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  1. 2250New
    Whose Values Are Generated? Measuring Visual Value Leakage in Multilingual Text-to-Image Generation

    Text-to-image (T2I) systems serve users across languages and cultural contexts. However, evaluations of cultural representation focus mainly on recognizable elements such as landmarks, clothing, and activities, overlooking the cultural values that generated images convey. To…

    arXiv: image generation03 NovPaper · image
  2. 1558New
    EDGAR: Efficient Diffusion-Driven Gaussian Avatar Refinement via Synthetic Failure

    EDGAR is a conditional diffusion framework with a fusion block and Mixture-of-Experts that refines degraded Gaussian head avatars by injecting self-supervised identity features, using refined multi-view renderings as pseudo-targets to optimize a single avatar.

    Hugging Face Daily Papers04 NovPaperOpen weights10 points
  3. 1329New
    IDPrior: Decoupling Identity Generation from Facial Image Synthesis

    Large-scale identity-labeled face datasets are essential for training modern face recognition models, yet collecting and annotating such data is costly and raises privacy and ethical concerns. Existing synthetic face generation methods typically derive identities from predefined…

    arXiv: image generation05 NovPaper · image
  4. 1328New
    StitchEdit: Stitching Depth Priors into Image Editors via Per-Layer Gradient Probing

    Text-guided image editing with diffusion models struggles with spatial edits (e.g. manipulating objects, changing viewpoints, manipulating occlusions) that depend on the 3D structure understanding of a scene. Marigold and follow-up work demonstrate that a pretrained diffusion…

    arXiv: image generation05 NovPaper · image
  5. 1327New
    TRACEMARK: Editor-Aware Watermarking for User Attribution in Text-Guided Image Editing

    An image editing service records which user requests each edit, but the edited image itself does not contain this account information. Once the image is downloaded and shared, the service cannot identify the requesting user from the image alone. Embedding a user-specific…

    arXiv: image generation05 NovPaper · image
  1. 327New
    Images 2.6Hacker News: image models04 NovDiscussion · image381 points
  2. 326New
    A Proxy-Based Method for Measuring Racial Disparities in~Text-to-Image~Generators

    We present an automated method for auditing text-to-image generators for representational bias against ground-truth demographic benchmarks. Our approach adapts the racial proxy variables used in U.S. antidiscrimination law to provide a fine-grained comparative analysis of…

    arXiv: image generation04 NovPaper · image
  3. 324New
    ChatGPT can now turn text prompts and photos into animated GIFs with OpenAI's Images 2.6 model

    OpenAI is adding a GIF feature to ChatGPT. With the new Images 2.6 model, users can create animated GIFs from text prompts or photos and then adjust speed and individual frames. The model's image consistency is strong enough to keep characters recognizable across frames, making…

    The Decoder04 NovNews
  4. 323New
    Segment What It Can Draw: Querying a Text-to-Image Generator with Its Own Images for Skin Lesion Segmentation

    Skin lesion segmentation without annotated masks requires the target to be specified through other cues. Existing approaches rely on dermoscopy-specific rules, annotation-derived model priors, or user-selected regions. A general text-to-image diffusion transformer offers a text…

    arXiv: image generation04 NovPaper · image
  5. 321New
    LongBanana: An Expert-Verified Benchmark for Long-Context Multi-Reference Image Synthesis

    We introduce LongBanana, to our knowledge the first expert-designed and expert-verified benchmark for long-context multi-reference image synthesis in the age of agent-driven image generation. The task requires retrieving dispersed visual evidence, rejecting distractors, binding…

    arXiv: image generation04 NovPaper · image
  1. 320New
    Context-Aware Autoregressive Image Generation for Emerging Reasoning Properties

    Autoregressive image generation has recently emerged as a competitive visual generation paradigm, yet its generation order is often predefined or heuristic, such as next-patch prediction, random-order prediction, or one-step generation for specific scales. We observe that these…

    arXiv: image generation04 NovPaper · image
  2. 319New
    TailSteer: Trajectory-Tail Steering for Drift-Resistant Multi-turn Image Editing

    Strong single-turn editing performance does not guarantee reliable multi-turn interaction: as edited outputs are repeatedly reused, small unintended changes can accumulate into severe visual drift. This long-term drift is often mitigated through training on multi-turn data or…

    arXiv: image generation04 NovPaper · image
  3. 309New
    Introducing Images 2.6

    Images 2.6 brings faster image generation, GIF creation, more precise editing, and new API controls across ChatGPT, Codex, and the API.

