Leveraging SwarmUI & Stable Diffusion 3 on Cloud Platforms: A Guide to Kaggle (No-Cost), Massed Comp
https://youtu.be/XFUZof6Skkw?embedable=true
This tutorial demonstrates the installation and usage of SwarmUI on various cloud platforms. For those lacking a high-performance GPU or seeking enhanced GPU capabilities, this guide is invaluable. You'll discover how to set up and leverage SwarmUI, a cutting-edge Generative AI interface, on Massed Compute, RunPod, and Kaggle (which provides complimentary dual T4 GPU access for 30 hours per week).
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This instructional video will enable you to utilize SwarmUI on cloud GPU services as seamlessly as on your personal computer. Additionally, I'll guide you through using Stable Diffusion 3 (#SD3) in the cloud environment. SwarmUI operates on the ComfyUI backend.
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🔗 Comprehensive Public Post (no registration required) Featured In The Video, Including All Relevant Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985
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🔗 Windows Guide: Mastering SwarmUI Usage ➡️ https://youtu.be/HKX8_F1Er_w
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🔗 Tutorial: Rapid Model Download for Massed Compute, RunPod, and Kaggle, plus Swift Model/File Upload to Hugging Face ➡️ https://youtu.be/X5WVZ0NMaTg
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🔗 Join SECourses Discord Community ➡️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
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🔗 Stable Diffusion GitHub Repository (Please Star, Fork, and Watch) ➡️ https://github.com/FurkanGozukara/Stable-Diffusion
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Exclusive Discount Code for Massed Compute: SECourses
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Valid for Alt Config RTX A6000 and standard RTX A6000 GPUs
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0:00 Overview of SwarmUI cloud services tutorial (Massed Compute, RunPod & Kaggle)
3:18 SwarmUI installation and usage on Massed Compute virtual Ubuntu machines
4:52 ThinLinc client synchronization folder setup for Massed Compute virtual machine access
6:34 Connecting to and initiating Massed Compute virtual machine post-initialization
7:05 One-click SwarmUI update on Massed Compute prior to use
7:46 Configuring multiple GPUs on SwarmUI backend for simultaneous image generation
7:57 GPU status monitoring using nvitop command
8:43 Pre-installed Stable Diffusion models on Massed Compute
9:53 Model download speed assessment on Massed Compute
10:44 Troubleshooting 4 GPU backend setup errors
11:42 Monitoring all 4 GPUs' operational status
12:22 Image generation and step speed analysis on RTX A6000 (Massed Compute) for SD3
12:50 CivitAI API key configuration for accessing gated models
13:55 Efficient bulk image download from Massed Compute
15:22 Latest SwarmUI installation on RunPod with precise template selection
16:50 Port configuration for SwarmUI connectivity post-installation
17:50 RunPod SwarmUI installation via sh file download and execution
19:47 Resolving backend loading issues through Pod restart
20:22 Relaunching SwarmUI on RunPod
1:14 Stable Diffusion 3 (SD3) implementation on RunPod
22:01 Multi-GPU backend system configuration on RunPod
23:22 RTX 4090 generation speed analysis (SD3 step speed)
24:04 Bulk image download technique for RunPod
24:50 SwarmUI and Stable Diffusion 3 setup on free Kaggle accounts
28:39 Modifying SwarmUI model root folder path on Kaggle for temporary storage utilization
29:21 Secondary T4 GPU backend addition on Kaggle
29:32 SwarmUI restart procedure on Kaggle
31:39 Stable Diffusion 3 model deployment and image generation on Kaggle
33:06 RAM error troubleshooting and resolution on Kaggle
33:45 Disabling one backend to prevent RAM errors with dual T5 XXL text encoder usage
34:04 Stable Diffusion 3 image generation speed evaluation on Kaggle's T4 GPU
34:35 Comprehensive image download process from Kaggle to local device
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In this comprehensive article, we will explore how to use SwarmUI, Stable Diffusion 3, and other Stable Diffusion models on various cloud computing platforms. This guide is designed to help users who don't have access to powerful GPUs locally leverage cloud resources for running these advanced AI image generation models. We'll cover three main platforms: Massed Compute, RunPod, and Kaggle.
1.1 Overview of Platforms
1.1.1 Massed Compute
Massed Compute is introduced as the cheapest and most powerful cloud server provider. It offers pre-installed SwarmUI and the latest versions of necessary software, making it easy to start generating images quickly.
1.1.2 RunPod
RunPod is another cloud service provider that offers access to high-performance GPUs. This platform allows users to deploy custom environments and install SwarmUI manually.
1.1.3 Kaggle
Kaggle, a popular platform for data science and machine learning, offers free GPU access. This article demonstrates how to use SwarmUI on a free Kaggle account, utilizing the provided T4 GPUs.
1.2 Prerequisites
Before diving into the specifics of each platform, it's strongly recommended to watch the 90-minute SwarmUI tutorial mentioned in the article. This comprehensive guide covers the details of using SwarmUI and is essential for understanding the full capabilities of the software.
Using SwarmUI on Massed Compute
2.1 Registration and Deployment
To begin using SwarmUI on Massed Compute, follow these steps:
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Use the provided registration link to create an account.
