Update app.py

app.py

@@ -1,21 +1,54 @@
 import gradio as gr
-from diffusers import StableDiffusionPipeline
 import torch
+from transformers import CLIPProcessor, CLIPModel
+from torch import nn
+import numpy as np
+import PIL
+from PIL import Image
+from torchvision import transforms
 
-# Load the Stable Diffusion model from Hugging Face's diffusers library
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4-original", torch_dtype=torch.float16)
-pipe.to("cuda")
+# 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)
 
-def generate_image(prompt):
-    # Generate the image based on the text prompt
-    image = pipe(prompt).images[0]
-    return image
+# Generate a random noise tensor (this will be transformed into an image)
+def generate_image_from_text(text_input):
+    # Preprocess the input text for CLIP model
+    inputs = clip_processor(text=text_input, return_tensors="pt", padding=True)
 
-# Create Gradio interface
-interface = gr.Interface(fn=generate_image,
-                         inputs="text",
-                         outputs="image",
-                         title="Text to Image Generator",
-                         description="Generate images from text using Stable Diffusion.")
+    # Extract image-text features using CLIP
+    text_features = clip_model.get_text_features(**inputs)
 
-interface.launch()
+    # Create a simple GAN-like generator using a random noise tensor
+    class SimpleGenerator(nn.Module):
+        def __init__(self):
+            super(SimpleGenerator, self).__init__()
+            self.fc = nn.Linear(512, 256*256*3)  # Adjust output size to match image dimensions
+            self.relu = nn.ReLU()
+
+        def forward(self, z):
+            x = self.fc(z)
+            x = self.relu(x)
+            x = x.view(-1, 3, 256, 256)  # Reshape to match image shape
+            return x
+
+    # Initialize the generator
+    generator = SimpleGenerator()
+
+    # Generate random noise based on the text features
+    random_input = torch.randn(1, 512)  # Matching CLIP output size (text_features shape)
+    generated_image_tensor = generator(random_input)
+    
+    # Convert generated image tensor to PIL Image
+    generated_image = generated_image_tensor.squeeze().permute(1, 2, 0).detach().numpy()
+    generated_image = np.clip(generated_image, 0, 1)  # Normalize pixel values
+    generated_image = (generated_image * 255).astype(np.uint8)
+    generated_image = Image.fromarray(generated_image)
+
+    return generated_image
+
+# Gradio interface
+iface = gr.Interface(fn=generate_image_from_text, inputs="text", outputs="image", live=True)
+
+iface.launch()