app.py

import torch
from PIL import Image
from torchvision import transforms
from transformers import CLIPProcessor, CLIPModel

# load the CLIP model
device = "cuda" if torch.cuda.is_available() else "cpu"
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
model.to(device)

# load the CLIP processor
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# load the image
image_path = "path/to/image.jpg"
image = Image.open(image_path)

# resize the image
resize = transforms.Resize((224, 224))
image = resize(image)

# convert the image to a tensor
tensor = transforms.ToTensor()(image)
tensor = tensor.to(device)

# get the image features using the CLIP model
with torch.no_grad():
    features = model.encode_image(tensor.unsqueeze(0))

# generate variations of the image using the CLIP model and processor
with torch.no_grad():
    outputs = model.generate_images(
        features=features,
        num_images=5,  # number of different variations to generate
        max_length=50,  # maximum length of the generated caption for the variation
        clip=processor,
        temperature=1.0,  # temperature of the sampling process
        top_p=0.9,  # top-p probability for the sampling process
        batch_size=1,
        device=device,
    )

# save the generated images
for i, output in enumerate(outputs):
    generated_image = transforms.functional.to_pil_image(output[0])
    generated_image.save(f"output/image_variation_{i}.jpg")