VDE accelerates rectified flow models like Flux by 3.22x with LPIPS of 0.069 via velocity decomposition into parallel/orthogonal components plus periodic full-pass anchoring.
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Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851
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CoReDiT reduces self-attention FLOPs in DiTs by up to 55% via linear-time spatial coherence pruning and neighbor-based reconstruction, delivering 1.33x-1.72x speedups with maintained quality.
HP-Edit introduces a post-training framework and RealPref-50K dataset that uses a VLM-based HP-Scorer to align diffusion image editing models with human preferences, improving outputs on Qwen-Image-Edit-2509.
ASTRA disentangles subject identity from pose structure in diffusion transformers via retrieval-augmented pose guidance, asymmetric EURoPE embeddings, and a DSM adapter to improve multi-subject generation.
AvatarPointillist autoregressively generates adaptive 3D point clouds via Transformer for photorealistic 4D Gaussian avatars from one image, jointly predicting animation bindings and using a conditioned Gaussian decoder.
MixGRPO speeds up GRPO for flow-based image generators by restricting SDE sampling and optimization to a sliding window while using ODE elsewhere, cutting training time by up to 71% with better alignment performance.
AttriStory adds a benchmark and AttriLoss-based latent optimization to improve faithful rendering of fine-grained attributes such as clothing color and texture in diffusion-model visual storytelling.
GOR-IS removes objects from 3D Gaussian Splatting reconstructions by performing inpainting in an intrinsic decomposition space that explicitly models light transport for consistent global lighting and non-Lambertian surfaces.
Patch Forcing enables diffusion models to denoise image patches at varying rates based on predicted difficulty, advancing easier regions first to improve context and achieve better generation quality on ImageNet while scaling to text-to-image tasks.
Seen-to-Scene unifies propagation-based and generation-based approaches for video outpainting via fine-tuned flow completion and reference-guided latent propagation to deliver superior temporal coherence and efficiency.
BVE framework enables text-guided 3D editing beyond voxel limits by combining self-constructed data, lightweight semantic injection, and annotation-free masking to preserve local invariance.
ISTS watermarking dynamically controls injection based on prompt semantics and uses two-sided detection to resist removal and forgery attacks in diffusion models.
Unlearning methods that strongly erase concepts from text-to-image diffusion models consistently degrade performance on attribute binding, spatial reasoning, and counting tasks.
HandDreamer is the first zero-shot text-to-3D method for hands that uses MANO initialization, skeleton-guided diffusion, and corrective shape guidance to produce view-consistent models.
HVG-3D uses a 3D-aware diffusion architecture with ControlNet to synthesize high-fidelity hand-object interaction videos from 3D control signals, achieving state-of-the-art spatial fidelity and temporal coherence on the TASTE-Rob dataset.
WorldPlay uses dual action representation, reconstituted context memory, and context forcing distillation to produce consistent 720p streaming video at 24 FPS for interactive world modeling.
Fine-tuning text-to-video models on sparse low-quality synthetic data for physical camera controls outperforms fine-tuning on photorealistic data.
Ouroboros uses two single-step diffusion models with cycle consistency for forward and inverse rendering, extending intrinsic decomposition to indoor/outdoor scenes with faster inference than multi-step methods.
RealDiffusion uses heat diffusion as a dissipative prior and a region-aware stochastic process inside a training-free physics-informed attention mechanism to improve multi-character coherence while preserving narrative dynamism in sequential image generation.
A scalable pipeline generates an intra-consistent, inter-diverse 1.4M style image dataset from text-to-image models and uses it to train a style encoder and generalizable style transfer model.
PureCC introduces a decoupled learning objective, dual-branch training pipeline with frozen extractor, and adaptive guidance scale λ* for high-fidelity concept customization while preserving original model behavior in text-to-image generation.
citing papers explorer
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VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation
VDE accelerates rectified flow models like Flux by 3.22x with LPIPS of 0.069 via velocity decomposition into parallel/orthogonal components plus periodic full-pass anchoring.
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CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers
CoReDiT reduces self-attention FLOPs in DiTs by up to 55% via linear-time spatial coherence pruning and neighbor-based reconstruction, delivering 1.33x-1.72x speedups with maintained quality.
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HP-Edit: A Human-Preference Post-Training Framework for Image Editing
HP-Edit introduces a post-training framework and RealPref-50K dataset that uses a VLM-based HP-Scorer to align diffusion image editing models with human preferences, improving outputs on Qwen-Image-Edit-2509.
