Twitter mentions hit 100k per day right after Stable Diffusion release, while OpenArt lists 1M registered users for the platform. Civitai adds about 1k new models each week. On the model side, Stable Diffusion XL totals 2.6 billion parameters, tying community momentum to the architecture that powers fast image generation.
Key Takeaways
- Stability AI Discord has 250k members discussing Stable Diffusion
- Reddit r/StableDiffusion subreddit has 500k subscribers
- Civitai community uploads 1k models weekly for Stable Diffusion
- Stable Diffusion 1.5 has 860 million parameters in UNet backbone
- VAE in Stable Diffusion uses 83 million parameters with 3x3 convolutions
- CLIP text encoder in Stable Diffusion has 123 million parameters (ViT-L/14)
- Stable Diffusion generates 512x512 image in 2 seconds on A100 GPU at 50 steps
- Stable Diffusion XL achieves FID score of 18.1 on MS COCO 2014
- Inference speed of Stable Diffusion v2-1 is 512x512 in 1.5s on RTX 3090
- Stable Diffusion v1.5 was trained on LAION-5B dataset containing 5.85 billion image-text pairs
- LAION-5B dataset used for Stable Diffusion has an average image resolution of 512x512 pixels across its samples
- Stable Diffusion training filtered out 12.8% of LAION-5B samples due to low quality or safety issues
- Stable Diffusion model on Hugging Face has 45 million downloads as of 2024
- Automatic1111 Stable Diffusion WebUI has 120k GitHub stars
- Replicate hosts 10B Stable Diffusion inferences monthly
Stable Diffusion thrives on massive community momentum, with millions of models, prompts, and daily generations worldwide.
Related reading
01 · Category
Community and Ecosystem Metrics22 stats
01
Stability AI Discord has 250k members discussing Stable Diffusion
02
Reddit r/StableDiffusion subreddit has 500k subscribers
03
Civitai community uploads 1k models weekly for Stable Diffusion
04
Hugging Face Stable Diffusion discussions: 10k+ threads
05
Stable Diffusion GitHub issues resolved: 5k+ across repos
06
LoRA competitions on Civitai attract 1k entries monthly
07
Stable Diffusion Twitter mentions peak at 100k/day post-release
08
80% of Stable Diffusion fine-tunes are community-driven on HF
09
PromptHero database has 500k Stable Diffusion prompts curated
10
Stable Diffusion hackathons hosted by Stability AI: 10+ events with 5k participants
11
SeaArt.ai community generates 10M images daily with SD
12
OpenArt Stable Diffusion platform has 1M registered users
13
Diffusers library stars: 20k on GitHub for SD support
14
Stable Diffusion ethical guidelines signed by 1k artists
15
TensorArt hosts 50k SD models with 100M monthly visits
16
NightCafe SD creations exceed 50M user artworks
17
Stable Diffusion YouTube tutorials: 10k+ videos with 100M views
18
Patreon supporters for SD creators: average $5k/month top 100
19
Kaggle Stable Diffusion competitions: 50k participants total
20
Stable Diffusion NFT collections: 100k+ minted on OpenSea
21
Forum posts on Stable Diffusion subreddit: 1M+ total
22
Stability AI funding rounds total $150M with community backers
Interpretation
Community and Ecosystem Metrics Interpretation
Stable Diffusion has spurred a global, community-fueled art and AI wave—with 250k Discord members, 500k Reddit subscribers, 1k weekly Civitai models, 10M daily SeaArt images, 100M YouTube views, 1M forum posts, and $150M in community funding—where hobbyists mint NFTs, 1k artists sign ethical guidelines, and hackathons draw 5k participants, all while Hugging Face hums with 10k+ threads and Kaggle sees 50k contributors, proving creativity and collaboration aren’t just add-ons—they’re the real "prompt" fueling this explosion.
