Stable Diffusion - Mac vs RTX4090 vs RTX3060 vs Google Colab - how they perform.

Render Realm
29 Aug 202309:25

TLDRIn this video, the creator compares the performance of Stable Diffusion across different systems, including a MacBook Pro M1 Max, a mid-range PC with an RTX 3060, a high-end PC with an RTX 4090, and Google Colab. The tests involve text-to-image and image-to-image benchmarks at various resolutions. The RTX 4090 outperforms all other systems, especially in high-resolution tasks, while the RTX 3060 is a solid mid-range option. Google Colab, using an older Tesla T4 GPU, shows limitations in performance. The M1 Max Mac struggles with Stable Diffusion, indicating a lack of optimization. The video concludes that for those requiring high computing power and willing to spend, the RTX 4090 is the top choice, while budget-conscious users might consider the RTX 3060 or Google Colab.

Takeaways

  • 🖥️ The comparison is between Mac with M1 Max, RTX 3060, RTX 4090, and Google Colab for stable diffusion performance.
  • 💻 The RTX 4090 outperforms all other systems, taking only 2.1 seconds for the first benchmark.
  • 📉 The RTX 3060 follows with a slightly longer time of 3.6 seconds for the same benchmark.
  • 🚀 The RTX 4090 maintains a significant lead in performance, especially at higher resolutions.
  • 🔍 Google Colab's Tesla T4 GPU, being 5 years old, shows expectedly lower performance.
  • 🍎 The Mac M1 Max, despite its power, struggles with stable diffusion, indicating a lack of optimization.
  • 📊 At 768x768 resolution, the performance gap widens, with RTX 4090 being the clear winner.
  • 🚫 The Mac experiences issues with high-resolution fixes and throws an error with automatic 1111.
  • 💰 The RTX 4090 is the most expensive but offers the best performance, making it ideal for those with a high budget.
  • 💼 For a mid-range system, the RTX 3060 or similar GPUs are recommended for a good balance of price and performance.
  • 🍏 Mac is not recommended for those specifically looking to use stable diffusion due to its current performance issues.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is a comparison of how stable diffusion performs on different systems, including a Mac with M1 Max, a mid-range PC with an RTX 3060, a high-end PC with an RTX 4090, and Google Colab.

  • Why did the user switch from Mac to a mid-range PC?

    -The user switched to a mid-range PC because they needed to work on projects with the Unreal Engine, which does not work well on a Mac, and also wanted to use it for stable diffusion.

  • What is the significance of the benchmarks conducted in the video?

    -The benchmarks are significant as they provide a quantitative measure of the performance of each system when running stable diffusion, allowing for a direct comparison of their capabilities.

  • What are the key specifications of the high-end PC used in the comparison?

    -The high-end PC has a Ryzen 9 processor, an RTX 4090 GPU, 24 GB of VRAM, and 64 GB of RAM.

  • How does Google Colab provide GPU power for its users?

    -Google Colab provides GPU power by running on Google's servers. The free version offers an Nvidia Tesla T4 GPU, while subscription plans provide access to more powerful GPUs.

  • What was the user's main surprise regarding the Mac's performance with stable diffusion?

    -The user was surprised by the Mac's poor performance with stable diffusion, despite the M1 Max being a powerful chip with 32 GPU cores, indicating that stable diffusion is not yet optimized for the Mac.

  • What was the outcome of the benchmarks at a resolution of 768x768?

    -At 768x768 resolution, the performance gap between the systems increased, with the RTX 4090 being the clear winner, while the Mac struggled, especially with the high-res fix.

  • Which model of stable diffusion did the user encounter issues with on the Mac?

    -The user encountered issues with the automatic 1111 model on the Mac, as it threw an error when used with the stxl model.

  • What is the user's recommendation for someone with a tight budget interested in stable diffusion?

    -For someone with a tight budget, the user recommends considering the RTX 3060 or a similar mid-range GPU, or trying out Google Colab, which is free in its basic version.

  • Why might the RTX 4090 not show any performance issues even with high resolutions?

    -The RTX 4090 might not show any performance issues with high resolutions due to its 24 GB of VRAM and the system having 64 GB of RAM, which allows for handling more intensive tasks.

  • What is the user's final conclusion about the best system for someone needing great computing power for stable diffusion?

    -The user concludes that for someone needing great computing power and not minding the cost, the RTX 4090 would be the first choice due to its superior performance in the benchmarks.

Outlines

00:00

🖥️ Cross-Platform Performance Comparison

The video script presents a detailed comparison of the performance of stable diffusion across different systems. The narrator, a long-time Mac user, initially used stable diffusion on a MacBook Pro M1 Max, but later transitioned to a mid-range PC with an NVIDIA RTX 3060 for projects involving Unreal Engine. To handle larger projects, a high-end PC with an RTX 4090 was acquired. Google Colab is also mentioned as a platform for demanding tasks like dream Booth trainings. The narrator conducted nine benchmarks across these platforms, focusing on text-to-image and image-to-image tasks, as well as rendering animations. The results highlight the superior performance of the RTX 4090, with the RTX 3060 and Google Colab showing good performance at lower resolutions. The Mac, however, struggled with high-resolution tasks and was the worst performer overall, indicating that stable diffusion is not yet optimized for Mac.

