“‘Diffusion Model’ Tag”,2019-09-12 (anime AI, GAN, AI scaling, robotics; backlinks)⁠:

Bibliography for tag ai/nn/diffusion, most recent first: 12 related tags, 256 annotations, & 159 links (parent).

• See Also
• Gwern
  • “Research Ideas”, Gwern2017
• Links
  • “Data Scaling Laws in Imitation Learning for Robotic Manipulation”, Lin et al 2024
  • “SANA: Efficient High-Resolution Image Synthesis With Linear Diffusion Transformers”, Xie et al 2024
  • “Copying Style, Extracting Value: Illustrators’ Perception of AI Style Transfer and Its Impact on Creative Labor”, Porquet et al 2024
  • “Improvements to SDXL in NovelAI Diffusion V3”, Ossa et al 2024
  • “[Taylor Swift Endorses Kamala Harris due to Deepfakes]”, Swift2024
  • “Diffusion Is Spectral Autoregression”, Dieleman2024
  • “My Dead Father Is ‘Writing’ Me Notes Again”
  • “The Rise of Terminator Zero With Writer Mattson Tomlin & Director Masashi Kudo”, Baron2024
  • “NovelAI Diffusion V1 Weights Release”, NovelAI 2024
  • “Transfusion: Predict the Next Token and Diffuse Images With One Multi-Modal Model”, Zhou et al 2024
  • “Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget”, Sehwag et al 2024
  • “Diffusion Forcing: Next-Token Prediction Meets Full-Sequence Diffusion”, Chen et al 2024
  • “MAR: Autoregressive Image Generation without Vector Quantization”, Li et al 2024
  • “Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI”, Hönig et al 2024
  • “Consistency-Diversity-Realism Pareto Fronts of Conditional Image Generative Models”, Astolfi et al 2024
  • “Interpreting the Weight Space of Customized Diffusion Models”, Dravid et al 2024
  • “SF-V: Single Forward Video Generation Model”, Zhang et al 2024
  • “Diffusion On Syntax Trees For Program Synthesis”, Kapur et al 2024
  • “Lateralization MLP: A Simple Brain-Inspired Architecture for Diffusion”, Hu & Rostami2024
  • “Dynamic Typography: Bringing Text to Life via Video Diffusion Prior”, Liu et al 2024
  • “Long-Form Music Generation With Latent Diffusion”, Evans et al 2024
  • “VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time”, Xu et al 2024
  • “ControlNet++: Improving Conditional Controls With Efficient Consistency Feedback”, Li et al 2024
  • “Measuring Style Similarity in Diffusion Models”, Somepalli et al 2024
  • “Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data”, Gerstgrasser et al 2024
  • “TextCraftor: Your Text Encoder Can Be Image Quality Controller”, Li et al 2024
  • “Improving Text-To-Image Consistency via Automatic Prompt Optimization”, Mañas et al 2024
  • “SDXS: Real-Time One-Step Latent Diffusion Models With Image Conditions”, Song et al 2024
  • “Stability AI Announcement”, Stability2024
  • “CMD: Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition”, Yu et al 2024
  • “ZigMa: Zigzag Mamba Diffusion Model”, Hu et al 2024
  • “Atomically Accurate de Novo Design of Single-Domain Antibodies”, Bennett et al 2024
  • “Sketch2Manga: Shaded Manga Screening from Sketch With Diffusion Models”, Lin et al 2024
  • “ELLA: Equip Diffusion Models With LLM for Enhanced Semantic Alignment”, Hu et al 2024
  • “Transparent Image Layer Diffusion Using Latent Transparency”, Zhang & Agrawala2024
  • “Neural Network Parameter Diffusion”, Wang et al 2024
  • “CartoonizeDiff: Diffusion-Based Photo Cartoonization Scheme”, Jeon et al 2024
  • “Discovering Universal Semantic Triggers for Text-To-Image Synthesis”, Zhai et al 2024
  • “AnimeDiffusion: Anime Diffusion Colorization”, Cao et al 2024
  • “Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift”, Qiu et al 2024
  • “Fixed Point Diffusion Models”, Bai & Melas-Kyriazi2024
