Stable Diffusion - Mac vs RTX4090 vs RTX3060 vs Google Colab - how they perform.
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 benchmarks include text-to-image and image-to-image tasks at various resolutions. The RTX 4090 outperforms all other systems, especially with high-resolution tasks, while the RTX 3060 offers a good balance between cost and performance. Google Colab, even with its subscription plans, lags behind the dedicated GPUs. The M1 Max MacBook Pro shows significant performance issues with Stable Diffusion, suggesting it's not yet optimized for Apple's silicon. The video concludes that for those requiring high computing power and willing to spend, the RTX 4090 is the top choice, while the RTX 3060 is recommended for a mid-range system. For budget-conscious users or those new to the platform, Google Colab is suggested as an alternative.
Takeaways
- 💻 The comparison is between a MacBook Pro M1 Max, a mid-range PC with an RTX 3060, a high-end PC with an RTX 4090, and Google Colab for running Stable Diffusion.
- 📈 The RTX 4090 outperforms the RTX 3060 and the Mac in benchmarks, taking only 2.1 seconds compared to 3.6 seconds for the RTX 3060.
- 🚀 The RTX 4090 is the clear winner in performance, nearly four times better than the RTX 3060 and significantly faster than the Mac and Google Colab.
- 🔍 Google Colab's performance was expectedly lower, using an older Tesla T4 GPU.
- 🍎 The Mac, despite having a powerful M1 Max chip, showed performance issues with Stable Diffusion and wasn't optimized for it.
- 📦 The high-end PC with RTX 4090 also has 24 GB of VRAM and 64 GB of RAM, which contributes to its superior performance.
- 📉 The performance gap widens at higher resolutions, with the RTX 4090 remaining the top performer.
- 💔 The Mac struggled with high-resolution tasks and threw an error when using the automatic 1111 version of Stable Diffusion.
- 💰 The RTX 4090, while offering the best performance, also has the highest power consumption and cost.
- 💭 For those on a tighter budget, the RTX 3060 or similar mid-range GPUs are recommended, or considering Google Colab which offers a free basic version.
- ⚙️ If you already own an Apple Silicon Mac, it can be used for Stable Diffusion, but it's not recommended to buy one solely for this purpose due to its current performance limitations.
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, mid-range PC with an RTX 3060, a high-end PC with an RTX 4090, and Google Colab.
What are the specifications of the Mac used in the comparison?
-The Mac used is a MacBook Pro M1 Max with 10 CPU cores, 32 GPU cores, and 32GB of memory.
Which two versions of stable diffusion were tested on the Mac?
-The two versions of stable diffusion tested on the Mac are the usual automatic 1111 and the Vlad stable diffusion, which is optimized for the Apple silicon GPU.
What are the approximate costs of the mid-range and high-end PCs used in the comparison?
-The mid-range PC with an AMD Ryzen 5 and Nvidia RTX 3060 costs about a thousand Euro or less, while the high-end PC with a Ryzen 9 and RTX 4090 costs over 3,000 Euro.
What GPU does Google Colab provide in its free version?
-In the free version, Google Colab provides an Nvidia Tesla T4 GPU.
How many benchmarks were conducted and what was the method to determine the results?
-Nine benchmarks were conducted, each with five iterations. The mean value of the last four iterations was used as the result for each benchmark.
What was the surprise result regarding the Mac's performance with stable diffusion?
-The surprise result was that the Mac's performance with stable diffusion was significantly lower than expected, indicating that stable diffusion is not yet optimized for the Mac.
Which system had the best performance in the benchmarks?
-The RTX 4090 system had the best performance in the benchmarks, performing nearly four times better than the RTX 3060 and several times better than the Mac and Google Colab.
What was the conclusion for users with different budgets and needs regarding the use of stable diffusion?
-For users who need great computing power and can afford it, the RTX 4090 is the best choice. For those with a tighter budget, the RTX 3060 or a similar mid-range system is recommended. For Apple silicon Mac users, the machine can be used for stable diffusion but is not optimal. For users with a very low budget, Google Colab is suggested as an alternative.
What was the issue encountered when using the automatic 1111 version of stable diffusion on the Mac?
-The Mac threw an error when using the automatic 1111 version of stable diffusion, suggesting that it may not be compatible or there might be a configuration issue.
What is the advantage of using Google Colab for stable diffusion tasks?
