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Karen Akers

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repliedto Sabith64's post about 20 hours ago
We’re releasing Lunara Art Eval, an art evaluation dataset for studying aesthetic quality, emotional resonance and content integrity in image generation. It contains 8,000 generated images across 1,000 shared prompts, covering diverse artistic styles. The eight models are Qwen-Image, AuraFlow, GPT-Image-1 Mini, Stable Diffusion 3.5 Large Turbo, HiDream-I1 Fast, FLUX.2-Klein-4B, Z-Image-Turbo and Moonworks Lunara. Each image includes scores on the three dimensions, allowing comparisons across models using the same prompts and evaluator. Dataset: https://huggingface.co/datasets/moonworks/lunara-art-eval Paper: https://arxiv.org/abs/2609.22272 The paper also introduces Lunara’s Diffusion Mixture Transformer architecture, with fewer than 10B active parameters, and CAT, an active-learning-inspired training algorithm that iteratively updates the training distribution. We’re a new lab focused on model architectures and training algorithms, working closely with artists and creators. Our earlier public datasets are: - [Lunara Aesthetic I](https://huggingface.co/datasets/moonworks/lunara-aesthetic): art images with human-refined prompts and region, style and topic labels. - [Lunara Aesthetic II](https://huggingface.co/datasets/moonworks/lunara-aesthetic-image-variations): original images paired with contextual variations. We’d appreciate feedback from people working on image generation and evaluation, particularly on where automated scores with your judgment.
repliedto fffiloni's post about 20 hours ago
📖 A gamebook you can play in your own words, powered by local Decision Models. Traditional gamebooks offer explicit branches: “To enter the forest, turn to page 42.” In Choose your own adventure, you can describe an action, question a character, or ask what you already know before deciding. Clickable choices remain available. The story’s branches and consequences are authored. Natural language adds a flexible way to interact with them, with conversations between actions. I built this project to explore a concrete role for Decision Models in an interactive application: • Kev-4B makes structured decisions: matching intentions to available actions and distinguishing acting from discussing. • Ink manages story progression, discoveries, inventory and endings. • Qwen3-8B selects dialogue responses from authored content available in the current state. Both models run locally inside the Space through llama.cpp, using quantized GGUFs. You can inspect the model calls, scores and routing decisions directly in the interface. The current release includes three adventures in French and English, with a pack format for adding new stories without changing the engine. It’s still experimental, and interpretations can go wrong. I’d love feedback on how it feels to play—and on this use of Decision Models to connect natural language with explicit game logic. Try and play here: https://huggingface.co/spaces/fffiloni/choose-your-own-adventure-dm 🤗
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