Techniques for High-Fidelity Image Synthesis

𝐕𝐢𝐬𝐮𝐚𝐥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐓𝐨𝐩𝐢𝐜 #106: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬. Diffusion models have rapidly become a go‑to for state‑of‑the‑art image synthesis - powering models like Stable Diffusion and DALL·E 2- thanks to their stability and sample quality. It is a class of GenAI algorithms that learn to gradually add noise to data (the “forward” process) and then reverse that noise to reconstruct the original signal (the “reverse” process). By training a neural network- often a U‑Net with ResNet and self‑attention blocks - to predict and subtract noise at each step, these models can generate high‑fidelity images from pure noise. Sharing a visual and interactive article where author explains how diffusion models work, here you will explore - A step‑by‑step comparison of forward vs. reverse diffusion, scaled down from 1,000 to just 10 iterations - How predicted noise is subtracted at each stage to recover the underlying image - The core equations behind the diffusion process (feel free to skim the math!) - The architecture of the modified U‑Net denoiser and why ResNet + Self‑Attention matters - Pro tips on customizing your noise schedule for faster, crisper outputs - A concise training pseudocode snippet to kick‑start your own diffusion model Link is in first comment

𝗜𝘀 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗲𝗱𝗶𝘁𝗼𝗿 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗛𝗼𝗹𝗹𝘆𝘄𝗼𝗼𝗱-𝗴𝗿𝗮𝗱𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? 𝗪𝗲 𝘀𝗲𝗲 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗔𝗜 𝗲𝗱𝗶𝘁𝗶𝗻𝗴 𝗱𝗲𝗺𝗼𝘀 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆. 𝗕𝘂𝘁 𝗼𝗻𝗰𝗲 𝘆𝗼𝘂 𝗽𝘂𝘁 𝘁𝗵𝗲𝗺 𝗶𝗻𝘁𝗼 𝗮 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, 𝘁𝗵𝗲𝘆 𝗼𝗳𝘁𝗲𝗻 𝗳𝗮𝗹𝗹 𝘀𝗵𝗼𝗿𝘁. 👀 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Most current editors treat editing as “re-painting.” To fit compute constraints, they implicitly downsample high-res assets and regenerate the whole scene. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁: fine textures disappear—and worse, the background starts to hallucinate. Regions you never asked to touch quietly drift and degrade across turns. To solve this problem, we present Agent Banana: High-Fidelity Image Editing with Agentic Thinking and Tooling. We argue the fix requires a shift from simple generation to agentic reasoning and tool-calling—separating the “brain” from the “hands”: 🧠 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 (𝗣𝗹𝗮𝗻𝗻𝗲𝗿): A VLM-based Planner acts like a creative lead—interpreting vague instructions (even “vibes”) and turning them into a precise sequence of atomic actions. 🛠️ 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗧𝗼𝗼𝗹𝗶𝗻𝗴 (𝗘𝘅𝗲𝗰𝘂𝘁𝗼𝗿): Instead of a global diffusion pass, we introduce Image Layer Decomposition, enabling the agent to isolate, crop, and edit specific regions at native 4K resolution (up to 11.8M pixels). 🍌 Agent Banana introduces two key mechanisms:  ❶ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗙𝗼𝗹𝗱𝗶𝗻𝗴, which compresses long interaction histories into structured memory for stable long-horizon control, and  ❷ 𝗜𝗺𝗮𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, which performs localized layer-based edits to preserve non-target regions while enabling native-resolution outputs. The benefit: the unedited background remains mathematically identical to the input. Zero drift. Zero quality loss. We also introduce 𝗛𝗗𝗗-𝗕𝗲𝗻𝗰𝗵, a benchmark for High-Definition, Dialogue-based editing that emphasizes multi-turn consistency and fine detail preservation. 🔗 Preprint: arxiv.org/abs/2602.09084 🤗 Huggingface: https://lnkd.in/g4qE5ugJ 🌐 Project page: agent-banana.github.io #GenerativeAI #ComputerVision #AgenticAI #ImageEditing #Research #NeurIPS #AI #4K #Photoshop #CVPR 

Sharp Monocular View Synthesis in Less Than a Second https://lnkd.in/djBTUjUE Real-time photorealistic view synthesis from a single image. Given a single photograph, regresses the parameters of a 3D Gaussian representation of the depicted scene. Synthesis in less than a second on a standard GPU via a single feedforward pass through a neural network. The synthesized representation is then rendered in real time, yielding high-resolution photorealistic images for nearby views. The representation is metric, with absolute scale, supporting metric camera movements. Robust zero-shot generalization. SOTA on multiple datasets while lowering the synthesis time by three orders of magnitude. Code and weights (try it on your images!) at https://lnkd.in/dMjfhnP4 . Project page with videos: https://lnkd.in/dGbuDaht with Lars Mescheder, Wei Dong, Shiwei Li, Xuyang Bai, Marcel Santana, Peiyun Hu, Bruno Lecouat, Mingmin Zhen, Amaël Delaunoy, Tian Fang, Yanghai Tsin, Stephan Richter

