Ultimate FLUX LoRA Training Tutorial: Windows and Cloud Deployment
I have done total 104 different LoRA trainings and compared each one of them to find the very best hyper parameters and the workflow for FLUX LoRA training by using Kohya GUI training script.
You can see all the done experiments' checkpoint names and their repo links in following public post: https://www.patreon.com/posts/110838414
After completing all these FLUX LoRA trainings by using the most VRAM optimal and performant optimizer Adafactor I came up with all of the following ranked ready to use configurations.
You can download all the configurations, all research data, installers and instructions at the following link : https://www.patreon.com/posts/110879657
Tutorials
I also have prepared 2 full tutorials. First tutorial covers how to train and use the best FLUX LoRA locally on your Windows computer : https://youtu.be/nySGu12Y05k
This is the main tutorial that you have to watch without skipping to learn everything. It has total 74 chapters, manually written English captions. It is a perfect resource to become 0 to hero for FLUX LoRA training.
FLUX LoRA Training Simplified: From Zero to Hero with Kohya SS GUI (8GB GPU, Windows) Tutorial Guide
The second tutorial I have prepared is for how to train FLUX LoRA on cloud. This tutorial is super extremely important for several reasons. If you don't have a powerful GPU, you can rent a very powerful and very cheap GPU on Massed Compute and RunPod. I prefer Massed Compute since it is faster and cheaper with our special coupon SECourses. Another reason is that in this tutorial video, I have fully in details shown how to train on a multiple GPU setup to scale your training speed. Moreover, I have shown how to upload your checkpoints and files ultra fast to Hugging Face for saving and transferring for free. Still watch first above Windows tutorial to be able to follow below cloud tutorial : https://youtu.be/-uhL2nW7Ddw
Blazing Fast & Ultra Cheap FLUX LoRA Training on Massed Compute & RunPod Tutorial — No GPU Required!
Example Generated Images
These images are generated on SwarmUI with the above shared configs trained LoRA on my poor 15 images dataset. Everything shown in tutorial videos for you to follow. Then I have used SUPIR the very best upscaler to 2x upscale them with default parameters except enabling face enhancement : https://youtu.be/OYxVEvDf284
Complete Guide to SUPIR Enhancing and Upscaling Images Like in Sci-Fi Movies on Your PC
All the prompts used to generate below images shared in the below public link:
Revolutionizing AI Image Generation: A Deep Dive into FLUX LoRA Training
In a groundbreaking development in the world of artificial intelligence and image generation, a comprehensive tutorial has been released detailing the process of training LoRA (Low-Rank Adaptation) on FLUX, the latest state-of-the-art text-to-image generative AI model. This tutorial, presented by an unnamed expert in the field, promises to revolutionize the way individuals and professionals approach AI-generated imagery.
The presenter, who has spent the past week immersed in intensive research, has completed an impressive 72 full training sessions, with more underway. This dedication has resulted in the development of a range of unique training configurations tailored to GPUs with varying VRAM capacities, from as little as 8GB up to 48GB. These configurations have been meticulously optimized for VRAM usage and ranked by training quality, ensuring that users can achieve outstanding results regardless of their hardware limitations.
One of the most remarkable aspects of this tutorial is its inclusivity. Even users with modest 8GB RTX GPUs can train an impressive FLUX LoRA at a respectable speed. The primary difference between configurations lies in the training speed, making this technology accessible to a wide range of users with varying hardware capabilities.
The tutorial utilizes Kohya GUI, a user-friendly interface built on the acclaimed Kohya training scripts. This graphical user interface allows users to install, set up, and start training with just a few mouse clicks, dramatically simplifying what has traditionally been a complex process. While the demonstration is conducted on a local Windows machine, the presenter assures viewers that the process is identical for cloud-based services, making this tutorial valuable for users across different platforms.
The comprehensive nature of this tutorial cannot be overstated. It covers everything from basic concepts to expert settings, ensuring that even complete beginners can fully train and utilize an impressive FLUX LoRA model. The tutorial is thoughtfully organized into chapters and includes manually written English captions, enhancing its accessibility and ease of use.
