For image-to-image generation, make sure that `num_inference_steps * strength` is larger or equal to 1.
The image-to-image pipeline will run for `int(num_inference_steps * strength)` steps, e.g. `0.5 * 2.0 = 1` step in
our example below.  
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image, make_image_grid

# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline_image2image = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")

init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
init_image = init_image.resize((512, 512))

prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"

image = pipeline_image2image(prompt, image=init_image, strength=0.5, guidance_scale=0.0, num_inference_steps=2).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```  
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/sdxl-turbo-img2img.png" alt="Image-to-image generation sample using SDXL Turbo"/>
</div>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl_turbo.md