TEXTUAL INVERSION - How To Do It In Stable Diffusion (It's Easier Than You Think) + Files!!!
TLDRTextual inversion in Stable Diffusion is a technique that allows for adding new styles or objects to text-to-image models without altering the base model. The process involves creating an embedding for new keywords, which can be used to initialize styles in Stable Diffusion. By training with a set of images, users can generate robust datasets and train models to produce accurate outputs. The tutorial covers the steps from installation to training, emphasizing the importance of image selection and prompt construction. It showcases how textual inversion can enhance AI-generated art by personalizing styles and encouraging creative experimentation.
Takeaways
- ๐ Textual inversion is a technique that can be used with Stable Diffusion to create AI-generated images based on specific styles or subjects.
- ๐ The process begins by understanding what textual inversion is and how it can be applied within the Stable Diffusion framework.
- ๐ To get started, one needs to have a local installation of Stable Diffusion and follow the provided installation guide.
- ๐ Downloading pre-trained styles and subjects from the Stable Diffusion Conceptualizer can be a quick way to start experimenting with textual inversion.
- ๐ Properly saving the downloaded style files within the Stable Diffusion folder is crucial for the process to work correctly.
- ๐จ Creating a unique name for your textual inversion project helps avoid accidental use of the same style in other projects.
- ๐ผ๏ธ Selecting a good number of input images (15-100) that are similar but not identical ensures a more accurate training process.
- ๐ Setting the correct image resolution is important for maintaining the aspect ratio and reducing training time.
- ๐ Training the model can take a significant amount of time, depending on the complexity and the steps set for the training process.
- ๐ The ability to continue training after an initial session allows for refining the results and achieving better outcomes.
- ๐ญ Experimenting with different animals or subjects can lead to interesting and unique AI-generated images, showcasing the creative potential of textual inversion.
Q & A
What is the main topic of the video?
-The main topic of the video is textual inversion in Stable Diffusion and how to perform it using one's own images.
What is textual inversion in the context of Stable Diffusion?
-Textual inversion in Stable Diffusion is a technique where the AI is trained with a specific style or subject using a set of images to generate new images that match the input style or subject.
How can one acquire the pre-trained styles for Stable Diffusion?
-Pre-trained styles for Stable Diffusion can be found on the Stable Diffusion Conceptualizer, where users can download and use them immediately.
What is the recommended value for the number of vectors per token in textual inversion?
-A value between 8 and 10 is recommended for the number of vectors per token in textual inversion.
What is the significance of the number of vectors per token in the process?
-The number of vectors per token determines the size of the embedding. A larger value means more information about the subject can fit into the embedding, but it also reduces the prompt allowance.
How many input images are suggested for the textual inversion process?
-At least 15 input images are suggested, though one can also use 50 or 100 pictures, depending on the project.
What should be the characteristics of the input images for textual inversion?
-The input images should be fairly similar to each other but not too similar. They should also be a little bit different but not too different to provide variety during the training process.
What is the recommended resolution for the input images in the textual inversion process?
-The recommended resolution for the input images is 512 by 768 pixels.
How long does the training process typically take?
-The training process can take a considerable amount of time. For instance, on a powerful computer with a 3080 TI, 20,000 steps took two and a half hours.
What is the purpose of the 'Create Flipped Copies' option in the process?
-The 'Create Flipped Copies' option doubles the amount of training images by flipping each image to the other side, enhancing the training data variety.
How can one use the trained textual inversion in Stable Diffusion?
-After training, the textual inversion can be used in the Stable Diffusion 'Text to Image' or 'Image to Image' features by entering the prompt and appending the project name at the end.
Outlines
๐จ Introduction to Textual Inversion with Stable Diffusion
This paragraph introduces the concept of textual inversion using Stable Diffusion, a method that may seem complex but is quite straightforward. The speaker uses Stable Diffusion's local installation to demonstrate textual inversion, guiding viewers on how to install and use it. The explanation includes what textual inversion is and how it can be utilized with pre-trained styles and subjects available on the Stable Diffusion Conceptualizer. It also provides instructions on downloading and using these styles, emphasizing the importance of saving the style files within the Stable Diffusion folder for proper functionality.
๐ผ๏ธ Preparing Images for Textual Inversion
The second paragraph delves into the specifics of preparing images for textual inversion. It emphasizes the need for a sufficient number of similar yet distinct images, such as multiple bunny pictures created using Mid-journey. The speaker discusses the importance of image resolution, suggesting a reduction to 512 by 768 pixels to optimize training time. The paragraph outlines the process of copying source and destination directories, setting the correct image size, and utilizing options like creating flipped copies and using BLIP captions for file names. It also touches on the limitations of Stable Diffusion's prompt allowance and how token usage can affect it.
๐ ๏ธ Training Textual Inversion with Stable Diffusion
This paragraph explains the training process of textual inversion in Stable Diffusion. It covers the steps to create a file for textual inversion, including setting up the number of vectors per token, which affects the size of the embedding and the prompt allowance. The speaker provides guidance on selecting the right prompts, using the textual inversion template, and setting the correct resolution for training. It also discusses the significance of the number of training steps, offering advice on how to balance the quality of results with the duration of the training process. The paragraph further explains how to process the images, set up the training parameters, and the importance of creating a backup of the embedding during training.
๐ธ Using and Evaluating Textual Inversion Results
The final paragraph discusses how to use the textual inversion results in Stable Diffusion. It explains the structure of the textual inversion folder and how to identify the project-specific embeddings and test images. The speaker shares insights on evaluating the training results, suggesting that sometimes less-trained versions can yield better outcomes. The paragraph also highlights the flexibility of using different animals and styles for creating unique AI-generated images. It concludes with the speaker's encouragement for viewers to experiment with textual inversion, emphasizing its potential for artistic exploration and creativity within AI art.
Mindmap
Keywords
๐กTextual Inversion
๐กStable Diffusion
๐กEmbeddings Folder
๐กPrompt
๐กTokens
๐กInput and Output Folders
๐กTraining
๐กSample Images
๐กTextual Inversion Folder
๐กArtificial Intelligence (AI)
Highlights
Textual inversion in Stable Diffusion is easier than it sounds.
Stable Diffusion local install automatic 1111 is used for demonstration.
Textual inversion allows you to train Stable Diffusion with your own images.
The stable diffusion conceptualizer offers pre-trained styles and subjects.
Input images are not output images from Stable Diffusion, but originals for comparison.
To download styles, right-click the name and open the link in a new tab.
Save the downloaded style file inside the Stable Diffusion embeddings folder.
An outdated version of Stable Diffusion can be updated by downloading a new zip file from the website.
Textual inversion requires a unique name to avoid accidental reuse of styles.
The number of vectors per token affects the size of the embedding and the prompt allowance.
Images for training should be similar but not too similar, and at least 15 in number.
The resolution of images is important for defining the ratio and training time.
Processing images involves a source and destination directory for input and output.
Flipped copies can be created to double the amount of training images.
The AI uses a prompt template file with descriptions for training images.
Training can be paused and resumed with different settings for further refinement.
Sample images are rendered every 150 steps during training.
Textual inversion enables the creation of unique styles and artistic AI-generated images.
Different versions of the trained model can be tested for varying results.