Stable Diffusion

image · generative · anchor · artifact

Open-weight latent diffusion model for text-to-image generation, cited in the corpus at version 1.4.

Note

the latent-diffusion paper behind it is Rombach et al., CVPR 2022, but no search returned its identifier from a source, so no anchor is recorded. The artifact is the official repository for the 1.4 checkpoint this corpus cites

Variants

Stable Diffusion 1.4, SDXL, Stable Diffusion v1.5, Stable Diffusion 2.1, Stable Diffusion 2, Stable Diffusion 3, Stable Diffusion 2.0, Stable Diffusion 3, Stable Diffusion 3.5, Epic Diffusion, Realistic Vision 1.4, Stable Diffusion 3, Stable Diffusion 1.5, Stable Diffusion 3, Stable Diffusion v2.1, Stable Diffusion XL

Findings

• IC-023Local scaling, rank, and complexity of Stable Diffusion correlate with generation aesthetics, diversity, and memorization
• IC-024Reward model trained on local scaling of Stable Diffusion can guide generation to increase diversity and aesthetic scores
• IC-1012Connected regions in the latent space of Stable Diffusion v2.1, v1.5, and GLIDE produce distorted images independent of the text prompt
• IC-1013Latent samples in Stable Diffusion v2.1, v1.5, and GLIDE can produce images of associated backgrounds rather than the key object, with failure rates of 2.7%, 9.2%, and 50.5% under random sampling respectively
• IC-1014A single adversarial token embedding appended to any input prompt overwrites the prompt in Stable Diffusion v2.1 to generate a target object, with CLIP similarity to the original prompt (0.742) remaining higher than to the target (0.546)
• IC-1069Stable Diffusion v2.1's compositional understanding on the ARO benchmark is significantly higher than previously reported by MMSE-based scoring
• IC-1070In Stable Diffusion v2.1, attention maps do not reliably predict the effect of prompt interventions on generated images, while conditional mutual information does
• IC-1071Stable Diffusion v2.1's pixel-wise conditional mutual information localizes abstract words (adjectives, adverbs, verbs) more effectively than attention, but is less effective than attention for object segmentation
• IC-1123All seven published concept erasure methods applied to Stable Diffusion 1.4 can be circumvented by learned word embeddings, demonstrating that targeted concepts are input-filtered rather than truly removed from the model
• IC-1137Stable Diffusion represents certain concepts primarily through specific named exemplars rather than abstract category features
• IC-1138Stable Diffusion simultaneously encodes multiple meanings of homograph concepts in a single representation
• IC-1139Stable Diffusion encodes social biases in its internal concept representations that are not always visually apparent
• IC-1140Stable Diffusion's internal concept representations encode visual and structural similarities (shape, texture, color) that transcend textual semantics
• IC-1202Stable Diffusion 1.5 and 2.1 exhibit a cropping failure mode where synthesized objects are cut off at image boundaries
• IC-1203Stable Diffusion 1.5 and 2.1 receive low user-preference win rates (7.91% and 6.71%) in a four-way comparison against SDXL
• IC-1204SD-VAE 1.x and SD-VAE 2.x achieve lower reconstruction quality than the new SDXL-VAE on COCO 2017
• IC-1391SLD concept removal variants and SD with negative prompts are bypassable by Ring-a-Bell adversarial prompts, increasing attack success rate from single digits to 90-100% for nudity
• IC-257327 DNNs approach or exceed human accuracy on object depth order but are near chance on VPT-basic, while humans show the opposite pattern
