masking.py

import os
import sys

import matplotlib.pyplot as plt
import torch

sys.path.append("taming-transformers")
import functools

import gradio as gr
from transformers import CLIPModel, CLIPProcessor

import edit
# import importlib
# importlib.reload(edit)
from backend import ImagePromptOptimizer, ImageState, ProcessorGradientFlow
from loaders import load_default

device = "cuda"
vqgan = load_default(device)
vqgan.eval()
processor = ProcessorGradientFlow(device=device)
clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip.to(device)
promptoptim = ImagePromptOptimizer(vqgan, clip, processor, quantize=True)
state = ImageState(vqgan, promptoptim)
mask = torch.load("eyebrow_mask.pt")
x = state.blend("./test_data/face.jpeg", "./test_data/face2.jpeg", 0.5)
plt.imshow(x)
plt.show()
state.apply_prompts("a picture of a woman with big eyebrows", "", 0.009, 40, None, mask=mask)
print('done')