  • “Why a Chinese Court’s Landmark Decision Recognising the Copyright for an AI-Generated Image Benefits Creators in This Nascent Field”, Shen2024
  • “Bridging the Gap: Sketch to Color Diffusion Model With Semantic Prompt Learning”, Wang et al 2024
  • “Applying Conditional Information in Guiding Diffusion-Based Method for Anime-Style Face Drawing”, Bảo 2024
  • “GenCast: Diffusion-Based Ensemble Forecasting for Medium-Range Weather”, Price et al 2023
  • “Training Stable Diffusion from Scratch Costs <$160k”, Stephenson & Seguin2023
  • “Generative AI Beyond LLMs: System Implications of Multi-Modal Generation”, Golden et al 2023
  • “DreamTuner: Single Image Is Enough for Subject-Driven Generation”, Hua et al 2023
  • “Rich Human Feedback for Text-To-Image Generation”, Liang et al 2023
  • “ECLIPSE: A Resource-Efficient Text-To-Image Prior for Image Generations”, Patel et al 2023
  • “Self-Conditioned Image Generation via Generating Representations”, Li et al 2023
  • “Retrieving Conditions from Reference Images for Diffusion Models”, Tang et al 2023
  • “Analyzing and Improving the Training Dynamics of Diffusion Models”, Karras et al 2023
  • “DiffiT: Diffusion Vision Transformers for Image Generation”, Hatamizadeh et al 2023
  • “Diffusion Models Without Attention”, Yan et al 2023
  • “MicroCinema: A Divide-And-Conquer Approach for Text-To-Video Generation”, Wang et al 2023
  • “AnyLens: A Generative Diffusion Model With Any Rendering Lens”, Voynov et al 2023
  • “Visual Anagrams: Generating Multi-View Optical Illusions With Diffusion Models”, Geng et al 2023
  • “Stability AI Explores Sale As Investor Urges CEO to Resign: Move Follows Letter from Investor Coatue Calling for Changes; Coatue Concerned about Stability AI’s Financial Position”, Bergen & Metz2023
  • “TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering”, Chen et al 2023
  • “MobileDiffusion: Subsecond Text-To-Image Generation on Mobile Devices”, Zhao et al 2023
  • “Adversarial Diffusion Distillation”, Sauer et al 2023
  • “Generative Models: What Do They Know? Do They Know Things? Let’s Find Out!”, Du et al 2023
  • “Test-Time Adaptation of Discriminative Models via Diffusion Generative Feedback”, Prabhudesai et al 2023
  • “Diffusion Model Alignment Using Direct Preference Optimization”, Wallace et al 2023
  • “Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models”, Gandikota et al 2023
  • “Introducing NovelAI Diffusion Anime V3”, NovelAI 2023
  • “UFOGen: You Forward Once Large Scale Text-To-Image Generation via Diffusion GANs”, Xu et al 2023
  • “I2VGen-XL: High-Quality Image-To-Video Synthesis via Cascaded Diffusion Models”, Zhang et al 2023
  • “AnyText: Multilingual Visual Text Generation And Editing”, Tuo et al 2023
  • “Idempotent Generative Network”, Shocher et al 2023
  • “Beyond U: Making Diffusion Models Faster & Lighter”, Calvo-Ordonez et al 2023
  • “CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling”, Sadat et al 2023
  • “CommonCanvas: An Open Diffusion Model Trained With Creative-Commons Images”, Gokaslan et al 2023
  • “Nightshade: Prompt-Specific Poisoning Attacks on Text-To-Image Generative Models”, Shan et al 2023
  • “Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task”, Okawa et al 2023
  • “Text Embeddings Reveal (Almost) As Much As Text”, Morris et al 2023
  • “Generalization in Diffusion Models Arises from Geometry-Adaptive Harmonic Representation”, Kadkhodaie et al 2023
  • “Intriguing Properties of Generative Classifiers”, Jaini et al 2023
  • “Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack”, Dai et al 2023
  • “Generating and Imputing Tabular Data via Diffusion and Flow-Based Gradient-Boosted Trees”, Jolicoeur-Martineau et al 2023
