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import streamlit as st | |
from transformers import CLIPProcessor, CLIPModel | |
import torch | |
from PIL import Image | |
import requests | |
from io import BytesIO | |
st.title("Text to Image Generation with CLIP") | |
# Load pretrained models | |
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16") | |
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16") | |
text = st.text_area("Enter a description:") | |
if st.button("Generate Image") and text: | |
# Process text and get CLIP features for text | |
text_features = clip_processor(text, return_tensors="pt", padding=True) | |
# Load an example image from the web (replace this with your image loading logic) | |
example_image_url = "https://example.com/your-image.jpg" | |
example_image_response = requests.get(example_image_url) | |
example_image = Image.open(BytesIO(example_image_response.content)) | |
# Process image and get CLIP features for image | |
image_features = clip_processor(images=example_image, return_tensors="pt", padding=True) | |
# Concatenate text and image features | |
combined_features = { | |
"pixel_values": torch.cat([text_features.pixel_values, image_features.pixel_values], dim=-1) | |
} | |
# Forward pass through CLIP | |
image_representation = clip_model(**combined_features).last_hidden_state.mean(dim=1) | |
# For visualization, you can convert the image representation back to an image | |
image_array = image_representation.squeeze().cpu().numpy() | |
image = Image.fromarray((image_array * 255).astype('uint8')) | |
# Display the generated image | |
st.image(image, caption="Generated Image", use_column_width=True) | |