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Stable Diffusion v1.1

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Stable Diffusion v1.1 is an open-weights image model from CompVis, released August 2022. Its published size is 0.86B parameters. Licence: creativeml-openrail-m.

Catalogue identity notes

CompVis/stable-diffusion-v1-1 checkpoint. params_b = 860M UNet (github.com/CompVis/stable-diffusion README); memory is the site's params x 2 bytes x 1.2 + 0.5 estimate.

25.8±2.8#138 of 144 image modelsfrom 3 benchmark results across 3 sources

Score within the LMArena text-to-image comparison set only.

$ how it compares

The image models ranked either side of Stable Diffusion v1.1, out of 197 with a score, then more from CompVis.

#modelscorewhereRAMreleased
186Stable Diffusion v1.4 CompVis27±2local~2.1GB2022-08
187Stable Diffusion v1.3 CompVis27±4local~2.1GB2022-08
188Stable Diffusion v1.1 CompVis26±3local~2.1GB2022-08
189Stable Diffusion v1.2 CompVis26±3local~2.1GB2022-08
190HiDream-E1.1 HiDream26±11comparison only2025-07
more from CompVis
106Latent Diffusion text2img-large (LDM-KL-8-G, LAION-400M) CompVis48±15local~7.5GB2022-04

see all image models · find the ones that fit your machine

$ what it's good at

text to image▃▅▅▃▄▄█▃▁▂▁▁ #123 of 126 · 97% of its score · 3 tests

Trails CogView2 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GenEval41.0% ✎▅█···▅▃▃▃··▃ beats 0/10easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 53%at its level
TIFA v1.0 (overall, mPLUG VQA)75.7% ✎█·······▄▄▄▄ beats 2/5very easy · avg 95%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40%typical
REAL realism benchmark for T2I (average of attribute, relationship and style scores)52.7% ✎█··········▄ beats 1/2easy · avg 65%▇▇ 4.1%blunt · at its level · small run

$ where to run it

Other ways to run it

version

  1. Run it as published · needs ~2.6 GB, NVIDIA only · from huggingface.co/CompVis (official)
    uv run --with diffusers --with transformers --with accelerate --with torch python -c "import torch; from diffusers import StableDiffusionPipeline; p=StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-1', torch_dtype=torch.float16).to('cuda'); p('a cat holding a sign').images[0].save('out.png')"

Official weights and documentation: huggingface.co/CompVis ↗

$ how the score was worked out · GenEval authors' results: djg… 53%, TIFA: Accurate and Interpreta… 40%, REAL: Realism Evaluation of T… 4%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
GenEval authors' results: djghosh13/geneval README and arXiv 2310.11513v11▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 53%MIT (README); CC-BY-4.0 (arXiv 2310.11513v1)
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering (arXiv v3)1▇▇▇▇▇▇▇▇▇▇▇▇ 40%CC-BY-4.0
REAL: Realism Evaluation of Text-to-Image Generation Models for Effective Data Augmentation (arXiv:2502.10663v1)1▇ 4%CC-BY-4.0
prior · pull toward the middle▇ 3%
weighted average= 25.8

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Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json