Update app.py

app.py

@@ -3,9 +3,7 @@ import torch
 from diffusers import I2VGenXLPipeline
 from diffusers.utils import export_to_gif, load_image
 import tempfile
-import spaces
 
-@spaces.GPU
 def initialize_pipeline(device):
     # Initialize the pipeline with CUDA support
     pipeline = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16")
@@ -51,25 +49,45 @@ def generate_gif(prompt, image, negative_prompt, num_inference_steps, guidance_s
 
 # Create the Gradio interface with tabs
 with gr.Blocks() as demo:
-    with gr.TabItem("Generate from Text or Image"):
-        with gr.Row():
-            with gr.Column():
-                prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
-                image = gr.Image(type="filepath", label="Input Image (optional)")
-                negative_prompt = gr.Textbox(lines=2, placeholder="Enter your negative prompt here...", label="Negative Prompt")
-                num_inference_steps = gr.Slider(1, 100, step=1, value=50, label="Number of Inference Steps")
-                guidance_scale = gr.Slider(1, 20, step=0.1, value=9.0, label="Guidance Scale")
-                seed = gr.Number(label="Seed", value=8888)
-                generate_button = gr.Button("Generate GIF")
+    with gr.Tabs():
+        with gr.TabItem("Generate from Text"):
+            with gr.Row():
+                with gr.Column():
+                    text_prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
+                    text_negative_prompt = gr.Textbox(lines=2, placeholder="Enter your negative prompt here...", label="Negative Prompt")
+                    text_num_inference_steps = gr.Slider(1, 100, step=1, value=50, label="Number of Inference Steps")
+                    text_guidance_scale = gr.Slider(1, 20, step=0.1, value=9.0, label="Guidance Scale")
+                    text_seed = gr.Number(label="Seed", value=8888)
+                    text_generate_button = gr.Button("Generate GIF")
 
-            with gr.Column():
-                output_video = gr.Video(label="Generated GIF")
+                with gr.Column():
+                    text_output_video = gr.Video(label="Generated GIF")
 
-        generate_button.click(
-            fn=generate_gif,
-            inputs=[prompt, image, negative_prompt, num_inference_steps, guidance_scale, seed],
-            outputs=output_video
-        )
+            text_generate_button.click(
+                fn=generate_gif,
+                inputs=[text_prompt, None, text_negative_prompt, text_num_inference_steps, text_guidance_scale, text_seed],
+                outputs=text_output_video
+            )
+
+        with gr.TabItem("Generate from Image"):
+            with gr.Row():
+                with gr.Column():
+                    image_prompt = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
+                    image_input = gr.Image(type="filepath", label="Input Image")
+                    image_negative_prompt = gr.Textbox(lines=2, placeholder="Enter your negative prompt here...", label="Negative Prompt")
+                    image_num_inference_steps = gr.Slider(1, 100, step=1, value=50, label="Number of Inference Steps")
+                    image_guidance_scale = gr.Slider(1, 20, step=0.1, value=9.0, label="Guidance Scale")
+                    image_seed = gr.Number(label="Seed", value=8888)
+                    image_generate_button = gr.Button("Generate GIF")
+
+                with gr.Column():
+                    image_output_video = gr.Video(label="Generated GIF")
+
+            image_generate_button.click(
+                fn=generate_gif,
+                inputs=[image_prompt, image_input, image_negative_prompt, image_num_inference_steps, image_guidance_scale, image_seed],
+                outputs=image_output_video
+            )
 
 # Launch the interface
-demo.launch()
+demo.launch()