02 · Category
Model Architecture and Parameters21 stats
01
Stable Diffusion 1.5 has 860 million parameters in UNet backbone
02
VAE in Stable Diffusion uses 83 million parameters with 3x3 convolutions
03
CLIP text encoder in Stable Diffusion has 123 million parameters (ViT-L/14)
04
Stable Diffusion v2 UNet expanded to 900 million parameters
05
Latent space dimension in Stable Diffusion is 64x64x4 for 512x512 images
06
Stable Diffusion XL base model totals 2.6 billion parameters
07
Cross-attention layers in Stable Diffusion UNet: 11 blocks with 8 heads each
08
Stable Diffusion 3 Medium has 2 billion parameters with MMDiT architecture
09
Quantized Stable Diffusion (8-bit) reduces parameters to effective 500M active
10
Stable Diffusion Inpainting encoder adds 10M parameters for mask handling
11
ControlNet adds 300M twin parameters to Stable Diffusion base
12
Stable Diffusion v1.5 scheduler uses DDIM with 50 inference steps standard
13
FlashAttention integration in Stable Diffusion reduces KV cache by 50%
14
Stable Diffusion LoRA fine-tune uses rank 4-16 adapters with 1M params
15
Textual Inversion in Stable Diffusion embeds 512-dim vectors for concepts
16
Stable Diffusion Turbo distills to 1-step generation with 900M params
17
Cascade model in SDXL uses three stages with 3B total params
18
RoPE positional embeddings in SD3 span 128k context
19
Stable Diffusion FP16 model size is 4GB VRAM minimum
20
Depth conditioner in Stable Diffusion adds MiDaS encoder with 100M params
21
AnimateDiff motion module has 16 layers of 320-dim adapters
Interpretation
Model Architecture and Parameters Interpretation
Stable Diffusion, that artful AI workhorse, spans models from SD 1.5 (860 million UNet parameters, plus 83 million VAE and 123 million CLIP) to SDXL (2.6 billion total, with a 3 billion cascade model and 1-step Turbo), each packed with tools like FlashAttention (cutting KV cache by 50%), DDIM (50 steps standard), add-ons (ControlNet: 300 million twin parameters, Inpainting: 10 million for masks, AnimateDiff: 16 layers of 320-dim adapters), and fine-tuning options (LoRA: 4-16 rank, 1 million params; Textual Inversion: 512-dim embeddings) while some scale up (SDv2: 900 million UNet, SD3 Medium: 2 billion with MMDiT) and others expand capabilities (SD3: 128k context via RoPE embeddings), all fitting into just 4GB of VRAM and handling 512x512 images with a 64x64x4 latent space—truly a versatile, powerful AI.
03 · Category
Performance and Speed Metrics20 stats
01
Stable Diffusion generates 512x512 image in 2 seconds on A100 GPU at 50 steps
02
Stable Diffusion XL achieves FID score of 18.1 on MS COCO 2014
03
Inference speed of Stable Diffusion v2-1 is 512x512 in 1.5s on RTX 3090
04
Stable Diffusion 3 Turbo generates 1MP images in <1s with 4 steps
05
CLIP score for Stable Diffusion v1.5 averages 0.32 on prompt alignment
06
Stable Diffusion on Apple M1 Max: 10 it/s for 512x512 at FP16
07
Distilled Stable Diffusion 2.1 reaches 25 it/s on A6000 GPU
08
Stable Diffusion XL refiner improves FID by 15% over base model
09
VRAM usage for Stable Diffusion v1.5 at 512x512 is 5.5GB peak
10
Stable Diffusion ControlNet adds 20% latency overhead on inference
11
Human preference win rate for SDXL vs Midjourney v5 is 48%
12
Stable Diffusion FP8 quantization speeds up 1.8x with 1% FID drop
13
Batch size 4 for Stable Diffusion on RTX 4090 yields 40 it/s
14
Stable Diffusion Inpainting CLIP score 0.35 vs 0.32 base
15
ELO score for Stable Diffusion 3 is 1025 on Artificial Analysis leaderboard
16
Inference steps reduction to 20 maintains 95% quality in Stable Diffusion
17
Stable Diffusion on T4 GPU: 3 it/s at 25 steps 512x512
18
Aesthetic score predictor correlates 0.85 with human ratings for SD outputs
19
Stable Diffusion Turbo 1-step FID 23.5 vs 12.0 at 50 steps
20
AnimateDiff FPS output averages 15 for 16-frame clips
Interpretation
Performance and Speed Metrics Interpretation
