New Release: Stable Diffusion 2.1 with GUI on Colab

Stability AI has launched Stable Diffusion 2.1, an update to their AI model that addresses previous concerns about anatomy and certain prompts not working well. The new release has removed adult content and some artists to improve the quality of generated images. Users can now access Stable Diffusion 2.1 through a lightweight GUI on GitHub, which is easy to set up and use on Google Colab. The video demonstrates how to use the new version, highlighting features like text-to-image, image-to-image, in-painting, and upscaling. The presenter also discusses the importance of reproducibility and provides a simple prompt example to generate a portrait. The community's response to the update is positive, and the video encourages viewers to try out Stable Diffusion 2.1 for themselves.

Takeaways

• 📈 Stable Diffusion 2.1 has been released by Stability AI, offering improvements over the previous version.
• 🎨 The new version addresses issues with anatomy and certain keywords not working as expected in the 2.0 version.
• 🚫 Stability AI has removed adult content and certain artists from the training dataset to improve the quality of the generated images.
• 🌐 Users can access Stable Diffusion 2.1 through various platforms, including Colab, using the diffusers library.
• 🖼️ The blog post announcing Stable Diffusion 2.1 features an impressive image, although the speaker was unable to reproduce it.
• 📸 There is a call for better reproducibility from Stability AI, including sharing more details about the seed value and configuration used.
• 🤖 The model has been trained on Stable Diffusion 2.0 with additional information and has removed adult content for a more refined output.
• 🌟 The new version has enabled the generation of celebrity and superhero images, which was one of the complaints in previous versions.
• 💻 A lightweight GUI for Stable Diffusion has been created by kunash and is available on GitHub, making it easier to use on Colab.
• ⚙️ The GUI allows for text-to-image, image-to-image, in-painting, and upscaling models, offering users more creative control.
• 🔍 Users have reported improvements in the final output, especially with keywords like 'training on ArtStation', indicating a positive response to the update.

Q & A

What is the latest version of Stable Diffusion released by Stability AI?

The latest version of Stable Diffusion released by Stability AI is 2.1.

What are some of the improvements made in Stable Diffusion 2.1?

Stable Diffusion 2.1 has improvements in addressing bad anatomy, removal of adult content, and certain artists that had resulted in bad anatomy. It also includes better handling of prompts and an enhanced training dataset.

How can one access and use Stable Diffusion 2.1?

You can access and use Stable Diffusion 2.1 by going to a specific GitHub repository that provides a lightweight UI GUI using Colab.

What is the issue with reproducibility that the speaker mentions?

The issue with reproducibility is that the shared prompts do not include the seed value, guidance scale, or number of steps, making it difficult for others to recreate the images.

What is the significance of the change in the training dataset in Stable Diffusion 2.1?

The change in the training dataset addresses the complaints regarding bad anatomy and the necessity to use negative prompts by removing certain content and artists.

How does the new Stable Diffusion 2.1 handle prompts related to 'trending on ArtStation'?

The new Stable Diffusion 2.1 model can generate images based on prompts that include 'trending on ArtStation', which was not working well in the previous version.

What are some of the features available in the new UI for Stable Diffusion 2.1?

The new UI for Stable Diffusion 2.1 includes text-to-image, image-to-image, in-painting, upscaling, and the ability to adjust negative prompts.

What is the recommended approach to using the number of steps in the Stable Diffusion 2.1 UI?

It is recommended not to go blindly by a higher number of steps. Instead, start with a smaller number and observe how the image changes as the steps increase.

How can one contribute to the creator of the Stable Diffusion UI?

One can contribute to the creator by buying them a coffee or supporting them with GitHub Stars if they are using the UI extensively.

What is the process to run Stable Diffusion 2.1 on Google Colab?

After opening the provided Colab link, you need to click the connect button, then run the provided buttons to install dependencies and run the notebook. Once set up, you can start using the UI to generate images.

How does Stable Diffusion 2.1 handle celebrity and superhero prompts?

Stable Diffusion 2.1 has enabled the generation of images related to celebrities and superheroes, addressing previous complaints about the lack of these features.

What are the ethical considerations mentioned in the script regarding the Stable Diffusion model?

The ethical considerations mentioned include the model's understanding of what is 'ugly' and the potential for creating better images, which could lead to further discussions about the ethical use of AI in image generation.

Outlines

00:00

🚀 Introduction to Stable Diffusion 2.1

The video introduces the release of Stable Diffusion 2.1 by Stability AI. The host discusses the accessibility of the new version and its improvements over the previous one. The speaker mentions an inability to reproduce an image from the announcement post due to lack of detailed information on the seed value and configuration. The focus is on the changes made to the training dataset, which included the removal of adult content and certain artists to address issues with anatomy and prompt effectiveness. The video also covers the reproducibility aspect and the community's feedback on the new model.

05:02

🖼️ Exploring Stable Diffusion 2.1 Features

The host demonstrates how to use Stable Diffusion 2.1 through a lightweight UI provided by Kunash on GitHub. The video showcases the process of generating images using specific prompts and discusses the reproducibility of results. It highlights the model's ability to generate images with trending prompts on ArtStation and introduces the inclusion of superheroes. The host also addresses the challenge of reproducibility and the community's positive response to the new version. The video provides a step-by-step guide on how to use the UI for text-to-image, image-to-image, in-painting, and upscaling models.

