metadata
license: cc-by-nc-4.0
Github repo: https://github.com/magic-research/piecewise-rectified-flow
PeRFlow accelerated SDXL-DreamShaper: https://huggingface.co/Lykon/dreamshaper-xl-1-0
Demo:
import random, os
import numpy as np
from pathlib import Path
import torch, torchvision
def setup_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
Path("demo").mkdir(parents=True, exist_ok=True)
from diffusers import StableDiffusionXLPipeline
pipe = StableDiffusionXLPipeline.from_pretrained("hansyan/perflow-sdxl-dreamshaper", torch_dtype=torch.float16, use_safetensors=True, variant="v0-fix")
from src.scheduler_perflow import PeRFlowScheduler
pipe.scheduler = PeRFlowScheduler.from_config(pipe.scheduler.config, prediction_type="ddim_eps", num_time_windows=4)
pipe.to("cuda", torch.float16)
prompts_list = [
["photorealistic, uhd, high resolution, high quality, highly detailed; RAW photo, a handsome man, wearing a black coat, outside, closeup face",
"distorted, blur, low-quality, haze, out of focus",],
["photorealistic, uhd, high resolution, high quality, highly detailed; masterpiece, A closeup face photo of girl, wearing a rain coat, in the street, heavy rain, bokeh,",
"distorted, blur, low-quality, haze, out of focus",],
["photorealistic, uhd, high resolution, high quality, highly detailed; RAW photo, a red luxury car, studio light",
"distorted, blur, low-quality, haze, out of focus",],
["photorealistic, uhd, high resolution, high quality, highly detailed; masterpiece, A beautiful cat bask in the sun",
"distorted, blur, low-quality, haze, out of focus",],
]
num_inference_steps = 6 # suggest steps >= num_win=4
cfg_scale_list = [2.0] # suggest values [1.5, 2.0, 2.5]
num_img = 2
seed = 42
for cfg_scale in cfg_scale_list:
for i, prompts in enumerate(prompts_list):
setup_seed(seed)
prompt, neg_prompt = prompts[0], prompts[1]
samples = pipe(
prompt = [prompt] * num_img,
negative_prompt = [neg_prompt] * num_img,
height = 1024,
width = 1024,
num_inference_steps = num_inference_steps,
guidance_scale = cfg_scale,
output_type = 'pt',
).images
cfg_int = int(cfg_scale); cfg_float = int(cfg_scale*10 - cfg_int*10)
save_name = f'step_{num_inference_steps}_txt{i+1}_cfg{cfg_int}-{cfg_float}.png'
torchvision.utils.save_image(torchvision.utils.make_grid(samples, nrow = num_img), os.path.join("demo", save_name))