Create app.py

app.py

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+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")