Maxed Out Macbook M3 Max for Stable Diffusion: A Powerhouse or a Costly Mistake?
TLDRThis video explores whether the MacBook M3 Max is a worthy investment for those working with AI and generative technologies. The presenter shares their experience using the MacBook Pro M1 for tasks like image and video generation, highlighting its strengths and limitations. They discuss installation challenges with AI libraries and compare the M3 Max with other high-end options from Tuxedo Computers, Razer, and MSI. The video includes performance tests on video processing and model training, demonstrating the M3 Max's capabilities and evaluating its practicality for professional AI work. The presenter concludes with tips for optimizing the MacBook for AI tasks and invites viewers to share their experiences.
Takeaways
- 😀 The speaker has been using a MacBook Pro M1 since 2021 for AI and generative AI tasks without major issues.
- 🔧 Initially, there were difficulties installing TensorFlow and PyTorch on the M1 chip, with no native solution provided by Apple.
- 💻 The speaker prefers using cloud-based servers for deploying technology but uses Mac for personal work and experimentation due to the ecosystem investment.
- 🚀 The MacBook Pro M1 Pro Max was tested for video processing and generative AI tasks, showing significant performance improvements over the M1.
- 💡 The speaker suggests that for professional use, especially in AI and video processing, maxing out the RAM is crucial, and the M3 Pro Max offers this capability.
- 💰 The price comparison between the M3 Pro Max and other high-end laptops (Linux and Windows) shows they are in a similar price range, making price less of a deciding factor.
- 🔥 The M3 Pro Max performed well in tests, completing tasks faster than the M1 Pro Max, but the fan was always running, indicating high usage.
- 🔄 The speaker recommends installing PyTorch via the 'nightly' channel in Conda for optimal performance with ComfyUI on Mac.
- 📈 The script emphasizes the importance of using the '-m torch.ampere' flag when running ComfyUI to enable GPU utilization.
- 🔍 The speaker calls for better support from Apple and the AI community to accommodate the needs of professionals using their devices for AI work.
- 📝 A suggestion is made for users to share their experiences and test results in the comments to help others make informed decisions about their hardware choices.
Q & A
What type of laptop does the speaker use for AI work, specifically generative AI?
-The speaker uses a MacBook Pro M1 Pro Max for AI work, including running image and video generation tasks.
What issues did the speaker initially face with the MacBook Pro M1 Pro Max?
-The speaker initially had problems installing TensorFlow and PyTorch on the MacBook Pro M1 Pro Max, as there were no native solutions for these libraries on Apple's M1 chips.
How does the speaker deploy technology for their work?
-The speaker uses cloud-based servers to run their codes and does not rely on these servers for day-to-day personal work or experimentation.
Why does the speaker prefer to use their Mac for certain tasks despite the initial software compatibility issues?
-The speaker prefers using their Mac due to the significant investment in the Mac ecosystem and the convenience of features like AirDrop and seamless device integration.
What is the speaker's opinion on the Mac's hardware upgrades?
-The speaker appreciates the continuous hardware upgrades by Apple but criticizes the lack of consideration for compatibility with AI libraries and the broader tech ecosystem.
What are the alternatives the speaker considered to the MacBook Pro M3 Pro Max?
-The speaker considered Linux machines from Tuxedo Computers and Windows laptops like the Razer Blade 16 and MSI models with Nvidia RTX 4090 GPUs.
How does the MacBook Pro M3 Pro Max compare to the alternatives in terms of price?
-The price range of the MacBook Pro M3 Pro Max, when maxed out with RAM and a 4K screen, is similar to the alternatives, making price not a significant differentiating factor.
What was the result of the speaker's test using the MacBook Pro M3 Pro Max with ComfyUI for video restyling?
-The speaker was able to restyle a 702-frame video in 45 minutes using the MacBook Pro M3 Pro Max with ComfyUI.
What issue did the speaker encounter when trying to use Vid2Vid workspace on Runway with the MacBook Pro M3 Pro Max?
-The speaker encountered an out-of-memory error when attempting to process a Vid2Vid workspace on Runway with the MacBook Pro M3 Pro Max.
How did the MacBook Pro M3 Pro Max perform in the speaker's tests with FaceFusion?
-The MacBook Pro M3 Pro Max was able to run FaceFusion with both face enhancer and frame enhancer without any issues, completing a 17-second video in 15 minutes and 50 seconds.
What advice does the speaker give for installing PyTorch on a Mac for use with ComfyUI?