  • “InstaFlow: One Step Is Enough for High-Quality Diffusion-Based Text-To-Image Generation”, Liu et al 2023
  • “Generating Tabular Datasets under Differential Privacy”, Truda2023
  • “MetaDiff: Meta-Learning With Conditional Diffusion for Few-Shot Learning”, Zhang & Yu2023
  • “Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior”, Block et al 2023
  • “FABRIC: Personalizing Diffusion Models With Iterative Feedback”, Rütte et al 2023
  • “Diffusion Models Beat GANs on Image Classification”, Mukhopadhyay et al 2023
  • “SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis”, Podell et al 2023
  • “SDXL § Micro-Conditioning: Conditioning the Model on Image Size”, Podell et al 2023 (page 3 org stability)
  • “DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models”, Xing et al 2023
  • “Fighting Uncertainty With Gradients: Offline Reinforcement Learning via Diffusion Score Matching”, Suh et al 2023
  • “Semi-Implicit Denoising Diffusion Models (SIDDMs)”, Xu et al 2023
  • “Evaluating the Robustness of Text-To-Image Diffusion Models against Real-World Attacks”, Gao et al 2023
  • “StyleTTS 2: Towards Human-Level Text-To-Speech through Style Diffusion and Adversarial Training With Large Speech Language Models”, Li et al 2023
  • “Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model”, Chen et al 2023
  • “Exposing Flaws of Generative Model Evaluation Metrics and Their Unfair Treatment of Diffusion Models”, Stein et al 2023
  • “StyleDrop: Text-To-Image Generation in Any Style”, Sohn et al 2023
  • “Artificial Intelligence and Art: Identifying the Esthetic Judgment Factors That Distinguish Human & Machine-Generated Artwork”, Samo & Highhouse2023
  • “Spontaneous Symmetry Breaking in Generative Diffusion Models”, Raya & Ambrogioni2023
  • “Tree-Ring Watermarks: Fingerprints for Diffusion Images That Are Invisible and Robust”, Wen et al 2023
  • “UDPM: Upsampling Diffusion Probabilistic Models”, Abu-Hussein & Giryes2023
  • “Generalizable Synthetic Image Detection via Language-Guided Contrastive Learning”, Wu et al 2023
  • “Common Diffusion Noise Schedules and Sample Steps Are Flawed”, Lin et al 2023
  • “Diffusart: Enhancing Line Art Colorization With Conditional Diffusion Models”, Carrillo et al 2023
  • “Continual Diffusion: Continual Customization of Text-To-Image Diffusion With C-LoRA”, Smith et al 2023
  • “Reference-Based Image Composition With Sketch via Structure-Aware Diffusion Model”, Kim et al 2023
  • “HyperDiffusion: Generating Implicit Neural Fields With Weight-Space Diffusion”, Erkoç et al 2023
  • “Masked Diffusion Transformer Is a Strong Image Synthesizer”, Gao et al 2023
  • “Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering”, Oppenlaender et al 2023
  • “TRACT: Denoising Diffusion Models With Transitive Closure Time-Distillation”, Berthelot et al 2023
  • “Consistency Models”, Song et al 2023
  • “Understanding the Diffusion Objective As a Weighted Integral of ELBOs”, Kingma & Gao2023
  • “Adding Conditional Control to Text-To-Image Diffusion Models”, Zhang et al 2023
  • “Glaze: Protecting Artists from Style Mimicry by Text-To-Image Models”, Shan et al 2023
  • “Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery”, Wen et al 2023
  • “Dreamix: Video Diffusion Models Are General Video Editors”, Molad et al 2023
  • “Imitating Human Behavior With Diffusion Models”, Pearce et al 2023
  • “Msanii: High Fidelity Music Synthesis on a Shoestring Budget”, Maina2023
  • “Archisound: Audio Generation With Diffusion”, Schneider2023
  • “DIRAC: Neural Image Compression With a Diffusion-Based Decoder”, Goose et al 2023
