# ControlNet

Regional-Prompting-FLUX is a training-independent regional prompting diffusion transformer model that provides fine-grained combined text-to-image generation capabilities for diffusion transformers (such as FLUX) without the need for training. The model not only delivers impressive results but also exhibits high compatibility with LoRA and ControlNet, minimizing GPU memory usage while maintaining high speed.

x-flux is a collection of deep learning model training scripts released by the XLabs AI team, featuring LoRA and ControlNet models. These models leverage DeepSpeed for training and support image sizes of 512x512 and 1024x1024, along with corresponding training configuration files and examples. The x-flux model training aims to enhance the quality and efficiency of image generation, making it significant in the field of AI image generation.

This is a SDXL-based ControlNet Tile model trained on the Hugging Face Diffusers dataset and is compatible with Stable Diffusion SDXL ControlNet. It was initially developed for my own realistic model training, used in the ultimate upscaling process to enhance image details. With the right workflow, it can provide good results for high-detail, high-resolution image repair. As most open-source models lack SDXL Tile models, I decided to share this one. This model supports high-resolution repair, style transfer, and image enhancement functions, providing you with a high-quality image processing experience.

PIXART LCM is a text-to-image synthesis framework that integrates the latent consistency model (LCM) and ControlNet into the advanced PIXART-α model. PIXART LCM is renowned for its ability to generate high-quality 1024px resolution images through an efficient training process. Integrating LCM in PIXART-δ significantly accelerates inference speed, allowing for the generation of high-quality images in just 2-4 steps. Notably, PIXART-δ achieves the milestone of generating 1024x1024 pixel images within 0.5 seconds, a 7-fold improvement over PIXART-α. Furthermore, PIXART-δ is meticulously designed for efficient training on a 32GB V100GPU within a single day. With 8-bit inference capability, PIXART-δ can synthesize 1024px images under an 8GB GPU memory constraint, considerably enhancing its usability and accessibility. Additionally, the introduction of a ControlNet-like module enables fine-grained control over text-to-image diffusion models. We propose a novel ControlNet-Transformer architecture, specifically tailored for Transformers, achieving explicit controllability and high-quality image generation. As a leading open-source image generation model, PIXART-δ offers a promising alternative within the stable diffusion model family, significantly contributing to the field of text-to-image synthesis.

QR Diffusion is a free QR code art generator that utilizes Stable Diffusion and ControlNet technology to generate breathtaking artworks of QR codes within seconds. It transcends the pixelated grids of traditional QR codes, employing the powerful generative AI model Stable Diffusion to create intricate images reminiscent of art. The ControlNet model ensures that the final QR code retains all the crucial details you require.