REVIEW 24 cited by
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
read the original abstract
With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb.
Forward citations
Cited by 24 Pith papers
-
Navigating the Open-Source Model Ecosystem: An Empirical Study of Creator Practices in Artistic Image Generation
A novel 6M-image Pixiv dataset shows open-source image generation has long-tail model usage, slow life cycles with version inertia, and surging multi-LoRA customization linked to higher engagement.
-
Semantic Browsing: Controllable Diversity for Image Generation
A technique for controllable diversity in text-to-image generation by inducing structured semantic variations at the prompt level via VLM and agentic workflow.
-
Gender Artifacts from Art History to Text-to-Image Generation
Introduces the StyleGender dataset and PixelSGA/MaskSGA metrics showing that text-to-image models amplify gender artifacts present in artistic styles beyond historical baselines.
-
OctoT2I: A Self-Evolving Agentic Text-to-Image Router
OctoT2I uses a no-supervision PSEL loop to discover model capability frontiers and route T2I tasks, reaching 0.96 GenEval score with 90.3% speedup over Flow-GRPO.
-
LoRA-Key: User-Centric LoRA Watermarking for Text-to-Image Diffusion Models
LoRA-Key creates a standalone user-specific Watermark LoRA trained with a latent watermark prior and GOP, attachable via training-free superposition to protect LoRA ownership while preserving quality.
-
Beyond Prompts: Unconditional 3D Inversion for Out-of-Distribution Shapes
Text-to-3D models lose prompt sensitivity for out-of-distribution shapes due to sink traps but retain geometric diversity via unconditional priors, enabling a decoupled inversion method for robust editing.
-
SEED: A Large-Scale Benchmark for Provenance Tracing in Sequential Deepfake Facial Edits
SEED is a new benchmark for sequential provenance tracing in diffusion-edited deepfake faces, with the FAITH baseline showing that wavelet-based high-frequency signals aid detection of accumulated editing artifacts.
-
VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion
FVD applies Fleming-Viot population dynamics to diffusion model sampling at inference time to reduce diversity collapse while improving reward alignment and FID scores.
-
Toward Generalizable Forgery Detection and Reasoning
FakeReasoning is an MLLM-based framework for unified forgery detection and reasoning on AI-generated images, supported by the new MMFR-Dataset of 120K images and 378K annotations across 10 generators.
-
KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers
Using learned 32×32 Kronecker block transforms as an online activation smoother improves W4A4 image quality of PixArt-Sigma, SANA, and FLUX.1-schnell over SVDQuant and LoRaQ, with a kernel up to 14% faster than SmoothQuant.
-
Beyond Prompts: Unconditional 3D Inversion for Out-of-Distribution Shapes
Empty-prompt (unconditional) inversion into a native text-to-3D model avoids "sink traps" and reconstructs and edits out-of-distribution 3D shapes more faithfully than text-guided inversion.
-
VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion
VASR separates continuation and residual variance in reward-guided diffusion SMC, using optimal mass allocation and systematic resampling to achieve up to 26% better FID scores and faster runtimes than prior SMC and M...
-
Direct Diffusion Score Preference Optimization via Stepwise Contrastive Policy-Pair Supervision
Diffusion image models can be aligned without human labels by supervising every denoising step with score targets from original versus degraded prompts.
-
Elastic ViTs from Pretrained Models without Retraining
A single-shot, label-free, retraining-free structured pruning method generates elastic ViTs at any sparsity by reweighting gradient-based importance scores with block correlations learned by an evolutionary strategy.
-
TetriServe: Efficiently Serving Mixed DiT Workloads
TetriServe's step-level, deadline-aware sequence parallelism improves SLO attainment for mixed-resolution diffusion transformer serving by up to 32% over fixed-SP systems.
-
Maestro: Self-Improving Text-to-Image Generation via Agent Orchestration
A multi-agent prompt-refinement system using pairwise AI judging and targeted edit signals outperforms prior automated methods on complex text-to-image tasks.
-
Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference
Direct-Align and SRPO fine-tune FLUX using ground-truth-noise recovery and text-conditional relative rewards, improving human-evaluated realism and aesthetics roughly 3x.
-
Understanding and evaluating computer vision models through the lens of counterfactuals
Counterfactual-based methods for concept attribution in classifiers and for dynamic bias evaluation and mitigation in text-to-image models.
-
Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM
Slot-MLLM introduces a slot-attention-based object-centric visual tokenizer with Q-Former encoder, diffusion decoder, and residual vector quantization for improved local visual comprehension and generation in multimodal LLMs.
-
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
HPD v2 is the largest human preference dataset for text-to-image images with 798k choices, and HPS v2 is the resulting CLIP-based scorer that better predicts human judgments and responds to model improvements.
-
Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
A compact unified model that reuses a frozen VLM encoder and hybrid continuous/discrete tokens reaches competitive image understanding and generation with 15.6M training images and about $2,000 in compute.
-
ACPO: Anchor-Constrained Perceptual Optimization for Diffusion Models with No-Reference Quality Guidance
ACPO uses anchor-based regularization with NR-IQA guidance to enable stable perceptual quality improvements in diffusion model fine-tuning.
-
Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration
A DAAM-based visual analytics workflow links step-resolved token attention trajectories, phase summaries, and spatial competition maps for Stable Diffusion-class models on a 60-prompt benchmark.
-
Deepfakes: we need to re-think the concept of "real" images
This position paper contends that the concept of 'real' images must be rethought because most modern photographs are computationally generated, undermining current deepfake detection methods.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.