  • “Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-To-Video Generation”, Wu et al 2022
  • “Scalable Adaptive Computation for Iterative Generation”, Jabri et al 2022
  • “Character-Aware Models Improve Visual Text Rendering”, Liu et al 2022
  • “Diffusion Transformers (DiTs): Scalable Diffusion Models With Transformers”, Peebles & Xie2022
  • “Point·E: A System for Generating 3D Point Clouds from Complex Prompts”, Nichol et al 2022
  • “The Stable Artist: Steering Semantics in Diffusion Latent Space”, Brack et al 2022
  • “Multi-Concept Customization of Text-To-Image Diffusion”, Kumari et al 2022
  • “Multi-Resolution Textual Inversion”, Daras & Dimakis2022
  • “Latent Video Diffusion Models for High-Fidelity Video Generation With Arbitrary Lengths”, He et al 2022
  • “VectorFusion: Text-To-SVG by Abstracting Pixel-Based Diffusion Models”, Jain et al 2022
  • “DreamArtist: Towards Controllable One-Shot Text-To-Image Generation via Contrastive Prompt-Tuning”, Dong et al 2022
  • “DiffusionDet: Diffusion Model for Object Detection”, Chen et al 2022
  • “Null-Text Inversion for Editing Real Images Using Guided Diffusion Models”, Mokady et al 2022
  • “InstructPix2Pix: Learning to Follow Image Editing Instructions”, Brooks et al 2022
  • “Versatile Diffusion: Text, Images and Variations All in One Diffusion Model”, Xu et al 2022
  • “Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models”, Struppek et al 2022
  • “EDiff-I: Text-To-Image Diffusion Models With an Ensemble of Expert Denoisers”, Balaji et al 2022
  • “DiffusionDB: A Large-Scale Prompt Gallery Dataset for Text-To-Image Generative Models”, Wang et al 2022
  • “Imagic: Text-Based Real Image Editing With Diffusion Models”, Kawar et al 2022
  • “Hierarchical Diffusion Models for Singing Voice Neural Vocoder”, Takahashi et al 2022
  • “Flow Matching for Generative Modeling”, Lipman et al 2022
  • “On Distillation of Guided Diffusion Models”, Meng et al 2022
  • “Improving Sample Quality of Diffusion Models Using Self-Attention Guidance”, Hong et al 2022
  • “Rectified Flow: A Marginal Preserving Approach to Optimal Transport”, Liu2022
  • “DreamFusion: Text-To-3D Using 2D Diffusion”, Poole et al 2022
  • “RealSinger: Ultra-Realistic Singing Voice Generation via Stochastic Differential Equations”, Anonymous2022
  • “g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints”, Peebles et al 2022
  • “PFGM: Poisson Flow Generative Models”, Xu et al 2022
  • “This Artist Is Dominating AI-Generated Art. And He’s Not Happy about It. Greg Rutkowski Is a More Popular Prompt Than Picasso”, Heikkilä2022
  • “Brain Imaging Generation With Latent Diffusion Models”, Pinaya et al 2022
  • “Soft Diffusion: Score Matching for General Corruptions”, Daras et al 2022
  • “Flow Straight and Fast: Learning to Generate and Transfer Data With Rectified Flow”, Liu et al 2022
  • “Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis”, Fan et al 2022
  • “Understanding Diffusion Models: A Unified Perspective”, Luo2022
  • “DreamBooth: Fine Tuning Text-To-Image Diffusion Models for Subject-Driven Generation”, Ruiz et al 2022
  • “Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise”, Bansal et al 2022
  • “Diffusion-QL: Diffusion Policies As an Expressive Policy Class for Offline Reinforcement Learning”, Wang et al 2022
  • “An Image Is Worth One Word: Personalizing Text-To-Image Generation Using Textual Inversion”, Gal et al 2022
  • “Prompt-To-Prompt Image Editing With Cross Attention Control”, Hertz et al 2022
  • “Text-Guided Synthesis of Artistic Images With Retrieval-Augmented Diffusion Models”, Rombach et al 2022
  • “NUWA-∞: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis”, Wu et al 2022
