Skip to content

DiffEdit

Image editing typically requires providing a mask of the area to be edited. DiffEdit automatically generates the mask for you based on a text query, making it easier overall to create a mask without image editing software. The DiffEdit algorithm works in three steps:

  1. the diffusion model denoises an image conditioned on some query text and reference text which produces different noise estimates for different areas of the image; the difference is used to infer a mask to identify which area of the image needs to be changed to match the query text
  2. the input image is encoded into latent space with DDIM
  3. the latents are decoded with the diffusion model conditioned on the text query, using the mask as a guide such that pixels outside the mask remain the same as in the input image

This guide will show you how to use DiffEdit to edit images without manually creating a mask.

Before you begin, make sure you have the following libraries installed:

# uncomment to install the necessary libraries
#!pip install mindone transformers

The StableDiffusionDiffEditPipeline requires an image mask and a set of partially inverted latents. The image mask is generated from the generate_mask function, and includes two parameters, source_prompt and target_prompt. These parameters determine what to edit in the image. For example, if you want to change a bowl of fruits to a bowl of pears, then:

source_prompt = "a bowl of fruits"
target_prompt = "a bowl of pears"

The partially inverted latents are generated from the invert function, and it is generally a good idea to include a prompt or caption describing the image to help guide the inverse latent sampling process. The caption can often be your source_prompt, but feel free to experiment with other text descriptions!

Let's load the pipeline, scheduler, inverse scheduler, and enable some optimizations to reduce memory usage:

import mindspore as ms
from mindone.diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionDiffEditPipeline

pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1",
    mindspore_dtype=ms.float16,
    safety_checker=None,
    use_safetensors=True,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)

Load the image to edit:

from mindone.diffusers.utils import load_image, make_image_grid

img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
raw_image = load_image(img_url).resize((768, 768))
raw_image

Use the generate_mask function to generate the image mask. You'll need to pass it the source_prompt and target_prompt to specify what to edit in the image:

from PIL import Image

source_prompt = "a bowl of fruits"
target_prompt = "a basket of pears"
mask_image = pipeline.generate_mask(
    image=raw_image,
    source_prompt=source_prompt,
    target_prompt=target_prompt,
)
Image.fromarray((mask_image.squeeze()*255).astype("uint8"), "L").resize((768, 768))

Next, create the inverted latents and pass it a caption describing the image:

inv_latents = pipeline.invert(prompt=source_prompt, image=raw_image)[0]

Finally, pass the image mask and inverted latents to the pipeline. The target_prompt becomes the prompt now, and the source_prompt is used as the negative_prompt:

output_image = pipeline(
    prompt=target_prompt,
    mask_image=mask_image,
    image_latents=inv_latents,
    negative_prompt=source_prompt,
)[0][0]
mask_image = Image.fromarray((mask_image.squeeze()*255).astype("uint8"), "L").resize((768, 768))
make_image_grid([raw_image, mask_image, output_image], rows=1, cols=3)
original image
edited image