-Google Colab offers the advantage of not requiring a powerful personal GPU, as it provides access to Google's servers with powerful GPUs. It also has a free basic version and offers subscription plans with more powerful GPU options.
Outlines
🖥️ Mac vs PC Performance on Stable Diffusion
The speaker begins by introducing the topic of comparing the performance of Stable Diffusion across different systems, specifically highlighting their personal journey from using a Mac to a mid-range PC and then to a high-end PC. They discuss their initial use of Stable Diffusion on a MacBook Pro M1 Max and the subsequent acquisition of a PC with an AMD Ryzen 5 and Nvidia RTX 3060 for projects requiring Unreal Engine, which is not well-supported on Macs. The need for more computing power led to the purchase of another PC with an RTX 4090. The speaker also mentions using Google Colab for demanding tasks like training Stable Diffusion models. They conducted 9 benchmarks across different platforms to compare performance, using various Stable Diffusion versions and models, and rendering tasks. The results were surprising, particularly the underperformance of the Mac despite its powerful M1 Max chip, indicating a lack of optimization for Macs.
📊 Benchmark Results and System Performance Conclusions
The speaker presents the results of their benchmarks, noting the RTX 4090's superior performance, especially at higher resolutions, with the RTX 3060 and Google Colab also performing well at lower resolutions. However, the Mac struggled with high-resolution tasks. The RTX 4090 was the clear winner in terms of performance, outperforming the RTX 3060 by nearly four times, the Mac by five to six times, and Google Colab by three and a half times. Despite its high cost and power consumption, the RTX 4090 was deemed the best choice for those requiring significant computing power. For those on a tighter budget, the RTX 3060 or similar mid-range systems were recommended. The speaker advised against purchasing a Mac solely for Stable Diffusion due to its current performance issues with the software. Google Colab was suggested as a cost-effective alternative for those with low budgets or who wish to avoid high expenses on hardware.
Mindmap
Keywords
💡Stable Diffusion
💡MacBook Pro M1 Max
💡RTX 3060
💡RTX 4090
💡Google Colab
💡Benchmarks
💡Reliberate Model
💡SDXL 1.0 Base Model
💡Control Nets
💡High-Res Fix
💡Animation Rendering
💡Performance Optimization
Highlights
A comparison of Stable Diffusion performance on different systems: Mac, RTX 3060, RTX 4090, and Google Colab.
The user's experience with Stable Diffusion on a MacBook Pro M1 Max, highlighting its powerful capabilities but noting optimization issues.
Introduction of a mid-range PC with an AMD Ryzen 5 and Nvidia RTX 3060 for Unreal Engine projects and Stable Diffusion.
The acquisition of a high-end PC with a Ryzen 9 and RTX 4090 for more powerful computing needs.
Utilization of Google Colab for demanding tasks like Dream Booth trainings for custom Stable Diffusion models.
Benchmarking process involving 9 tests with five iterations each to assess system performance.
Surprising results from the benchmarks, indicating the RTX 4090's superior performance in comparison to other systems.
The RTX 4090 completed the first benchmark in just 2.1 seconds, significantly faster than the RTX 3060 at 3.6 seconds.
Google Colab's use of an older Tesla T4 GPU resulted in expectedly slower performance.
The Mac's performance was a significant letdown, indicating a lack of optimization for Stable Diffusion on Apple Silicon.
At 768x768 resolution, the performance gap between systems widened, with the RTX 4090 being the clear winner.
High-res fix tests showed the RTX 4090 maintaining performance while other systems struggled.
The RTX 3060 and Google Colab performed well at lower resolutions but faced challenges at higher resolutions.
The Mac encountered errors when using the automatic 1111 version of Stable Diffusion, suggesting compatibility issues.
For image-to-image tests with control nets, the RTX 4090 showed no issues even at high resolutions due to its 24GB of VRAM.
The Mac struggled with high-resolution images, particularly when using the automatic 1111, and threw errors.
In animation rendering tests, the RTX 4090 was unbeatable at 512x512 pixels, while other systems performed similarly.
The final conclusion: the RTX 4090 is the top choice for those needing great computing power and willing to spend, while the RTX 3060 is a solid mid-range option.
Google Colab is recommended for those with a low budget or who wish to try Stable Diffusion without a significant financial investment.
The Mac is not recommended for purchasing solely for Stable Diffusion purposes due to its current performance issues.
