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+pip install diffusers transformers torch torchvision torchaudio moviepy pillow openai accelerate safetensors
+
+# ai_cartoon_animated.py
+
+import os
+import torch
+from diffusers import StableDiffusionPipeline, StableVideoDiffusionPipeline
+from transformers import pipeline
+from moviepy.editor import concatenate_videoclips, AudioFileClip, VideoFileClip
+from PIL import Image
+
+# ========= SETTINGS =========
+huggingface_token = "YOUR_HUGGINGFACE_TOKEN"
+output_dir = "ai_cartoon_movie"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+os.makedirs(output_dir, exist_ok=True)
+
+# ========= 1. STORY CREATION =========
+story_theme = "A brave robot exploring an alien planet with its cat companion."
+num_scenes = 20  # about 30 seconds per scene
+
+story_gen = pipeline("text-generation", model="gpt2")
+story_text = story_gen(
+    f"Write a {num_scenes}-scene cartoon story about {story_theme}. "
+    "Each scene should include a one-sentence visual description and one sentence of narration.",
+    max_length=1200,
+    temperature=0.8,
+    do_sample=True
+)[0]["generated_text"]
+
+scenes = [s.strip() for s in story_text.split(".") if len(s.strip()) > 10][:num_scenes]
+
+# ========= 2. IMAGE GENERATION =========
+print("\n🎨 Generating base cartoon frames...")
+sd_pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    use_auth_token=huggingface_token
+).to(device)
+
+image_paths = []
+for i, desc in enumerate(scenes):
+    prompt = f"cartoon style, vibrant colors, {desc}"
+    img = sd_pipe(prompt).images[0]
+    img_path = os.path.join(output_dir, f"scene_{i}.png")
+    img.save(img_path)
+    image_paths.append(img_path)
+
+# ========= 3. VIDEO ANIMATION =========
+print("\n🎞️ Generating motion for each scene (Stable Video Diffusion)...")
+svd_pipe = StableVideoDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-video-diffusion-img2vid",
+    use_auth_token=huggingface_token
+).to(device)
+
+video_paths = []
+for i, img_path in enumerate(image_paths):
+    image = Image.open(img_path).convert("RGB")
+    print(f"Animating scene {i+1}/{len(image_paths)}...")
+    frames = svd_pipe(image, num_frames=16).frames[0]  # 16 short frames
+    frame_dir = os.path.join(output_dir, f"frames_{i}")
+    os.makedirs(frame_dir, exist_ok=True)
+    frame_paths = []
+    for j, frame in enumerate(frames):
+        fpath = os.path.join(frame_dir, f"frame_{j}.png")
+        frame.save(fpath)
+        frame_paths.append(fpath)
+
+    # Combine frames into a video
+    import cv2
+    frame = cv2.imread(frame_paths[0])
+    height, width, _ = frame.shape
+    out_path = os.path.join(output_dir, f"scene_{i}.mp4")
+    out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'mp4v'), 8, (width, height))
+    for fpath in frame_paths:
+        out.write(cv2.imread(fpath))
+    out.release()
+    video_paths.append(out_path)
+
+# ========= 4. VOICE / NARRATION =========
+print("\n🎤 Generating narration...")
+tts = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits")
+audio_paths = []
+for i, desc in enumerate(scenes):
+    narration = f"Scene {i+1}. {desc}."
+    speech = tts(narration)
+    audio_path = os.path.join(output_dir, f"scene_{i}.wav")
+    with open(audio_path, "wb") as f:
+        f.write(speech["audio"])
+    audio_paths.append(audio_path)
+
+# ========= 5. ASSEMBLE FINAL MOVIE =========
+print("\n🎬 Combining all animated scenes and voiceovers...")
+final_clips = []
+for i, video_path in enumerate(video_paths):
+    clip = VideoFileClip(video_path)
+    audio = AudioFileClip(audio_paths[i])
+    clip = clip.set_audio(audio).set_duration(audio.duration)
+    final_clips.append(clip)
+
+final_video = concatenate_videoclips(final_clips, method="compose")
+output_path = os.path.join(output_dir, "ai_cartoon_animated.mp4")
+final_video.write_videofile(output_path, fps=24)
+print(f"\n✅ Finished cartoon: {output_path}")