Run Stable Diffusion XL For Free In Colab: Including Your Own LoRA Files

All Your Tech AI
26 Jan 202407:10

TLDRThe video tutorial demonstrates how to utilize a custom stable, diffusion model called Focus in Google Colab, enabling users to render high-quality images without the need for a powerful gaming computer. It guides viewers through the process of connecting to a GPU instance, installing Focus, and generating images using various settings and styles. The tutorial also explains how to upload a personal 'Laura' file to customize the model further and emphasizes the ease of use and accessibility of Focus for AI enthusiasts.

Takeaways

  • ๐ŸŒŸ The video discusses training a stable, diffusion model using a tool called Focus, which simplifies the process.
  • ๐Ÿ’ป Training and using the model can be done without a powerful gaming computer, utilizing Google Colab instead.
  • ๐Ÿ”— Focus is praised for its ease of use, abstracting complex inner workings and specialized prompting techniques.
  • ๐Ÿ“„ The GitHub page of Focus provides an open and collab link for easy access to the service.
  • ๐Ÿ–ฅ๏ธ A T4 GPU instance is provided by Google Colab to run stable diffusion with enough memory and disk space.
  • ๐Ÿ”„ After installation, Focus runs and provides two URLs; one local and one gradio app link for accessible use.
  • ๐Ÿ–ผ๏ธ The Focus UI allows users to generate high-quality images with simple prompts and offers advanced settings for further customization.
  • ๐ŸŽจ Users can select different preset styles to modify the appearance of generated images, such as an origami style.
  • ๐Ÿ”ง The model tab in Focus lets users select different stable diffusion models and even upload custom Laura files.
  • ๐Ÿ“‚ Uploading custom Laura files involves placing the file in the correct directory within the Google Colab environment.
  • ๐Ÿš€ With custom Laura files, users can generate stable diffusion images using their own models in the cloud.
  • โณ The session will time out eventually, and uploaded files will be deleted, so users should save their images before they are lost.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about training your own stable, diffusion model using a tool called Focus, and running it on Google Colab without needing a powerful gaming computer.

  • What is the significance of the Laura file in the context of the video?

    -The Laura file is a low-rank adaptation file that helps stable, diffusion render new and interesting characters, objects, places, and styles. It is crucial for customizing the stable, diffusion model.

  • How does Google Colab help in using stable, diffusion without a powerful machine?

    -Google Colab provides a platform where you can run the stable, diffusion model using a T4 GPU instance, which has enough memory to execute the model without needing a local, powerful machine.

  • What does Focus abstract away in using stable, diffusion?

    -Focus abstracts away the complex inner workings, specialized prompting techniques, and other technical aspects of running stable, diffusion, making it simpler and easier to use.

  • How can you access the Focus UI in Google Colab?

    -After installing Focus and running the notebook in Google Colab, you can access the Focus UI by clicking on the Gradio URL provided at the bottom of the page.

  • What is the quality of the images generated by the stable, diffusion model with Focus?

    -The images generated are of very high quality, almost photographic, due to the fine-tuning and tweaking done by Focus on the back end.

  • What are some advanced settings available in Focus?

    -In Focus, you can adjust the dimensions and aspect ratios of the generated images, change the number of images generated, and enter a negative prompt performance.

  • How can you apply different styles to the generated images in Focus?

    -You can select different preset styles in the 'Style' section of Focus, which modifies the appearance of the generated images. For example, you can apply an origami style or an MRE dark cyberpunk style.

  • How do you upload your custom Laura file to Google Colab?

    -You can upload your custom Laura file to the 'luras' directory within the 'focus' folder in Google Colab's file system. After uploading, you need to rename the file and move it to the 'luras' directory.

  • What happens if the Google Colab session times out?

    -If the session times out, all the files in the Colab environment, including the uploaded models and generated images, will be deleted. It is important to save any desired images or files before this happens.

  • Can Focus run on a low-end graphics card?

    -Yes, Focus can run on as little as 4 GB of VRAM, which means it can be executed on fairly old video cards without a problem.

Outlines

00:00

๐Ÿš€ Training Stable Diffusion with Google Colab

This paragraph introduces the process of training a stable diffusion model using Google Colab, a platform that allows users to train models without the need for a powerful desktop computer. The speaker explains how they previously demonstrated the creation of a low-rank adaptation file for stable diffusion, which can render new characters, objects, and styles. The focus here is on utilizing a project called Focus, which simplifies the use of stable diffusion. The speaker guides the audience through connecting to a GPU instance in Google Colab, installing Focus, and using it to generate high-quality images with simple prompts. The paragraph emphasizes the ease of use and the high-quality results produced by Focus, even for those without access to advanced hardware.

05:01

๐ŸŒŸ Customizing Stable Diffusion with Your Own Laura File

In this paragraph, the speaker delves into the customization of stable diffusion using personal Laura files. After explaining the process of uploading the Laura file to the correct directory in Google Colab, the speaker demonstrates how to integrate the file into the Focus interface. This allows users to generate images using their custom models. The speaker also discusses the ability to change the style of the generated images by selecting different preset styles, such as origami or cyberpunk. Additionally, the paragraph covers the option to upload different checkpoint models for further customization. The speaker concludes by reminding viewers to save their images before the session times out, as all files will be deleted. The paragraph highlights the flexibility and adaptability of the Focus tool for stable diffusion, enabling users to create unique and personalized content.