Stable Diffusion is a veritable workhorse of AI image generation—quick to produce, consistent in quality, and adaptable across hardware, churning out 512x512 images in under two seconds, 1MP shots in less than a second with just four steps, and even outlasting MidJourney v5 48% of the time; it’s impressively consistent, with FID scores as low as 12.0, CLIP alignment averaging 0.32, and 20 steps still retaining 95% of the quality, while performing well across GPUs (from Apple M1 Max’s 10 it/s to NVIDIA RTX 4090s’ 40 it/s with batch size 4) and even T4s at 3 it/s, with extras like the XL refiner boosting FID by 15%, FP8 quantization speeding things up 1.8x with minimal FID loss, ControlNet adding 20% latency, Inpainting nudging CLIP to 0.35, AnimateDiff hitting 15 FPS for 16-frame clips, and an aesthetic predictor that correlates 0.85 with human ratings—all while sitting at a solid ELO score of 1025 on benchmarks.
04 · Category
Training Data Statistics24 stats
01
Stable Diffusion v1.5 was trained on LAION-5B dataset containing 5.85 billion image-text pairs
02
LAION-5B dataset used for Stable Diffusion has an average image resolution of 512x512 pixels across its samples
03
Stable Diffusion training filtered out 12.8% of LAION-5B samples due to low quality or safety issues
04
The aesthetic quality score threshold for LAION-Aesthetics subset used in Stable Diffusion training was set at 4.5 out of 10
05
Stable Diffusion v2 used LAION-Aesthetics V2 with 2.1 billion high-quality samples
06
NSFW content in LAION-5B was estimated at 1.6% before filtering for Stable Diffusion
07
Stable Diffusion fine-tuning on 150k images took 100 A100-GPU hours for DreamBooth
08
LAION-5B metadata includes captions generated by CLIP ViT-L/14, covering 5.85B entries
09
Stable Diffusion XL training dataset size estimated at over 1 billion tokens post-filtering
10
Watermark detection filtered 2% of LAION-5B images during Stable Diffusion prep
11
Stable Diffusion v1.4 used 2.3B subset of LAION-400M refined
12
Text encoder in Stable Diffusion trained on 380M image-text pairs initially
13
Stable Diffusion 3 uses a synthetic dataset augmentation increasing effective size by 4x
14
LAION-COCO subset for Stable Diffusion captioning has 80k high-quality pairs
15
Blur detection removed 5.4% of LAION-5B for Stable Diffusion training
16
Stable Diffusion Inpainting model trained on 500k masked images from LAION
17
Multilingual LAION-5B++ covers 17 languages with 10B pairs, influencing Stable Diffusion variants
18
Stable Diffusion v1.5 depth model used 1M depth-map annotated images
19
Caption length in Stable Diffusion training data averages 12.5 tokens
20
Stable Diffusion XL filtered dataset for 1024x1024 resolution using 600M samples
21
Hate speech filtering in LAION for Stable Diffusion removed 0.1% samples
22
Stable Diffusion ControlNet trained on 3.5M edge-map pairs
23
LAION-Art dataset subset of 400k artistic images used in fine-tunes
24
Stable Diffusion AnimateDiff uses 100k video frame pairs for motion
Interpretation
Training Data Statistics Interpretation
Stable Diffusion, that AI image-maker, was shaped using a hodgepodge of datasets—like LAION-5B with 5.85 billion image-text pairs (most 512x512 pixels), filtered thoroughly (losing 12.8% to low quality, safety issues, NSFW, hate speech, blur, or watermarks) and spiced with a LAION-Aesthetics subset rated 4.5/10; LAION-400M added 2.3B samples, newer models like SDXL use over 1 billion tokens and 600M 1024x1024 pixels, and SD3 doubles its effective size via synthetic data; even the nuts and bolts matter, such as DreamBooth taking 100 A100-GPU hours on 150k images, LAION-COCO's 80k high-quality captions, CLIP-generated metadata, 1M depth-map annotated pictures, 12.5-token average captions, niche subsets like LAION-Art (400k) or ControlNet (3.5M edge-maps), and AnimateDiff's 100k video frame pairs for smooth motion.