DistriFusion Distributed Parallel Inference for High-Resolution Diffusion Models Diffusion models have achieved great success in synthesizing high-quality images. However, generating high-resolution images with diffusion models is still challenging due to the enormous computational costs, resulting in a prohibitive latency for interactive applications. In this paper, we propose DistriFusion to tackle this problem by leveraging parallelism across multiple GPUs. Our method splits the model input into multiple patches and assigns each patch to a GPU. However, na\"{\i}vely implementing such an algorithm breaks the interaction between patches and loses fidelity, while incorporating such an interaction will incur tremendous communication overhead. To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step. Therefore, our method supports asynchronous communication, which can be pipelined by computation. Extensive experiments show that our method can be applied to recent Stable Diffusion XL with no quality degradation and achieve up to a 6.1times speedup on eight NVIDIA A100s compared to one.

New paper "Stochastic Deep Restoration Priors for Imaging Inverse Problems." This work was done with a dream team of experts: Yuyang Hu, Albert Peng, Weijie Gan, Peyman Milanfar, and Mauricio Delbracio. The prevailing approach in score-based modeling for imaging is to employ a deep neural network trained as a Gaussian denoiser to serve as a proxy for the image distribution. While this method has proven effective in plug-and-play (PnP) methods and diffusion models (DMs), we show that priors from deep models pre-trained as more general restoration operators can perform better. We introduce Stochastic deep Restoration Priors (ShaRP), a new method that uses an ensemble of such restoration models. ShaRP improves upon methods using Gaussian denoisers by better handling structured artifacts and enabling self-supervised training without fully sampled data. We prove ShaRP minimizes an objective function involving a regularizer derived from the restoration score functions, and theoretically analyze its convergence. ShaRP achieves SOTA performance on MRI and image super-resolution, surpassing both denoiser- and diffusion-model-based methods without requiring retraining. Read here: https://lnkd.in/gRfCNvKa

I learned recently that Stable Diffusion was no longer the most widely used approach for generating images. There is an interesting and very cool technique called flow matching (also rectified flow matching, which we’ll cover another day). This is my best understanding of it right now. Help me if I miss any key considerations. The main thread for the diffusion approach was predicting the error or residual of a stochastic random process and continually predicting that error iteratively until the image was in the domain or distribution you were looking for. In practice, you start with an image of random noise and repeatedly predict and subtract away noise until an image appears. Pretty cool approach, but costly in terms of the number of times you have to iterate to get acceptable results. In comes flow matching (https://lnkd.in/ghgigQ3Q ). Instead of predicting the error or residual, we predict the velocity of each point in the sample and the direction it needs to move to go from noise to the in-distribution image. We frame the problem in a way such that we assume there exists a smooth transformation between the random noise distribution and the resulting image distribution, and we train a model to predict the derivative of that transformation over time. Doing this allows us to know the direction and magnitude each point needs to change at every step between the starting noise and ending picture, such that we can use traditional ODEs to solve for those values and reduce the number of inference calls to the model required to turn noise into a pretty picture. I love how many ways there are to solve the same problem. It turns out that the error-predicting diffusion approach can be viewed as a special Gaussian case of flow matching, but we won’t get into that here. Hope this helps shed some light on the technical approaches behind how your favorite image generation model is creating that cute picture of a puppy for you.

What if generating a high-quality image took 5 seconds instead of 10 minutes? ByteDance just published NextFlow, a unified autoregressive model that rivals diffusion models in image quality while being dramatically faster. The core problem: Pure autoregressive image models have always been painfully slow. Generating a single 1024×1024 image via traditional raster-scan prediction can take over 10 minutes. Meanwhile, hybrid approaches that bolt diffusion onto transformers create architectural complexity and representational gaps between understanding and generation. NextFlow's key insight is replacing raster-scan with next-scale prediction. Instead of predicting pixels one by one left-to-right, the model generates images hierarchically from coarse structure to fine details. This cuts sequence length dramatically since visual tokens grow quadratically with resolution in raster-scan approaches. Two additional mechanisms make this work: A dual-codebook tokenizer that captures both semantic meaning and pixel-level detail in the same representation, enabling true unification of understanding and generation. And a scale-aware loss reweighting strategy that prevents the model from ignoring crucial early scales that determine global layout. NextFlow generates 1024×1024 images in 5 seconds while matching state-of-the-art diffusion models on text-to-image benchmarks. It achieves 6x fewer FLOPs than MMDiT-based diffusion transformers and outperforms specialized models on image editing tasks. This suggests we may not need separate systems for understanding and generating visual content. A single autoregressive architecture can do both efficiently. ↓ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐮𝐩? Join my newsletter with 50k+ readers and be the first to learn about the latest AI research: llmwatch.com 💡

