Create app.py

app.py

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+import spaces
+import torch
+from diffusers import CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+import gradio as gr
+
+@spaces.GPU(duration=250)
+def generate_video(prompt, image):
+    # Carregar a pipeline pré-treinada
+    pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+        "THUDM/CogVideoX-5b-I2V",
+        torch_dtype=torch.bfloat16
+    )
+    
+    pipe.enable_sequential_cpu_offload()
+    pipe.vae.enable_tiling()
+    pipe.vae.enable_slicing()
+    
+    video = pipe(
+        prompt=prompt,
+        image=image,
+        num_videos_per_prompt=1,
+        num_inference_steps=50,
+        num_frames=49,
+        guidance_scale=6,
+        generator=torch.Generator(device="cuda").manual_seed(42),
+    ).frames[0]
+    
+    video_path = "output.mp4"
+    export_to_video(video, video_path, fps=8)
+    
+    return video_path
+
+# Interface Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("# Image to Video Generation")
+    
+    with gr.Row():
+        # Entrada de texto para o prompt
+        prompt_input = gr.Textbox(label="Prompt", value="A little girl is riding a bicycle at high speed. Focused, detailed, realistic.")
+        
+        # Upload de imagem
+        image_input = gr.Image(label="Upload an Image", type="pil")
+    
+    # Botão para gerar o vídeo
+    generate_button = gr.Button("Generate Video")
+    
+    # Saída do vídeo gerado
+    video_output = gr.Video(label="Generated Video")
+    
+    # Ação ao clicar no botão
+    generate_button.click(fn=generate_video, inputs=[prompt_input, image_input], outputs=video_output)
+
+# Rodar a interface
+demo.launch()