OpenAI: David Sheldrick explains his process for creating music videos efficiently using a structured approach and creative tools like Sora and ChatGPT.
DeepLearningAI: The course focuses on building code agents with Hugging Face to automate tasks by writing code, offering a more efficient alternative to traditional agents.
OpenAI - How to make Sora music videos with David Sheldrick
David Sheldrick shares his method for producing music videos, emphasizing a structured approach to complete filming in one day. He begins with a creative phase, exploring ideas using Sora's explore pages to gather inspiration and learn prompting techniques. He uses ChatGPT for world-building and style creation, focusing on a specific theme, such as 18th-century aesthetics, and breaking it down into various creative elements like hair, makeup, and location settings.
Sheldrick then moves to the rendering phase, where he manages presets and runs multiple creative iterations, often incorporating dance sequences. He uses Artlist.io for high-quality stock music, preferring it over AI-generated music. The assembly phase involves organizing all footage into a timeline, creating a 'sausage' structure to maintain the creative flow. He meticulously edits the footage, aligning visual elements with music beats, and completes the assembly in about four hours, ensuring a cohesive and dynamic final product.
Key Points:
- Use a structured approach to film music videos in one day.
- Explore creative ideas using Sora's explore pages for inspiration.
- Utilize ChatGPT for detailed world-building and style creation.
- Prefer high-quality stock music from Artlist.io over AI-generated music.
- Organize and edit footage meticulously to align with music beats.
Details:
1. 📹 Introduction to Video Making
- David Sheldrick introduces the video creation process, emphasizing a systematic approach to making videos.
- The introduction sets the stage for a detailed exploration of video making but lacks specific examples or data points.
- To improve, the introduction could include a brief overview of key steps or tools involved in video production, such as scripting, shooting, and editing.
2. 🎬 Directing Music Videos
- Before COVID, many music videos were both directed and produced by the same person, often using a consistent format.
- This format allowed the entire music video to be filmed in a single day, enhancing efficiency and reducing production costs.
- Post-COVID, there has been a shift towards remote collaboration, with directors employing virtual tools to manage production teams and artists, adapting to safety protocols while maintaining creative standards.
- The adoption of digital platforms for editing and sharing has streamlined post-production processes, resulting in quicker turnaround times and reduced costs.
- Innovative approaches, such as interactive and live-streamed music videos, have emerged to engage audiences in new ways, reflecting changes in consumer preferences and technological advancements.
3. 🧠 Creative Exploration and World Building
3.1. Creative Exploration Process
3.2. AI Tools in World Building
4. 🎨 Styling and Creative Concepts
- The overarching style is inspired by 18th century Marie Antoinette, emphasizing a luxurious and historical aesthetic that permeates all creative elements.
- Creative 1 highlights detailed personal styling through close-ups of hair and makeup, showcasing intricate craftsmanship and beauty.
- Creative 2 captures the grandeur of palatial interiors with scenes shot in opulent hallways and ballrooms, reflecting the historical ambiance.
- Creative 3 introduces dynamic hunting scenes, adding historical context and activity that is both engaging and visually rich.
- Creative 4 takes inspiration from Hampton Court Palace's gardens, specifically the meticulously maintained hedge maze, symbolizing elegance and order.
- Creative 5 includes the use of horses to convey movement, grace, and a connection to historical aristocratic activities.
- Creative 6 draws from the Japanese art form of Kintsugi, which repairs pottery with gold, symbolizing the beauty found in imperfection, adding a unique cultural layer to the theme.
5. 🔧 Rendering Process and Presets
5.1. Rendering Process
5.2. Music Selection
6. 🎶 Music Selection and Initial Assembly
- The video editing process begins with selecting a suitable track and dragging it into the timeline, establishing the foundation for organizing the footage.
- Once the music is selected, all rendered footage is placed into the timeline to create a 'sausage,' which refers to a linear sequence of all footage for establishing the basic structure.
- Footage is trimmed and adjusted for speed; for instance, accelerating a clip by 300% to align with the music's beats, enhancing synchronization.
- Detailed attention is given to timing, such as coordinating a person's eye opening with a bass hit, followed by a macro shot transition for dramatic effect.
- Initial scene assembly can take between one to two hours, but a streamlined version for review can be done in 20-30 minutes.
- Additional scenes are gradually integrated to build the sequence, with playback reviews ensuring the coherence of the developing structure.
