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Luo-Yihong/
yoso_sd1.5_lora

Name: yoso_sd1.5_lora
Rating: 2.5 (38 reviews)

stable-diffusion-v1-5/stable-diffusion-v1-5

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You Only Sample Once (YOSO)

The YOSO was proposed in "You Only Sample Once: Taming One-Step Text-To-Image Synthesis by Self-Cooperative Diffusion GANs" by Yihong Luo, Xiaolong Chen, Xinghua Qu, Jing Tang.

Official Repository of this paper: YOSO.

Note

This is our old-version LoRA. We have re-trained the YOSO-LoRA via more computational resources and better data, achieving better one-step performance. Check the technical report for more details! The newly trained LoRA may be released in the next few months.

Usage

1-step inference

1-step inference is only allowed based on SD v1.5 for now. And you should prepare the informative initialization according to the paper for better results.

python

import torch
from diffusers import DiffusionPipeline, LCMScheduler
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype = torch.float16)
pipeline = pipeline.to('cuda')
pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_lora_weights('Luo-Yihong/yoso_sd1.5_lora')
generator = torch.manual_seed(318)
steps = 1
bs = 1
latents = ... # maybe some latent codes of real images or SD generation
latent_mean = latent.mean(dim=0)
init_latent = latent_mean.repeat(bs,1,1,1) + latents.std()*torch.randn_like(latents) 
noise = torch.randn([bs,4,64,64])
input_latent = pipeline.scheduler.add_noise(init_latent,noise,T)
imgs= pipeline(prompt="A photo of a dog",
                    num_inference_steps=steps, 
                    num_images_per_prompt = 1,
                        generator = generator,
                        guidance_scale=1.5,
                    latents = input_latent,
                   )[0]
imgs

The simple inference without informative initialization, but worse quality:

python

pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype = torch.float16)
pipeline = pipeline.to('cuda')
pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_lora_weights('Luo-Yihong/yoso_sd1.5_lora')
generator = torch.manual_seed(318)
steps = 1
imgs = pipeline(prompt="A photo of a corgi in forest, highly detailed, 8k, XT3.",
                    num_inference_steps=1, 
                    num_images_per_prompt = 1,
                        generator = generator,
                        guidance_scale=1.,
                   )[0]
imgs[0]

2-step inference

We note that a small CFG can be used to enhance the image quality.

python

pipeline = DiffusionPipeline.from_pretrained("stablediffusionapi/realistic-vision-v51", torch_dtype = torch.float16)
pipeline = pipeline.to('cuda')
pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_lora_weights('Luo-Yihong/yoso_sd1.5_lora')
generator = torch.manual_seed(318)
steps = 2
imgs= pipeline(prompt="A photo of a man, XT3",
                    num_inference_steps=steps, 
                    num_images_per_prompt = 1,
                        generator = generator,
                        guidance_scale=1.5,
                   )[0]
imgs

Moreover, it is observed that when combined with new base models, our YOSO-LoRA is able to use some advanced ode-solvers:

python

import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
pipeline = DiffusionPipeline.from_pretrained("stablediffusionapi/realistic-vision-v51", torch_dtype = torch.float16)
pipeline = pipeline.to('cuda')
pipeline.load_lora_weights('Luo-Yihong/yoso_sd1.5_lora')
pipeline.scheduler = DPMSolverMultistepScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
generator = torch.manual_seed(323)
steps = 2
imgs= pipeline(prompt="A photo of a girl, XT3",
                    num_inference_steps=steps, 
                    num_images_per_prompt = 1,
                        generator = generator,
                        guidance_scale=1.5,
                   )[0]
imgs[0]

We encourage you to experiment with various solvers to obtain better samples. We will try to improve the compatibility of the YOSO-LoRA with different solvers.

You may try some interesting applications, like:

python

generator = torch.manual_seed(318)
steps = 2
img_list = []
for age in [2,20,30,50,60,80]:
    imgs = pipeline(prompt=f"A photo of a cute girl, {age} yr old, XT3",
                        num_inference_steps=steps, 
                        num_images_per_prompt = 1,
                            generator = generator,
                            guidance_scale=1.1,
                       )[0]
    img_list.append(imgs[0])
make_image_grid(img_list,rows=1,cols=len(img_list))

You can increase the steps to improve sample quality.

Bibtex

@misc{luo2024sample,
      title={You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs}, 
      author={Yihong Luo and Xiaolong Chen and Xinghua Qu and Jing Tang},
      year={2024},
      eprint={2403.12931},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}