Support-conditioned flow matching under the Gaussian OT path is exactly Nadaraya-Watson kernel smoothing with time-decreasing bandwidth, implemented by a single Gaussian attention head.
super hub Canonical reference
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Canonical reference. 90% of citing Pith papers cite this work as background.
134
Pith papers citing it
Background
90% of classified citations
abstract
Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images using only text prompt as it often involves complex prompt engineering. An alternative to text prompt is image prompt, as the saying goes: "an image is worth a thousand words". Although existing methods of direct fine-tuning from pretrained models are effective, they require large computing resources and are not compatible with other base models, text prompt, and structural controls. In this paper, we present IP-Adapter, an effective and lightweight adapter to achieve image prompt capability for the pretrained text-to-image diffusion models. The key design of our IP-Adapter is decoupled cross-attention mechanism that separates cross-attention layers for text features and image features. Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model. As we freeze the pretrained diffusion model, the proposed IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. With the benefit of the decoupled cross-attention strategy, the image prompt can also work well with the text prompt to achieve multimodal image generation. The project page is available at \url{https://ip-adapter.github.io}.
hub tools
citation-role summary
background 26
dataset 3
method 1
citation-polarity summary
claims ledger
- abstract Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images using only text prompt as it often involves complex prompt engineering. An alternative to text prompt is image prompt, as the saying goes: "an image is worth a thousand words". Although existing methods of direct fine-tuning from pretrained models are effective, they require large computing resources and are not compatible with other base models, text prompt, and structural controls. I
authors
co-cited works
representative citing papers
SplatShot is a training-free method that inserts per-step 3DGS refitting and photometric feedback into diffusion denoising to enforce multi-view consistency for single-photo 3D face avatars.
Chameleon proposes the first large-scale cross-domain compositing dataset and a disentangled encoder plus gated diffusion transformer that outperforms prior in-domain and cross-domain methods on plausibility and fidelity.
DEMON is a streaming diffusion engine that exposes denoising parameters as playable controls at up to 12.3 decoder completions per second via per-slot scheduling, shared state, source blending, and accelerated decoding.
EM-Vid introduces an entity-centric latent patch memory bank with sparse token conditioning and budgeted updates for training-free consistent multi-shot video generation.
PIU suppresses target identity generation in Arc2Face by replacing it with a proximity-selected anchor identity through localized fine-tuning of cross-attention layers while preserving output quality for other identities.
Tiny-Engram uses small n-gram-indexed memory tables to bind trigger phrases to target visual identities in diffusion models while preserving compositional control from the surrounding prompt.
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
DirectTryOn achieves state-of-the-art one-step virtual try-on performance by applying pure conditional transport, garment preservation loss, and self-consistency loss to straighten trajectories in pretrained generative models.
INSET embeds images as native tokens in interleaved instructions, outperforming prior methods on multi-image consistency and text alignment as complexity grows.
MoCam unifies static and dynamic novel view synthesis by temporally decoupling geometric alignment and appearance refinement within the diffusion denoising process.
Deepfake detection must shift from classifying media realism to detecting communicative deception by applying Speech Act Theory, Grice's Cooperative Principle, and Cialdini's influence principles.
Delta-Adapter extracts a semantic delta from a single image pair via a pre-trained vision encoder and injects it through a Perceiver adapter to enable scalable single-pair supervised editing.
A training-free adaptive subspace projection method mitigates semantic collapsing in generative personalization by isolating and adjusting drift in a low-dimensional subspace using the stable pre-trained embedding as anchor.
Presents the first large-scale benchmark for multi-frame geometric distortion removal in videos under severe refractive warping, using real and synthetic data across four distortion levels and evaluating classical and learning-based methods including a proposed diffusion-based V-cache.
CA-IDD is the first diffusion model for face swapping that integrates multi-modal cross-attention guidance from identity embeddings, gaze, and facial parsing to achieve better identity consistency and an FID of 11.73 over GAN baselines.
MuSS is a new movie-sourced dataset and benchmark that enables AI models to generate multi-shot videos with improved narrative coherence and subject identity preservation.
StyleID supplies human-perception-aligned benchmarks and fine-tuned encoders that improve facial identity recognition robustness across stylization types and strengths.
AttentionBender applies 2D transforms to cross-attention maps in video diffusion transformers, producing distributed distortions and glitch aesthetics that reveal entangled attention mechanisms while serving as both an XAI probe and creative tool.
A dual-path consistency framework for text-driven 3D scene editing that models cross-view dependencies via structural correspondence and semantic continuity, trained on a newly constructed paired multi-view dataset.
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.
A video generation approach conditions a base model with multi-scale 3D latent features and a cross-attention adapter to produce geometrically realistic and consistent orbital videos from one image.
UDAPose improves low-light human pose estimation by synthesizing realistic images via DHF and LCIM modules and dynamically balancing image cues with pose priors using DCA, yielding AP gains of 10.1 and 7.4 over prior methods.
NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
citing papers explorer
-
Support-Conditioned Flow Matching Is Kernel Smoothing
Support-conditioned flow matching under the Gaussian OT path is exactly Nadaraya-Watson kernel smoothing with time-decreasing bandwidth, implemented by a single Gaussian attention head.
