Techniques for High-Fidelity Image Synthesis

NeRF-Casting Improved View-Dependent Appearance with Consistent Reflections Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint. Recent works have improved NeRF's ability to render detailed specular appearance of distant environment illumination, but are unable to synthesize consistent reflections of closer content. Moreover, these techniques rely on large computationally-expensive neural networks to model outgoing radiance, which severely limits optimization and rendering speed. We address these issues with an approach based on ray tracing: instead of querying an expensive neural network for the outgoing view-dependent radiance at points along each camera ray, our model casts reflection rays from these points and traces them through the NeRF representation to render feature vectors which are decoded into color using a small inexpensive network. We demonstrate that our model outperforms prior methods for view synthesis of scenes containing shiny objects, and that it is the only existing NeRF method that can synthesize photorealistic specular appearance and reflections in real-world scenes, while requiring comparable optimization time to current state-of-the-art view synthesis models.

Researchers from Virginia Tech, Meta, and UC Davis have introduced AR-RAG (Autoregressive Retrieval Augmentation), a novel approach that significantly improves AI image generation by incorporating dynamic patch-level retrieval during the generation process. The Problem with Current Methods: Existing retrieval-augmented image generation methods retrieve entire reference images once at the beginning and use them throughout generation. This static approach often leads to over-copying irrelevant details, stylistic bias, and poor instruction following when prompts contain multiple objects or complex spatial relationships. The AR-RAG Solution Instead of static image-level retrieval, AR-RAG performs dynamic retrieval at each generation step: - Uses already-generated image patches as queries to retrieve similar patch-level visual references - Maintains a database of patch embeddings with spatial context from real-world images - Implements two frameworks: DAiD (training-free) and FAiD (parameter-efficient fine-tuning) - Enables context-aware retrieval that adapts to evolving generation needs Key Results: Testing on three benchmarks (GenEval, DPG-Bench, Midjourney-30K) showed substantial improvements: - 7-point increase in overall GenEval score (0.71 → 0.78) - 2.1-point improvement on DPG-Bench - Significant FID score reduction on Midjourney-30K (14.33 → 6.67) - Particularly strong gains in multi-object generation and spatial positioning tasks Why This Matters: AR-RAG addresses fundamental limitations in current image generation models, especially for complex prompts requiring precise object placement and interaction. The method's ability to selectively incorporate relevant visual elements while avoiding over-copying makes it valuable for applications requiring high fidelity and instruction adherence. The research demonstrates that fine-grained, dynamic retrieval can substantially improve image generation quality while maintaining computational efficiency. AR-RAG: Autoregressive Retrieval Augmentation for Image Generation: https://lnkd.in/g7cjJ32J. Paper and research by Jingyuan Qi, Zhiyang X., Qifan Wang, Huang Lifu

🔥LCMs are speeding past traditional Latent Diffusion Models (LDMs). They crank out high-res images in just a few steps – sometimes in just one! It's not just about speed, though; it's about smarter, more efficient processing that is less resource-intensive. This is big news for creators, developers, and tech enthusiasts. Abstract: "Latent Diffusion models (LDMs) have achieved remarkable results in synthesizing high-resolution images. However, the iterative sampling process is computationally intensive and leads to slow generation. Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al). Viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, mitigating the need for numerous iterations and allowing rapid, high-fidelity sampling. Efficiently distilled from pre-trained classifier-free guided diffusion models, a high-quality 768 x 768 2~4-step LCM takes only 32 A100 GPU hours for training. Furthermore, we introduce Latent Consistency Fine-tuning (LCF), a novel method that is tailored for fine-tuning LCMs on customized image datasets. Evaluation on the LAION-5B-Aesthetics dataset demonstrates that LCMs achieve state-of-the-art text-to-image generation performance with few-step inference." Credit: Tsinghua University Project Page: https://lnkd.in/eKwMVd8S arXiv: https://lnkd.in/ehAY_n8Z GitHub: https://lnkd.in/eJe8Hb5P MIT License: https://lnkd.in/ePxaywMF 🤗 Demo: https://lnkd.in/ekcVh2Wk For more like this ⤵ 👉 Follow Orbis Tabula #generativeai #latentconsistencymodel #stablediffusion