Diffusion Model with Perceptual Loss
Paper • 2401.00110 • Published • 13
How to use ByteDance/sd2.1-base-zsnr-laionaes6 with Diffusers:
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]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]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.
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")
@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}
}