✨OmniRefiner: Reinforcement-Guided Local Diffusion Refinement

📝 Summary:
OmniRefiner enhances reference-guided image generation by overcoming fine detail loss. It uses a two-stage framework: a fine-tuned diffusion editor for global coherence, then reinforcement learning for localized detail accuracy. This significantly improves detail preservation and consistency.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19990
• PDF: https://arxiv.org/pdf/2511.19990
• Github: https://github.com/yaoliliu/OmniRefiner

==================================

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#DiffusionModels #ImageGeneration #ReinforcementLearning #GenerativeAI #ComputerVision

✨The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment

📝 Summary:
ImageCritic corrects inconsistent fine-grained details in generated images using a reference-guided post-editing approach. It employs attention alignment loss and a detail encoder to precisely rectify inconsistencies and improve accuracy.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20614
• PDF: https://arxiv.org/pdf/2511.20614
• Project Page: https://ouyangziheng.github.io/ImageCritic-Page/
• Github: https://github.com/HVision-NKU/ImageCritic

🔹 Models citing this paper:
• https://huggingface.co/ziheng1234/ImageCritic

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ziheng1234/Critic-10K

✨ Spaces citing this paper:
• https://huggingface.co/spaces/ziheng1234/ImageCritic

==================================

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#ImageGeneration #ComputerVision #DeepLearning #AI #ImageEditing

✨Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning

📝 Summary:
Flash-DMD accelerates generative diffusion models via efficient timestep-aware distillation and joint reinforcement learning. This framework achieves faster convergence, high-fidelity few-step generation, and stabilizes RL training using distillation as a regularizer, all with reduced computation...

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20549
• PDF: https://arxiv.org/pdf/2511.20549

==================================

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#DiffusionModels #ImageGeneration #ReinforcementLearning #ModelDistillation #GenerativeAI

✨CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation

📝 Summary:
CookAnything is a diffusion framework generating coherent, multi-step recipe image sequences from instructions. It uses step-wise regional control, flexible positional encoding, and cross-step consistency for consistent, high-quality visual synthesis.

🔹 Publication Date: Published on Dec 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03540
• PDF: https://arxiv.org/pdf/2512.03540
• Github: https://github.com/zhangdaxia22/CookAnything

==================================

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#CookAnything #ImageGeneration #DiffusionModels #AI #RecipeGeneration

✨Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation

📝 Summary:
Echo-4o-Image is a 180K synthetic dataset from GPT-4o. It enhances image generation by covering rare scenarios and providing clean text to image supervision. This improves model performance and transferability across various foundation models.

🔹 Publication Date: Published on Aug 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.09987
• PDF: https://arxiv.org/pdf/2508.09987
• Project Page: https://yejy53.github.io/Echo-4o/
• Github: https://yejy53.github.io/Echo-4o

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Yejy53/Echo-4o-Image

==================================

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#ImageGeneration #GPT4o #SyntheticData #AIResearch #FoundationModels

✨Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion

📝 Summary:
Semantic-First Diffusion SFD asynchronously denoises semantic and texture latents for image generation. This method prioritizes semantic formation, providing clearer guidance for texture refinement. SFD significantly improves convergence speed by up to 100x and enhances image quality.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04926
• PDF: https://arxiv.org/pdf/2512.04926

==================================

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#DiffusionModels #ImageGeneration #SemanticAI #GenerativeAI #DeepLearning

✨UltraImage: Rethinking Resolution Extrapolation in Image Diffusion Transformers

📝 Summary:
UltraImage tackles content repetition and quality degradation in high-resolution image generation by correcting dominant frequency periodicity and applying entropy-guided attention. It achieves extreme extrapolation, producing high-fidelity images up to 6Kx6K without low-resolution guidance.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04504
• PDF: https://arxiv.org/pdf/2512.04504
• Project Page: https://thu-ml.github.io/ultraimage.github.io/
• Github: https://thu-ml.github.io/ultraimage.github.io/

==================================

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#ImageGeneration #DiffusionModels #Transformers #HighResolution #DeepLearning

✨PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling

📝 Summary:
PaCo-RL is a reinforcement learning framework for consistent image generation. It introduces PaCo-Reward for human-aligned consistency evaluation and PaCo-GRPO for efficient RL optimization. The framework achieves state-of-the-art consistency with improved training efficiency.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04784
• PDF: https://arxiv.org/pdf/2512.04784
• Project Page: https://x-gengroup.github.io/HomePage_PaCo-RL/
• Github: https://x-gengroup.github.io/HomePage_PaCo-RL

🔹 Models citing this paper:
• https://huggingface.co/X-GenGroup/PaCo-Reward-7B
• https://huggingface.co/X-GenGroup/PaCo-Reward-7B-Lora
• https://huggingface.co/X-GenGroup/PaCo-FLUX.1-dev-Lora

==================================

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#ReinforcementLearning #ImageGeneration #AI #DeepLearning #GenerativeAI

✨Vibe Spaces for Creatively Connecting and Expressing Visual Concepts

📝 Summary:
Vibe Blending uses Vibe Space, a hierarchical graph manifold, to create coherent and creative image hybrids. It learns geodesics in feature spaces, outperforming current methods in creativity and coherence as rated by humans.

🔹 Publication Date: Published on Dec 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.14884
• PDF: https://arxiv.org/pdf/2512.14884
• Project Page: https://huzeyann.github.io/VibeSpace-webpage/
• Github: https://github.com/huzeyann/VibeSpace

==================================

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#ImageGeneration #ComputerVision #AI #MachineLearning #CreativeAI

✨Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing

📝 Summary:
This paper proposes a framework using a semantic-pixel reconstruction objective to adapt encoder features for generation. It creates a compact, semantically rich latent space, leading to state-of-the-art image reconstruction and improved text-to-image generation and editing.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17909
• PDF: https://arxiv.org/pdf/2512.17909
• Project Page: https://jshilong.github.io/PS-VAE-PAGE/
• Github: https://jshilong.github.io/PS-VAE-PAGE/

==================================

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#TextToImage #ImageGeneration #DeepLearning #ComputerVision #AIResearch

✨Unified Thinker: A General Reasoning Modular Core for Image Generation

📝 Summary:
Unified Thinker introduces a modular reasoning core for image generation, decoupling a Thinker from the generator. It uses reinforcement learning to optimize visual correctness, substantially improving image reasoning and generation quality.

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03127
• PDF: https://arxiv.org/pdf/2601.03127

==================================

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#ImageGeneration #AIResearch #ReinforcementLearning #DeepLearning #GenerativeAI