DiffEdit

DiffEdit: Diffusion-based semantic image editing with mask guidance is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord.

The abstract from the paper is:

Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast, our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of-the-art editing performance on ImageNet. In addition, we evaluate semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images.

The original codebase can be found at Xiang-cd/DiffEdit-stable-diffusion, and you can try it out in this demo.

This pipeline was contributed by clarencechen. ❤️

Tips

The pipeline can generate masks that can be fed into other inpainting pipelines. Check out the code examples below to know more.
In order to generate an image using this pipeline, both an image mask (manually specified or generated using generate_mask) and a set of partially inverted latents (generated using invert) must be provided as arguments when calling the pipeline to generate the final edited image. Refer to the code examples below for more details.
The function generate_mask exposes two prompt arguments, source_prompt and target_prompt, that let you control the locations of the semantic edits in the final image to be generated. Let's say, you wanted to translate from "cat" to "dog". In this case, the edit direction will be "cat -> dog". To reflect this in the generated mask, you simply have to set the embeddings related to the phrases including "cat" to source_prompt_embeds and "dog" to target_prompt_embeds. Refer to the code example below for more details.
When generating partially inverted latents using invert, assign a caption or text embedding describing the overall image to the prompt argument to help guide the inverse latent sampling process. In most cases, the source concept is sufficently descriptive to yield good results, but feel free to explore alternatives. Please refer to this code example for more details.
When calling the pipeline to generate the final edited image, assign the source concept to negative_prompt and the target concept to prompt. Taking the above example, you simply have to set the embeddings related to the phrases including "cat" to negative_prompt_embeds and "dog" to prompt_embeds. Refer to the code example below for more details.
If you wanted to reverse the direction in the example above, i.e., "dog -> cat", then it's recommended to:
- Swap the source_prompt and target_prompt in the arguments to generate_mask.
- Change the input prompt for invert to include "dog".
- Swap the prompt and negative_prompt in the arguments to call the pipeline to generate the final edited image.
Note that the source and target prompts, or their corresponding embeddings, can also be automatically generated. Please, refer to this discussion for more details.

Usage example

Based on an input image with a caption

When the pipeline is conditioned on an input image, we first obtain partially inverted latents from the input image using a DDIMInverseScheduler with the help of a caption. Then we generate an editing mask to identify relevant regions in the image using the source and target prompts. Finally, the inverted noise and generated mask is used to start the generation process.

First, let's load our pipeline:

import torch
from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionDiffEditPipeline

sd_model_ckpt = "stabilityai/stable-diffusion-2-1"
pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
    sd_model_ckpt,
    torch_dtype=torch.float16,
    safety_checker=None,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing()
generator = torch.manual_seed(0)

Then, we load an input image to edit using our method:

from diffusers.utils import load_image

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

Then, we employ the source and target prompts to generate the editing mask:

# See the "Generating source and target embeddings" section below to
# automate the generation of these captions with a pre-trained model like Flan-T5 as explained below.

source_prompt = "a bowl of fruits"
target_prompt = "a basket of fruits"
mask_image = pipeline.generate_mask(
    image=raw_image,
    source_prompt=source_prompt,
    target_prompt=target_prompt,
    generator=generator,
)

Then, we employ the caption and the input image to get the inverted latents:

inv_latents = pipeline.invert(prompt=source_prompt, image=raw_image, generator=generator).latents

Now, generate the image with the inverted latents and semantically generated mask:

image = pipeline(
    prompt=target_prompt,
    mask_image=mask_image,
    image_latents=inv_latents,
    generator=generator,
    negative_prompt=source_prompt,
).images[0]
image.save("edited_image.png")

Generating image captions for inversion

The authors originally used the source concept prompt as the caption for generating the partially inverted latents. However, we can also leverage open source and public image captioning models for the same purpose. Below, we provide an end-to-end example with the BLIP model for generating captions.

