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No camera. No Photoshop. Just a text prompt. egaku-ai.com โ€” 40 AI tools, free to start. #AIart #AIgenerated #TextToImage Model: Flux Pro
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Got an idea? Turn it into a stunning AI image in seconds โœจ Create visuals for marketing, social posts, design projects, and storyiesust by typing what you imagined! Click here to start ๐Ÿ”— bit.ly/4uN8En6 #ImageGenerator #TextToImage #ContentCreation #Wondershare #Mediaio
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Krea 2 API is now live on Pixazo. ๐Ÿš€ ๐ŸŽจ Advanced text-to-image generation with next-level creative control. ๐Ÿ–ผ๏ธ Use reference images for consistency โœจ Apply LoRA style presets instantly ๐ŸŽฏ Guide outputs with moodboard conditioning Create better visuals with fewer iterations. API: pixazo.ai/models/krea #PixazoAPI #Krea #AIImages #TextToImage #GenerativeAI #Developers
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We've teamed up with @vm0_ai ๐ŸคAtlas Cloud is now a built-in connector in VM0. Generate video, images and text from 300 models just by asking Zero in plain English. One API key. No JSON wrangling. No babysitting render queues. How it works ๐Ÿงต๐Ÿ‘‡ #AtlasCloud #vm0 #API #AIVideo #AIimage #TextToVideo #TextToImage #ImageToVideo #VideoGeneration #AIart
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#NLProc Introducing ImageEval 2026 - a new shared task on Cultural Grounding in Arabic Multimodal AI, organized with #ArabicNLP2026 and co-located with #EMNLP2026. The task evaluates how well multimodal AI systems understand and generate culturally grounded Arabic visual content from the MENA region. Tracks: ๐Ÿ”น Arabic Visual QA & Hallucination Detection ๐Ÿ”น Cultural Accuracy Evaluation for Text-to-Image Generation Registration: shorturl.at/utvGK ๐Ÿ“… Registration deadline: July 20, 2026 ๐Ÿ“… System papers due: August 15, 2026 ๐ŸŒ imageeval2026.github.io/ ๐Ÿ“‚ github.com/ImageEval2026/Imaโ€ฆ Open to researchers in Arabic NLP, multimodal AI, computer vision, generative AI, and cultural computing. Please feel free to share! W/ @sabdaljalil_ @AhlamBashiti Farina Amir @shammur_absar @NadirDurrani5 @dalvifahim @baselmousi995 Hunzalah Hassan Bhattihtt @Zein_5 Erchin Serpedin Hasan Kurban Mustafa Jarrar #ImageEval2026 #ArabicNLP #MultimodalAI #VQA #TextToImage @emnlpmeeting @_ArabicNLP
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๐ŸŽจ TEXT-TO-IMAGE MODELS Which image model are you using most today? โšซ GPT Image 2 ๐ŸŸก Ideogram 4.0 ๐ŸŸข Flux 2 Max ๐Ÿ”ต Recraft V4 Each excels in different areas: ๐Ÿ“ธ Photorealism ๐Ÿ“ Typography ๐ŸŽจ Design โš™๏ธ Control What's working best for your projects? ๐Ÿ‘‡ #AI #TextToImage #GenerativeAI #AIArt #Design
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AI Art Challenge - Crochet ๐Ÿงถ Art ๐ŸŽจ Feel free to join this Artwork Challenge #AI #AIArt #Gemini #NewArt #AIChallenge #AIPhoto #TextToImage #AIArt #AIImageGeneration #CreativeAI #AIArtworks #TextToArt #AIIllustration #AICreativity #AIArtists
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AI Art Challenge - New Theme: Dream Home ๐Ÿก Made by @FlowbyGoogle Welcome to all Join ๐Ÿ”ฅ๐Ÿ˜Ž #AI #AIArt #House #DreamHome #Home #TextToImage #AIArt #AIImageGeneration #CreativeAI #AIArtworks #TextToArt #AIIllustration #AICreativity #AIArtists
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AI Art Challenge - Hat ๐ŸŽฉ๐Ÿ‘’๐Ÿงข Made by Nano Banana ๐ŸŒ #TextToImage #AIArt #AIImageGeneration #CreativeAI #AIArtworks #TextToArt #AIIllustration #AICreativity #AIArtists
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Fooocus is an AI image generator for creators and designers, offering advanced inpainting, multiโ€‘prompt support, style controls and InsightFaceโ€‘based face swapping to turn prompts into highโ€‘quality visuals instantly. fooocus.one/ #TexttoImage #PhotoImageEditor

