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import torch
from PIL import Image
def get_fashion_text_embeddings(fclip, cfg, device):
print(f'Target text prompt is {cfg.text_prompt}')
print(f'Base text prompt is {cfg.base_text_prompt}')
with torch.no_grad():
text_embeds = fclip.encode_text_tensors([cfg.text_prompt]).detach()
base_text_embeds = fclip.encode_text_tensors([cfg.base_text_prompt]).detach()
target_text_embeds = text_embeds.clone() / text_embeds.norm(dim=1, keepdim=True)
delta_text_embeds = text_embeds - base_text_embeds
delta_text_embeds = delta_text_embeds / delta_text_embeds.norm(dim=1, keepdim=True)
return target_text_embeds.to(device), delta_text_embeds.to(device)
def get_fashion_img_embeddings(fclip, cfg, device, normalize=True):
print(f'Target image path is {cfg.image_prompt}')
print(f'Base image path is {cfg.base_image_prompt}')
with torch.no_grad():
target_image_embeds = fclip.encode_images([cfg.image_prompt], 1)
target_image_embeds = torch.tensor(target_image_embeds, device=device).detach()
base_image_embeds = fclip.encode_images([cfg.base_image_prompt], 1)
base_image_embeds = torch.tensor(base_image_embeds, device=device).detach()
delta_img_embeds = target_image_embeds - base_image_embeds
if normalize:
delta_img_embeds = delta_img_embeds / delta_img_embeds.norm(dim=1, keepdim=True)
target_image_embeds = target_image_embeds.clone() / target_image_embeds.norm(dim=1, keepdim=True)
return target_image_embeds.to(device), delta_img_embeds.to(device)
def get_text_embeddings(clip, model, cfg, device):
print(f'Target text prompt is {cfg.text_prompt}')
print(f'Base text prompt is {cfg.base_text_prompt}')
text_embeds = clip.tokenize(cfg.text_prompt).to(device)
base_text_embeds = clip.tokenize(cfg.base_text_prompt).to(device)
with torch.no_grad():
text_embeds = model.encode_text(text_embeds).detach()
target_text_embeds = text_embeds.clone() / text_embeds.norm(dim=1, keepdim=True)
delta_text_embeds = text_embeds - model.encode_text(base_text_embeds)
delta_text_embeds = delta_text_embeds / delta_text_embeds.norm(dim=1, keepdim=True)
return target_text_embeds, delta_text_embeds
def get_img_embeddings(model, preprocess, cfg, device):
print(f'Target image path is {cfg.image_prompt}')
print(f'Base image path is {cfg.base_image_prompt}')
image = preprocess(Image.open(cfg.image_prompt)).unsqueeze(0).to(device)
base_image = preprocess(Image.open(cfg.base_image_prompt)).unsqueeze(0).to(device)
with torch.no_grad():
target_image_embeds = model.encode_image(image).to(device).detach()
base_image_embeds = model.encode_image(base_image).to(device)
delta_img_embeds = target_image_embeds - base_image_embeds
delta_img_embeds = delta_img_embeds / delta_img_embeds.norm(dim=1, keepdim=True)
return target_image_embeds, delta_img_embeds
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