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#Image generation

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  1. InternLM (Shanghai AI Lab) · new models on Hugging FaceAI score60

    Shanghai AI Lab releases Intern Lumina U2 unified multimodal model on Hugging Face

    AIShanghai AI Lab's InternLM has published Intern Lumina U2, a 16B-parameter MoE model with 1B active parameters that handles text QA, image generation and editing, and image, video, and 3D understanding. The model uses an 8-codebook fully-discrete visual representation built on AToken. Checkpoints are provided for Huawei Ascend NPUs and NVIDIA GPUs under Apache 2.0, with the technical report still listed as coming soon.

    Why it matters: The model unifies text, image, video, and 3D understanding with image generation in one framework, a broader scope than single-modality releases.

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  1. Google · new models on Hugging FaceAI score22

    Google releases TIPS B/14 v1 vision-language model on Hugging Face

    AIGoogle has published TIPS B/14 (v1) on Hugging Face, a contrastive vision-language model that produces spatially rich image features aligned with text embeddings. The model has 86M vision parameters and 110M text parameters at native 448 resolution, and is licensed under Apache 2.0. The release includes usage code for image and text encoding, zero-shot classification, and spatial feature visualization.

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  1. Stability AIAI score38

    Stability AI launches Stable Audio 3.0 plugin and enhanced web experience

    AIStability AI has released two beta tools for Stable Audio 3.0: a plugin that brings audio generation into digital audio workstations (DAWs) and an upgraded experience at StableAudio.com with more editing options. Both are powered by commercially-safe models, so users own their outputs and can distribute them freely. Some features are experimental, and the company says it will keep iterating in real time.

  2. Stability AIAI score40

    Stability AI launches Stable Audio plugin and enhanced web app for Stable Audio 3.0

    AIStability AI released a Stable Audio plugin that runs Stable Audio 3.0 generation inside DAWs as an instrument, available as a macOS AU and VST3 with Apple Silicon and Intel support. The enhanced StableAudio.com web app adds iterative prompting, audio-to-audio variations, per-track mixing controls, and export, and both tools are in beta and powered by commercially-safe models that users can distribute freely.

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  1. ByteDance · new models on Hugging FaceAI score52

    ByteDance releases Bernini-Diffusers-v2 video generation and editing model

    AIByteDance has released Bernini-Diffusers-v2 on Hugging Face, a video generation and editing pipeline combining a Qwen2.5-VL planner with Wan2.2 diffusion components. The model card recommends it over Bernini-R for complex requests needing stronger instruction following and multi-step semantic planning. Code and weights are available under Apache License 2.0.

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  1. MiniMax BlogAI score72

    MiniMax H3 unifies text, image, video, and audio generation in one model

    AIMiniMax launches H3, a general-purpose multimodal generation model that understands text, images, video, and audio as unified context. It generates video up to 15 seconds at 2K resolution with native stereo sound, and the company says model weights will be opened in the coming days, subject to applicable laws and regulations. MiniMax also says H3 is priced below mainstream models at 2K and 768p.

    Why it matters: The post explains how a unified multimodal design and training choices enable 2K video with native stereo sound, useful for comparing against closed video generators.

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  1. Meta AI BlogAI score75

    Meta launches Muse Image, an agentic image model with search and code tools

    AIMeta Superintelligence Labs has released Muse Image, which can invoke search and coding tools and self-refine its generations before output. It is available today in the Meta AI app, meta.ai, Instagram Stories in the US, and WhatsApp in limited countries, with Facebook coming soon. Meta also previewed Muse Video, which is coming soon to creators and Meta AI and is reported as ranking No. 3 on Arena for text-to-video at the time of writing.

    Why it matters: The source describes how search, code execution, and self-refinement change image generation, which matters to anyone comparing agentic media models with plain prompt-to-image systems.

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