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 on different systems including a Mac with an M1 Max chip, a mid-range PC with an RTX 3060, a high-end PC with an RTX 4090, and Google Colab. The benchmarks include text-to-image and image-to-image tasks at various resolutions. The RTX 4090 outperforms all other systems, especially at higher resolutions, while the Mac struggles with optimization for Stable Diffusion. Google Colab, using an older Tesla T4 GPU, shows expected limitations. The video concludes that for those requiring significant computing power and willing to invest, the RTX 4090 is the top choice. For a mid-range system, the RTX 3060 is recommended. The Mac is deemed unsuitable for Stable Diffusion tasks at this time, and Google Colab is suggested for those with a low budget or experimentation purposes.

Takeaways

  • 🖥️ The comparison is between the performance of Stable Diffusion on a Mac, RTX 3060, RTX 4090, and Google Colab.
  • 🍎 The Mac used is a MacBook Pro M1 Max with 10 CPU, 32 GPU cores, and 32GB of memory.
  • 💰 The mid-range PC with an AMD Ryzen 5 and Nvidia RTX 3060 costs about a thousand Euro or less.
  • ⚙️ The high-end PC features a Ryzen 9 and an RTX 4090 with 24GB of VRAM and 64GB of RAM, costing over 3000 Euro.
  • 🚀 Google Colab offers free access to Nvidia Tesla T4 GPUs, with more powerful options available on subscription plans.
  • 📈 Nine benchmarks were conducted, each with five iterations, to assess performance across different systems.
  • 📊 The RTX 4090 outperformed all other systems, completing tasks in as little as 2.1 seconds.
  • 🔍 The Mac struggled with high-resolution tasks and was the slowest in most benchmarks.
  • 💡 The RTX 3060 and Google Colab performed well at lower resolutions but struggled with high-resolution tasks.
  • 📉 The Mac's performance issues with Stable Diffusion suggest it is not yet optimized for Apple's silicon GPU.
  • 💭 The RTX 4090 is recommended for those needing high computing power and willing to spend, while the RTX 3060 is a good mid-range option.
  • 🍎 For Apple silicon Mac users, the machine can be used for Stable Diffusion but is not recommended for purchase solely for this purpose.

Q & A

  • What is the main topic of the video transcript?

    -The main topic of the video transcript 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 using a Mac to a PC?

    -The user switched to a 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 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.

  • What is the GPU used by Google Colab in its free version?

    -In its free version, Google Colab uses an Nvidia Tesla T4 GPU.

  • How many benchmarks were conducted and what were they?

    -Nine benchmarks were conducted, including text to image at 512x512, 768x768, and 512x512 with high-res fix using the Reliberate model, image to image using two control nets for the Reliberate model, and rendering a standard animation of the Forum with 120 frames using both the Reliberate and SdxL models.

  • What was the most surprising result in the benchmarks?

    -The most surprising result was the poor performance of the Mac with M1 Max, despite its powerful chip and 32 GPU cores, indicating that stable diffusion is not yet optimized for Mac.

  • Which system performed the best in the benchmarks?

    -The RTX 4090 in the high-end PC performed the best in the benchmarks, showing significantly better performance than the other systems.

  • What was the conclusion regarding the Mac's suitability for stable diffusion?

    -The conclusion was that while the Mac is a great machine with high performance and low power consumption, it currently has severe performance issues with stable diffusion and is not worth the investment for this specific purpose.

  • What advice was given for someone with a low budget interested in stable diffusion?

    -For someone with a low budget, the advice was to try Google Colab, which is free in the basic version and offers powerful GPUs through its subscription plans.

  • How did the RTX 3060 perform compared to the RTX 4090?

    -The RTX 3060 performed significantly worse than the RTX 4090, with the 4090 being nearly four times better in performance.

  • What was the final recommendation for someone needing great computing power?

    -For someone needing great computing power and not minding the cost, the recommendation was to go for an RTX 4090, which was the clear winner in terms of performance.

Outlines

00:00

🖥️ System Comparison for Stable Diffusion Performance

The speaker, a long-time Mac user, discusses their experience with stable diffusion across different systems. They compare a MacBook Pro M1 Max, a mid-range PC with an AMD Ryzen 5 and Nvidia RTX 3060, a high-end PC with a Ryzen 9 and RTX 4090, and Google Colab. The comparison includes benchmark tests with various resolutions and models, revealing surprising results. The RTX 4090 outperforms significantly, while the Mac struggles with optimization for stable diffusion and lacks specific Nvidia GPU features.