Beyond the training process itself, the tutorial also demonstrates how to use the generated LoRAs within the Swarm UI and how to perform grid generation to identify the best training checkpoint. This holistic approach ensures that users not only learn how to train models but also how to effectively implement and optimize them.
In an exciting development for the AI community, the presenter reveals that at the end of the video, they will demonstrate how to train Stable Diffusion 1.5 and SDXL models using the latest Kohya GUI interface. This addition significantly expands the scope of the tutorial, offering valuable insights into multiple cutting-edge AI models.
To support the tutorial, the presenter has prepared an extensive written post containing all necessary instructions, links, and guides. This post, which will be continuously updated with new information, hyperparameters, and features, serves as the ultimate companion guide to the video tutorial.
The presenter goes into great detail about the installation process, emphasizing the importance of having the correct prerequisites installed, including Python 3.10.11, FFmpeg, CUDA 11.8, C++ tools, and Git. They provide step-by-step instructions for verifying the installation of these components, ensuring that users have a solid foundation before proceeding with the FLUX LoRA training.
One of the key aspects highlighted in the tutorial is the preparation of training data. The presenter stresses the importance of dataset quality, recommending that users include a variety of poses, expressions, clothing, and backgrounds in their training images. They also emphasize the need for excellent focus, sharpness, and lighting in the training dataset, as these factors significantly impact the quality of the generated images.
The tutorial delves into the intricacies of setting up the training parameters, explaining concepts such as epochs, repeating, and the use of regularization images. The presenter provides insights into how to optimize these settings based on the size and characteristics of the training dataset.
An interesting feature of the tutorial is the demonstration of multi-GPU usage for faster processing. The presenter shows how to configure Swarm UI to utilize multiple GPUs, significantly speeding up the image generation process.
The tutorial also covers advanced topics such as grid generation for comparing different checkpoints and LoRA models. This feature allows users to systematically evaluate and select the best performing model for their specific needs.
In addition to the technical aspects, the presenter shares valuable resources with the audience. They mention a Discord channel with over 8,000 members, a GitHub repository with 2,000 stars, and a subreddit where users can find additional support and information. The presenter's openness is evident as they provide their real LinkedIn profile, encouraging professional connections and further discussions.
The tutorial concludes with exciting prospects for future content, including guides for training on cloud platforms like Runpod and Massed Compute, as well as fine-tuning techniques that promise even better results.
In conclusion, this comprehensive tutorial on FLUX LoRA training represents a significant step forward in democratizing advanced AI image generation techniques. By making these complex processes accessible to a wide range of users, from beginners to experts, and across various hardware configurations, the presenter is contributing to the rapid advancement and widespread adoption of AI-generated imagery. As the field continues to evolve at a breakneck pace, tutorials like this one play a crucial role in keeping the community informed and empowered to leverage the latest technological advancements.
FLUX AI Training: A Comprehensive Guide to Cloud-Based Machine Learning
In a groundbreaking development in the field of artificial intelligence, a new tutorial has emerged, showcasing the incredible potential of FLUX AI training on cloud services. This comprehensive guide, spanning over an hour, offers an in-depth look at how individuals can harness the power of cloud computing to train and utilize FLUX models, even without access to high-end GPUs.
The tutorial, presented by an unnamed expert in the field, begins with a bold promise: the ability to train "amazing FLUX models in under 1 hour" using cloud services like Massed Compute and RunPod. This claim immediately captures attention, given the typically resource-intensive nature of AI model training.
One of the most striking aspects of this tutorial is its focus on cost-effectiveness. The presenter asserts that users can achieve high-quality results for as little as $1.25 per hour when utilizing 4x GPU setups. This pricing model potentially democratizes access to advanced AI training, making it accessible to a broader range of researchers, developers, and enthusiasts.
The guide doesn't just stop at training; it extends to the practical application of these models. The presenter introduces tools like SwarmUI and Forge Web UI, which allow users to generate images rapidly, perform grid generation, and compare checkpoints efficiently. This holistic approach ensures that learners can not only train models but also put them to immediate use.
A significant portion of the tutorial is dedicated to demonstrating these processes on both Massed Compute and RunPod platforms. This dual-platform approach provides viewers with flexibility in their choice of cloud service provider, catering to different preferences and requirements.