-The speaker advises installing PyTorch using the 'nightly' version in a Conda environment and ensuring that the MPS (Mac's equivalent to CUDA) is available for GPU acceleration.
Outlines
🤖 AI and MacBook Pro M1 Experience
The speaker discusses the experience of using a MacBook Pro M1 for AI work, particularly in generative AI. They mention the challenges of installing TensorFlow and PyTorch on the M1 chip, which were eventually resolved using conda. The speaker expresses disappointment in Apple and the library developers for not providing native support for these libraries on non-NVIDIA and non-Intel platforms. They also share their preference for using cloud-based servers for deployment and Mac for day-to-day work due to the investment in the Apple ecosystem and its convenience features like AirDrop and seamless device integration.
💻 Comparing Laptop Options for AI Work
The speaker compares different laptop options for AI work, including a Linux laptop from Tuxedo Computers, a Windows-based Razer Blade 16, and an MSI laptop. They discuss the specifications, such as screen resolution, GPU, RAM, and storage, and compare the prices of these options with the MacBook Pro M3 Pro Max. The speaker concludes that the price difference is not significant and that they decided to upgrade to the M3 Pro Max due to the ability to max out the RAM and the convenience of the Apple ecosystem.
🚀 Testing the M3 Pro Max for AI Tasks
The speaker presents test results of using the M3 Pro Max for AI tasks, such as video restyling with ComfyUI and running diffusion. They highlight the ability to run complex tasks that were not possible on the M1 Pro Max, such as using frame enhancer and face enhancer simultaneously in FaceFusion. The speaker also notes the M3 Pro Max's performance during video processing, mentioning that the machine did not suffer despite the fan running continuously. They express satisfaction with the upgrade but also urge Apple to consider the needs of the AI community and improve compatibility with AI libraries.
🛠️ Tips for Mac Users Working with AI
The speaker provides tips for Mac users working with AI, focusing on the installation of PyTorch using the 'nightly' version of conda, which is crucial for compatibility with ComfyUI. They explain the process of verifying the installation and the availability of MPS, which is similar to CUDA for Mac. The speaker also emphasizes the importance of using the '-mps' flag when running ComfyUI to enable GPU acceleration, which is often overlooked in other tutorials.
📊 Performance Monitoring and Community Input
The speaker discusses the performance monitoring of the M3 Pro Max during video processing tasks, noting that the RAM usage stayed around 60-70% and the CPU usage remained low. They invite the audience to share their experiences with Mac, Windows, or Linux machines in the comments to help others make informed decisions about their hardware choices for AI work. The speaker also suggests that community feedback could be valuable for understanding the performance and compatibility of different systems with AI applications.
Mindmap
Keywords
💡MacBook Pro M1
💡TensorFlow and PyTorch
💡cond
💡ecosystem
💡M3 Pro Max
💡Linux machine
💡NVIDIA RTX 4090
💡comfyUI
💡Stable Diffusion
💡Vid2Vid
💡MPS
Highlights
Discussion on whether a high-end MacBook Pro M3 Max is suitable for AI and generative AI work.
The user's experience with the MacBook Pro M1 since 2021, including running image generation with Comi and Stable Diffusion.
Challenges faced with installing TensorFlow and PyTorch on the M1 Pro Max and the reliance on conda.
Criticisms of Apple and the AI community for not supporting native installations of key libraries.
The user's preference for using cloud-based servers for deploying technology rather than for daily personal work.
Advantages of the Mac ecosystem, including seamless device integration and convenience.
Comparison between the MacBook Pro Max and Linux laptops, focusing on specs and price.
The user's decision to upgrade to the M3 Pro Max for improved video processing capabilities.
Performance test results of the M3 Pro Max using ComfyUI for video restyling.
Comparison of processing times between the M3 Pro Max and cloud-based services like Run Diffusion.
Issues with memory limitations when using Vid2Vid workspace on both M3 Pro Max and cloud platforms.
Successful use of FaceFusion on the M3 Pro Max, which was not possible on the M1 Pro Max.
The user's satisfaction with the M3 Pro Max's performance during video processing tasks.
Recommendation for maxing out RAM when purchasing a laptop for AI tasks, especially for video processing.
A call to action for Apple to better support the AI community and adapt their hardware to industry standards.
Tips for Mac users on installing PyTorch correctly for ComfyUI and enabling GPU usage.
Invitation for users to share their experiences with different machines and operating systems for AI tasks.
The importance of community feedback for making informed decisions on hardware choices for AI work.