• IC-330Low local intrinsic dimension (LIDθ) of the learned manifold predicts memorization in Stable Diffusion v1.5, IDDPM, and StyleGAN2-ADA
• IC-331In Stable Diffusion v1.5, specific tokens in text prompts drive memorization, and GPT-4-based perturbation of high-attribution tokens reduces SSIM similarity to training images while maintaining CLIP score
• IC-394Text generation in SDXL, DeepFloyd IF, and SD3 is controlled by less than 1% of parameters concentrated in specific cross- or joint-attention layers, and these layers are specialised for text content rather than visual template
• IC-445Stable Diffusion v1.5 generates nudity for 796 out of 4703 prompts in the I2P inappropriate prompts dataset
• IC-446In Stable Diffusion v1.5, concept-generating neurons are localized in the second layer of FFNs, spanning less than 3% of FFN parameters, and are disentangled from object-generating neurons
• IC-473ResNet-18 lacks a clear multifractal structure while ResNet-152 shows one with irregular shifts, and a 160M diffusion model exhibits lower degree of emergence than Pythia 160M
• IC-473ResNet-18 lacks a clear multifractal structure while ResNet-152 shows one with irregular shifts, and a 160M diffusion model exhibits lower degree of emergence than Pythia 160M [context]
• IC-495All evaluated multimodal foundation models achieve average non-hallucination accuracy below 50% across six hallucination scenarios
• IC-497Text-to-image models experience performance drops exceeding 10% under adversarial prompts, with spatial reasoning being the most vulnerable task across all models
• IC-498Multimodal foundation models exhibit severe group unfairness, with race and age biases more pronounced than gender bias in text-to-image models while gender bias is stronger in image-to-text models
• IC-539GPT-4o in text-code-image mode achieves the highest scores on SCIMAGE but remains below 4 on all three evaluation dimensions, and all models degrade substantially on prompts requiring combined understanding types
• IC-540Spatial understanding is the most challenging dimension for code-based models while numerical understanding is most challenging for direct image models
• IC-541Code-based output (Python or TikZ) produces images with notably higher scientific style scores than direct image generation from Stable Diffusion and DALL-E
• IC-554In Stable Diffusion 1.4, 1.5, 2.0, and 3.0, parameters with the smallest absolute values (below ~10^-3) do not contribute to the generative process, and this ineffectiveness is caused by stochastic training dynamics rather than architectural redundancy.
• IC-783Stable Diffusion v1.5's conditional probability pθ(x|c) is heavily biased by the unconditional probability pθ(x), making it unreliable as a condition-alignment metric
• IC-785Different released diffusion models produce images with distinguishable probability signatures, enabling source attribution
• IC-904Stable Diffusion XL generates non-empty cups when prompted for 'empty cup'
• IC-982In Stable Diffusion's UNET, visual attribute knowledge is distributed across multiple components with attribute-specific patterns, concentrated more in the up-block, and cross-attention layers are not the primary causal states
• IC-983In Stable Diffusion's CLIP text-encoder, knowledge about all visual attributes is localized to a single causal state: the first self-attention layer at the last subject token
• IC-999FLUX1 and Stable Diffusion 3.5 exhibit high local dependency ratio and produce text hallucinations when generating text content
• IC-999FLUX1 and Stable Diffusion 3.5 exhibit high local dependency ratio and produce text hallucinations when generating text content [context]

