whatmodel.sh · find the best image models you can run

Best image models to run locally on a 8GB PC without a graphics card

A leaderboard of the best image models you can run locally on a computer with 8GB of RAM and no graphics card. Dreamlike Photoreal 2.0 is #1 right now, followed by Openjourney and Stable Diffusion v1.4. Models are ranked by one score that combines 73 published benchmarks. Each model shows the download that fits and the command to run it.

8 models · scores combine 73 benchmarks · updated how it works · find a leaderboard · benchmark your machine
Models for 8GB RAM · no GPU → ~3GB for models (CPU, slow) · local only · reset
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$ terminal

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Score vs memory to run

#modelscore↓where↓speed↓RAM↓get itsources
178Dreamlike Photoreal 2.0 · q8_0 (sd.cpp --type) Dreamlike.art30±3local (slow)~2.1GBhow to run › EvalAlign: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models (arXiv:2406.16562v3)HEIM: Holistic Evaluation of Text-to-Image Models (arXiv 2311.04287v1)tgxs002/HPSv2 repository README, HPS v2 benchmark table
185Openjourney · q8_0 (sd.cpp --type) PromptHero28±2local (slow)~2.1GBhow to run › GenAI Arena: An Open Evaluation Platform for Generative Models (arXiv v3)EvalAlign: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models (arXiv:2406.16562v3)ImagenHub: Standardizing the evaluation of conditional image generation models (arXiv v4)HEIM: Holistic Evaluation of Text-to-Image Models (arXiv 2311.04287v1)tgxs002/HPSv2 repository README, HPS v2 benchmark table
186Stable Diffusion v1.4 · q8_0 (sd.cpp --type) CompVis27±2local (slow)~2.1GBhow to run › GenEval authors' results: djghosh13/geneval README and arXiv 2310.11513v1EvalAlign: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models (arXiv:2406.16562v3)ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty (arXiv v1)ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation (arXiv 2304.05977v4)HEIM: Holistic Evaluation of Text-to-Image Models (arXiv 2311.04287v1)tgxs002/HPSv2 repository README, HPS v2 benchmark table
187Stable Diffusion v1.3 · q8_0 (sd.cpp --type) CompVis27±4*local (slow)~2.1GBhow to run › GenEval authors' results: djghosh13/geneval README and arXiv 2310.11513v1
188Stable Diffusion v1.1 · q8_0 (sd.cpp --type) CompVis26±3local (slow)~2.1GBhow to run › GenEval authors' results: djghosh13/geneval README and arXiv 2310.11513v1TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering (arXiv v3)REAL: Realism Evaluation of Text-to-Image Generation Models for Effective Data Augmentation (arXiv:2502.10663v1)
189Stable Diffusion v1.2 · q8_0 (sd.cpp --type) CompVis26±3*local (slow)~2.1GBhow to run › GenEval authors' results: djghosh13/geneval README and arXiv 2310.11513v1EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation (arXiv v2)
191Stable Diffusion v1.5 · q8_0 (sd.cpp --type) Stability AI26±2local (slow)~2.1GBhow to run › GenEval authors' results: djghosh13/geneval README and arXiv 2310.11513v1EvalAlign: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models (arXiv:2406.16562v3)EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model Evaluation (arXiv v2)TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering (arXiv v3)HEIM: Holistic Evaluation of Text-to-Image Models (arXiv 2311.04287v1)Evaluating Numerical Reasoning in Text-to-Image Models (GeckoNum, arXiv v3)+1
193LCM Dreamshaper v7 · openvino-fp16 (OpenVINO GenAI) SimianLuo (LCM authors)23±4*local (slow)~3GBhow to run › GenAI Arena: An Open Evaluation Platform for Generative Models (arXiv v3)

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202 of 202 catalogue models listed here · sources and licences

* new or thinly tested: ranked on too few benchmark results · † only the maker’s own numbers so far · ~RAM is estimated; full context may need more memory