|
--- |
|
license: cc-by-nc-4.0 |
|
--- |
|
**Github repo**: https://github.com/magic-research/piecewise-rectified-flow <br> |
|
**PeRFlow accelerated SDXL-DreamShaper**: https://huggingface.co/Lykon/dreamshaper-xl-1-0 |
|
|
|
**Demo:** |
|
```python |
|
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)) |
|
|
|
``` |