Mindmap

Keywords

๐Ÿ’กStable Diffusion

Stable Diffusion is a type of artificial intelligence (AI) model used for generating images from textual descriptions. It is designed to create new and interesting visual content, such as characters, objects, places, and styles. In the context of the video, Stable Diffusion is the core technology that the user is learning to utilize without needing a powerful computer, by leveraging Google Colab and Focus.

๐Ÿ’กGoogle Colab

Google Colab is a cloud-based platform that allows users to run Python code in a Jupyter notebook environment without needing to install any software locally. It provides free access to computational resources, including GPUs, which are essential for running machine learning models like Stable Diffusion. In the video, Google Colab is used to train and run the Stable Diffusion model without the need for a personal, high-performance computer.

๐Ÿ’กFocus

Focus is a project designed to simplify the use of Stable Diffusion, making it as easy to use as other AI models like Midjourney. It abstracts away the complex inner workings and specialized prompting techniques required to operate Stable Diffusion, providing a user-friendly interface for generating images. In the video, the speaker uses Focus to run Stable Diffusion in Google Colab, demonstrating its ease of use and effectiveness in producing high-quality images.

๐Ÿ’กGradio

Gradio is a library used for creating web applications for machine learning models. It allows users to quickly deploy models and interact with them through a simple interface. In the context of the video, Gradio is the tool that provides the accessible URL for users to interact with the Focus application and generate images using the Stable Diffusion model.

๐Ÿ’กGPU Instance

A GPU Instance refers to a virtual machine equipped with a Graphics Processing Unit (GPU), which is specialized hardware for processing complex calculations faster than traditional CPUs. In the context of the video, the GPU instance in Google Colab provides the necessary computational power to run the Stable Diffusion model, allowing users to generate high-quality images without the need for a local, powerful machine.

๐Ÿ’กLoopback Address

A loopback address is a network address that a computer uses to communicate with itself. It is a local address and cannot be accessed from outside the host machine. In the video, the loopback address is mentioned when discussing the URLs provided after starting the Focus application, emphasizing that it cannot be used because the user is not on the same network as the Colab machine.

๐Ÿ’กAdvanced Settings

Advanced settings refer to the optional configurations and fine-tuning options available within a software application or system. These settings allow users to customize the behavior and output of the system to better suit their needs. In the context of the video, advanced settings in Focus enable users to modify parameters such as image dimensions, aspect ratios, the number of images generated, and negative prompt performance.

๐Ÿ’กPreset Styles

Preset styles are pre-defined configurations or filters that can be applied to the output of a generative model to achieve a specific visual effect or aesthetic. They simplify the process of creating content with a consistent style by reducing the need for manual adjustments. In the video, preset styles in Focus are used to transform the generated images into different visual styles, such as 'origami' or 'MRE dark cyberpunk,' with a single selection.

๐Ÿ’กLaura File

A Laura file, short for a Low-Rank Adaptation file, is a type of file used in the context of Stable Diffusion models to customize and adapt the model's output according to specific user preferences or requirements. It contains the adapted weights that the model uses to generate images. In the video, the user learns how to upload and utilize their own Laura file within the Focus application on Google Colab to generate images with their personalized Stable Diffusion model.

๐Ÿ’กCheckpoints

Checkpoints in the context of machine learning models are saved states of the model that can be used to resume training or to continue the model's operation from a specific point. They are crucial for maintaining progress and ensuring that the model's performance does not degrade over time. In the video, checkpoints refer to different versions of the Stable Diffusion model that can be uploaded and used within the Focus application.

๐Ÿ’กSession Storage

Session storage is a type of web storage that stores data for the duration of a single browser session. It is cleared when the session ends, typically when the browser is closed. In the video, session storage is used to temporarily store the uploaded Laura file before it is moved to the appropriate directory within the Google Colab environment.

Highlights

Training your own stable, diffusion model using Excel, Laura, file, and low rank, adaptation file without the need for a powerful gaming computer.

Utilizing Google Colab to run stable diffusion models for those without the capability to run them locally.

Focus, a project that simplifies the use of stable diffusion, making it as easy to use as mid-journey.

Accessing Focus through an open and collab link on their GitHub page.

Connecting to a T4 GPU instance in Google Colab to run stable diffusion with adequate memory and disk space.

Running the Focus application by clicking the play button and proceeding despite the collab notebook not being authored by Google.

Installation of Focus on the Google Colab instance, which takes a minute to complete.

Accessing the Focus UI using the provided gradio app link.

Generating high-quality images with simple prompts through Focus's fine-tuning and tweaking capabilities.

Exploring advanced settings in Focus to modify dimensions, aspect ratios, number of images generated, and negative prompt performance.

Applying preset styles in Focus to alter the appearance of generated images, such as the origami style.

Changing the default Juggernaut XL stable diffusion model to other variations like stable diffusion XL.

Uploading custom Laura files to the Focus application for generating stable diffusion images with personalized models.

Renaming and uploading the pytorch Laura weights file to the correct directory in Google Colab.

Refreshing files in Focus to display the uploaded Laura file and selecting it for image generation.

Selecting different styles and prompts for generating images with custom Laura files in Focus.

Saving generated images from Google Colab before the session times out and files are deleted.

Running Focus locally on a minimum of 4GB of VRAM, allowing for the use of older video cards.

The video provides a comprehensive tutorial on using Focus for stable diffusion, including tips and tricks for beginners.