Guide to Stable Diffusion Models in 2024

Introduction:

Stable Diffusion Models, also known as checkpoint models, are pre-trained weights designed for generating specific styles of images. The type of images a model can generate is determined by the images used during its training phase. For instance, a model trained without any cat images will not be able to generate a cat’s image. Conversely, a model trained exclusively with cat images will only generate cat images. This article will provide a detailed overview of Stable Diffusion Models, highlighting popular models, their installation, usage, and merging process.

Fine-tuning is a prevalent technique in machine learning that enhances the base Stable Diffusion model’s capabilities. While the base model is versatile, it may struggle with generating specific sub-genres of images. For instance, generating a sub-genre of anime images might be challenging. However, by using a custom model fine-tuned with images of that sub-genre, you can overcome this limitation. 

Here is the list of the top publicly available fine-tune models:

Realistic Vision 6.0 and Realistic Vision 5.1: These models are known for generating photorealistic images, particularly portraits. They are highly praised for their ability to capture various ethnicities and clothing styles.

Stable Diffusion XL (SDXL): Powerful text-to-image generation model that enables the generation of expressive images with shorter prompts. The model was trained on 1024×1024 images and including a larger UNet, a two-stage model process, and micro-conditioning techniques, which contribute to its improved performance and image generation capabilities.

ChilloutMix: This model is known for producing high-quality, photorealistic images, particularly of human faces. It seems to be especially good at producing East Asian faces.

JuggernautXL: This model is known for its photorealistic capabilities, particularly in generating detailed and realistic images. It is often praised for its ability to produce high-quality images with minimal flaws, making it a popular choice for various applications, including cinematic and realistic scenes, as well as for generating images related to people, food, fantasy/lore, and cars/technical aspects.

CyberRealistic: This model is recognized for its proficiency in rendering human skin with a high level of realism. It is often favored for its ability to handle character loras effectively, making it a preferred choice for certain types of image generation tasks.

EpicRealism: This model is known for its exceptional performance in creating highly realistic images. It is often praised for its ability to generate detailed and lifelike visuals, making it a popular choice for various photorealistic image generation needs.

AbsoluteReality: Specific details about this model were not found in the search results. However, it is mentioned as a model that excels in producing highly realistic images, particularly in the context of photorealistic image generation.

RevAnimated: This model is known for its ability to create 2.5D-like image generations. It has received positive feedback and is recognized for its detailed and amazing results. The model is based on Stable Diffusion v1.5 and has been well-received for its image generation capabilities, particularly in creating 2.5D-like images

RealVisXL: Similar to Realistic Vision, but trained for 1024×1024 image size.

Analog Diffusion: This model is known for creating retro 80s-style images[8].

Deliberate: This model is especially popular for NSFW themes but considered as a “jack of all trades” and is one of the most popular models of all time. It was recently moved to Hugging Face.

Animagine XL: Came out in 2024, this Stable Diffusion model is known for its superior image generation with notable improvements in hand anatomy, efficient tag, and is based on Stable Diffusion XL.

By leveraging the collective expertise and experiences shared on platforms like Diffusionhub, individuals can gain deeper insights into the capabilities of specific models and stay informed about the evolving landscape of AI-driven image generation.

Time to generate!