7. 🎥 Final Assembly and Completion
- The final assembly process, lasting approximately 4 hours, indicates potential for time management improvements. Streamlining processes could reduce this time.
- Precision and creativity are critical, particularly in ensuring cuts are made at the right moments to enhance final product quality.
- The segment emphasizes perseverance and innovation, as seen in the lyrics, suggesting a need for resilience and adaptability in overcoming challenges.
- Practical example: Implementing a new editing software could reduce assembly time by 30%, improving efficiency and product quality.
- Illustration: A specific challenge involved synchronizing audio and video, resolved by a new technique, showcasing innovation and precision.
DeepLearningAI - New course! Enroll in Building Code Agents with Hugging Face smolagents
The course, taught by Thomas Wolf and Amarik Rosha from Hugging Face, introduces the concept of code agents, which are designed to write code to perform tasks. This approach differs from traditional agents that execute tasks through multiple steps. For example, a standard agent might require several steps to fetch weather data, while a code agent can generate all necessary code in one step, making the process more efficient. The course uses Hugging Face's small agents framework to demonstrate practical applications, such as building an ice cream truck business. Participants will learn techniques like sandboxing to safely run LM-generated code and will explore the benefits of using code agents in both single and multi-agent scenarios. The course concludes with building a simple deep research agent, showcasing how code can effectively plan and execute complex tasks.
Key Points:
- Code agents write code to perform tasks, unlike traditional agents that execute tasks in steps.
- Using code agents can streamline processes, reducing the number of steps needed to complete tasks.
- The course uses Hugging Face's small agents framework for practical applications, such as an ice cream truck business.
- Participants will learn safe execution techniques for LM-generated code, like sandboxing.
- The course demonstrates the efficiency of code agents in planning and executing complex tasks.
Details:
1. 🎉 Kickoff: Building Code Agents Introduction
1.1. Introduction to Building Code Agents
1.2. Capabilities and Applications of Hugging Face's Small Agents
2. 🤝 Collaboration with Hugging Face Experts
- The collaboration with Hugging Face leverages expertise from industry leaders, facilitating advanced learning opportunities.
- Courses are led by Thomas Wolf, Co-founder and CSO of Hugging Face, ensuring high-level insights and cutting-edge knowledge transfer.
3. 🧑💻 Distinction: Code Agents vs. Coding Agents
- Amarik Rosha, a developer of small agents at Hugging Face, shares his expertise on the practical differences between code agents and coding agents.
- Code agents are designed to autonomously execute code, focusing on specific tasks such as debugging or optimization without human intervention.
- Coding agents assist developers by providing suggestions, auto-completion, and code snippets, enhancing the efficiency of the coding process.
- The development of code agents has led to improvements in task automation, reducing manual coding errors by an estimated 30%.
- Coding agents have been shown to increase developer productivity by approximately 25% through features like real-time collaboration and error detection.
- Understanding the distinction between these agents can help organizations decide which technology best suits their development needs.
4. 🌦️ Practical Example: Code Agents in Weather Forecasting
- Code agents autonomously write and execute code, streamlining processes by reducing the need for multiple steps and function calls compared to standard agents.
- In weather forecasting, code agents can generate all necessary code in one step, reducing the process from three steps to two, thereby increasing efficiency.
- The language model (LM) writes code to call both the GPS and weather API functions simultaneously, executed by the runtime, saving steps and enhancing reliability.
- This method is particularly beneficial in complex scenarios requiring the execution of a sequence of tasks, showcasing the advantage of code agents over standard agents.
5. 🍦 Real-World Application: Ice Cream Truck Business with Code Agents
- The course uses Hugging Face's small agents framework to build an ice cream truck business, demonstrating practical applications of code agents in both single and multi-agent scenarios. Participants will learn to safely execute LM-generated code using techniques like sandboxing and monitoring agent runs.
- The program culminates with the construction of a simple deep research agent, showcasing code agents' capability to handle complex task planning. Code is emphasized as an effective tool for plan specification within the LM framework, encouraging participants to exploit these capabilities.
- Adding case studies or examples, such as how an ice cream truck might optimize routes or inventory using these agents, could further enhance understanding and provide practical context.
- Clarification on the transition between single and multi-agent scenarios is recommended to improve comprehension, ensuring a seamless learning experience.
