Create animate/animate.py

animate/animate.py

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+import gradio as gr
+import os
+from PIL import Image
+from pathlib import Path
+import tempfile
+import uuid
+
+# Hugging Face video generation inference
+from diffusers import AnimateDiffPipeline
+import torch
+
+# Optional: Replace with your model of choice from HF
+ANIMATEDIFF_MODEL = "guoyww/animatediff-light"
+
+pipe = AnimateDiffPipeline.from_pretrained(
+    ANIMATEDIFF_MODEL,
+    torch_dtype=torch.float16,
+    variant="fp16"
+).to("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def generate_motion_video(concept, motion_prompt, sketch_img):
+    if sketch_img is None:
+        return "Please upload or generate a sketch first.", None
+
+    unique_id = str(uuid.uuid4())
+    out_dir = Path(tempfile.gettempdir()) / f"motion_{unique_id}"
+    out_dir.mkdir(exist_ok=True)
+
+    # Generate text prompt from sketch concept and motion
+    prompt = (
+        f"You are given a sketch of a {concept} composed of strokes over a grid. "
+        f"Imagine this sketch being brought to life in a short animation.\n\n"
+        f"The motion task is: '{motion_prompt}'\n\n"
+        f"Generate a short animation (5–8 seconds) that shows this action applied naturally "
+        f"to the sketch, while keeping the line-drawn aesthetic."
+    )
+
+    # AnimateDiff generation
+    video = pipe(prompt, num_frames=16).frames[0]  # Get 1st sample’s frames
+
+    gif_path = out_dir / f"{concept.replace(' ', '_')}_motion.gif"
+    video[0].save(
+        gif_path,
+        save_all=True,
+        append_images=video[1:],
+        duration=100,
+        loop=0
+    )
+
+    return "GIF created successfully!", str(gif_path)