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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. This tutorial shows how to use it for text-guided image-to-image generation with Stable Diffusion model.

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.

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

    from diffusers import StableDiffusionImg2ImgPipeline

Load the pipeline:

    device = "cuda"
    pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
        device
    )

Download an initial image and preprocess it so we 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

Define the prompt and run the pipeline:

    prompt = "A fantasy landscape, trending on artstation"

`strength` is a value between 0.0 and 1.0, that controls the amount of noise that is 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.

Let’s generate two images with same pipeline and seed, but with different values for `strength`:

    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

    image = pipe(prompt=prompt, image=init_image, strength=0.5, guidance_scale=7.5, generator=generator).images[0]
    image

As you can see, when using a lower value for `strength`, the generated image is more closer to the original `image`.

Now let’s use a different scheduler - LMSDiscreteScheduler:

    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