WOW! NEW ControlNet feature DESTROYS competition!

Sebastian Kamph
13 May 202309:07

TLDRThe video introduces a groundbreaking update to ControlNet, a feature within the stable diffusion software. This new update allows users to input an image and maintain the same facial style while manipulating the subject's pose and expressions, such as making them laugh, cry, or appear angry. The video demonstrates the use of the 'reference only' preprocessor, which generates images resembling the input image without utilizing additional tools like Dream Booth or LoRAs. Despite some initial image quality issues, which are being addressed by the developers, the feature shows significant promise. The video also explores the potential of multi-control settings and the ability to combine ControlNet with other tools for even more impressive results. The presenter concludes by highlighting the power of ControlNet and its potential to revolutionize image generation for both experienced users and newcomers alike.

Takeaways

  • ๐Ÿš€ New ControlNet feature allows users to maintain a consistent face style across different poses and expressions.
  • ๐Ÿ“ธ The ControlNet preprocessor 'reference only' is a game-changing update that significantly enhances the customization of generated images.
  • ๐Ÿ“ˆ Users need to ensure they have the latest version of Stable Diffusion (1.1.162 or later) to utilize the new ControlNet features.
  • ๐Ÿ”„ Updating the system can be done through the extensions menu or by using 'git pull' in the Stable Diffusion folder for automatic updates.
  • ๐Ÿ’ก The script demonstrates how to use ControlNet to create images of a woman smiling, with variations in hair color and clothing details.
  • ๐Ÿ” There are ongoing efforts to address issues like blurring and collapsing in generated images, as discussed in the official GitHub by the author of ControlNet.
  • ๐Ÿ”„ By adjusting the control mode, users can prioritize either the prompt or ControlNet to refine the output and avoid common problems like blurring.
  • ๐ŸŒŸ Multi-ControlNet allows for even more control over the generated images, enabling users to input specific poses and expressions.
  • ๐ŸŽญ The tool is powerful enough for both experienced users and beginners to create high-quality, customized images.
  • ๐Ÿ‘ด A demonstration is provided using an image of an old man, showing how ControlNet can adapt to different styles and emotional expressions.
  • ๐Ÿ“‰ The script emphasizes the importance of the prompt in conjunction with the input image, as it can influence the style and emotional tone of the generated images.
  • ๐Ÿ“ The video presenter will continue to monitor updates and improvements to ControlNet, expecting further enhancements in the near future.

Q & A

  • What is the new feature in ControlNet that is being discussed in the video?

    -The new feature in ControlNet being discussed is the 'reference only' preprocessor, which allows users to input an image and generate new images with the same facial style in different poses and expressions.

  • What version of ControlNet is mentioned as necessary for this feature?

    -The video mentions that the necessary version for this feature is 1.1.162 or later.

  • How can users ensure they have the latest version of ControlNet?

    -Users can check for updates by going into the extensions and looking for the 'check for updates' option. If an update is available, they can apply it by pressing the 'apply and restart UI' button.

  • What is the process to update Stable Diffusion if the automatic update doesn't work?

    -If the automatic update doesn't work, users can manually update by navigating to their Stable Diffusion folder and typing 'git pull' to check for updates and apply them automatically.

  • What issue is being worked on to improve the quality of generated images?

    -The issue being worked on is the blurring and collapsing problems in the generated images, which are expected to be fixed in future updates.

  • How can the generated images be improved if there are blurring or grainy issues?

    -By changing the control mode from 'balance' to either 'my prompt is more important' or 'control net is more important', users can get around the blurring and grainy issues to generate clearer images.

  • What is the purpose of using a 'pose' in the ControlNet feature?

    -The purpose of using a 'pose' in ControlNet is to generate images where the subject is in a specific pose, such as hands going up or a particular facial expression, while maintaining the style and characteristics of the input image.

  • How does the multi-control feature enhance the capabilities of ControlNet?

    -The multi-control feature allows users to combine different control inputs, such as pose and facial expression, to generate images that closely resemble the desired outcome, offering more control and versatility in image creation.

  • What is the significance of the 'open pose' in the ControlNet feature?

    -The 'open pose' is a specific type of pose that allows for more flexibility in generating images. It is used when the desired outcome is to have the subject in a pose that is not predefined or when a custom pose is required.

  • How does the ControlNet feature handle changes in the prompt?

    -The ControlNet feature can adapt to changes in the prompt, allowing users to modify the generated images by altering the description, such as changing a smiling face to a crying one, without altering the control net input.

  • What is the potential of the ControlNet feature for both experienced and new users?

    -The ControlNet feature is powerful for both experienced users who are familiar with control techniques and new users alike, as it provides a high level of control and customization in image generation, making it easier to create impressive images.

  • What is the expected future improvement for the ControlNet feature?

    -The expected future improvement for the ControlNet feature includes bug fixes and enhancements that will likely result in better image quality and fewer issues with blurring and collapsing, as mentioned in the official GitHub discussions.

Outlines

00:00

๐Ÿš€ Introduction to Stable Diffusion and Control Net Update

The video begins with an exciting announcement about a game-changing update to stable fusion and control net. The host introduces the concept of using an input image to maintain the same face style while manipulating the person's poses and expressions. The video emphasizes that this is not clickbait and shows a humorous incident involving a book falling on the host's head. The focus then shifts to using the latest version of stable diffusion, specifically version 1.1.162, and the importance of keeping the software up to date. The host guides viewers on how to update their extensions and software, including using 'git pull' for updates. The new control net reference only is showcased, demonstrating how to use it with an input image to generate images of a woman smiling, without the need for a dream booth or additional models. The video also discusses ongoing improvements to address image blurring and collapsing issues, as noted in the official GitHub discussion.