Interestingly, the tutorial goes beyond just the training process. It delves into the intricacies of managing and transferring large datasets and model files. The presenter introduces a method for rapid uploading and downloading of checkpoints to and from Hugging Face, a popular platform for sharing machine learning models. This feature addresses a common pain point in AI development – the time-consuming process of transferring large files.
The tutorial is structured to cater to both novices and experienced practitioners. For those new to the field, there's a strong recommendation to watch a preceding Windows tutorial, which covers the fundamentals of using the Kohya GUI for FLUX LoRA training. This layered learning approach ensures that viewers have a solid foundation before diving into the more advanced cloud-based techniques.
One of the most technically intriguing aspects of the tutorial is its exploration of multi-GPU training. The presenter demonstrates how to effectively utilize multiple GPUs to dramatically speed up the training process. This section is particularly valuable for those looking to scale their AI projects efficiently.
The tutorial also touches on the nuances of different GPU types and their cost-effectiveness. For instance, it compares the performance of RTX A6000 GPUs on Massed Compute with A40 GPUs on RunPod, providing viewers with practical insights into hardware selection for their projects.
A significant portion of the video is dedicated to troubleshooting and problem-solving. The presenter encounters and resolves various issues in real-time, from file transfer problems to configuration errors. This aspect of the tutorial is invaluable, as it prepares viewers for the real-world challenges they might face in their own projects.
The guide also delves into the intricacies of model configuration. It explores different "ranks" of configuration, explaining how these affect the balance between training speed and model quality. This level of detail allows viewers to make informed decisions about their training parameters based on their specific needs and resources.
One of the most impressive demonstrations in the tutorial is the speed at which complex operations can be performed using cloud resources. For instance, the presenter shows how to generate a grid of 195 images, each with 48 steps, in just 22 minutes using multiple GPUs. This kind of performance can be game-changing for researchers and artists working with AI-generated imagery.
The tutorial doesn't shy away from comparing different cloud providers. While Massed Compute is often presented as the preferred option due to its speed and cost-effectiveness, the presenter also highlights the advantages of RunPod, such as its permanent storage options. This balanced approach allows viewers to make informed decisions based on their specific needs.
An interesting technical aspect covered in the tutorial is the use of the "apply T5 attention mask" option. The presenter explains how this can improve model quality but at the cost of training speed, allowing viewers to make informed trade-offs based on their priorities.
The guide also touches on more advanced topics like learning rate adjustment for multi-GPU setups. It provides a formula for calculating new learning rates when scaling up to multiple GPUs, demonstrating the presenter's deep understanding of the underlying principles of machine learning.
One of the most valuable aspects of the tutorial is its coverage of post-training workflows. It demonstrates how to use tools like SwarmUI and Forge Web UI to generate images using the trained models, comparing different checkpoints to find the optimal results. This end-to-end approach ensures that viewers can take their projects from training to practical application.
The presenter's attention to detail is evident throughout the tutorial. For instance, they explain the importance of proper file naming conventions and directory structures when working with these complex AI systems. This kind of practical knowledge can save viewers hours of troubleshooting in their own projects.
Throughout the tutorial, there's a strong emphasis on the rapidly evolving nature of the field. The presenter mentions ongoing research into fine-tuning techniques and the potential for training the CLIP large model, hinting at even more powerful capabilities in the near future.
In conclusion, this comprehensive tutorial represents a significant step forward in democratizing access to advanced AI training techniques. By leveraging cloud computing resources, it opens up possibilities for individuals and small teams to work with cutting-edge AI models that were previously the domain of large tech companies and research institutions.
The combination of technical depth, practical problem-solving, and forward-looking insights makes this tutorial a valuable resource for anyone interested in the intersection of cloud computing and artificial intelligence. As the field continues to evolve at a rapid pace, resources like this will be crucial in keeping the wider community informed and empowered to push the boundaries of what's possible with AI.
This tutorial not only provides a roadmap for training and using FLUX models but also offers a glimpse into the future of AI development, where cloud resources and collaborative platforms like Hugging Face play an increasingly central role. As we move forward, it's clear that the ability to efficiently leverage these tools will be a key skill for AI researchers and practitioners alike.
Top comments (0)