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import gradio as gr
import torch
from transformers import CLIPProcessor, CLIPModel
from torch import nn
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
# Load CLIP model and processor
model_name = "openai/clip-vit-base-patch16"
clip_model = CLIPModel.from_pretrained(model_name)
clip_processor = CLIPProcessor.from_pretrained(model_name)
# Define a simple generator network
class SimpleGenerator(nn.Module):
def __init__(self):
super(SimpleGenerator, self).__init__()
self.fc = nn.Sequential(
nn.Linear(512, 1024),
nn.ReLU(),
nn.Linear(1024, 256*256*3), # Output image pixels
nn.Tanh() # Normalize output between -1 and 1
)
def forward(self, z):
x = self.fc(z)
x = x.view(256, 256, 3) # Reshape to image format
return x
# Initialize the generator model
generator = SimpleGenerator()
# Function to generate an image based on text input
def generate_image_from_text(text_input):
# Preprocess text input using CLIP
inputs = clip_processor(text=[text_input], return_tensors="pt", padding=True)
text_features = clip_model.get_text_features(**inputs)
# Generate image tensor
with torch.no_grad():
generated_image_tensor = generator(text_features)
# Normalize tensor to (0, 255)
generated_image = (generated_image_tensor - generated_image_tensor.min()) / (generated_image_tensor.max() - generated_image_tensor.min())
generated_image = (generated_image * 255).cpu().numpy().astype(np.uint8)
# Convert to PIL Image
image = Image.fromarray(generated_image)
return image
# Gradio interface
iface = gr.Interface(fn=generate_image_from_text, inputs="text", outputs="image", live=True)
iface.launch()
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