Completed
Checking VRAM Usage with nvitop: Real-Time Monitoring During FLUX Training
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Blazing Fast and Ultra Cheap FLUX LoRA Training on Cloud Compute - No GPU Required
Automatically move to the next video in the Classroom when playback concludes
- 1 Introduction to FLUX Training on Cloud Services Massed Compute and RunPod
- 2 Overview of Platform Differences and Why Massed Compute is Preferred for FLUX Training
- 3 Quick Setup for Massed Compute and RunPod Accounts
- 4 Overview of FLUX, Kohya GUI, and Using 4x GPUs for Fast Training
- 5 Exploring Massed Compute Coupons and Discounts: How to Save on GPU Costs
- 6 Detailed Setup for Training FLUX on Massed Compute: Account Creation, Billing, and Deploying Instances
- 7 Deploying Multiple GPUs on Massed Compute for Faster Training
- 8 Setting Up ThinLinc Client for File Transfers Between Local Machine and Cloud
- 9 Troubleshooting ThinLinc File Transfer Issues on Massed Compute
- 10 Preparing to Install Kohya GUI and Download Necessary Models on Massed Compute
- 11 Upgrading to the Latest Version of Kohya for FLUX Training
- 12 Downloading FLUX Training Models and Preparing the Dataset
- 13 Checking VRAM Usage with nvitop: Real-Time Monitoring During FLUX Training
- 14 Speed Optimization Tips: Disabling T5 Attention Mask for Faster Training
- 15 Understanding the Trade-offs: Applying T5 Attention Mask vs. Training Speed
- 16 Setting Up Multi-GPU Training for FLUX on Massed Compute
- 17 Adjusting Epochs and Learning Rate for Multi-GPU Training
- 18 Achieving Near-Linear Speed Gain with 4x GPUs on Massed Compute
- 19 Uploading FLUX LoRAs to Hugging Face for Easy Access and Sharing
- 20 Using SwarmUI on Your Local Machine via Cloudflare for Image Generation
- 21 Moving Models to the Correct Folders in SwarmUI for FLUX Image Generation
- 22 Setting Up and Running Grid Generation to Compare Different Checkpoints
- 23 Downloading and Managing LoRAs and Models on Hugging Face
- 24 Generating Images with FLUX on SwarmUI and Finding the Best Checkpoints
- 25 Advanced Configurations in SwarmUI for Optimized Image Generation
- 26 How to Use Forge Web UI with FLUX Models on Massed Compute
- 27 Setting Up and Configuring Forge Web UI for FLUX on Massed Compute
- 28 Moving Models and LoRAs to Forge Web UI for Image Generation
- 29 Generating Images with LoRAs on Forge Web UI
- 30 Transition to RunPod: Setting Up FLUX Training and Using SwarmUI/Forge Web UI
- 31 RunPod Network Volume Storage: Setup and Integration with FLUX Training
- 32 Differences Between Massed Compute and RunPod: Speed, Cost, and Hardware
- 33 Deploying Instances on RunPod and Setting Up JupyterLab
- 34 Installing Kohya GUI and Downloading Models for FLUX Training on RunPod
- 35 Preparing Datasets and Starting FLUX Training on RunPod
- 36 Monitoring VRAM and Training Speed on RunPod’s A40 GPUs
- 37 Optimizing Training Speed by Disabling T5 Attention Mask on RunPod
- 38 Comparing GPU Performance Across Platforms: A6000 vs A40 in FLUX Training
- 39 Setting Up Multi-GPU Training on RunPod for Faster FLUX Training
- 40 Adjusting Learning Rate and Epochs for Multi-GPU Training on RunPod
- 41 Achieving Near-Linear Speed Gain with Multi-GPU FLUX Training on RunPod
- 42 Completing FLUX Training on RunPod and Preparing Models for Use
- 43 Managing Multiple Checkpoints: Best Practices for FLUX Training
- 44 Using SwarmUI on RunPod for Image Generation with FLUX LoRAs
- 45 Setting Up Multiple Backends on SwarmUI for Multi-GPU Image Generation
- 46 Generating Images and Comparing Checkpoints on SwarmUI on RunPod
- 47 Uploading FLUX LoRAs to Hugging Face from RunPod for Easy Access
- 48 Advanced Download Techniques: Using Hugging Face CLI for Batch Downloads
- 49 Fast Download and Upload of Models and LoRAs on Hugging Face
- 50 Using Forge Web UI on RunPod for Image Generation with FLUX LoRAs
- 51 Troubleshooting Installation Issues with Forge Web UI on RunPod
- 52 Generating Images on Forge Web UI with FLUX Models and LoRAs
- 53 Conclusion and Upcoming Research on Fine-Tuning FLUX with CLIP Large Models
