Text-to-Image
Diffusers
StableDiffusionPipeline
stable-diffusion
How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("ByteDance/sd2.1-base-zsnr-laionaes6", dtype=torch.bfloat16, device_map="cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

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

This model is used in Diffusion Model with Perceptual Loss paper as the MSE baseline.

This model is trained using zero terminal SNR schedule following Common Diffusion Noise Schedules and Sample Steps are Flawed paper on LAION aesthetic 6+ data.

This model is finetuned from stabilityai/stable-diffusion-2-1-base.

This model is meant for research demonstration, not for production use.

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")

Related Models

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}
}
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