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2.5 KiB
2.5 KiB
How to Use
You can run PixArt-α / PixArt-Σ with stable-diffusion.cpp.
PixArt is a DiT-based text-to-image model family conditioned by a T5-XXL text encoder and a 4-channel VAE: SDXL-style for PixArt-Σ and SD1.x-style for PixArt-α.
Download weights
- Download the transformer (diffusion model)
- PixArt-Σ XL-2 1024-MS: https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/transformer
- PixArt-α XL-2 1024-MS: https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS/tree/main/transformer
- Download the T5-XXL text encoder
- Download the VAE
- PixArt-Σ: https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/vae
- PixArt-α: https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS/tree/main/vae
- Use the VAE matching the checkpoint's latent space. For TAE decoding or preview, use TAESDXL for PixArt-Σ and TAESD for PixArt-α.
- Tokenizer: the T5 vocabulary is embedded; no extra tokenizer file is needed.
Examples
.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\pixart_sigma_xl2_1024_ms.safetensors --t5xxl ..\models\text_encoders\t5xxl.safetensors --vae ..\models\vae\pixart_vae.safetensors -p "a lovely cat" --cfg-scale 4.5 -W 1024 -H 1024 --steps 20 -v
Notes
- The VAE scaling factor defaults to
0.13025for PixArt-Σ. PixArt-α checkpoints with resolution micro-condition weights use0.18215. PixArt-α 512 has the same tensor layout as PixArt-Σ, so it requires an explicit override:--model-args "pixart_vae_scale_factor=0.18215". This argument can also override the scale for other compatible checkpoints. - PixArt-Σ checkpoints compute 2D sincos positional embeddings at runtime;
the trained grid is 64x64 patches with an interpolation scale of 2.
For checkpoints trained at a different resolution, the positional embedding
parameters can be adjusted via model args:
--model-args "pixart_pos_embed_base_size=<trained grid>,pixart_interpolation_scale=<scale>"(e.g.pixart_pos_embed_base_size=32,pixart_interpolation_scale=1,pixart_vae_scale_factor=0.18215for PixArt-α XL-2 512). - Checkpoints carrying resolution/aspect-ratio micro-condition weights are detected but those conditions are not applied yet; a warning is logged and generation proceeds with the timestep embedding only.
- The transformer predicts 8 channels (noise + learned variance); only the noise half is used for sampling, matching the reference implementation.