OpenAI sets two-stage Sora shutdown with app closing April 2026 and API following in September

Introduction

OpenAI's decision to shut down Sora in two stages — with the web app closing in April 2026 and the API following in September — marks a significant shift in the company's strategic focus. While Sora was a pioneering AI video generation tool, its shutdown highlights the challenges of deploying creative AI systems at scale. However, this doesn't mean we can't learn from Sora's technology or build similar systems using existing tools and frameworks.

In this tutorial, you'll learn how to create a basic video generation pipeline using Python and the Hugging Face Diffusion library, which is the foundation for many AI video generation systems. This will give you hands-on experience with the underlying technologies that powered Sora, even though the actual Sora API will no longer be available.

Prerequisites

• Python 3.8 or higher installed
• Basic understanding of Python programming
• Familiarity with machine learning concepts (optional but helpful)
• Access to a machine with at least 8GB RAM (preferably 16GB or more)
• Internet connection for downloading models and dependencies

Why these prerequisites? The Hugging Face Diffusion models are computationally intensive and require sufficient memory to run. While we won't be generating full-length videos (as Sora did), we'll build a system that simulates video generation using image diffusion techniques.

Step-by-Step Instructions

1. Set Up Your Python Environment

First, create a virtual environment to isolate your project dependencies:

python -m venv sora_tutorial
source sora_tutorial/bin/activate  # On Windows: sora_tutorial\Scripts\activate

Then install the required libraries:

pip install torch torchvision transformers accelerate diffusers

Why? These libraries form the backbone of modern diffusion models used in AI video generation. torch is the deep learning framework, diffusers provides pre-trained models, and transformers handles text processing.

2. Load a Pre-Trained Diffusion Model

Create a Python script called video_generator.py and add the following code to load a text-to-image diffusion model:

from diffusers import StableDiffusionPipeline
import torch

# Load the model
model_id = "runwayml/stable-diffusion-v1-5"
pipeline = StableDiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16
)

# Move to GPU if available
pipeline = pipeline.to("cuda" if torch.cuda.is_available() else "cpu")

Why? We're using a stable diffusion model that generates images from text prompts. While not video generation, this is the foundational technology behind Sora's video capabilities.

3. Generate Sample Images

Now, generate a few sample images to test the model:

prompt = "A futuristic cityscape at sunset"
image = pipeline(prompt).images[0]
image.save("output_image.png")
print("Image saved as output_image.png")

Why? This step validates that your environment is set up correctly and gives you a tangible result to work with. The model will take a few minutes to run on the first prompt.

4. Simulate Video Generation

While true video generation is complex, we can simulate a video by generating a sequence of images:

from PIL import Image
import os

# Create a directory for frames
os.makedirs("video_frames", exist_ok=True)

# Generate a sequence of images
prompts = [
    "A bird flying over a mountain",
    "A bird flying over a lake",
    "A bird flying over a forest"
]

for i, prompt in enumerate(prompts):
    image = pipeline(prompt).images[0]
    image.save(f"video_frames/frame_{i:03d}.png")
    print(f"Frame {i} saved")

Why? This simulates how Sora might have generated video by stitching together frames. In a real system, you'd interpolate between these frames to create smooth motion.

5. Create a Video from Frames

Use OpenCV to combine the frames into a video:

import cv2
import os

# Set video parameters
frame_rate = 1
frame_width = 512
frame_height = 512

# Get all frames
frame_files = sorted([f for f in os.listdir("video_frames") if f.endswith(".png")])

# Create video writer
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
video = cv2.VideoWriter("output_video.mp4", fourcc, frame_rate, (frame_width, frame_height))

# Add frames to video
for frame_file in frame_files:
    frame_path = os.path.join("video_frames", frame_file)
    frame = cv2.imread(frame_path)
    frame = cv2.resize(frame, (frame_width, frame_height))
    video.write(frame)

video.release()
print("Video saved as output_video.mp4")

Why? This step demonstrates how to convert a sequence of images into a video file, mimicking how AI video systems might compile frames into a final output.

6. Run the Full Pipeline

Run your script to generate the video:

python video_generator.py

Why? This final step ties everything together, showing how to build an AI video generation pipeline from scratch using open-source tools.

Summary

This tutorial demonstrated how to create a video generation pipeline using open-source tools similar to those used in Sora. While we didn't replicate Sora's full capabilities, we built a foundation that shows how AI video generation works at a high level. Understanding these techniques is crucial for developers working with AI systems, especially as companies shift focus from consumer-facing tools to enterprise applications.

As OpenAI moves away from creative AI tools, developers and researchers can still experiment with these technologies using frameworks like Hugging Face, Diffusers, and Stable Diffusion. This tutorial provides a practical starting point for exploring AI video generation in your own projects.