Update app.py

app.py

@@ -1,15 +1,20 @@
-from diffusers import DiffusionPipeline
-import torch
+import gradio as gr
+from transformers import DiffusionPipeline
 
-device = "cuda"if torch.cuda.is _available() else :"cpu"
+# Load the pre-trained Diffusion model from Hugging Face
+pipeline = DiffusionPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V")
 
-pipeline=DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt")
-pipeline = pipeline.to(device)
+def generate_image(prompt):
+    image = pipeline(prompt)["sample"][0]
+    return image
 
-prompt = "A beautiful sunset over the ocean"
-video = pipeline(prompt,num_interface_steps=50).videos
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=gr.Textbox(label="Enter your prompt"),
+    outputs=gr.Image(type="pil"),
+    title="Diffusion Model Image Generator",
+    description="Enter a prompt to generate an image using the DiffusionPipeline from Hugging Face."
+)
 
-video path = "generated_video.mp4"with open(video_path, "wb") as f:
-    f.write(video[0])
-
-print(f"video generated and saved as {video_path}")
+if __name__ == "__main__":
+    iface.launch()