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Text-guided image-to-image generation

The StableDiffusionImg2ImgPipeline lets you pass a text prompt and an initial image to condition the generation of new images.

Before you begin, make sure you have all the necessary libraries installed:

    !pip install diffusers transformers ftfy accelerate

Get started by creating a StableDiffusionImg2ImgPipeline with a pretrained Stable Diffusion model like `nitrosocke/Ghibli-Diffusion`.

    import torch
    import requests
    from PIL import Image
    from io import BytesIO
    from diffusers import StableDiffusionImg2ImgPipeline

    device = "cuda"
    pipe = StableDiffusionImg2ImgPipeline.from_pretrained("nitrosocke/Ghibli-Diffusion", torch_dtype=torch.float16).to(
        device
    )

Download and preprocess an initial image so you can pass it to the pipeline:

    url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

    response = requests.get(url)
    init_image = Image.open(BytesIO(response.content)).convert("RGB")
    init_image.thumbnail((768, 768))
    init_image

💡 `strength` is a value between 0.0 and 1.0 that controls the amount of noise added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.

Define the prompt (for this checkpoint finetuned on Ghibli-style art, you need to prefix the prompt with the `ghibli style` tokens) and run the pipeline:

    prompt = "ghibli style, a fantasy landscape with castles"
    generator = torch.Generator(device=device).manual_seed(1024)
    image = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
    image

You can also try experimenting with a different scheduler to see how that affects the output:

    from diffusers import LMSDiscreteScheduler

    lms = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
    pipe.scheduler = lms
    generator = torch.Generator(device=device).manual_seed(1024)
    image = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
    image

Check out the Spaces below, and try generating images with different values for `strength`. You’ll notice that using lower values for `strength` produces images that are more similar to the original image.

Feel free to also switch the scheduler to the LMSDiscreteScheduler and see how that affects the output.