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ASTRA: Enhancing Multi-Subject Generation with Retrieval-Augmented Pose Guidance and Disentangled Position Embedding
ASTRA disentangles subject identity from pose structure in diffusion transformers via retrieval-augmented pose guidance, asymmetric EURoPE embeddings, and a DSM adapter to improve multi-subject generation.
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AvatarPointillist: AutoRegressive 4D Gaussian Avatarization
AvatarPointillist autoregressively generates adaptive 3D point clouds via Transformer for photorealistic 4D Gaussian avatars from one image, jointly predicting animation bindings and using a conditioned Gaussian decoder.
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MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE
MixGRPO speeds up GRPO for flow-based image generators by restricting SDE sampling and optimization to a sliding window while using ODE elsewhere, cutting training time by up to 71% with better alignment performance.
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AttriStory: Fine-grained Attribute Realization for Visual Storytelling with Diffusion Models
AttriStory adds a benchmark and AttriLoss-based latent optimization to improve faithful rendering of fine-grained attributes such as clothing color and texture in diffusion-model visual storytelling.
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GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space
GOR-IS removes objects from 3D Gaussian Splatting reconstructions by performing inpainting in an intrinsic decomposition space that explicitly models light transport for consistent global lighting and non-Lambertian surfaces.
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Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
Patch Forcing enables diffusion models to denoise image patches at varying rates based on predicted difficulty, advancing easier regions first to improve context and achieve better generation quality on ImageNet while scaling to text-to-image tasks.
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Seen-to-Scene: Keep the Seen, Generate the Unseen for Video Outpainting
Seen-to-Scene unifies propagation-based and generation-based approaches for video outpainting via fine-tuned flow completion and reference-guided latent propagation to deliver superior temporal coherence and efficiency.
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Beyond Voxel 3D Editing: Learning from 3D Masks and Self-Constructed Data
BVE framework enables text-guided 3D editing beyond voxel limits by combining self-constructed data, lightweight semantic injection, and annotation-free masking to preserve local invariance.
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Towards Robust Content Watermarking Against Removal and Forgery Attacks
ISTS watermarking dynamically controls injection based on prompt semantics and uses two-sided detection to resist removal and forgery attacks in diffusion models.
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Erasure or Erosion? Evaluating Compositional Degradation in Unlearned Text-To-Image Diffusion Models
Unlearning methods that strongly erase concepts from text-to-image diffusion models consistently degrade performance on attribute binding, spatial reasoning, and counting tasks.
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HandDreamer: Zero-Shot Text to 3D Hand Model Generation using Corrective Hand Shape Guidance
HandDreamer is the first zero-shot text-to-3D method for hands that uses MANO initialization, skeleton-guided diffusion, and corrective shape guidance to produce view-consistent models.
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HVG-3D: Bridging Real and Simulation Domains for 3D-Conditional Hand-Object Interaction Video Synthesis
HVG-3D uses a 3D-aware diffusion architecture with ControlNet to synthesize high-fidelity hand-object interaction videos from 3D control signals, achieving state-of-the-art spatial fidelity and temporal coherence on the TASTE-Rob dataset.
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WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
WorldPlay uses dual action representation, reconstituted context memory, and context forcing distillation to produce consistent 720p streaming video at 24 FPS for interactive world modeling.
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Less is More: Data-Efficient Adaptation for Controllable Text-to-Video Generation
Fine-tuning text-to-video models on sparse low-quality synthetic data for physical camera controls outperforms fine-tuning on photorealistic data.
-
Ouroboros: Single-step Diffusion Models for Cycle-consistent Forward and Inverse Rendering
Ouroboros uses two single-step diffusion models with cycle consistency for forward and inverse rendering, extending intrinsic decomposition to indoor/outdoor scenes with faster inference than multi-step methods.
-
RealDiffusion: Physics-informed Attention for Multi-character Storybook Generation
RealDiffusion uses heat diffusion as a dissipative prior and a region-aware stochastic process inside a training-free physics-informed attention mechanism to improve multi-character coherence while preserving narrative dynamism in sequential image generation.
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MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping
A scalable pipeline generates an intra-consistent, inter-diverse 1.4M style image dataset from text-to-image models and uses it to train a style encoder and generalizable style transfer model.
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PureCC: Pure Learning for Text-to-Image Concept Customization
PureCC introduces a decoupled learning objective, dual-branch training pipeline with frozen extractor, and adaptive guidance scale λ* for high-fidelity concept customization while preserving original model behavior in text-to-image generation.