ComfyUI - Hands are finally FIXED! This solution works with all models!
TLDRIn this video, the creator demonstrates a method to fix hand images using AI, overcoming previous issues encountered. They showcase the effectiveness of a sponsored Gigabyte laptop in enhancing their artwork creation process. The tutorial focuses on using a specific node for depth map preprocessing to accurately correct hand depictions in images. The creator shares their experience with various control nets and masking techniques to refine the AI's output, resulting in improved hand representation. They also emphasize the importance of using different seeds for the random elements in the process. The video concludes with a recommendation to upscale the corrected images for further enhancement and acknowledges the support from the community and sponsor.
Takeaways
- 🎥 The video is a tutorial on fixing hands in images using AI, with a claimed 90% success rate.
- 🖌️ The speaker uses a specific model (Juggernaut) and a simple prompt to generate an image of a woman with incorrect hand depiction.
- 💻 Gigabyte赞助了频道并提供了一台配备48显卡的17x笔记本电脑，用于直播和视频制作。
- 🌟 The speaker emphasizes the importance of using a fixed seed for consistency in the image generation process.
- 🔍 A custom node for the empty latent and a standard case sampler are used to generate the initial image.
- 🛠️ The 'mesh grafer' node is introduced as a key tool for identifying and correcting hand issues in the image.
- 🎨 The use of a control net and a depth map is crucial for guiding the AI to correct the hand shape and position.
- 📏 The speaker advises on using a mask to focus the corrections only on the hands, leaving the rest of the image untouched.
- 🔄 The process involves running the image through a refiner to apply the mask and correct only the targeted area.
- 🔧 The speaker shares a mistake to avoid: using the same seed for both the initial generation and the refining process.
- ⚙️ The video concludes with a recommendation to upscale the final image for further improvements and details on where to access the tutorial files.
Q & A
What is the primary focus of the video content?
-The primary focus of the video content is to demonstrate a method for fixing hands in images using AI, with a specific emphasis on improving the depiction of hands in artwork generated through AI models.
Which company sponsored the video?
-The video was sponsored by Gigabyte, who provided a 17x laptop for use during live streams and video production.
What type of laptop did Gigabyte provide?
-Gigabyte provided a laptop equipped with a 48-card system, suitable for creating high-quality artwork and handling the demands of live streaming and video production.
What model is the presenter using for the demonstration?
-The presenter is using the Juggernaut model for the demonstration, but notes that any preferred model can be used for this process.
What is the significance of the word 'hands' in the context of the video?
-In the context of the video, the word 'hands' is used as a prompt to help correct issues with the depiction of hands in AI-generated images.
What is the purpose of the mesh gra forer in the process?
-The mesh gra forer is used to identify and correct the hand shape in the image. It uses a small model to generate a depth map of the hands, which helps the main model understand the layout of the hands for correction.
How does the presenter ensure that only the hands are corrected in the image?
-The presenter uses a mask to isolate the area of the image that needs correction (the hands) and ensures that the AI model focuses only on that area, leaving the rest of the image unchanged.
What issue did the presenter encounter with the same seed value in the process?
-Using the same seed value for both the mask and the case sampler led to an issue where the corrected hands appeared 'crunchy' or distorted. The presenter advises using different seed values to avoid this problem.
How does the presenter address fingers that are too long in the corrected hands?
-The presenter suggests changing the mask from 'based on depth' to 'tight B boxes', which creates a bounding box around just the depth of the hand. This should help correct issues with fingers that are too long, as they will be contained within the bounding box.
What is the ultimate goal of the process described in the video?
-The ultimate goal of the process is to achieve a more realistic and accurate depiction of hands in AI-generated images, improving the overall quality and believability of the artwork.
Where can viewers find the files and additional resources mentioned in the video?
-Viewers can find the files and additional resources in the community area on the presenter's YouTube channel, which is accessible to those who support the channel as sponsors.
Outlines
🖌️ Fixing Hands in Artwork
The speaker begins by discussing a previous live stream where they attempted to fix hands in images using a specific model. They mention having resolved issues since then and are now prepared to demonstrate a method to fix hands in images with a success rate of around 90%. The speaker expresses gratitude to Gigabyte for sponsoring the channel and providing a powerful laptop that facilitates their work. They introduce a basic graph and a simple prompt to generate an image of a woman with waving hands. The speaker emphasizes the importance of using a methodical approach to correct hands rather than relying solely on a prompt. They explain the use of a custom node for the empty latent and a standard case sampler with a fixed seed to control variables. The goal is to correct the hands without needing further intervention from the case sampler later on.
🌟 Overcoming Challenges with Control Nets
The speaker shares their frustration from a previous live stream where they encountered issues with the process. They provide a solution by explaining the use of a depth map preprocessor node called 'mesh graer' to identify and correct hand shapes in the image. The speaker emphasizes the importance of using a control net to guide the correction process, specifically using an advanced control net with a depth map. They also discuss the creation of a mask to ensure only the hands are redrawn, not the entire image. The speaker provides a detailed walkthrough of how to connect the nodes and correct common mistakes, such as using the same seed for all processes, which can lead to issues with the final output.
🔍 Refining the Hand Correction Process
In the final paragraph, the speaker delves into refining the hand correction process by discussing the use of bounding boxes to address issues with hand size and finger length. They explain how changing the mask from depth-based to tight bounding boxes can improve the results by focusing only on the hand area. The speaker also highlights the importance of adjusting mask expansion settings to ensure a better fit. They recommend using an upscaler for final touches to enhance the overall quality of the image. The speaker expresses gratitude to their sponsor, Gigabyte, and to the community members who support their channel. They mention sharing files and resources in the community area on YouTube as a way to give back to their supporters.
Mindmap
Keywords
💡fix hands
💡live stream
💡Gigabyte
💡Juggernaut model
💡prompt
💡case sampler
💡mesh grafer
💡control net
💡mask
💡upscale
💡community area
Highlights
The speaker introduces a method to fix hands in images with a success rate of about 90%.
The process is applicable to various models and works well with 1.5, SDXL, and other models.
Gigabyte sponsors the channel and provides a 17x laptop with a 48-card for live streams and video production.
The speaker uses a very basic graph and the Juggernaut model for the demonstration.
A simple prompt is used to generate an image of a woman with incorrect hand depiction.
The speaker emphasizes the importance of using a fixed seed for consistency in the image generation process.
The Mesh Grafer node is utilized to identify and correct hand shapes in the image.
The Control Net and its auxiliary preprocessor play a crucial role in the hand-fixing process.
The speaker explains how to use a depth map to guide the model in correcting hand depictions.
A case sampler is used to test the image generation with the fixed variables.
The speaker discusses the creation of a mask to isolate the hand area for correction.
The importance of using different seeds for the positive and negative case samplers is highlighted to avoid issues.
The speaker provides a solution for hands that are too large or have too many fingers by adjusting the mask settings.
The use of bounding boxes to refine the mask and improve hand correction is discussed.
The speaker suggests upscaling the image after hand correction for better results.
The speaker plans to share the graph in the community area for supporters of the channel.
Gigabyte's sponsorship and support for the channel are acknowledged and appreciated.