Diffusion Models Beat GANs on Image Synthesis |
NeurIPS |
2021 |
Github |
Diffusion models on image generation |
Diffusion Models Beat GANs on Image Synthesis |
NeurIPS |
2021 |
Github |
Diffusion models on image generation |
Denoising Diffusion Probabilistic Models |
NeurIPS |
2020 |
Github |
Diffusion models on image generation |
SuS-X: Training-Free Name-Only Transfer of Vision-Language Models |
ICCV |
2023 |
Github |
Diffusion models on image generation |
Investigating Prompt Engineering in Diffusion Models |
NeurIPS Workshop |
2022 |
--- |
Semantic prompt design |
DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models |
arXiv |
2023 |
Github |
Diversify generation with prompt; Prompts for synthetic data generation |
Is synthetic data from generative models ready for image recognition? |
ICLR |
2023 |
Github |
Diversify generation with prompt |
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion |
ICLR |
2023 |
Github |
Complex control of synthesis results via prompts |
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation |
CVPR |
2023 |
Github |
Complex control of synthesis results via prompts |
Multi-Concept Customization of Text-to-Image Diffusion |
CVPR |
2023 |
Github |
Complex control of synthesis results via prompts |
Prompt-to-Prompt Image Editing with Cross Attention Control |
arXiv |
2022 |
--- |
Complex control of synthesis results via prompts |
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis |
ICLR |
2023 |
Github |
Controllable text-to-image generation |
Diffusion Self-Guidance for Controllable Image Generation |
arXiv |
2023 |
Page |
Controllable text-to-image generation |
Imagic: Text-Based Real Image Editing with Diffusion Models |
CVPR |
2023 |
Github |
Controllable text-to-image generation |
Adding Conditional Control to Text-to-Image Diffusion Models |
arXiv |
2023 |
Github |
Controllable text-to-image generation |
Prompt-to-Prompt Image Editing with Cross Attention Control |
arXiv |
2022 |
Github |
Complex control of synthesis results via prompts |
ImaginaryNet: Learning Object Detectors without Real Images and Annotations |
ICLR |
2023 |
Github |
Prompts for synthetic data generation |
Is synthetic data from generative models ready for image recognition? |
ICLR |
2023 |
Github |
Prompts for synthetic data generation |
Make-A-Video: Text-to-Video Generation without Text-Video Data |
ICLR |
2023 |
Page |
Prompts for text-to-video generation |
Imagen Video: High Definition Video Generation with Diffusion Models |
arXiv |
2022 |
Page |
Prompts for text-to-video generation |
FateZero: Fusing Attentions for Zero-shot Text-based Video Editing |
arXiv |
2023 |
Github |
Prompts for text-to-video generation |
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation |
ICCV |
2023 |
Github |
Prompts for text-to-video generation |
DiffRF: Rendering-Guided 3D Radiance Field Diffusion |
CVPR |
2023 |
Page |
Prompts for text-to-3D generation |
DreamFusion: Text-to-3D using 2D Diffusion |
arXiv |
2022 |
Page |
Prompts for text-to-3D generation |
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models |
CVPR |
2023 |
Page |
Prompts for text-to-3D generation |
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model |
arXiv |
2022 |
Page |
Prompts for text-to-motion generation |
FLAME: Free-form Language-based Motion Synthesis & Editing |
AAAI |
2023 |
Github |
Prompts for text-to-motion generation |
MDM: Human Motion Diffusion Model |
ICLR |
2023 |
Github |
Prompts for text-to-motion generation |
Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models |
arXiv |
2023 |
--- |
Prompts for complex tasks |
Multimodal Procedural Planning via Dual Text-Image Prompting |
arXiv |
2023 |
Github |
Prompts for complex tasks |
Prompt Stealing Attacks Against Text-to-Image Generation Models |
arXiv |
2023 |
--- |
Prompts for responsible AI |
Membership Inference Attacks Against Text-to-image Generation Models |
arXiv |
2022 |
--- |
Membership attacks against text-to-image models |
Are Diffusion Models Vulnerable to Membership Inference Attacks? |
ICML |
2023 |
Github |
Membership attacks against text-to-image models |
A Reproducible Extraction of Training Images from Diffusion Models |
arXiv |
2023 |
Github |
Membership attacks against text-to-image models |
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness |
arXiv |
2023 |
Github |
Prompts on text-to-image models considering fairness |
Social Biases through the Text-to-Image Generation Lens |
arXiv |
2023 |
--- |
Prompts on text-to-image models considering biases |
T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation |
ACL |
2023 |
--- |
Prompts on text-to-image models considering biases |
Stable Bias: Analyzing Societal Representations in Diffusion Models |
arXiv |
2023 |
--- |
Prompts on text-to-image models considering biases |
A Pilot Study of Query-Free Adversarial Attack Against Stable Diffusion |
CVPR |
2023 |
--- |
Adversarial robustness of text-to-image models |
Diffusion Models for Imperceptible and Transferable Adversarial Attack |
arXiv |
2023 |
Github |
Adversarial robustness of text-to-image models |
Diffusion Models for Adversarial Purification |
ICML |
2022 |
Github |
Adversarial robustness of text-to-image models |
Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis |
arXiv |
2022 |
--- |
Backdoor attack on text-to-image models |
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning |
arXiv |
2023 |
--- |
Backdoor attack on text-to-image models |
Zero-Day Backdoor Attack against Text-to-Image Diffusion Models via Personalization |
arXiv |
2023 |
--- |
Backdoor attack on text-to-image models |