05 · Category
Usage and Popularity Stats21 stats
01
Stable Diffusion model on Hugging Face has 45 million downloads as of 2024
02
Automatic1111 Stable Diffusion WebUI has 120k GitHub stars
03
Replicate hosts 10B Stable Diffusion inferences monthly
04
Stable Diffusion v1.5 checkpoint downloaded 50M+ times on Civitai
05
70% of AI art on DeviantArt generated with Stable Diffusion per 2023 survey
06
ComfyUI nodes for Stable Diffusion exceed 1k custom extensions
07
Stable Diffusion usage peaks at 5M daily generations on HF Spaces
08
40% of Fortune 500 companies use Stable Diffusion variants internally
09
Civitai hosts 100k+ Stable Diffusion LoRAs with 2B downloads
10
InvokeAI Stable Diffusion interface downloaded 500k times
11
Stable Diffusion prompts shared on Lexica.ai exceed 10M entries
12
25M users accessed DreamStudio Stable Diffusion platform by 2023
13
GitHub repos mentioning Stable Diffusion: over 20k as of 2024
14
Stable Diffusion fine-tunes on Civitai average 10k downloads each top 100
15
Fooocus UI for Stable Diffusion has 30k stars on GitHub
16
Stable Diffusion API calls on Replicate: 1B+ total inferences
17
Midjourney Discord vs Stable Diffusion: 15M vs 8M monthly actives 2023
18
Stable Diffusion models trained daily on HF: 500+
19
Pinterest AI art pins: 60% Stable Diffusion generated per analysis
20
Stable Diffusion WebUI extensions: 800+ available
21
Stable Diffusion Discord servers: 500k+ members across top communities
Interpretation
Usage and Popularity Stats Interpretation
Stable Diffusion isn’t just an AI trend—it’s a cultural, industrial, and creative behemoth, with 45 million downloads on Hugging Face, 120,000 stars for the Automatic1111 WebUI, 10 billion monthly Replicate inferences, over 50 million downloads of the v1.5 checkpoint on Civitai, 70% of DeviantArt’s AI art, 40% of Fortune 500 companies using variants, 10 million prompts on Lexica, 25 million DreamStudio users, 20,000 GitHub repos mentioning it, 100,000 LoRAs on Civitai with 2 billion downloads, 500,000 Discord members, 5 million daily generations on HF Spaces, more monthly Discord actives than Midjourney (15 million vs. 8 million), and 60% of Pinterest’s AI art pins—all by 2024.
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Nathan Caldwell. (2026, February 24). Stable Diffusion Statistics. Gitnux. https://gitnux.org/stable-diffusion-statistics
MLA
Nathan Caldwell. "Stable Diffusion Statistics." Gitnux, 24 Feb 2026, https://gitnux.org/stable-diffusion-statistics.
Chicago
Nathan Caldwell. 2026. "Stable Diffusion Statistics." Gitnux. https://gitnux.org/stable-diffusion-statistics.
Sources & references
25 datasets cited across this report · attribution is report-level