Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from diffusers import I2VGenXLPipeline
+from PIL import Image
+from moviepy.editor import ImageSequenceClip
+import io
+
+def generate_video(image, prompt, negative_prompt, video_length):
+    generator = torch.manual_seed(8888)
+
+    # Set the device to CPU or a non-NVIDIA GPU
+    device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # Load the pipeline
+    pipeline = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float32)
+    pipeline.to(device)  # Move the model to the selected device
+
+    # Generate frames with progress tracking
+    frames = []
+    total_frames = video_length * 30  # Assuming 30 frames per second
+
+    for i in range(total_frames):
+        frame = pipeline(
+            prompt=prompt,
+            image=image,
+            num_inference_steps=5,
+            negative_prompt=negative_prompt,
+            guidance_scale=9.0,
+            generator=generator,
+            num_frames=1
+        ).frames[0]
+        frames.append(np.array(frame))
+
+        # Update progress
+        yield (i + 1) / total_frames  # Yield progress
+
+    # Create a video clip from the frames
+    output_file = "output_video.mp4"
+    clip = ImageSequenceClip(frames, fps=30)  # Set the frames per second
+    clip.write_videofile(output_file, codec='libx264', audio=False)
+
+    return output_file
+
+# Gradio interface
+def interface(image, prompt, negative_prompt, video_length):
+    # Convert the uploaded image to a PIL Image
+    image = Image.open(io.BytesIO(image.read()))
+    
+    # Generate video and track progress
+    return generate_video(image, prompt, negative_prompt, video_length)
+
+# Create Gradio Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("# AI-Powered Video Generation")
+    
+    with gr.Row():
+        image_input = gr.Image(type="file", label="Upload Image")
+        prompt_input = gr.Textbox(label="Enter the Prompt")
+        negative_prompt_input = gr.Textbox(label="Enter the Negative Prompt")
+        video_length_input = gr.Number(label="Video Length (seconds)", value=10, precision=0)
+
+    generate_button = gr.Button("Generate Video")
+    output_video = gr.Video(label="Output Video")
+
+    # Define the button action
+    generate_button.click(
+        interface,
+        inputs=[image_input, prompt_input, negative_prompt_input, video_length_input],
+        outputs=output_video,
+        show_progress=True  # Show progress bar
+    )
+
+# Launch the Gradio app
+demo.launch()