Stable Diffusion and applications

Stable Diffusion, a generative AI model introduced in 2022, is designed to produce realistic images, videos, and animations based on text and image prompts. Beyond image generation, it excels in tasks like denoising, inpainting, super-resolution, and image-to-image translation. For instance, it can be trained to eliminate gaussian noise from images, showcasing its versatility.

The primary use of Stable Diffusion is in generating detailed images conditioned on textual descriptions, but it can also be applied to tasks like inpainting, outpainting, and guided image-to-image translations through text prompts. It stands out for its efficiency, requiring less processing power compared to other text-to-image models, making it widely accessible.

Despite its strengths, Stable Diffusion may pose challenges due to its computational intensity and time-consuming nature. The quality of results can vary based on input data and network parameters. It may not be ideal for certain image editing tasks, such as removing unwanted elements from a picture. Nonetheless, its capabilities span text-to-image, image-to-image, graphic artwork, image editing, and video creation.

The various applications beyond text-to-image generation, highlighting the advancements in diffusion models.

1. Text-Guided Creative Generation

1.1 Visual Art Generation

Diffusion models have been applied to artistic painting, overcoming issues faced by GAN-based painting.Techniques like Multimodal Guided Artwork Diffusion (MGAD), DiffStyler, and others improve the generative process and achieve high-quality digital artworks.Differ ent approaches personalize text-to-image generation, extend styles, and enhance computation efficiency.

1.2 Video Generation and Story Visualization

Text-to-video generation involves adapting text-to-image models for video creation, with methods like Make-A-Video and Video Imagen. Text-to-story generation (story synthesis) explores creating videos based on text, addressing challenges like actor and background consistency. Examples include Make-A-Story and AR-LDM.

1.3 3D Generation

DeepFusion pioneers the application of diffusion models to 3D object synthesis. Magic3D proposes a coarse-to-fine optimization for higher-resolution results. 3DDesigner focuses on consistency in 3D object generation.

2. Text-Guided Image Editing

2.1 General Image Editing

DiffusionCLIP introduces diffusion models to alleviate issues in zero-shot image editing, showing superior performance. Other methods like LDEdit, Prompt-to-Prompt, and CycleDiffusion improve image editing tasks using diffusion models.

2.2 Image Editing with Masks

Blended Diffusion and its variants address local (masked) image editing challenges, ensuring coherence between edited regions and the background. DiffEdit proposes automatic mask generation based on noise estimates.

2.3 Model Training with a Single Image

Various approaches, such as SinDDM, SinDiffusion, and UniTune, explore training generative models with a single image.

2.4 3D Object Editing

3DDesigner is the first to perform 360-degree manipulation of 3D objects. DATID-3D focuses on text-guided domain adaptation of 3D objects.

2.5 Other Interesting Tasks

Imagic performs text-based semantic edits to a single image. MagicMix introduces semantic mixing, blending different semantics to create new concepts. InstructPix2Pix edits images based on human-written instructions. PITI proposes pretraining-based image-to-image translation for synthesizing realistic images.