10:03

🎨 Testing and Comparing Stable Diffusion 2.1

The video continues with testing Stable Diffusion 2.1 by generating images using different prompts, including a portrait of an old Chinese grandpa. The host discusses the UI's efficiency, the importance of steps in the image generation process, and the ethical considerations regarding the model's understanding of aesthetics. The video also explores the use of negative prompts and their impact on image quality. The host compares the results with previous versions and emphasizes the improvements in keyword training, particularly with ArtStation prompts. The video concludes with a call to action for viewers to share their experiences with Stable Diffusion 2.1, especially regarding improvements in human anatomy.

Keywords

Stable Diffusion 2.1

Stable Diffusion 2.1 is an updated version of a machine learning model used for generating images from textual descriptions. It is a significant topic in the video as the host discusses its new features and improvements over the previous version. The host also demonstrates how to access and use this model, making it central to the video's content.

Reproducibility

Reproducibility refers to the ability to create the same output from the same input, which is crucial in machine learning models. In the context of the video, the host expresses a desire for better reproducibility in image generation, particularly concerning seed values and configurations. This is important as it affects the reliability and consistency of the results produced by Stable Diffusion 2.1.

Adult Content

Adult content typically includes material intended for mature audiences and may not be suitable for all users. The video mentions that Stable Diffusion 2.1 has removed adult content from its training dataset, which is a change from the previous version. This decision was made to improve the quality of the generated images and to make the model more widely applicable.

Anatomy

Anatomy, in the context of this video, refers to the accurate and realistic representation of human or animal body structures in generated images. The host discusses how the updated model addresses concerns about poor anatomy in images produced by the previous version, which is a significant improvement for users looking to generate more realistic images.

Training Dataset

A training dataset is a collection of data used to teach a machine learning model how to perform a task. The video explains that Stable Diffusion 2.1 has been trained on a modified dataset that excludes certain content to improve the quality of the generated images, particularly in terms of anatomy and the removal of adult content.

Negative Prompts

Negative prompts are terms or descriptions that users can include in their prompts to guide the image generation model away from producing certain unwanted features or styles. The host mentions the use of negative prompts, such as 'cartoonish,' to refine the output of the model and avoid undesired results.

UI (User Interface)

UI, or user interface, is the part of a computer program that users interact with to perform tasks. The video introduces a lightweight UI for Stable Diffusion 2.1 that allows users to easily input prompts and generate images. This UI is accessible through Google Colab and is highlighted for its ease of use and quick setup.

GitHub Repository

A GitHub repository is a location where developers can store and share their code with others. In the video, the host directs viewers to a specific GitHub repository where they can access the Stable Diffusion 2.1 UI. This repository is essential for users who want to try out the new features of Stable Diffusion 2.1.

Colab

Colab, short for Google Colaboratory, is a cloud-based platform that allows users to write and execute code in a simple and collaborative environment. The video emphasizes the use of Colab to run the Stable Diffusion 2.1 model for free, which is a significant benefit for users who may not have access to powerful hardware.

Text-to-Image Model

A text-to-image model is a type of machine learning model that generates images based on textual descriptions. The video discusses the capabilities of Stable Diffusion 2.1 as a text-to-image model and how it has been improved to better handle prompts and generate higher quality images.

Upscaling

Upscaling refers to the process of increasing the resolution of an image while maintaining or enhancing its quality. The video mentions the upscaling feature of Stable Diffusion 2.1, which allows users to generate images at a higher resolution, such as 768 by 768 pixels, and then upscale them for even more detail.

Highlights

Stability AI has released Stable Diffusion 2.1, which can be accessed using various methods including Colab.

The new version addresses issues with anatomy and removes adult content from the training dataset.

Stable Diffusion 2.1 is trained on Stable Diffusion 2.0 but with additional improvements.

The update includes better handling of prompts related to trending on ArtStation.

Celebrity and superhero images have been enabled in Stable Diffusion 2.1, addressing previous complaints.

Reproducibility of images generated by Stable Diffusion 2.1 is emphasized, with a focus on seed value and configuration.

A lightweight UI for Stable Diffusion has been created by kunash, making it easier to use on Colab.

The UI does not suffer from the black image issue that some other UIs have, providing a smoother experience.

Users can now generate images with prompts, upscale images, and perform in-painting with the new UI.

The UI allows for easy adjustment of steps and seed values for generating images.

Stable Diffusion 2.1 shows promise in generating better images, as seen in user-generated content on Reddit.

The text encoder has been changed in Stable Diffusion 2.1, with the open-source version called OpenCLIP.

The new version has started to impact the final outcome of images, especially with keywords like 'training on ArtStation'.

Users can directly use Stable Diffusion 2.1 from the diffusers Library by changing the model ID.

The CKPT file for Stable Diffusion 2.1 can be downloaded for use with other UIs.

The video demonstrates the generation of a close-up portrait of a young Chinese girl using the new version.

The video also shows an attempt to generate an old Chinese Grandpa portrait, highlighting the improvements in human anatomy.

The presenter encourages viewers to try Stable Diffusion 2.1 and share their findings, especially regarding human anatomy.