Update app.py

app.py

@@ -5,6 +5,19 @@ from diffusers.utils import export_to_gif, load_image
 import tempfile
 import spaces
 
+@spaces.GPU
+def initialize_pipeline():
+    # Initialize the pipeline without CUDA support
+    pipeline = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16")
+    return pipeline
+
+import gradio as gr
+import torch
+from diffusers import I2VGenXLPipeline
+from diffusers.utils import export_to_gif, load_image
+import tempfile
+import spaces
+
 @spaces.GPU
 def initialize_pipeline():
     # Initialize the pipeline without CUDA support
@@ -19,7 +32,7 @@ def generate_gif(prompt, image, negative_prompt, num_inference_steps, guidance_s
     generator = torch.Generator().manual_seed(seed)
 
     # Check if an image is provided
-    if image is not None:
+    if image is not None and image != "":  # Ensure image is not empty
         image = load_image(image).convert("RGB")
         frames = pipeline(
             prompt=prompt,
@@ -61,10 +74,10 @@ with gr.Blocks() as demo:
                 with gr.Column():
                     text_output_video = gr.Video(label="Generated GIF")
 
-            # Fix: Do not use `None` in the inputs, instead pass an empty input for image
+            # When generating from text, pass an empty string as the image input
             text_generate_button.click(
                 fn=generate_gif,
-                inputs=[text_prompt, None, text_negative_prompt, text_num_inference_steps, text_guidance_scale, text_seed],
+                inputs=[text_prompt, "", text_negative_prompt, text_num_inference_steps, text_guidance_scale, text_seed],
                 outputs=text_output_video
             )
 
@@ -89,4 +102,4 @@ with gr.Blocks() as demo:
             )
 
 # Launch the interface
-demo.launch()
+demo.launch()