Lol, this guy was dissed by ChatGpt! How to Improve Picture Generation in ChatGPT: The Prompting Skills That Deliver Professional Results AI image generation can feel like magic, until it doesn’t. Many people try prompting ChatGPT for an image and get something close… but not quite right: strange hands, unclear faces, mismatched styles, awkward composition, or details that don’t align with the vision in your head. The truth is, high-quality image results aren’t random, they’re designed. And the difference between an average AI image and a stunning one often comes down to one thing: how you prompt. Start with clarity: subject, setting, and action A strong prompt begins with a clear anchor. Instead of writing “a woman in a park,” specify who she is, what she’s doing, and where she is. Better: “A woman jogging on a tree-lined path in a city park, smiling mid-stride.” This immediately reduces ambiguity and increases realism. Many prompts fail because they don’t tell the system what kind of image to produce. Adding a style reference prevents generic outputs and strengthens consistency. Examples of style instructions: Photorealistic DSLR photo Cinematic film still Anime key visual Watercolor illustration 3D Pixar-style render Use lighting and camera language to boost realism. For realistic images, lighting and camera cues matter more than people expect. These terms improve quality dramatically: “soft natural window light” “golden hour lighting” “studio softbox lighting” “shallow depth of field” “50mm lens” “crisp focus, high detail” These instructions guide the image toward professional photography rather than “AI-looking.” Without composition guidance, the framing can be random. Give simple instructions like: close-up portrait wide shot centered composition rule of thirds background bokeh If you want a clean professional look, tell it where the focus should go. Add specifics, but avoid overload Details make images feel real: clothing, textures, mood, time of day. But stuffing 40 details into one prompt can confuse the generation. Focus on the top 6–10 details that matter most. Use iteration: don’t restart, refine If the image is close, don’t write a whole new prompt. Instead say: “Keep everything the same, but change the background to a beach at sunset.” “Same image, but make it more cinematic and less cartoon-like.” “Same pose, remove the hat, add glasses.” Iteration produces consistency, especially for branding, characters, and repeated scenes. Great image generation isn’t about luck. It’s about communication. When we learn to describe a vision clearly, we get better results, not only from AI tools, but from people, teams, and systems. Leadership principle: Clarity creates quality. #AIArt #PromptEngineering #ChatGPT #ImageGeneration #DesignTools #Creativity #ContentCreation #DigitalMarketing #Leadership

Breaking the Vicious Cycle: Coherent 3D Gaussian Splatting from Sparse and Motion-Blurred Views https://lnkd.in/eAAzE7PZ 3D Gaussian Splatting (3DGS) has emerged as a state-of-the-art method for novel view synthesis. However, its performance heavily relies on dense, high-quality input imagery, an assumption that is often violated in real-world applications, where data is typically sparse and motion-blurred. These two issues create a vicious cycle: sparse views ignore the multi-view constraints necessary to resolve motion blur, while motion blur erases high-frequency details crucial for aligning the limited views. Thus, reconstruction often fails catastrophically, with fragmented views and a low-frequency bias. To break this cycle, we introduce CoherentGS, a novel framework for high-fidelity 3D reconstruction from sparse and blurry images. Our key insight is to address these compound degradations using a dual-prior strategy. Specifically, we combine two pre-trained generative models: a specialized deblurring network for restoring sharp details and providing photometric guidance, and a diffusion model that offers geometric priors to fill in unobserved regions of the scene. This dual-prior strategy is supported by several key techniques, including a consistency-guided camera exploration module that adaptively guides the generative process, and a depth regularization loss that ensures geometric plausibility. We evaluate CoherentGS through both quantitative and qualitative experiments on synthetic and real-world scenes, using as few as 3, 6, and 9 input views. Our results demonstrate that CoherentGS significantly outperforms existing methods, setting a new state-of-the-art for this challenging task. The code and video demos are available at this https URL. --- Newsletter https://lnkd.in/emCkRuA More story https://lnkd.in/eMFcEekQ LinkedIn https://lnkd.in/ehrfPYQ6 #AINewsClips #AI #ML #ArtificialIntelligence #MachineLearning #ComputerVision