to enhance final generation quality.
+
+</Tip>
+
 Generate an image from the base model, and set the model output to **latent** space:
 
 ```py
diff --git a/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py b/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py
index f888fb6c1de3..093616f5432d 100644
--- a/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py
@@ -62,7 +62,7 @@
         >>> pipe = pipe.to(device)
 
         >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-        >>> init_image = load_image(url).resize((512, 512))
+        >>> init_image = load_image(url).resize((1024, 1024))
 
         >>> prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"