Recent research from Alibaba's Tongyi Laboratory indicates that existing text-to-image Diffusion Transformer models already possess the ability to generate multiple images with specific relational contexts. With just a little 'guidance', they can achieve a synthesis of understanding and produce high-quality multi-image collections. Traditional Diffusion models are more like 'rote learning' students, requiring massive datasets to generate high-quality images. However, with the support of IC-LoRA, they become 'high-achieving students', capable of learning through analogy.

Recently, the image AI startup Recraft released its latest text-generating image model, Recraft v3, which has shown exceptional performance in independent testing, redefining the standards for image generation performance. According to Recraft, the v3 model excels in generating text within images, maintaining anatomical accuracy, understanding prompts, and creating high-quality visual content. Its biggest breakthrough is the ability to accurately present long segments of text in a single generation, while many other models struggle with more than a few words.

Recently, a research team from the Beijing Academy of Artificial Intelligence launched a new image generation model called OmniGen. Unlike previous tools such as Stable Diffusion, which focused on a single task, OmniGen's biggest highlight is its multi-capability: it can handle a variety of image generation tasks within a unified framework, including text-to-image generation and image editing, making it a versatile player. This means that users only need to provide simple prompts.

Google's DeepMind, in collaboration with the Massachusetts Institute of Technology (MIT), recently announced a significant research breakthrough. The research team developed a new autoregressive model called 'Fluid', which has made groundbreaking progress in the field of text-to-image generation. After scaling up to 10.5 billion parameters, the model has demonstrated outstanding performance. This research overturns the industry's common perception; while autoregressive models have dominated the language processing domain, they have traditionally been seen as inferior to Stable Diffusion and Goog.

Recently, a research team jointly launched an open-source AI image generation model named Meissonic. Surprisingly, this model uses only one billion parameters while being able to generate high-quality images. This compact design gives Meissonic the potential to enable localized text-to-image applications on mobile devices. The technology behind this model involves a research team that includes Alibaba, Skywork AI, and researchers from various universities. They employed a unique transformer architecture and innovative training methods, which allow

Google's Gemini platform recently announced that their next-generation image generation model, Imagen3, is now open for use by all users worldwide. Imagen3 is touted as Google's highest-quality image generation model to date, delivering greater realism and improved instruction-following capability. Compared to previous versions, Imagen3 has significantly reduced distracting artifacts during the image generation process, enabling users to achieve the desired image effects more easily. This means that if you have any creative ideas and concepts...

Recently, the German AI startup Black Forest Labs launched its latest image generation model, Flux 1.1 Pro. This new model boasts an astonishing speed, with Black Forest Labs claiming it's six times faster than the previous generation, Flux 1 Pro, while also significantly enhancing image quality. In addition to Flux 1.1 Pro, Black Forest Labs has also released the beta version of its BFL API, allowing developers to integrate it with their applications.

Recently, Zhichu AI has open-sourced its latest creation—CogView3 and its upgraded version CogView-3Plus-3B, injecting new vitality into the field of text-to-image generation. The debut of CogView3 is undoubtedly a significant milestone. As the first model to achieve relay diffusion in the text-to-image generation domain, it employs a unique cascade diffusion method. This innovative approach first generates low-resolution images and then completes the final output through relay-based super-resolution technology. This not only greatly enhances...

The Stepping Star Open Platform Experience Center has launched the image generation model Step-1X, which debuted at the 2024 World Artificial Intelligence Conference, alongside the Step-2 language model and Step-1.5V multimodal model. Partnering with Shanghai Film, Step-1X developed an H5 interactive experience based on the classic IP 'Havoc in Heaven', and despite scarce data resources, it can still generate high-quality images. It emphasizes deep semantic alignment and detail generation, utilizes a self-developed DiT model architecture, supports long instruction input, and is suitable for advertising and gaming.

FLUX.1-dev-LoRA is an innovative image generation model that specializes in merging elements of reality with illustrations to create unique visual effects. Trained based on FLUX.1-dev, it can depict foreground characters in an illustrated style while maintaining realism in the background, making it suitable for generating creative images on social platforms. Users can generate images that meet their needs by providing scene descriptions and character action prompts. Official prompt examples demonstrate how to describe for specific art style fusion. Online experience and model download links are provided.

Freepik's AI image generation model Mystic, in collaboration with Magnific AI, has ushered in a new era of AI creation. This model not only generates ultra-realistic images but also captures intricate details in complex scenes, even generating realistic text within images, achieving full HD quality. The uniqueness of Mystic lies in its integration of the strengths of Stable Diffusion, Flux, and Magnific's proprietary models, finely tuned by a professional team.

Apple recently launched an innovative image and video generation technology called Matryoshka Diffusion Models (MDM), aptly named the 'Nested Diffusion Model'. MDM employs a groundbreaking approach that processes images at different resolutions simultaneously, achieving seamless generation from low-resolution sketches to high-definition details. The core technical concept is similar to Russian nesting dolls, where each layer contains a smaller but fully functional substructure. The core architecture of this innovative method is referred to as Nested.

Midjourney -- personalize. First, collect images that users like and dislike, using a multimodal model to infer the reasons behind their preferences. Then, utilize these attributes to generate images that align with the user's preferences.

Quick Hands released a big move today by opening its in-house image generation model——“Kolors”. This is not an ordinary model; it has been trained on tens of billions of text-image pairs, equipped with a General Language Model (GLM) as a text encoder, supporting bilingual Chinese and English prompts, and can handle contexts up to 256 tokens. Key Features of Kolors:Bilingual Support:Utilizes the General Language Model (GLM) as a text encoder, enabling the model to not only master English but

Stable Diffusion 3 is the most powerful text-to-image model. It adopts the MMDiT architecture, demonstrating performance that surpasses existing text-to-image generation systems. Stable Diffusion 3 excels in visual aesthetics, text adherence, and layout compared to other advanced models. The MMDiT architecture combines DiT and rectangular flow forms, processing image and language representations through independent weight sets. Stable Diffusion 3 offers flexibility.