  • “IHDM: Generative Modeling With Inverse Heat Dissipation”, Rissanen et al 2022
  • “DiffC: Lossy Compression With Gaussian Diffusion”, Theis et al 2022
  • “Diffusion-GAN: Training GANs With Diffusion”, Wang et al 2022
  • “Compositional Visual Generation With Composable Diffusion Models”, Liu et al 2022
  • “DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps”, Lu et al 2022
  • “Score-Based Generative Models Detect Manifolds”, Pidstrigach2022
  • “Elucidating the Design Space of Diffusion-Based Generative Models”, Karras et al 2022
  • “Text2Human: Text-Driven Controllable Human Image Generation”, Jiang et al 2022
  • “Improved Vector Quantized Diffusion Models”, Tang et al 2022
  • “Maximum Likelihood Training of Implicit Nonlinear Diffusion Models”, Kim et al 2022
  • “Imagen: Photorealistic Text-To-Image Diffusion Models With Deep Language Understanding”, Saharia et al 2022
  • “Flexible Diffusion Modeling of Long Videos”, Harvey et al 2022
  • “Planning With Diffusion for Flexible Behavior Synthesis”, Janner et al 2022
  • “Diffusion Models for Adversarial Purification”, Nie et al 2022
  • “Retrieval-Augmented Diffusion Models: Semi-Parametric Neural Image Synthesis”, Blattmann et al 2022
  • “Video Diffusion Models”, Ho et al 2022
  • “KNN-Diffusion: Image Generation via Large-Scale Retrieval”, Ashual et al 2022
  • “Perception Prioritized Training of Diffusion Models”, Choi et al 2022
  • “Diffusion Probabilistic Modeling for Video Generation”, Yang et al 2022
  • “Diffusion Causal Models for Counterfactual Estimation”, Sanchez & Tsaftaris2022
  • “Truncated Diffusion Probabilistic Models and Diffusion-Based Adversarial Autoencoders”, Zheng et al 2022
  • “Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality”, Watson et al 2022
  • “From Data to Functa: Your Data Point Is a Function and You Should Treat It like One”, Dupont et al 2022
  • “Denoising Diffusion Restoration Models”, Kawar et al 2022
  • “DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents”, Pandey et al 2022
  • “Itô-Taylor Sampling Scheme for Denoising Diffusion Probabilistic Models Using Ideal Derivatives”, Tachibana et al 2021
  • “High-Resolution Image Synthesis With Latent Diffusion Models”, Rombach et al 2021
  • “High Fidelity Visualization of What Your Self-Supervised Representation Knows About”, Bordes et al 2021
  • “More Control for Free! Image Synthesis With Semantic Diffusion Guidance”, Liu et al 2021
  • “Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction”, Chung et al 2021
  • “VQ-DDM: Global Context With Discrete Diffusion in Vector Quantized Modeling for Image Generation”, Hu et al 2021
  • “Diffusion Autoencoders: Toward a Meaningful and Decodable Representation”, Preechakul et al 2021
  • “Blended Diffusion for Text-Driven Editing of Natural Images”, Avrahami et al 2021
  • “Vector Quantized Diffusion Model for Text-To-Image Synthesis”, Gu et al 2021
  • “Classifier-Free Diffusion Guidance”, Ho & Salimans2021
  • “Unleashing Transformers: Parallel Token Prediction With Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes”, Bond-Taylor et al 2021
  • “Restormer: Efficient Transformer for High-Resolution Image Restoration”, Zamir et al 2021
  • “Tackling the Generative Learning Trilemma With Denoising Diffusion GANs”, Xiao et al 2021
  • “Diffusion Normalizing Flow”, Zhang & Chen2021
  • “Palette: Image-To-Image Diffusion Models”, Saharia et al 2021
  • “Progressive Distillation for Fast Sampling of Diffusion Models”, Salimans & Ho2021
  • “DiffusionCLIP: Text-Guided Image Manipulation Using Diffusion Models”, Kim & Ye2021
  • “Unconditional Diffusion Guidance”, Ho & Salimans2021