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๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜†๐—ผ๐—ป๐—ฒ ๐˜„๐—ฎ๐—ป๐˜๐˜€ ๐Ÿฐ๐—ž ๐—”๐—œ. But true native-4K data is still surprisingly scarce. ๐Ÿš€ Excited to share our new work: ๐Ÿฐ๐—ž๐—Ÿ๐—ฆ๐——๐—•: ๐—” ๐—Ÿ๐—ฎ๐—ฟ๐—ด๐—ฒ-๐—ฆ๐—ฐ๐—ฎ๐—น๐—ฒ ๐——๐—ฎ๐˜๐—ฎ๐˜€๐—ฒ๐˜ ๐—ณ๐—ผ๐—ฟ ๐Ÿฐ๐—ž ๐—œ๐—บ๐—ฎ๐—ด๐—ฒ ๐—ฅ๐—ฒ๐˜€๐˜๐—ผ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฎ๐—ป๐—ฑ ๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป, accepted to CVPR 2026 DataCV. Most public datasets are built around sub-1K, HD, or 2K images. But at 4K resolution, small artifacts become big problems: blurry textures, distorted boundaries, repeated patterns, and missing fine details. To address this gap, we introduce ๐Ÿฐ๐—ž๐—Ÿ๐—ฆ๐——๐—•, a large-scale native-4K dataset and benchmark for high-resolution restoration and generation. ๐Ÿ“Œ ๐Ÿฐ๐—ž๐—Ÿ๐—ฆ๐——๐—• ๐—ถ๐—ป๐—ฐ๐—น๐˜‚๐—ฑ๐—ฒ๐˜€: โœ… 129K native-4K training images โœ… 2K validation images and 1,984 test images โœ… Diverse categories: nature, urban scenes, people, food, artwork, CGI, and more โœ… Aligned 4K imageโ€“text pairs for generative modeling โœ… Paired LR/HR evaluation sets for super-resolution We also build a multi-stage curation pipeline combining resolution filtering, LMM-based quality scoring, texture-richness filtering, and human verification. Across classical SR, real-world blind SR, and 4K text-to-image generation, fine-tuning on 4KLSDB consistently improves fidelity, local detail, perceptual quality, and human preference. ๐Ÿ’ก Main takeaway: ๐—ป๐—ฎ๐˜๐—ถ๐˜ƒ๐—ฒ-๐Ÿฐ๐—ž ๐˜€๐˜‚๐—ฝ๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—บ๐—ฎ๐˜๐˜๐—ฒ๐—ฟ๐˜€. As visual AI moves toward ultra-high-resolution restoration and generation, we need datasets and benchmarks that expose the fine-scale failures hidden by low-resolution evaluation. ๐Ÿ“„ ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ ๐—ฝ๐—ฎ๐—ด๐—ฒ: 4klsdb.github.io ๐Ÿ’ป ๐—š๐—ถ๐˜๐—›๐˜‚๐—ฏ: github.com/taco-group/4KLSDB ๐Ÿ’ฝ ๐——๐—ฎ๐˜๐—ฎ๐˜€๐—ฒ๐˜: huggingface.co/datasets/Singโ€ฆ #ComputerVision #GenerativeAI #ImageRestoration #SuperResolution #TextToImage #DiffusionModels #Dataset #Benchmarking #TAMU
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๐Ÿ—ฝ๐Ÿ—ผ New York & Paris โ€” reimagined as glowing 2K city scroll art. Generated entirely from a text prompt with AVCLabs AI Image Generator. No design skills. Batch generation. No software.๐ŸŽจ ๐Ÿ‘‰ Try it free: avclabs.com/ai-tools/ai-imagโ€ฆ #AVCLabs #AIImageGenerator #AIArt #TextToImage
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๐ŸŽจ TEXT-TO-IMAGE MODELS Which image model are you using most today? โšซ GPT Image 2 ๐ŸŸก Ideogram 4.0 ๐ŸŸข Flux 2 Max ๐Ÿ”ต Recraft V4 Each excels in different areas: ๐Ÿ“ธ Photorealism ๐Ÿ“ Typography ๐ŸŽจ Design โš™๏ธ Control What's working best for your projects? ๐Ÿ‘‡ #AI #TextToImage #GenerativeAI #AIArt #Design
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Weโ€™re excited to share our new work on CVPR 2026, Understanding Reward Hacking in Text-to-Image Reinforcement Learning. Reinforcement learning is becoming an increasingly important tool for post-training text-to-image generation models. But as we optimize these models with learned rewards, an important question arises: Are reward models truly improving generation quality, or are they creating new ways for models to game the objective? In this work, we take a closer look at reward hacking in T2I RL post-training. We study a range of reward designs, including aesthetic and preference rewards, prompt-image consistency rewards, and multi-reward ensembles. Our analysis shows that models can easily over-optimize a single reward across reward setups. Human preference reward may push generations toward exaggerated colors or superficial appeal, while a prompt-image consistency reward may improve alignment at the cost of realism and structure. Even combining multiple rewards only partially mitigates the issue. To mitigate this, we introduce ArtifactReward, a lightweight artifact-aware reward trained from a small curated dataset of artifact-free and artifact-containing samples. ArtifactReward can be integrated into existing T2I RL pipelines as a simple safeguard, improving realism and reducing reward hacking across multiple reward configurations. Paper: arxiv.org/pdf/2601.03468 Code: github.com/yq-hong/ArtifactRโ€ฆ Poster Session: June 6, 7:30am ExHall A Many thanks to our amazing team: Yunqi Hong @yyqq_hong , Kuei-Chun Kao @KueiChunKao, Hengguang Zhou @hgzhou42 , and Cho-Jui Hsieh @cho_jui_hsieh . #CVPR2026 #TextToImage #ReinforcementLearning #RewardHacking #GenerativeAI #UCLA #TurningPointAI
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