✨DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion

📝 Summary:
DyPE enhances diffusion transformers for ultra-high-resolution image generation by dynamically adjusting positional encodings. This training-free method allows pre-trained models to synthesize images far beyond their training resolution, achieving state-of-the-art fidelity without extra sampling ...

🔹 Publication Date: Published on Oct 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.20766
• PDF: https://arxiv.org/pdf/2510.20766
• Project Page: https://noamissachar.github.io/DyPE/
• Github: https://github.com/guyyariv/DyPE

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#DiffusionModels #ImageGeneration #HighResolution #DeepLearning #ComputerVision

✨Qwen-Image Technical Report

📝 Summary:
Qwen-Image is an image generation model that significantly advances complex text rendering through a comprehensive data pipeline and progressive training across languages. It also improves precise image editing via a dual-encoding mechanism and multi-task training for enhanced consistency and vis...

🔹 Publication Date: Published on Aug 4

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/qwen-image-technical-report
• PDF: https://arxiv.org/pdf/2508.02324
• Github: https://github.com/QwenLM/Qwen-Image

🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen-Image
• https://huggingface.co/Qwen/Qwen-Image-Edit
• https://huggingface.co/Qwen/Qwen-Image-Edit-2509

✨ Spaces citing this paper:
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-Angles
• https://huggingface.co/spaces/tori29umai/Qwen-Image-2509-MultipleAngles
• https://huggingface.co/spaces/linoyts/Qwen-Image-Edit-next-scene

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#ImageGeneration #AI #DeepLearning #ComputerVision #TextToImage

✨One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models

📝 Summary:
LUA performs efficient super-resolution directly in diffusion models' latent space. This lightweight module enables faster, high-quality image synthesis by upscaling before VAE decoding, cutting time versus pixel-space methods, and generalizing across VAEs.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10629
• PDF: https://arxiv.org/pdf/2511.10629

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#DiffusionModels #SuperResolution #LatentSpace #ImageGeneration #AIResearch

✨Benchmarking Diversity in Image Generation via Attribute-Conditional Human Evaluation

📝 Summary:
This paper introduces a framework to robustly evaluate diversity in text-to-image models. It uses a novel human evaluation template, curated prompts with variation factors, and systematic analysis of image embeddings to rank models and identify diversity weaknesses.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10547
• PDF: https://arxiv.org/pdf/2511.10547

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#ImageGeneration #TextToImage #AIDiversity #Benchmarking #HumanEvaluation

✨WEAVE: Unleashing and Benchmarking the In-context Interleaved Comprehension and Generation

📝 Summary:
WEAVE introduces a suite with a large dataset and benchmark to assess multi-turn context-dependent image generation and editing in multimodal models. It enables new capabilities like visual memory in models while exposing current limitations in these complex tasks.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11434
• PDF: https://arxiv.org/pdf/2511.11434
• Project Page: https://weichow23.github.io/weave/
• Github: https://github.com/weichow23/weave

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#MultimodalAI #ImageGeneration #GenerativeAI #ComputerVision #AIResearch