Imagic: Text-Based Real Image Editing with Diffusion Models (2210.09276v3)

Abstract: Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, style transfer), or apply to synthetically generated images, or require multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to apply complex (e.g., non-rigid) text-guided semantic edits to a single real image. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics. Our method can make a standing dog sit down or jump, cause a bird to spread its wings, etc. -- each within its single high-resolution natural image provided by the user. Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object). Our method, which we call "Imagic", leverages a pre-trained text-to-image diffusion model for this task. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. We demonstrate the quality and versatility of our method on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework.

Imagic: Text-Based Real Image Editing with Diffusion Models

The paper "Imagic: Text-Based Real Image Editing with Diffusion Models" addresses the problem of applying complex text-based semantic edits to a single real image. Standout features of this method include the ability to make non-rigid changes to objects within a high-resolution natural image and the operation without any auxiliary inputs beyond the input image and accompanying text prompt. This method presents a significant improvement over current leading text-conditioned image editing methods, which are often constrained by either the type of edit they can handle or the necessity of additional auxiliary inputs such as image masks or multiple viewpoints.

The research utilizes a pre-trained text-to-image diffusion model to navigate this challenge, developing a text embedding that integrates both the real input image and the desired target edit specified via text. The paper's notable contribution is its capability to manipulate real images through text prompts to execute complex edits, for instance, altering an animal's posture or altering multiple objects in a single image.

Methodology

The proposed method, Imagic, proceeds through a structured 3-step process:

1. Text Embedding Optimization: The target text is converted into a text embedding. This embedding is then optimized to produce an image similar to the input image.
2. Model Fine-Tuning: Subsequently, the pre-trained diffusion model is fine-tuned to reconstruct the input image with higher fidelity using the optimized embedding.
3. Text Embedding Interpolation: The text embedding undergoes interpolation between the optimized and the original target text embedding, facilitating a blend that incorporates both the input image and the target text's characteristics.

Results

Imagic demonstrates superior versatility and quality in text-based image editing across various domains. The strong numerical results emphasize the model's capabilities. The paper includes a user paper where the proposed method outperformed previous image editing techniques. Additionally, the introduction of TEdBench, a challenging image editing benchmark, facilitates the assessment and comparison of different methods, reinforcing Imagic's credibility in handling complex edits.

Implications

The practical implications of this research are profound. It simplifies the image editing pipeline, requiring users to provide only a single image and a text prompt. This opens up numerous applications in fields ranging from digital art and content creation to augmented reality and user-generated content platforms.

The theoretical implications are equally significant. The capability to semantically interpolate between text embeddings uncovers robust compositional properties of text-to-image diffusion models. This mechanism hints at a broader potential to merge disparate data modalities through latent space interpolation—a concept that could extend beyond image editing into other generative tasks such as video and 3D content generation.

Future Directions

Looking forward, research can expand on enhancing the method's fidelity to the input image and controlling its sensitivity to random seeds and interpolation parameters. Automating the interpolation parameter selection to suit different edits or optimizing the model in a way that reduces artifacts and biases inherent in the diffusion model are viable areas for further exploration.

There is also a possibility to address and minimize societal risks associated with synthetic image editing, such as the misuse of the technology for creating misleading or altered imagery. This necessitates accompanying developments in methods to detect and authenticate edited content.

In summary, the paper "Imagic: Text-Based Real Image Editing with Diffusion Models" presents a meticulously developed, robust, and versatile method for text-based semantic image editing. By leveraging diffusion models, it enables complex non-rigid edits with high fidelity, paving the way for future advancements in both practical applications and theoretical understanding in the field of generative models and image processing.