Shared mechanisms

• Depth-dependent structurealso in Baichuan 2, BERT, BLIP-2, BLOOM, Chameleon, CLIP / CLIP-ViT (LC), DeepFloyd IF, DeiT-III, DINO, DINOv2, Falcon, Gemma, Gemma 2, GPT-2, GPT-J, GPT-NeoX-20B, Griffin, I3D, Idefics, InstructBLIP, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama 3.2, Llama-3.2-3B, LLaVA, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Phi, MAE, MAE-B/16, Mamba, MiniGPT-4, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, mPLUG-Owl, MPT, MultiBERTs, MViT V2, OLMo / OLMo base, OpenCLIP, OPT, Phi-2, Pythia, Qwen 2, Qwen2-VL, Qwen2.5, RoBERTa / RoBERTa-L, RWKV, SALMONN, SAM, SlowFast, Swin Transformer, TerraMind, TimesFormer, TSM, Uniformer, Vicuna, VideoMAE, ViT, X3D, Yi
• Explanation faithfulnessalso in BakLLaVA, CF2, Claude 3, Claude 3.5, CLIP / CLIP-ViT (LC), DRUM, Fuyu, GEM, Gemini 1.5 / Gemini Pro 1.5, Gemma 2, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4o, GPT-J, Idefics, Llama 3.1, LLaVA-NeXT / LLaVA 1.6, MAE-B/16, MobileNetV2, mPLUG-Owl3, OpenFlamingo, PGExplainer, Pythia, RCExplainer, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, SigLIP, SigLIP-2, TAGExplainer, ViT
• Failure modealso in AASIST, ADM, Aegis-Guard-Defensive, Alpaca, AnyLoc, AutoTikZ / DataTikZ, Baichuan, Baichuan 2, Baichuan2-13B, BakLLaVA, Bard, BEiT, BERT, BingChat, BLIP, BLIP-2, BLOOM, BSRNet, CF2, Chat-UniVi-7B, ChatGLM-6B / ChatGLM-6b-2, ChatGLM2, ChatGPT, CLAP, Claude 1.3, Claude 2.0, Claude 2.1, Claude 3, Claude 3.5, CLEAR, CLIP / CLIP-ViT (LC), CLIP4Clip, CLIPBERT, CLIPCap, CLMBR-T-BASE, CloFNet, Code Llama, CodeGeex2, CodeGen, CodeLlama-13B, CodeLlama-34B, CogVLM2, Cohere Command R, CoMEt, Command R+, CONCH, CycleGAN, DALL-E, DALL·E 2, DALL·E 3, DASR, DECAF, DeepSeek-2-Chat, DeepSeek-2-Coder, DeepSeek-V2-0628, DeepSeek-VL, DeepSeek-VL2, DeiT, DeiT-III, Depth Anything, DETR-R50, DimeNet++, DINO, DINO-FocalNet-Large, DINOv2, EGNN, Emu2, EquiformerV2, ESCN, ESM-2, ESM3, ESRGAN, EVA-CLIP, EVE, Falcon, Faster R-CNN / Faster R-CNN R50 / Faster R-CNN X101, FLAN-T5, Florence-2, FLUX / FLUX1, FoveaBox, Fuyu, Galactica-6.7B, GAT, 3D Gaussian Splatting, GCN, Gemini, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GIN, GLIDE, GLM-4, GLM-4V, GloVe, GP-UNIT, GPT-2, GPT-3 / GPT base, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4.1, GPT-4o, GPT-J, GPT-NeoX-20B, GraphSAGE, Grounding DINO, Guanaco, GVP, Hawkeye, HiFaceGAN, HPS, HuggingGPT, IDDPM, Idefics, Idefics2, ImageBind, ImageBind-LLM-7B, Imagen Video, ImageReward, 12-in-1, InstructBLIP, InstructGPT, InternLM-2.5-7B, InternLM-XComposer2-VL, InternVideo, InternVL-1.5, InternVL2, Koala, LegalBERT, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama 3.2, Llama-3.2-3B, Llama-3-2-Vision, LLaMA-Adapter v2, Llama Guard, Llama Guard 2, Llama-Guard 3, Llama-VID, LLaVA, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Med, LLaVA-NeXT / LLaVA 1.6, LLaVA-OneVision, LongVA-7B, LOVT, LWM-1M-JAX, MACE, MAE, Med-Flamingo, Merlot Reserve, MGCA, MiDaS, MiniCPM-V, MiniGPT-4, Mip-Splatting, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mistral Large 2, Mistral Large V2, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, MobileNetV2, Molmo, MolmoE-7B, Momentor, Moondream2, Moonshot-v1-8k, mPLUG-2, mPLUG-Owl, mPLUG-Owl3, mPLUG-Owl2, MPT, MSA Transformer, MultiBERTs, Nova Canvas, Nova Lite, Nova Pro, O1 / OpenAI-o1-preview, O3, O4-mini, OLMo / OLMo base, OneLLM, OpenAI Moderation, OpenChat-3.5-0106, OpenCLIP, OpenFlamingo, OPT, Otter, Otter-7B, PaLM 2, PaLM 62B, PandaGPT-7B, PerSAM, Phi-3, Phi-3.5 Mini Instruct, PickScore, PLIP, Prismatic, ProGen-2, Pythia, Qwen1.5, Qwen 2, Qwen 2.5 72B Instruct, Qwen2-VL, Qwen-Audio, Qwen-VL, Qwen2.5, Qwen2-Audio, R2D2, RadFM, RCExplainer, RD-SR, RealESRNet, Reprover, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, RetinaNet-R50, RivaGAN, RS-LDS, RT-1, RT-2, SALMONN, SAM, SAM 