How To Change Clothes In Stable Diffusion With Inpainting & ControlNet

In this video, the host demonstrates how to change clothing in photos for free using Stable Diffusion, a powerful AI tool. The process involves installing the ControlNet extension and downloading necessary models like the Open Pose model and an inpainting checkpoint model. The user is guided through the steps of using the inpainting feature to cover clothes and then applying positive and negative prompts to transform the image into a more formal look. The ControlNet extension is crucial for preserving the original pose of the subject. The video also suggests creative uses for this skill, such as creating professional headshots for LinkedIn or transforming oneself into a fashion icon. The host encourages viewers to explore their creativity with Stable Diffusion and provides additional prompt ideas on their website.

Takeaways

• 🎨 **Free Clothing Change**: You can change clothes in photos for free using Stable Diffusion.
• 📚 **Setup Requirements**: Ensure you have Automatic1111 web UI and the ControlNet extension installed for Stable Diffusion.
• 🔗 **Extension Installation**: Install the ControlNet extension from the provided URL within the extensions tab of Automatic1111.
• 📁 **Model Placement**: Download and place the Open pose model for ControlNet and the inpainting checkpoint model in their respective directories.
• 🖌️ **Image Transformation**: Use the img2img tab in Automatic1111, paint over the clothes, and then apply positive and negative prompts for transformation.
• ⚙️ **Configuration Settings**: Adjust settings like sampling steps, CFG scale, and noise strength for the inpainting process.
• 🧍 **Pose Preservation**: If the pose is distorted, use the ControlNet extension with the Open pose model to preserve the original pose.
• 📈 **Enhanced Results**: ControlNet helps to maintain the human pose, leading to more accurate clothing transformations.
• 💼 **Professional Use**: The technique can be used to create professional headshots for LinkedIn or job interviews.
• 🌟 **Fashion Icon**: You can also use this skill to transform into a fashion icon with the help of creative prompts.
• ✅ **Limitless Possibilities**: With Stable Diffusion, you can virtually become anyone you want, from a rockstar to a chef or a model.
• 📢 **Stay Updated**: For more tutorials and inspiration, subscribe and stay tuned to the channel.

Q & A

What is the main topic of the video?

The main topic of the video is teaching viewers how to change clothes in photos for free using Stable Diffusion with inpainting and ControlNet.

What are the prerequisites before starting the process?

To start the process, you need to have the Automatic1111 web UI installed for running Stable Diffusion and the ControlNet extension for Automatic1111.

How do you install the ControlNet extension?

To install the ControlNet extension, go to the extensions tab in Automatic1111, click on the install from URL tab, and enter the URL of the extensions page. After clicking install, restart Stable Diffusion.

Which model is needed for ControlNet and where should it be placed?

You need to download the Open pose model for ControlNet and place it in the ControlNet folder in your Stable Diffusion directory.

What is the recommended inpainting checkpoint model?

The recommended inpainting checkpoint models are Realistic Vision or Clarity inpainting models, as they are very good.

What is the process of changing clothes in an image?

The process involves uploading the image to the img2img tab in Automatic1111, painting over the clothes, using positive and negative prompts, adjusting configuration settings, and generating the transformed image.

What might be a potential issue with the initial transformation?

A potential issue is that the pose of the person in the image might not match the original image after the initial transformation.

How does ControlNet help with the transformation process?

ControlNet helps by preserving the pose of the human in the image when changing clothes, using the ControlNet Open Pose model.

What are some uses for the skill of changing clothes in Stable Diffusion?

You can use this skill to create professional headshots for LinkedIn or job interviews, or to turn yourself into a fashion icon with various styles.

How can one find cool prompts for changing clothes in Stable Diffusion?

One can find cool prompts on the presenter's website, which provides examples and inspiration for different styles.

What is the final advice given by the presenter?

The final advice is to stay creative and explore the possibilities of changing one's appearance in photos using Stable Diffusion.

How can viewers get more tutorials like this?

Viewers can get more tutorials by liking, subscribing, and hitting the notification bell for updates on more content.