05:01

📊 Benchmark Results and Recommendations

The video script concludes with a summary of the benchmark results and the narrator's recommendations. The RTX 4090 emerged as the clear winner with nearly four times better performance than the RTX 3060 and significantly outperforming the Mac and Google Colab. Despite its high cost and power consumption, the RTX 4090 is the narrator's top choice for those requiring significant computing power. For a mid-range system, the RTX 3060 or similar GPUs are suggested. The Mac is not recommended for stable diffusion tasks due to its performance issues, although it is acknowledged as a high-performance machine with low power consumption. Lastly, Google Colab is proposed as a cost-effective option for those with a low budget or who are unwilling to invest heavily in hardware for stable diffusion.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is an AI model used for generating images from textual descriptions. It is a theme of the video as the host compares its performance across different systems. The host mentions using it on various platforms, including Mac, RTX 3060, RTX 4090, and Google Colab.

💡MacBook Pro M1 Max

The MacBook Pro M1 Max is a high-end laptop from Apple that features Apple's own silicon chip with powerful CPU and GPU cores. In the video, it is used to demonstrate the performance of Stable Diffusion, highlighting that while the machine is powerful, it struggles with the software due to optimization issues.

💡RTX 3060

The RTX 3060 is a mid-range graphics card from Nvidia, known for its good performance in gaming and graphics-intensive tasks. The video compares its performance with other systems when running Stable Diffusion, showing it to be a solid choice for those on a tighter budget.

💡RTX 4090

The RTX 4090 is a high-end graphics card from Nvidia, designed for the most demanding tasks and offering superior performance. The video presents it as the best-performing system for Stable Diffusion, although it comes with a high price tag and power consumption.

💡Google Colab

Google Colab is a cloud-based platform that provides access to computing resources, including GPUs, for machine learning and data analysis. The video discusses its use for running Stable Diffusion, noting that it offers a free version with limited resources and subscription plans for more power.

💡Benchmarks

Benchmarks are tests that measure the performance of hardware or software. In this context, the host conducts benchmarks to compare how different systems handle Stable Diffusion. The benchmarks include text-to-image generation and image-to-image processing at various resolutions.

💡Control Nets

Control Nets are additional neural networks used with Stable Diffusion to control the generation process, allowing for more precise image outputs. The video mentions their use in image-to-image tests, noting that not all models support them.

💡High-Res Fix

High-Res Fix is a feature or setting within Stable Diffusion that allows for higher resolution image generation. The video discusses its impact on performance, showing that some systems struggle with the increased demands of high-resolution images.

💡VRAM

Video RAM (VRAM) is the memory used by graphics cards to store image data. The video highlights the importance of VRAM in handling high-resolution images, with the RTX 4090's 24GB of VRAM being a key factor in its superior performance.

💡RAM

Random Access Memory (RAM) is the main memory that a computer uses to store data temporarily and run programs. The video mentions that the RTX 4090 system has 64GB of RAM, which contributes to its high performance in benchmarks.

💡Performance Optimization

Performance optimization refers to the process of improving the efficiency and effectiveness of a system, in this case, how well it runs Stable Diffusion. The video discusses the lack of optimization for Mac systems and the varying levels of optimization across different hardware.

Highlights

Comparison of stable diffusion performance on different systems: Mac, RTX 3060, RTX 4090, and Google Colab.

MacBook Pro M1 Max with 10 CPU and 32 GPU cores used for comparison.

Mid-range PC with AMD Ryzen 5 and Nvidia RTX 3060 detailed.

High-end PC with Ryzen 9 and RTX 4090 for larger projects.

Google Colab used for demanding tasks like dream Booth trainings.

9 benchmarks conducted with 5 iterations each for accuracy.

RTX 4090 outperformed RTX 3060 significantly in the first benchmark.

Mac's performance with stable diffusion was surprisingly poor, indicating lack of optimization.

RTX 4090 maintained a clear lead in performance at 768x768 resolution.

High-res fix caused performance issues for Mac and other systems except RTX 4090.

RTX 3060 and Google Colab struggled with high-resolution tasks.

Mac encountered errors when using the automatic 1111 model.

RTX 4090 showed no issues with high-resolution image to image tasks.

Mac's performance was adequate at lower resolutions but struggled with high-resolution tasks.

RTX 4090 was unbeatable at 512x512 pixels in animation rendering.

RTX 4090 is the top performer but with higher power consumption and cost.

RTX 3060 is recommended for a mid-range system with a good price-performance ratio.

Google Colab is suggested for those with a low budget or new to the platform.

Mac is not recommended solely for stable diffusion purposes due to performance issues.