MODEL PROFILE

PixArt-Sigma-XL-2-1024-MS

PixArt-alpha · PixArt-Sigma-XL-2-1024-MS

PixArt-Σ consists of pure transformer blocks for latent diffusion: It can directly generate 1024px, 2K and 4K images from text prompts within a single sampling process. For research purposes, we recommend our `generative-models` Github repository (https://github.com/PixArt-alpha/PixArt-sigma), which is more suitable for both training and inference and for which most advanced diffusion sampler like SA-Solver will be added over time. Hugging Face provides free PixArt-Σ inference. - **Repository:** https://github.com/PixArt-alpha/PixArt-sigma - **Demo:** https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma > [!IMPORTANT] > Make sure to upgrade diffusers to >= 0.28.0: > > In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`: > > For `diffusers = 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:

Image generationlicense:openrail++
Context windowNot disclosed
Input → OutputText → Image
Model weightsNot verified
Profile updated2026/11/2 19:17:11
OVERVIEW

Model overview

PixArt-Σ consists of pure transformer blocks for latent diffusion: It can directly generate 1024px, 2K and 4K images from text prompts within a single sampling process. For research purposes, we recommend our `generative-models` Github repository (https://github.com/PixArt-alpha/PixArt-sigma), which is more suitable for both training and inference and for which most advanced diffusion sampler like SA-Solver will be added over time. Hugging Face provides free PixArt-Σ inference. - **Repository:** https://github.com/PixArt-alpha/PixArt-sigma - **Demo:** https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma > [!IMPORTANT] > Make sure to upgrade diffusers to >= 0.28.0: > > In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`: > > For `diffusers = 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:openrail++。模型介绍与第三方评测均以原来源的具体版本和方法为准。