Create app.py

app.py

@@ -0,0 +1,86 @@
+import gradio as gr
+import torch
+from diffusers import AnimateDiffPipeline
+from PIL import Image
+import imageio
+import tempfile
+import os
+
+MODEL_ID = "guoyww/animatediff-motion-adapter-v1-5-2"
+
+print("Loading pipeline...")
+
+pipe = AnimateDiffPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float32
+)
+
+pipe.to("cpu")
+
+# CPU optimizations
+pipe.enable_attention_slicing()
+pipe.enable_vae_slicing()
+
+print("Model Ready")
+
+
+def generate_video(image, prompt, frames, steps, seed):
+
+    generator = torch.Generator("cpu").manual_seed(int(seed))
+
+    image = Image.fromarray(image).resize((384,384))
+
+    with torch.inference_mode():
+
+        result = pipe(
+            prompt=prompt,
+            image=image,
+            num_frames=frames,
+            num_inference_steps=steps,
+            generator=generator
+        )
+
+    frames_list = result.frames
+
+    temp_dir = tempfile.mkdtemp()
+    video_path = os.path.join(temp_dir, "video.mp4")
+
+    imageio.mimsave(video_path, frames_list, fps=6)
+
+    return video_path
+
+
+with gr.Blocks() as demo:
+
+    gr.Markdown("# 🚀 Image → Video AI (CPU Optimized)")
+
+    with gr.Row():
+
+        with gr.Column():
+
+            input_image = gr.Image(label="Upload Image")
+
+            prompt = gr.Textbox(
+                value="cinematic camera movement",
+                label="Motion Prompt"
+            )
+
+            frames = gr.Slider(8,20,value=12,step=1,label="Frames")
+
+            steps = gr.Slider(4,8,value=6,step=1,label="Steps")
+
+            seed = gr.Number(value=42)
+
+            btn = gr.Button("Generate Video")
+
+        with gr.Column():
+
+            output_video = gr.Video()
+
+    btn.click(
+        generate_video,
+        inputs=[input_image,prompt,frames,steps,seed],
+        outputs=output_video
+    )
+
+demo.launch()