05:01

📊 Benchmark Results and Conclusions

The video script details the results of nine benchmarks, each with five iterations, using different models and resolutions. The RTX 4090 proves to be the fastest, especially at higher resolutions, while the RTX 3060 and Google Colab (with a Tesla T4 GPU) show decent performance at lower resolutions. The Mac encounters issues, particularly with the automatic 1111 model. The speaker concludes that for those requiring high computing power and willing to spend, the RTX 4090 is the top choice. For a mid-range system, the RTX 3060 is recommended. Existing Apple silicon Mac users can use their machines for stable diffusion but should not purchase one solely for this purpose. Google Colab is suggested for those on a tight budget or new to the platform.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is a term referring to a type of machine learning model used for generating images from textual descriptions. In the video, it is the central application being tested on various hardware setups to determine performance differences. The script discusses its usage on different platforms, emphasizing the importance of computational power for efficient image generation.

💡Mac

Mac refers to the line of personal computers designed and developed by Apple Inc. In the context of the video, the presenter initially used a Mac for Stable Diffusion but encountered limitations due to the platform's incompatibility with certain software and hardware requirements. The video compares the Mac's performance with other systems.

💡RTX 4090

RTX 4090 is a high-end graphics processing unit (GPU) developed by Nvidia, known for its powerful performance in gaming and professional applications. The video highlights the RTX 4090 as the most capable system tested for running Stable Diffusion, showcasing its superior processing speed and handling of high-resolution images.

💡RTX 3060

RTX 3060 is a mid-range GPU from Nvidia, designed for a balance between performance and cost. The video script compares the RTX 3060's performance with other systems, noting it as a more budget-friendly option that still provides competent performance for Stable Diffusion tasks.

💡Google Colab

Google Colab is a cloud-based platform that offers users access to computing resources, including GPUs, for machine learning and data analysis. The video discusses using Google Colab for demanding tasks like Stable Diffusion, emphasizing its accessibility and the convenience of not needing a personal high-end GPU.

💡Benchmarks

Benchmarks are tests used to measure the performance of hardware or software. In the video, the presenter conducts benchmarks to compare the efficiency of different systems running Stable Diffusion. The benchmarks include text-to-image generation, image-to-image processing, and animation rendering at various resolutions.

💡Apple Silicon

Apple Silicon refers to the custom-designed processors by Apple for its Mac computers, starting with the M1 chip. The video mentions the Apple Silicon GPU's performance with Stable Diffusion, indicating that while powerful, it may not be fully optimized for the application, resulting in suboptimal performance.

💡Unreal Engine

Unreal Engine is a game engine developed by Epic Games, widely used for creating 3D video games. The video mentions the Unreal Engine as one of the reasons the presenter purchased a PC with an RTX 3060, as it does not perform well on a Mac, highlighting the engine's demanding hardware requirements.

💡VRAM

Video RAM (VRAM) is a type of memory used by graphics processing units to store image data. The video compares systems with different amounts of VRAM, such as the RTX 4090 with 24GB, noting its significance in handling high-resolution images for Stable Diffusion without performance issues.

💡Control Nets

Control Nets are additional neural networks used in conjunction with Stable Diffusion to guide the image generation process based on specific input conditions. The video script discusses their use in image-to-image tests, noting that certain models, like the sdxl, do not yet support them.

💡High-Res Fix

High-Res Fix refers to a feature or setting that allows for the generation of high-resolution images. The video script mentions the use of a high-res fix in benchmarks, which significantly impacts the performance of the systems tested, particularly highlighting the Mac's struggle with this feature.

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 for Unreal Engine projects.

High-end PC with Ryzen 9 and RTX 4090 for more powerful computing needs.

Google Colab used for demanding tasks like dream Booth trainings.

Benchmarks include text-to-image and image-to-image tests at various resolutions.

RTX 4090 outperforms RTX 3060 by a significant margin in benchmarks.

Google Colab with Tesla T4 GPU shows expected performance due to its age.

Mac's M1 Max shows underwhelming performance with stable diffusion.

RTX 4090 maintains high performance even with high-res fix and sdxl model.

RTX 3060 and Google Colab struggle with high-res fix and sdxl model.

Mac encounters errors when using automatic 1111 for certain tasks.

RTX 4090 excels in image-to-image tasks with control nets.

Mac struggles with high-resolution tasks and shows lower performance.

RTX 4090 is unbeatable in rendering animations at 512x512 pixels.

Mac is the worst performer in animation rendering and throws errors.

RTX 4090 is the top performer, nearly four times better than RTX 3060.

RTX 3060 is a good mid-range option for those on a budget.

Google Colab is a cost-effective alternative for those with low budgets.

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

Casual Browsing

Midjourney vs Dall-E vs Stable Diffusion vs Adobe Firefly

2024-06-12 15:00:00

Stable Diffusion vs Midjourney vs Dall E

2024-06-12 12:45:00

DALLE-2 vs Stable Diffusion vs Midjourney

2024-06-12 13:25:00

Best AI Image? Midjourney V6 vs DALL E 3 vs Stable Diffusion

2024-05-18 10:40:02

BEST AI Art Generator? Dall E 2 vs Midjourney vs Stable Diffusion

2024-05-18 10:30:02