Create app.py

app.py

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+import torch
+from diffusers import DiffusionPipeline
+import gradio as gr
+
+# Load the CogVideoX diffusion model
+pipe = DiffusionPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.float16)
+pipe.to("cuda")  # Send the model to GPU if available
+
+# Function to generate an image based on user prompt
+def generate_image(prompt):
+    # Generate an image using the CogVideoX model
+    with torch.inference_mode():
+        image = pipe(prompt).images[0]
+    return image
+
+# Set up the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Image Generation using CogVideoX (THUDM/CogVideoX-5b)")
+
+    # Input for user to provide a text prompt
+    prompt_input = gr.Textbox(label="Enter Text Prompt", placeholder="e.g. 'Astronaut in a jungle, cold color palette, muted colors, detailed, 8k'", value="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k")
+    
+    # Output to display the generated image
+    output_image = gr.Image(label="Generated Image")
+
+    # Button to trigger image generation
+    generate_button = gr.Button("Generate Image")
+
+    # Link button click to image generation function
+    generate_button.click(fn=generate_image, inputs=prompt_input, outputs=output_image)
+
+# Launch the Gradio app
+demo.launch()