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Vladimir Mandic edited this page Oct 29, 2024 · 30 revisions

Models

List of popular text-to-image generative models with their respective parameters and architecture overview
Original URL: https://github.com/vladmandic/automatic/wiki/Models

Publisher Model Version Size Diffusion Architecture Model Params Text Encoder(s) TE Params Auto Encoder Other
StabilityAI Stable Diffusion 1.5 2.28GB UNet 0.86B CLiP ViT-L 0.12B VAE
StabilityAI Stable Diffusion 2.1 2.58GB UNet 0.86B CLiP ViT-H 0.34B VAE
StabilityAI Stable Diffusion XL 6.94GB UNet 2.56B CLiP ViT-L + ViT+G 0.12B + 0.69B VAE
StabilityAI Stable Diffusion 3.0 Medium 15.14GB MMDiT 2.0B CLiP ViT-L + ViT+G + T5-XXL 0.12B + 0.69B + 4.76B 16ch VAE
StabilityAI Stable Diffusion 3.5 Medium 15.89GB MMDiT 2.25B CLiP ViT-L + ViT+G + T5-XXL 0.12B + 0.69B + 4.76B 16ch VAE
StabilityAI Stable Diffusion 3.5 Large 26.98GB MMDiT 8.05B CLiP ViT-L + ViT+G + T5-XXL 0.12B + 0.69B + 4.76B 16ch VAE
StabilityAI Stable Cascade Medium 11.82GB Multi-stage UNet 1.56B + 3.6B CLiP ViT-G 0.69B 42x VQE
StabilityAI Stable Cascade Lite 4.97GB Multi-stage UNet 0.7B + 1.0B CLiP ViT-G 0.69B 42x VQE
Black Forest Labs Flux 1 Dev/Schnell 32.93GB MMDiT 11.9B CLiP ViT-L + T5-XXL 0.12B + 4.769B 16ch VAE
FAL AuraFlow 0.3 31.90GB MMDiT 6.8B UMT5 12.1B VAE
AlphaVLLM Lumina Next SFT 8.67GB DiT 1.7B Gemma 2.5B VAE LM
PixArt Alpha XL 2 21.3GB DiT 0.61B T5-XXL 4.76B VAE
PixArt Sigma XL 2 21.3GB DiT 0.61B T5-XXL 4.76B VAE
Segmind SSD-1B N/A 8.72GB UNet 1.33B CLiP ViT-L + ViT+G 0.12B + 0.69B VAE
Segmind Vega N/A 6.43GB UNet 0.75B CLiP ViT-L + ViT+G 0.12B + 0.69B VAE
Segmind Tiny N/A 1.03GB UNet 0.32B CLiP ViT-L 0.12B VAE
Kwai Kolors N/A 17.40GB UNnet 2.58B ChatGLM 6.24B VAE LM
PlaygroundAI Playground 1.0 4.95GB UNet 0.86B CLiP ViT-L 0.12B VAE
PlaygroundAI Playground 2.x 13.35GB UNet 2.56B CLiP ViT-L + ViT+G 0.12B + 0.69B VAE
Tencent HunyuanDiT 1.2 14.09GB DiT 1.5B BERT + T5-XL 3.52B + 1.67B VAE LM
Warp AI Wuerstchen N/A 12.16GB Multi-stage UNet 1.0B + 1.05B CLiP ViT-L + ViT+G 0.12B + 0.69B 42x VQE
Kandinsky Kandinsky 2.2 5.15GB Unet 1.25B CLiP ViT-G 0.69B VQ
Kandinsky Kandinsky 3.0 27.72GB Unet 3.05B T5-XXXL 8.72B VQ
Thudm CogView 3 Plus 24.96GB DiT 2.85B T5-XXL 4.76B VAE
IDKiro SDXS N/A 2.05GB UNet 0.32B CLiP ViT-L 0.12B VAE
Open-MUSE aMUSEd 256 3.41GB ViT 0.60B CLiP ViT-L 0.12B VQ
Koala Koala 700M 6.58GB UNet 0.78B CLiP ViT-L + ViT+G 0.12B + 0.69B VAE
Thu-ML UniDiffuser v1 5.37GB U-ViT 0.95B CLiP ViT-L + CLiP ViT-B 0.12B + 0.16B VAE
Salesforce BLIP-Diffusion N/A 7.23GB UNet 0.86B CLiP ViT-L + BLiP-2 0.12B + 0.49B VAE
DeepFloyd IF M 12.79GB Multi-stage UNet 0.37B + 0.46B T5-XXL 4.76B Pixel
DeepFloyd IF L 15.48GB Multi-stage UNet 0.61B + 0.93B T5-XXL 4.76B Pixel
MeissonFlow Meissonic N/A 3.64GB DiT 1.18B CLiP ViT-H 0.35B VQ
VectorSpaceLab OmniGen v1 15.47GB Transformer 3.76B None 0 VAE Phi-3

Notes

  • Created using SD.Next built-in model analyzer
  • Number of parameters is proportional to model complexity and ability to learn
    Quality of generated images is also influenced by training data and duration of training
  • Size refers to original model variant in 16bit precision where available
    Quantized variations may be smaller
  • Distilled variants are not included as typical goal-distilling does not change underlying model params
    e.g. Turbo/LCM/Hyper/Lightning/etc. or even Dev/Schnell

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