Outlines

00:00

🎨 Free Clothing Transformation with Stable Diffusion

The video introduces a free method to change clothing in photos using Stable Diffusion. The host guides viewers through setting up Automatic 1111 web UI and installing the Control Net extension. They explain the necessity of downloading the Open pose model for Control Net and an inpainting checkpoint model. The process involves uploading an image, painting over the clothes, and using prompts to transform the image into a more formal attire. Configuration settings are adjusted for the inpainting process, and the video demonstrates how Control Net can correct pose discrepancies in the output image. The host also suggests using this skill for professional headshots and provides ideas for other creative applications.

Keywords

Stable Diffusion

Stable Diffusion is an AI model that generates images from textual descriptions. In the context of the video, it's used to change the clothes in a photo for free, which is typically a feature offered by paid tools. It's the core technology that enables the clothing transformation process shown in the tutorial.

Inpainting

Inpainting is a technique used in image processing to fill in missing or damaged parts of an image. In the video, inpainting is used to cover up the clothes in a photo, which is then followed by the transformation process to change the clothing using Stable Diffusion.

ControlNet

ControlNet is an extension for the Automatic1111 web UI that helps in controlling the generation process of Stable Diffusion, particularly useful for maintaining the original pose of a person in the image during transformations. It's a crucial tool in the video to ensure that the pose remains consistent with the original after the clothing change.

Automatic1111 Web UI

Automatic1111 Web UI is a user interface for running Stable Diffusion. It's necessary for the process outlined in the video as it provides the platform on which the extensions and models are installed and used to change clothes in a photo.

Open Pose Model

The Open Pose Model is a pre-trained model used by ControlNet to detect the pose of humans in an image. In the video, it's downloaded and placed in the ControlNet folder to help maintain the original pose during the clothing transformation process.

Checkpoint Model

A Checkpoint Model in the context of AI refers to a saved state of the model's training. In the video, the Realistic Vision or Clarity inpainting model is recommended as a checkpoint model to be downloaded and used for the clothing transformation in Stable Diffusion.

Positive and Negative Prompts

Positive and negative prompts are instructions given to the AI to guide the image generation process. Positive prompts describe the desired outcome, while negative prompts indicate what to avoid. In the video, these prompts are used to transform the image into a more formal and professional look.

Configuration Settings

Configuration settings are the parameters that are adjusted to control the behavior of the AI model during the image generation process. In the video, settings such as the inpainting checkpoint, sampling steps, and noise strength are configured to achieve the desired clothing transformation.

Professional Headshot

A professional headshot is a type of portrait photography used for professional purposes, like LinkedIn profiles or job interviews. In the video, the technique of changing clothes in Stable Diffusion is applied to convert casual photos into professional headshots.

Fashion Icon

A fashion icon is a person known for their influence on fashion trends and their ability to inspire others with their style. In the context of the video, the term is used to suggest that with Stable Diffusion, one can transform their image to emulate a fashion icon or any desired look.

Pose Detection

Pose detection is the process of identifying the position and orientation of a person's body in an image. The Open Pose model in ControlNet is used for this purpose in the video, ensuring that the pose of the person in the image is preserved after the clothing has been changed.

Highlights

Learn how to change clothes in photos for free using Stable Diffusion

Automatic 1111 web UI and ControlNet extension required

Download and install the Open Pose model for ControlNet

Use the Realistic Vision or Clarity inpainting model

Upload the image to the img2img tab and paint over the clothes

Use positive and negative prompts to transform the image

Adjust configuration settings: clarity inpainting checkpoint, uler ancestral sampler, sampling steps, CF FG scale

Set inpaint area to only masked and masked content to original

Set low to no noise strength between 0.5 to 0.7

ControlNet can fix pose issues in the generated image

Enable ControlNet, select Open Pose model, and enable low vrm and Pixel Perfect

ControlNet preserves the human pose in the output image

Use this skill to create professional headshots for LinkedIn

Turn yourself into a fashion icon with cool prompts

Stable Diffusion allows you to become whoever you want

Find more prompt ideas and inspiration on their website

This tutorial provides step-by-step instructions for changing clothes in Stable Diffusion

The process is easy to follow and requires minimal setup

ControlNet is extremely helpful for preserving pose accuracy

The output images showcase the effectiveness of the method