Enter your billing information and load balance to your account.
Navigate to the deployment section.
Select the RTX A6000 or RTX A6000 Alt config based on availability.
Choose the "creator" category and "SE courses" image.
Apply the special coupon code "SECourses verify" to reduce the hourly rate.
Click "deploy" to create your instance.
2.2 Connecting to the Virtual Machine
After deploying your instance, you'll need to connect to it:
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Download and install the ThinLinc client appropriate for your operating system.
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Configure the ThinLinc client: Go to "Options" > "Local devices" Uncheck all options except "Drives"
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Add a folder for synchronization to upload/download files
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Use the provided login IP address and credentials to connect to your virtual machine.
2.3 Updating and Starting SwarmUI
Once connected to your Massed Compute virtual machine:
Double-click the updater button to automatically update SwarmUI to the latest version.
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Wait for the update to complete and for SwarmUI to start.
2.4 Configuring Multiple GPUs
If you've deployed multiple GPUs, you can configure SwarmUI to use them all:
Go to "Server" > "Backends"
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Add additional ComfyUI self-starting backends
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Set unique GPU IDs for each backend to ensure proper distribution across available GPUs
2.5 Generating Images
With SwarmUI set up on Massed Compute, you can now start generating images:
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Select your desired model (e.g., Stable Diffusion 3, SDXL, etc.)
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Choose your preferred sampler and scheduler
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Enter your prompt and set the number of images to generate
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Click "Generate" to start the process
2.6 Downloading Generated Images
To download your generated images from Massed Compute:
Navigate to the "Files" folder
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Go to "apps" > "Stable SwarmUI" > "output"
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Copy the output folder to your synchronization folder
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Access the synchronized files on your local machine
2.7 Using CivitAI API
A new feature allows you to download gated CivitAI models:
Obtain your CivitAI API key from your account settings
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In SwarmUI, go to "User" and enter your API key
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Use the model downloader in "Utilities" to access CivitAI models Using SwarmUI on RunPod
3.1 Registration and Pod Deployment
To use SwarmUI on RunPod:
Register using the provided link
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Set up billing and load credits to your account
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Go to "Pods" and click "Deploy Pod"
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Select Community Cloud or set up permanent storage (refer to the separate tutorial for this)
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Choose your desired GPU configuration (e.g., 3x 4090 GPU)
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Select the "RunPod PyTorch 2.1 with CUDA 11.8" template
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Set disk volume and proxy port (7801 for SwarmUI)
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Deploy your pod
3.2 Installing SwarmUI
Once your pod is running:
Connect to JupyterLab
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Upload the provided "install_linux.sh" file
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Open a terminal and run the installation commands
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Wait for the installation to complete
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Restart the pod once after the first installation
3.3 Starting SwarmUI
After restarting:
Connect to JupyterLab again
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Run the provided start commands in the terminal
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Access SwarmUI through the HTTP port connection
3.4 Downloading Additional Models
To use models like Stable Diffusion 3 on RunPod:
Go to "Utilities" > "Model Downloader"
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Use the provided direct download link to add new models
3.5 Configuring Multiple GPUs
Similar to Massed Compute, configure multiple backends:
Go to "Server" > "Backends"
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Add ComfyUI self-starting backends
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Set unique GPU IDs for each backend
3.6 Generating Images
Follow the same process as described for Massed Compute to generate images using your chosen models and settings.
3.7 Downloading Generated Images
To download images from RunPod:
Navigate to the SwarmUI folder
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Right-click on the output folder and download as an archive
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Alternatively, use RunPodCTL or upload to Hugging Face (refer to the separate tutorial for these methods)
Using SwarmUI on Kaggle
4.1 Setting Up Kaggle Notebook
To use SwarmUI on a free Kaggle account:
Register for a free Kaggle account and verify your phone number
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Download the provided Kaggle notebook file
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Create a new notebook on Kaggle and import the downloaded file
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Select GPU T4 x2 as your accelerator
4.2 Installing SwarmUI
Follow the steps in the notebook to:
Download required models
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Execute installation cells
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Configure model paths and backends
4.3 Using SwarmUI on Kaggle
After installation:
Access SwarmUI through the provided link
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Configure backends to use both T4 GPUs
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Generate images using available models
4.4 Managing RAM Limitations
When using Stable Diffusion 3 on Kaggle:
Be aware of potential RAM limitations
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Use only one backend if encountering memory errors
4.5 Downloading Generated Images
To download images from Kaggle:
Use the provided cell to zip all generated images
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Refresh the file list and download the zip file
Additional Features and Resources
5.1 CivitAI Integration
SwarmUI now supports CivitAI API integration:
Obtain your CivitAI API key
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Enter the key in the SwarmUI user settings
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Use the model downloader to access CivitAI models
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This comprehensive guide provides detailed instructions on using SwarmUI, Stable Diffusion 3, and other Stable Diffusion models on Massed Compute, RunPod, and Kaggle. By following these steps, users without powerful local GPUs can leverage cloud resources to generate high-quality AI images. Remember to refer to the recommended tutorials and resources for more in-depth information on specific topics and advanced usage scenarios.
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