2, SAULLM 54B, Scaffold-GS, SchNet, Seed-LLaMA-8B, SGC, SIREN, Sketch Transformer, SLD-max, SLD-medium, SLD-strong, SLDS, SLIMG, SpeechGPT, SphereNet, SRResNet, StackLLaMA, Starcoder, StegaStamp, StyleGAN2-ADA, Swin Transformer, T5, TD-MPC, TerraMind, TimeChat, TranceptionEVE, TreeRing, Tulu 2, UnifiedQA, UniPerceiver, UNITER, UniVL, Van Gogh Diffusion, VERA, VGG / VGG13, Vicuna, Video-Chat-7B, Video-ChatGPT, Video-LLaMA, Video-LLaMA-2-13B, Video-LLaVA, VideoCLIP, ViLA-8B, ViLBERT, VindLU, VioLET, ViRTex, ViT, ViV1T, VTG-LLM, WildGuard, Wizardlm, X-CLIP, X-InstructBLIP-7B, XGen-MM, Xlm-R, Zephyr-7B-beta
• Method artefactalso in Baichuan, CLIP / CLIP-ViT (LC), ConvNeXt, EfficientNet, Falcon, Gemma, Gemma 2, GPT-2, GPT-3.5 / ChatGPT-3.5, GPT-4o, GPT-J, InternLM, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, MAP-NEO, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, OLMo / OLMo base, OpenCLIP, OpenLLaMA, OPT, Pythia, Qwen1.5, Qwen 2, Qwen2.5, RedPajama, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, Skywork, StableLM, TerraMind, ViT, Yi, ZiYA2
• Positional biasalso in BERT, ChatGPT, Claude 3, Claude 3.5, Falcon, Fuyu, Gemini, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GLIDE, GPT-2, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4.1, GPT-4o, GPT-J, InstructGPT, LLaMA, Llama 2 / Llama 2 base, Llama 3, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, MPT, O1 / OpenAI-o1-preview, O3, O4-mini, PaLM 2, Phi-3, Pythia, Qwen1.5, Qwen 2, Qwen 2.5 72B Instruct, Sybil, Vicuna
• Scale-dependent behaviouralso in Aquila-7B, BEiT, BERT, BLOOM, Claude 2.1, Claude 3, Claude 3.5, CLIP / CLIP-ViT (LC), Code Llama, CodeGen, Cohere Command R, DeepSeek LLM, DeepSeekMoE, DeiT-III, DINO, DINOv2, EquiformerV2, ESCN, Falcon, FLAN-T5, Gemini 1.0 Pro, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GPT-2, GPT-3 / GPT base, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4o, GPT-J, GPT-Neo, I3D, Idefics, InternLM-2.5-7B, InternLM-XComposer2-VL, InternLM2, InternVL-1.5, InternVL2, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama 3.2, Llama-3.2-3B, LLaVA-1.5 / LLaVA-v1.5, LLaVA-NeXT / LLaVA 1.6, LongVA-7B, MAE, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, Moirai, MPT, MViT V2, O1 / OpenAI-o1-preview, OLMo / OLMo base, OpenCLIP, OpenFlamingo, OpenLLaMA, OPT, PaLM 2, Phi-3, Platypus2-Instruct-70B, Pythia, Qwen, Qwen1.5, Qwen 2, Qwen 2.5 72B Instruct, Qwen2-VL, Qwen-Audio, Qwen2.5, RedPajama-INCITE, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, SlowFast, Solar 10.7B, StableLM, Swin Transformer, T5, TerraMind, text-ada-001, TigerBot-70B-Chat, TimesFormer, TSM, Tulu 2, Uniformer, Vicuna, VideoMAE, ViLA-8B, Wizardlm, X3D, XGLM, Xwin-LM-70B-v0.1, Yi
• Shortcutalso in BakLLaVA, BLIP-2, Claude 3, Claude 3.5, CLIP / CLIP-ViT (LC), CLIP4Clip, CLIPBERT, DALL·E 2, DALL·E 3, DeepSeek-VL2, Eurus-RM-7B, Falcon, FLUX / FLUX1, Gemini, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GPT-2, GPT-3 / GPT base, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4o, GPT-J, iFlytekSpark-13B, InstructBLIP, Internlm2-Reward, InternVideo, InternVL2, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama-3-2-Vision, LLaVA, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Med, LLaVA-NeXT / LLaVA 1.6, Med-Flamingo, Merlot Reserve, MiniGPT-4, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, Molmo, mPLUG-2, mPLUG-Owl3, Nova Canvas, O1 / OpenAI-o1-preview, OpenCLIP, OPT, Otter, PaLM 62B, Pythia, Qwen, Qwen 2, Qwen-VL, Qwen2.5, RadFM, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, Swin Transformer, Sybil, TerraMind, Tulu 2, UniPerceiver, UniVL, Vicuna, Video-LLaMA, VideoCLIP, VindLU, VioLET, X-CLIP