First, let's load our automatic image captioning model:

import torch
from transformers import BlipForConditionalGeneration, BlipProcessor

captioner_id = "Salesforce/blip-image-captioning-base"
processor = BlipProcessor.from_pretrained(captioner_id)
model = BlipForConditionalGeneration.from_pretrained(captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True)

Then, we define a utility to generate captions from an input image using the model:

@torch.no_grad()
def generate_caption(images, caption_generator, caption_processor):
    text = "a photograph of"

    inputs = caption_processor(images, text, return_tensors="pt").to(device="cuda", dtype=caption_generator.dtype)
    caption_generator.to("cuda")
    outputs = caption_generator.generate(**inputs, max_new_tokens=128)

    # offload caption generator
    caption_generator.to("cpu")

    caption = caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]
    return caption

Then, we load an input image for conditioning and obtain a suitable caption for it:

from diffusers.utils import load_image

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

Then, we employ the generated caption and the input image to get the inverted latents:

from diffusers import DDIMInverseScheduler, DDIMScheduler

pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
)
pipeline = pipeline.to("cuda")
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing()

pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)

generator = torch.manual_seed(0)
inv_latents = pipeline.invert(prompt=caption, image=raw_image, generator=generator).latents

Now, generate the image with the inverted latents and semantically generated mask from our source and target prompts:

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

mask_image = pipeline.generate_mask(
    image=raw_image,
    source_prompt=source_prompt,
    target_prompt=target_prompt,
    generator=generator,
)

image = pipeline(
    prompt=target_prompt,
    mask_image=mask_image,
    image_latents=inv_latents,
    generator=generator,
    negative_prompt=source_prompt,
).images[0]
image.save("edited_image.png")

Generating source and target embeddings

The authors originally required the user to manually provide the source and target prompts for discovering edit directions. However, we can also leverage open source and public models for the same purpose. Below, we provide an end-to-end example with the Flan-T5 model for generating source an target embeddings.

1. Load the generation model:

import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)

2. Construct a starting prompt:

source_concept = "bowl"
target_concept = "basket"

source_text = f"Provide a caption for images containing a {source_concept}. "
"The captions should be in English and should be no longer than 150 characters."

target_text = f"Provide a caption for images containing a {target_concept}. "
"The captions should be in English and should be no longer than 150 characters."

Here, we're interested in the "bowl -> basket" direction.

3. Generate prompts:

We can use a utility like so for this purpose.

@torch.no_grad
def generate_prompts(input_prompt):
    input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to("cuda")

    outputs = model.generate(
        input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
    )
    return tokenizer.batch_decode(outputs, skip_special_tokens=True)

And then we just call it to generate our prompts:

source_prompts = generate_prompts(source_text)
target_prompts = generate_prompts(target_text)

We encourage you to play around with the different parameters supported by the generate() method (documentation) for the generation quality you are looking for.

4. Load the embedding model:

Here, we need to use the same text encoder model used by the subsequent Stable Diffusion model.

from diffusers import StableDiffusionDiffEditPipeline 

pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
)
pipeline = pipeline.to("cuda")
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing()

generator = torch.manual_seed(0)

5. Compute embeddings:

import torch 

@torch.no_grad()
def embed_prompts(sentences, tokenizer, text_encoder, device="cuda"):
    embeddings = []
    for sent in sentences:
        text_inputs = tokenizer(
            sent,
            padding="max_length",
            max_length=tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
        prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
        embeddings.append(prompt_embeds)
    return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)

source_embeddings = embed_prompts(source_prompts, pipeline.tokenizer, pipeline.text_encoder)
target_embeddings = embed_prompts(target_captions, pipeline.tokenizer, pipeline.text_encoder)

And you're done! Now, you can use these embeddings directly while calling the pipeline:

from diffusers import DDIMInverseScheduler, DDIMScheduler
from diffusers.utils import load_image

pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)

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


mask_image = pipeline.generate_mask(
    image=raw_image,
    source_prompt_embeds=source_embeds,
    target_prompt_embeds=target_embeds,
    generator=generator,
)

inv_latents = pipeline.invert(
    prompt_embeds=source_embeds,
    image=raw_image,
    generator=generator,
).latents

images = pipeline(
    mask_image=mask_image,
    image_latents=inv_latents,
    prompt_embeds=target_embeddings,
    negative_prompt_embeds=source_embeddings,
    generator=generator,
).images
images[0].save("edited_image.png")

StableDiffusionDiffEditPipeline

[[autodoc]] StableDiffusionDiffEditPipeline - all - generate_mask - invert - call