-
Splatshot: 3D Face Avatar Generation from a Single Unconstrained Photo
SplatShot is a training-free method that inserts per-step 3DGS refitting and photometric feedback into diffusion denoising to enforce multi-view consistency for single-photo 3D face avatars.
-
Chameleon: Style-Content Disentangled Framework for Cross-Domain Object Compositing
Chameleon proposes the first large-scale cross-domain compositing dataset and a disentangled encoder plus gated diffusion transformer that outperforms prior in-domain and cross-domain methods on plausibility and fidelity.
-
DEMON: Diffusion Engine for Musical Orchestrated Noise
DEMON is a streaming diffusion engine that exposes denoising parameters as playable controls at up to 12.3 decoder completions per second via per-slot scheduling, shared state, source blending, and accelerated decoding.
-
EM-Vid: Training-Free Entity-Centric Memory for Efficient and Consistent Multi-Shot Video Generation
EM-Vid introduces an entity-centric latent patch memory bank with sparse token conditioning and budgeted updates for training-free consistent multi-shot video generation.
-
PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models
PIU suppresses target identity generation in Arc2Face by replacing it with a proximity-selected anchor identity through localized fine-tuning of cross-attention layers while preserving output quality for other identities.
-
Tiny-Engram: Trigger-Indexed Concept Tables for Generative Vision
Tiny-Engram uses small n-gram-indexed memory tables to bind trigger phrases to target visual identities in diffusion models while preserving compositional control from the surrounding prompt.
-
Functionalization via Structure Completion and Motion Rectification
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
-
DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport
DirectTryOn achieves state-of-the-art one-step virtual try-on performance by applying pure conditional transport, garment preservation loss, and self-consistency loss to straighten trajectories in pretrained generative models.
-
Images in Sentences: Scaling Interleaved Instructions for Unified Visual Generation
INSET embeds images as native tokens in interleaved instructions, outperforming prior methods on multi-image consistency and text alignment as complexity grows.
-
MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics
MoCam unifies static and dynamic novel view synthesis by temporally decoupling geometric alignment and appearance refinement within the diffusion denoising process.
-
Detecting Deception, Not Deepfakes: Why Media Forensics Needs Social Theories
Deepfake detection must shift from classifying media realism to detecting communicative deception by applying Speech Act Theory, Grice's Cooperative Principle, and Cialdini's influence principles.
-
Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision
Delta-Adapter extracts a semantic delta from a single image pair via a pre-trained vision encoder and injects it through a Perceiver adapter to enable scalable single-pair supervised editing.
-
Adaptive Subspace Projection for Generative Personalization
A training-free adaptive subspace projection method mitigates semantic collapsing in generative personalization by isolating and adjusting drift in a low-dimensional subspace using the stable pre-trained embedding as anchor.
-
A unified Benchmark for Multi-Frame Image Restoration under Severe Refractive Warping
Presents the first large-scale benchmark for multi-frame geometric distortion removal in videos under severe refractive warping, using real and synthetic data across four distortion levels and evaluating classical and learning-based methods including a proposed diffusion-based V-cache.
-
CA-IDD: Cross-Attention Guided Identity-Conditional Diffusion for Identity-Consistent Face Swapping
CA-IDD is the first diffusion model for face swapping that integrates multi-modal cross-attention guidance from identity embeddings, gaze, and facial parsing to achieve better identity consistency and an FID of 11.73 over GAN baselines.
-
MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation
MuSS is a new movie-sourced dataset and benchmark that enables AI models to generate multi-shot videos with improved narrative coherence and subject identity preservation.
-
StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition
StyleID supplies human-perception-aligned benchmarks and fine-tuned encoders that improve facial identity recognition robustness across stylization types and strengths.
-
AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probe
AttentionBender applies 2D transforms to cross-attention maps in video diffusion transformers, producing distributed distortions and glitch aesthetics that reveal entangled attention mechanisms while serving as both an XAI probe and creative tool.
-
View-Consistent 3D Scene Editing via Dual-Path Structural Correspondense and Semantic Continuity
A dual-path consistency framework for text-driven 3D scene editing that models cross-view dependencies via structural correspondence and semantic continuity, trained on a newly constructed paired multi-view dataset.
-
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.
-
Towards Realistic and Consistent Orbital Video Generation via 3D Foundation Priors
A video generation approach conditions a base model with multi-scale 3D latent features and a cross-attention adapter to produce geometrically realistic and consistent orbital videos from one image.
-
UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation
UDAPose improves low-light human pose estimation by synthesizing realistic images via DHF and LCIM modules and dynamically balancing image cues with pose priors using DCA, yielding AP gains of 10.1 and 7.4 over prior methods.
-
NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity
NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
-
Large-Scale Universal Defect Generation: Foundation Models and Datasets
A 300K quadruplet dataset and UniDG foundation model enable reference- or text-driven defect generation across categories, outperforming few-shot baselines on anomaly detection tasks.