  • “Generative Probabilistic Image Colorization”, Furusawa et al 2021
  • “Bilateral Denoising Diffusion Models”, Lam et al 2021
  • “ImageBART: Bidirectional Context With Multinomial Diffusion for Autoregressive Image Synthesis”, Esser et al 2021
  • “Variational Diffusion Models”, Kingma et al 2021
  • “LoRA: Low-Rank Adaptation of Large Language Models”, Hu et al 2021
  • “PriorGrad: Improving Conditional Denoising Diffusion Models With Data-Dependent Adaptive Prior”, Lee et al 2021
  • “Score-Based Generative Modeling in Latent Space”, Vahdat et al 2021
  • “CDM: Cascaded Diffusion Models for High Fidelity Image Generation”, Ho et al 2021
  • “Learning to Efficiently Sample from Diffusion Probabilistic Models”, Watson et al 2021
  • “Gotta Go Fast When Generating Data With Score-Based Models”, Jolicoeur-Martineau et al 2021
  • “Diffusion Models Beat GANs on Image Synthesis”, Dhariwal & Nichol2021
  • “DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism”, Liu et al 2021
  • “Image Super-Resolution via Iterative Refinement”, Saharia et al 2021
  • “Learning Energy-Based Models by Diffusion Recovery Likelihood”, Gao et al 2021
  • “Improved Denoising Diffusion Probabilistic Models”, Nichol & Dhariwal2021
  • “Denoising Diffusion Implicit Models”, Song et al 2021
  • “Maximum Likelihood Training of Score-Based Diffusion Models”, Song et al 2021
  • “Score-Based Generative Modeling through Stochastic Differential Equations”, Song et al 2020
  • “Denoising Diffusion Probabilistic Models”, Ho et al 2020
  • “NoGAN: Decrappification, DeOldification, and Super Resolution”, Antic et al 2019
  • “Conceptual Captions: A Cleaned, Hypernymed, Image Alt-Text Dataset For Automatic Image Captioning”, Sharma et al 2018
  • “Improving Sampling from Generative Autoencoders With Markov Chains”, Creswell et al 2016
  • “Deep Unsupervised Learning Using Nonequilibrium Thermodynamics”, Sohl-Dickstein et al 2015
  • “A Connection Between Score Matching and Denoising Autoencoders”, Vincent2011
  • “Optimal Approximation of Signal Priors”, Hyvarinen2008
  • “Estimation of Non-Normalized Statistical Models by Score Matching”, Hyvarinen2005
  • “The AI Art Apocalypse”
  • “Towards Pony Diffusion V7, Going With the Flow.”, AstraliteHeart2024
  • “Image Synthesis Style Studies Database (The List)”
  • “Public Folder for DD Studies”
  • “Negative Prompt”
  • “Combination of OpenAI GLIDE and Latent Diffusion”
  • “KaliYuga-Ai/Textile-Diffusion”
  • “V Objective Diffusion Inference Code for PyTorch”
  • “High-Resolution Image Synthesis With Latent Diffusion Models”
  • “Neonbjb/tortoise-Tts: A Multi-Voice TTS System Trained With an Emphasis on Quality”
  • “Openai/guided-Diffusion”
  • “The Annotated Diffusion Model”
  • “Imagen Video”
  • “PaintsUndo: A Base Model of Drawing Behaviors in Digital Paintings”
  • “Keypoint Based Anime Generation With Additional CLIP Guided Tuning”
  • “Rethinking The Danbooru2021 Dataset”
  • “A Closer Look Into The Latent-Diffusion Repo, Do Better Than Just Looking”
  • “Model Comparison Study for Disco Diffusion v. 5”
  • “Model Comparison Study for Disco Diffusion v. 5—PLMS Sampling Edition”
  • “Flexible Diffusion Modeling of Long Videos”
  • “Guidance: a Cheat Code for Diffusion Models”
  • “Stability AI CEO Resigns Because You Can’t Beat Centralized AI With More Centralized AI”
  • “ControlNet Game of Life”
  • “Case Study: Interpreting, Manipulating, and Controlling CLIP With Sparse Autoencoders”
  • “The AI Animal Letters of the Alphabet”
  • “Generative Modeling by Estimating Gradients of the Data Distribution”
  • Wikipedia
• Miscellaneous
• Bibliography

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