pip install diffusers -U

from diffusers import AutoPipelineForText2Image
import torch
from modelscope import snapshot_download
model_dir = snapshot_download("AI-ModelScope/sdxl-turbo")
pipe = AutoPipelineForText2Image.from_pretrained(model_dir, torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
prompt = "Beautiful and cute girl, 16 years old, denim jacket, gradient background, soft colors, soft lighting, cinematic edge lighting, light and dark contrast, anime, art station Seraflur, blind box, super detail, 8k"
image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]
image.save("image.png")

from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
from modelscope import snapshot_download
model_dir = snapshot_download("AI-ModelScope/sdxl-turbo")
pipe = AutoPipelineForImage2Image.from_pretrained(model_dir,  variant="fp16")
init_image = load_image("image.png").resize((512, 512))
prompt = "grey image"
image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0]
image.save("image1.png")

from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
import torch
from modelscope import snapshot_download
model_dir_lcm = snapshot_download("AI-ModelScope/lcm-sdxl",revision = "master")
model_dir_sdxl = snapshot_download("AI-ModelScope/stable-diffusion-xl-base-1.0",revision = "v1.0.9")
unet = UNet2DConditionModel.from_pretrained(model_dir_lcm, torch_dtype=torch.float16, variant="fp16")
pipe = DiffusionPipeline.from_pretrained(model_dir_sdxl, unet=unet, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
prompt = "Beautiful and cute girl, 16 years old, denim jacket, gradient background, soft colors, soft lighting, cinematic edge lighting, light and dark contrast, anime, art station Seraflur, blind box, super detail, 8k"
image = pipe(prompt, num_inference_steps=4, guidance_scale=8.0).images[0]
image.save("image.png")

# Step1: 我的notebook -> PAI-DSW -> GPU环境
# Step2: Entry the Notebook cell，clone FaceChain from github:
!GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/modelscope/facechain.git --depth 1
# Step3: Change the working directory to facechain, and install the dependencies:
import os
os.chdir('/mnt/workspace/facechain')    # You may change to your own path
print(os.getcwd())
!pip3 install gradio==3.50.2
!pip3 install controlnet_aux==0.0.6
!pip3 install python-slugify
!pip3 install onnxruntime==1.15.1
!pip3 install edge-tts
# Step4: Start the app service, click "public URL" or "local URL", upload your images to 
# train your own model and then generate your digital twin.
!python3 app.py