Sd2.1 Base Zsnr Laionaes6

🚀 SD v2.1-base with Zero Terminal SNR (LAION Aesthetic 6+)

This model is utilized in the Diffusion Model with Perceptual Loss paper as the MSE baseline. It is trained using the zero terminal SNR schedule, following the methodology presented in the Common Diffusion Noise Schedules and Sample Steps are Flawed paper, on LAION aesthetic 6+ data. This model is a fine - tuned version of stabilityai/stable-diffusion-2-1-base and is intended for research demonstration, not for production use.

🚀 Quick Start

This model is designed to generate text - to - image results. It is based on the StableDiffusion architecture and trained with specific schedules and datasets.

💻 Usage Examples

Basic Usage

from diffusers import StableDiffusionPipeline
prompt = "A young girl smiling"
pipe = StableDiffusionPipeline.from_pretrained("ByteDance/sd2.1-base-zsnr-laionaes6").to("cuda")
pipe(prompt, guidance_scale=7.5, guidance_rescale=0.7).images[0].save("out.jpg")

📚 Documentation

Related Models

• bytedance/sd2.1-base-zsnr-laionaes5
• bytedance/sd2.1-base-zsnr-laionaes6
• bytedance/sd2.1-base-zsnr-laionaes6-perceptual

Cite as

@misc{lin2024diffusion,
      title={Diffusion Model with Perceptual Loss}, 
      author={Shanchuan Lin and Xiao Yang},
      year={2024},
      eprint={2401.00110},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{lin2023common,
      title={Common Diffusion Noise Schedules and Sample Steps are Flawed}, 
      author={Shanchuan Lin and Bingchen Liu and Jiashi Li and Xiao Yang},
      year={2023},
      eprint={2305.08891},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

📄 License

This model is released under the OpenRAIL++ license.