# Image synthesis

DynamicControl is a framework designed to enhance the control of text-to-image diffusion models. It dynamically combines various control signals and supports adaptive selection of different numbers and types of conditions to synthesize images more reliably and in detail. The framework first utilizes a dual-loop controller, employing pre-trained conditional generation and discriminator models to generate initial real score rankings for all input conditions. Then, a multimodal large language model (MLLM) constructs an efficient condition evaluator to optimize the condition ordering. DynamicControl jointly optimizes MLLM and the diffusion model, leveraging the inference capabilities of MLLM to facilitate multi-condition text-to-image tasks. The final ordered conditions are input into a parallel multi-control adapter to learn dynamic visual condition feature maps and integrate them to adjust ControlNet, enhancing control over the generated images.

Sana is a text-to-image generation framework developed by NVIDIA, capable of efficiently producing images up to 4096×4096 resolution. With its rapid processing speed and robust text-image alignment capabilities, it can even be deployed on laptop GPUs. It is based on a linear diffusion transformer (text-to-image generative model) with 1648M parameters, specifically designed for generating multi-scale images at a base resolution of 1024px. Key advantages of the Sana model include high-resolution image generation, rapid synthesis speed, and strong text-image alignment capabilities. The model's background reveals that it is developed using open-source code, available on GitHub, and adheres to specific licensing (CC BY-NC-SA 4.0 License).

HiDiffusion is a pre-trained diffusion model that can enhance resolution and speed by simply adding one line of code. The model uses techniques such as Resolution-Aware U-Net (RAU-Net) and Modified Shifted Window Multi-head Self-Attention (MSW-MSA) to dynamically adjust feature map sizes to address the issue of object duplication and optimize window attention to reduce computational burden. HiDiffusion can extend image generation resolution to 4096×4096 while maintaining 1.5-6 times the inference speed of previous methods.

SDXL Turbo is a text-to-image generative model based on Adversarial Diffusion Distillation (ADD) technology. It can quickly generate high-quality images. As an enhanced version of SDXL 1.0, it can synthesize high-quality, realistic images with a single network evaluation.

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.