# Realism

Veo 2 is the latest video generation model developed by Google DeepMind, representing a significant advancement in video generation technology. Veo 2 can realistically simulate physical effects from the real world and a wide range of visual styles while following both simple and complex instructions. This model significantly surpasses other AI video models in detail, realism, and minimizing human artifacts. Veo 2’s advanced motion capabilities allow it to accurately represent movement and closely follow detailed instructions, enabling the creation of various lens styles, angles, and motions. Its importance in the field of video generation is reflected in the enhanced diversity and quality of video content, providing robust technical support for film production, game development, and virtual reality.

iPhone Photo [FLUX] is an AI model based on LoRA technology, designed to enhance the realism of images, especially excelling in mimicking iPhone photo effects. It not only enhances the quality of iPhone-captured photos but also adds a natural and realistic visual effect to non-iPhone photos. Developed by Anibaaal and released on October 2, 2024, this model belongs to the Civitai platform. A usage tip suggests setting the intensity to 1, and it boasts over 1000 likes and 375 comments, indicating its popularity.

The Flux_Realistic Style model is an AI model focused on generating extremely lifelike and natural everyday photos. Leveraging the latest artificial intelligence technologies and deep learning algorithms, it produces images in the realistic style typical of Xiaohongshu. This model is particularly suitable for users who need to post high-quality, realistic images on social media, as well as for professionals engaged in artistic creation and design work. The model offers a variety of parameter settings to adapt to different usage scenarios and requirements.

Deep floyd is an open-source text-to-image model with high realism and language understanding capabilities. It consists of a frozen text encoder and three cascaded pixel diffusion modules: a base model generates 64x64 pixel images based on text prompts, and two super-resolution models generate images with gradually increasing resolutions: 256x256 pixels and 1024x1024 pixels. All stages of the model utilize a frozen T5 transformer-based text encoder to extract text embeddings, which are then input into a UNet architecture enhanced with cross-attention and attention pooling. This efficient model surpasses current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset. Our work highlights the potential of larger UNet architectures in the first stage of cascaded diffusion models and demonstrates a promising future for text-to-image synthesis.