add to doc

Author: linoytsaban

docs/source/en/api/pipelines/sana_sprint.md

@@ -88,12 +88,41 @@ image.save("sana.png")
 
 Users can tweak the `max_timesteps` value for experimenting with the visual quality of the generated outputs. The default `max_timesteps` value was obtained with an inference-time search process. For more details about it, check out the paper.
 
+## Image to Image 
+The [`SanaSprintImg2ImgPipeline`] is a pipeline for image-to-image generation. It takes an input image and a prompt, and generates a new image based on the input image and the prompt.
+
+```py
+import torch
+from diffusers import SanaSprintImg2ImgPipeline
+from diffusers.utils.loading_utils import load_image
+
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png"
+)
+
+pipe = SanaSprintImg2ImgPipeline.from_pretrained(
+    "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers", 
+    torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+image = pipe(prompt="a cute pink bear", 
+             image=image, 
+             strength=0.5, height=832, width=480).images[0]
+image[0].save("output.png")
+```
+
 ## SanaSprintPipeline
 
 [[autodoc]] SanaSprintPipeline
   - all
   - __call__
 
+## SanaSprintImg2ImgPipeline
+
+[[autodoc]] SanaSprintImg2ImgPipeline
+  - all
+  - __call__
+
 
 ## SanaPipelineOutput