-
MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation
MoZoo generates high-fidelity animal videos with fur and muscle dynamics from coarse meshes by extending video diffusion with role-aware RoPE and asymmetric decoupled attention, trained on a new synthetic-to-real dataset.
-
Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors
Graph-PiT adds graph priors and a hierarchical GNN to part-based image synthesis to enforce relational constraints and improve structural coherence over vanilla PiT.
-
DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
DSH-Bench is a benchmark for subject-driven T2I generation that uses hierarchical taxonomy sampling, difficulty/scenario classification, and a new SICS metric showing 9.4% higher human correlation than prior measures.
-
Setting the Stage: Text-Driven Scene-Consistent Image Generation
A new data pipeline using real photos, entity removal, and image-to-video models plus a cross-view attention loss enables text-driven generation of actors in reference scenes with improved alignment.
-
Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
Omni-Attribute is a new open-vocabulary image attribute encoder trained on semantically linked pairs with dual objectives to produce disentangled representations for personalization and compositional generation.
-
RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing
RDSplat is the first 3D Gaussian Splatting watermarking method that maintains 0.701 bit accuracy against both 2D and 3D diffusion editing by embedding only in low-frequency primitives selected via FAPS.
-
One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer
One-to-All Animation enables alignment-free character animation and image pose transfer via self-supervised outpainting reformulation, reference extraction, hybrid fusion attention, identity-robust pose control, and token replacement for long videos.
-
ART-VITON: Measurement-Guided Latent Diffusion for Artifact-Free Virtual Try-On
ART-VITON uses residual prior initialization and artifact-free measurement-guided sampling with data consistency, frequency correction, and periodic denoising to generate artifact-free virtual try-on images on VITON-HD, DressCode, and SHHQ-1.0.
-
Durian: Dual Reference Image-Guided Portrait Animation with Attribute Transfer
Durian introduces a dual-reference diffusion model trained via self-reconstruction on video frames to enable cross-identity attribute transfer in portrait animations, supporting multi-attribute composition and interpolation.
-
ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing
ChangeBridge introduces a drift-asynchronous diffusion bridge with composed initialization, pixel-wise drift maps, and drift-aware denoising to produce spatially and temporally coherent post-event remote sensing images.
-
PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models
PacTure uses view packing and next-scale autoregressive prediction to generate consistent multi-view PBR textures faster than prior sequential or cross-attention methods.
-
UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
UniEdit-Flow presents tuning-free Uni-Inv and Uni-Edit methods for inversion and editing in flow models that achieve accurate reconstruction and robust region-preserving edits across generative models.
-
Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models
Gungnir shows that style-based triggers with RAN and STTR techniques can activate backdoors in diffusion models while evading detection and surviving fine-tuning.
-
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
ELLA introduces a timestep-aware semantic connector to link LLMs with diffusion models for improved dense prompt following, validated on a new 1K-prompt benchmark.
-
Controllable Texture Tiling with Transformed RoPE-Enhanced Diffusion Models
A Diffusion Transformer framework applies coordinate-transformed RoPE and disjoint attention masks to achieve controllable, high-fidelity texture tiling that preserves reference structure and scene lighting.
-
IP-Adapter Is All You Need: Towards Fine-Tuning-Free Diffusion-Based Talking Face Generation
A fine-tuning-free framework combines pretrained Stable Diffusion with IP-Adapter plus three parameter-free modules to achieve improved lip synchronization and visual quality in talking face generation.
-
Scheduled Style Injection: Expanding the Style-Content Pareto Frontier in Training-Free Diffusion-based Style Transfer
Scheduled decreasing style injection across decoder layers and denoising timesteps, combined with ControlNet scheduling, expands the style-content tradeoff frontier and achieves 6.1% better ArtFID than StyleID across 28k images.
-
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.
-
VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers
VISTA introduces a new synthetic triplet dataset and diffusion-transformer framework with style adapter that jointly models style, content, and motion to achieve state-of-the-art video style transfer.
-
DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers
DiRotQ uses PCA-based rotation-aware activation quantization combined with GPTQ to achieve better FID and PSNR in 4-bit diffusion transformers than prior methods like SVDQuant.
-
AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression
AdaEraser introduces token-wise adaptive attention suppression in diffusion denoising to enable high-quality training-free object removal by modulating suppression according to evolving self-attention maps.
-
MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer
MaTe proposes a training-free diffusion transformer that performs material transfer using only images by integrating them at the token level for unified multi-modal attention in a shared latent space.
-
CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesis
CRAFT adapts diffusion models to medical images via clinical reward alignment from LLMs and VLMs, improving alignment scores and cutting low-quality generations by 20.4% on average across modalities.
-
UniCustom: Unified Visual Conditioning for Multi-Reference Image Generation
A unified visual conditioning approach fuses semantic and appearance features before VLM processing, with two-stage training and slot-wise regularization, to improve consistency in multi-reference image generation.
-
L2P: Unlocking Latent Potential for Pixel Generation
L2P repurposes pre-trained LDMs for direct pixel generation via large-patch tokenization and shallow-layer training on synthetic data, matching source performance with 8-GPU training and enabling native 4K output.