Image Generation

StyleCodes enables easy style sharing for image generation by encoding styles as compact strings, enhancing control and collaboration while minimizing quality loss.

Researchers have developed Continuous Speculative Decoding, boosting autoregressive image generation speed by up to 2.33x while maintaining image quality.

FitDiT boosts virtual try-on realism by enhancing garment details via Diffusion Transformers, improving texture and size accuracy for high-fidelity virtual fashion.

MagicQuill: an intelligent interactive image editing system enabling intuitive, precise image edits via brushstrokes and real-time intent prediction by a multimodal LLM.

OmniEdit, a novel instruction-based image editing model, surpasses existing methods by leveraging specialist supervision and high-quality data, achieving superior performance across diverse editing ta…

Edify Image: groundbreaking pixel-perfect photorealistic image generation using cascaded pixel-space diffusion models with a novel Laplacian diffusion process, enabling diverse applications including …

Add-it: Training-free object insertion in images using pretrained diffusion models by cleverly balancing information from the scene, text prompt, and generated image, achieving state-of-the-art result…

StdGEN: Generate high-quality, semantically decomposed 3D characters from a single image in minutes, enabling flexible customization for various applications.

SVDQuant boosts 4-bit diffusion models by absorbing outliers via low-rank components, achieving 3.5x memory reduction and 3x speedup on 12B parameter models.

SG-I2V: Zero-shot controllable image-to-video generation using a self-guided approach that leverages pre-trained models for precise object and camera motion control.

Training-free Regional Prompting for FLUX boosts compositional text-to-image generation by cleverly manipulating attention mechanisms, achieving fine-grained control without retraining.

Randomized Autoregressive Modeling (RAR) sets a new state-of-the-art in image generation by cleverly introducing randomness during training to improve the model’s ability to learn from bidirectional c…

Constant Acceleration Flow (CAF) dramatically speeds up diffusion model generation by using a constant acceleration equation, outperforming state-of-the-art methods with improved accuracy and few-step…

In-Context LoRA empowers existing text-to-image models for high-fidelity multi-image generation by simply concatenating images and using minimal task-specific LoRA tuning.

HelloMeme enhances text-to-image models by integrating spatial knitting attentions, enabling high-fidelity meme video generation while preserving model generalization.