05:02

๐ŸŽจ Advanced Control Net Techniques and Results

The host delves into advanced techniques using the control net, starting with an open pose image sourced from 'post my art'. The video demonstrates the process of enabling control net and setting up the open pose without the need for a preprocessor. The results are shown with four new images that resemble the woman from the input, featuring similar hair color and clothing gradient. The host then discusses the ability to change the woman's expression to crying by adjusting the prompt, while keeping the control net settings the same. The video shows the effectiveness of control net in generating images that align with the new prompt, even though the second image does not perfectly follow the pose. The host concludes with a test using an image of an old man, changing the prompt to 'old man angry' and observing the results. The video highlights the power of control net and encourages both experienced and new users to explore its capabilities. The host signs off, promising to keep an eye on updates and improvements to control net, and looks forward to the next video.

Mindmap

Keywords

๐Ÿ’กControlNet

ControlNet is a term referring to a feature in the Stable Diffusion software that allows users to control the style and content of generated images more precisely. In the video, it is highlighted as a game-changing update that can manipulate facial expressions, poses, and emotions of a person in an image without altering the core identity.

๐Ÿ’กStable Diffusion

Stable Diffusion is a machine learning model used for generating images from textual descriptions. It is the platform where the ControlNet feature is being utilized. The video discusses an update to this software that significantly enhances its capabilities.

๐Ÿ’กPreprocessor

A preprocessor in the context of the video is a part of the ControlNet feature that prepares the input image for further manipulation. It is set to 'reference only' to ensure the input image's style is retained in the generated images.

๐Ÿ’กExtensions

Extensions refer to additional functionalities or tools that can be added to the main software. In the video, the speaker instructs viewers to check for updates in the extensions of Stable Diffusion to utilize the latest version of ControlNet.

๐Ÿ’กGit Pull

Git Pull is a command used in software development to update a local copy of a repository with the latest changes from a remote repository. The video suggests using this command to update Stable Diffusion if automatic updates are not working.

๐Ÿ’กDream Booth

Dream Booth is a technique used in image generation where a model is trained on a specific subject to generate images of that subject in various styles and poses. The video mentions that the new ControlNet feature does not require Dream Booth, indicating a more autonomous approach to image manipulation.

๐Ÿ’กLoras

Loras are additional neural network layers that can be used to influence the style and content of generated images in machine learning models. The video notes that the new ControlNet feature does not require Loras, suggesting an advancement in technology that reduces dependency on such layers.

๐Ÿ’กControl Mode

Control Mode in the video refers to the setting that determines the influence of the ControlNet feature versus the input prompt. It can be adjusted to prioritize either the prompt or the ControlNet's directives for the image generation process.

๐Ÿ’กMulti-ControlNet

Multi-ControlNet is a feature that allows for the use of multiple ControlNets simultaneously, enabling more complex and nuanced image manipulation. The video demonstrates how this can be used to combine different poses and expressions in a single image generation process.

๐Ÿ’กPose

In the context of the video, a pose refers to the physical position or arrangement of a person or object within an image. The ControlNet feature can manipulate the pose of a person in an image, as demonstrated by changing a neutral pose to an 'open pose' with raised hands.

๐Ÿ’กEmotional Expression

Emotional expression in the video pertains to the ability of ControlNet to alter the emotional state depicted in a person's face within an image. This is showcased by changing a smiling face to a crying one, based on the input prompt.

Highlights

ControlNet introduces a game-changing update with a new feature that allows users to maintain a consistent face style across different poses and expressions.

The update is not clickbait and promises to deliver a truly mind-blowing experience with the latest version of stable diffusion.

Users are advised to update to version 1.1.162 or later for the best experience with the new ControlNet feature.

Updating the automatic 1111 or vladder Fusion is as simple as navigating to the stable Fusion folder and executing a 'git pull' command.

The new ControlNet preprocessor 'reference only' is a significant addition, allowing for greater control over the output images.

By enabling ControlNet and setting the preprocessor to 'reference only', users can input an image and generate a series of images with similar facial features.

The generated images can depict a range of emotions such as smiling, laughing, crying, or being angry without the need for additional tools like Dream Booth.

Current limitations include occasional blurring or graininess in the generated images, which are actively being addressed by the development team.

Adjusting the control mode to prioritize the prompt or ControlNet can help mitigate issues with image quality.

The multi-controllet feature enhances the power of ControlNet, allowing for even more detailed and accurate image manipulation.

Users can input specific poses and facial expressions, such as an 'open pose' or 'crying', to achieve highly customized outputs.

The tool's effectiveness is demonstrated through the successful generation of images with the requested pose and emotional expression.

ControlNet's ability to generate photorealistic and hyper-realistic painting styles is showcased through various examples.

The prompt's wording plays a crucial role in determining the style and emotional content of the generated images.

The video demonstrates the successful generation of an 'angry old man' image, highlighting the flexibility of ControlNet's prompt system.

ControlNet is expected to improve further with upcoming bug fixes and updates, promising even more impressive results in the future.

The presenter encourages viewers to stay tuned for more updates and improvements to ControlNet, emphasizing its current and potential impact on image generation.