    OpenAI News04 NovNews · imageOfficial
  4. 307New
    Whose Values Are Generated? Measuring Visual Value Leakage in Multilingual Text-to-Image Generation

    Text-to-image (T2I) systems serve users across languages and cultural contexts. However, evaluations of cultural representation focus mainly on recognizable elements such as landmarks, clothing, and activities, overlooking the cultural values that generated images convey. To…

    arXiv: image generation03 NovPaper · image
  5. 303New
    MiniMax H3 in ComfyUI | Character Swap, Talk, Sing & Timeline (Ep41)

    In this episode, we test MiniMax H3 in ComfyUI with 25 free workflows, plus 2 bonus workflows. H3 is a video model that makes video with sound, from your images, videos and audio. We test them all, and see what H3 does well and where it struggles. Part 1, change your video: a…

    pixaroma03 NovVideo
  1. 302New
    PATTS: Parallel-Aware Test-Time Scaling for Thought-Image Generation in Multimodal Discrete Diffusion LLMs

    Test-time scaling (TTS) improves generative models by allocating additional inference-time computation to sampling, search, and verification. Existing image generation approaches, however, typically rank completed images or image-centric intermediate states. We study TTS for…

    arXiv: image generation03 NovPaper · image
  2. 299New
    FlowTranslator: Training-Free In-Image Machine Translation with Flow-Based Editing

    In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrounding visual context. Existing task-specific models achieve convincing performance but require massive paired training images before and after translation. As advanced…

    arXiv: image generation03 NovPaper · image
  3. 298New
    Improving Diffusion-Based Augmentation for Data-Scarce Image Classification via Mask-Guided Reference Attention and Repulsive Noise Optimization

    Diffusion-based data augmentation has emerged as a powerful strategy for addressing data-scarce image classification. Recent pipelines typically adapt a pretrained text-to-image diffusion model to a limited number of class examples and subsequently sample synthetic images using…

    arXiv: image generation03 NovPaper · image
  4. 297New
    InvokeAI 6.14.4InvokeAI03 NovRelease · image
  5. 293New
    Structured Visual Landscape: Generating Preferred Representations in Biological and Artificial Neural Networks

    Understanding how neurons respond to visual stimulus is an important question in both deep learning and neuroscience. It has significant implications for enhancing the interpretability of black-box artificial neural networks and for understanding the visual representation in the…

    arXiv: image generation02 NovPaper · image
  1. 275New
    Restoring Semantic Fidelity for Training-Free High-Resolution Image Generation in Diffusion Transformers

    Diffusion Transformer (DiT)-based text-to-image models have recently achieved remarkable success in image synthesis. However, when directly applied beyond their training resolution, these models often suffer from severe semantic degradation, where meaningful semantic content…

    arXiv: image generation01 NovPaper · image
  2. 274New
    MedNet and U$^2$-ViT: A Nested Vision Transformer Diffusion Framework for Diverse Medical Images Generation

    The scarcity of diverse, high-quality medical images remains a significant bottleneck in advancing AI-driven medical diagnostics and treatment planning. Traditional datasets often suffer from class imbalances, limited disease coverage, and insufficient variations in imaging…

    arXiv: image generation01 NovPaper · image
  3. 263New
    Training-free Conditional Object Composition for Multi-object Image Generation with Diffusion Models

    Multi-object image generation in text-to-image diffusion models remains challenging, as existing approaches struggle to reliably generate all objects specified in a prompt. Compositional approaches that generate objects independently often fail to capture the interactions among…

    arXiv: image generation31 OctPaper · image
  4. 261New
    FREE + LOCAL: Image to 3D for LOW Vram

    This will turn a single image of an object into a full 3D Gaussian Splat in seconds. ComfyUI-Idol adds native support for Speridlabs’ open Idol model. This model is optimized for LOW Vram I will also show a way to convert it with MeshLab (free) and Blender (free) into a OBJ ####…

    Olivio Sarikas30 OctVideo · image
  5. 258New
    AI Video Just Stepped Into the Real World!

    Your AI characters can now stand in your actual room, and react to what's in it! Simulon is back: turn a character image into a 3D "neural asset," place it in the real world through your phone's camera, act out the scene with your own camera move, and the app regenerates the…

    Theoretically Media29 OctVideo