ComfyUI - Hands are finally FIXED! This solution works with all models!
TLDRIn this video, the creator demonstrates a method to fix hands in images using AI, overcoming issues encountered in a previous live stream. Sponsored by Gigabyte, the video showcases the use of a powerful 17x laptop equipped with a 48-card setup. The creator guides viewers through the process using a basic graph and a specific model, emphasizing the importance of a well-crafted prompt and methodical approach. The video also highlights the use of a mesh generator for depth map preprocessing and a control net for refining the image, particularly the hands. The creator shares tips on avoiding common mistakes and achieving better results, ultimately offering the graph to supporters in the community area.
Takeaways
- 🎥 The video is a tutorial on fixing hands in images using AI, with a claimed 90% success rate.
- 🖌️ The presenter uses a Juggernaut model with 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卡的17x笔记本电脑，用于直播和视频制作。
- 🌟 The presenter highlights the laptop's portability and performance, especially compared to an older 3090 card.
- 📌 A basic graph is used to demonstrate the process, with a focus on the 'mesh grabber' node for hand correction.
- 🔍 The 'depth map pre-processor' is used to identify and correct hand shapes in the image.
- 🎨 A control net is employed to refine the image, specifically targeting the hands using a depth map and a mask.
- 🛠️ The presenter shares a mistake from a previous live stream regarding the use of a global seed for the case sampler.
- 📈 The video emphasizes the importance of using different seeds for the case sampler to avoid issues with the final output.
- 🔧 The presenter suggests using 'tight B boxes' for the mask to better handle individual fingers and hands.
- 🚀 An upscaler is recommended to further refine the image, particularly the face and any residual lines.
- 💖 The presenter expresses gratitude to Gigabyte and the community members for their support and shares resources in the YouTube community area.
Q & A
What was the main topic of the video?
-The main topic of the video was about fixing hands in images using AI, specifically focusing on improving the quality of the hands in a portrait.
Which company sponsored the video?
-Gigabyte sponsored the video and provided a 17x laptop for use during live streams and video production.
What type of model was used in the video for the AI image generation?
-The video used a Juggernaut model for the AI image generation, but it was mentioned that any preferred model could be used.
How did the video creator ensure the AI focused on fixing only the hands and not the entire image?
-The creator used a depth map preprocessor to identify and focus on the hands, and then applied a mask to ensure only the hands were corrected.
What was the issue encountered with the same seed value in the process?
-Using the same seed value for both the initial image generation and the case sampler resulted in a problem where the hands looked 'crunchy' or distorted, indicating the need for different seeds to avoid this issue.
How did the video creator address the issue of fingers being too long?
-The creator suggested changing the mask from based on depth to tight B boxes, which would create a bounding box around just the depth of the hand, potentially addressing the issue of overly long fingers.
What additional step was recommended after fixing the hands?
-After fixing the hands, the creator recommended using an upscaler to improve the overall quality of the image, particularly the face and to resolve any potential lines or other issues.
How did the video creator share the AI graph with the audience?
-The creator posted the AI graph in the community area on YouTube for the supporters of the channel to access and use.
What was the purpose of the Gigabyte 17x laptop in the video production?
-The Gigabyte 17x laptop was used for its high performance, allowing the creator to produce high-quality videos and live streams, and to create impressive artwork while on the go.
What was the significance of the fixed seed in the AI image generation process?
-The fixed seed was used to ensure consistency and control over the variables during the AI image generation process, preventing unwanted changes in subsequent iterations.
What was the role of the control net in the hand fixing process?
-The control net was used to guide the AI in correcting the hands based on the depth map and mask, ensuring that only the hands were modified and the rest of the image remained unchanged.
Outlines
🖌️ Fixing Hands in Images with AI
The paragraph introduces a tutorial on how to fix hands in images using AI, specifically addressing issues faced in a previous live stream. The speaker has resolved those issues and is now prepared to demonstrate a method that works well for various models, including 1.5 sdxl. The process is described as simple and quick, and the speaker thanks Gigabyte for sponsoring the channel and providing a powerful laptop that facilitates the creation of high-quality artwork. The tutorial begins with a basic graph and a straightforward prompt to generate an image of a woman with waving hands. The focus is on correcting the hands without altering the rest of the image.
🌟 Overcoming Challenges in AI Image Editing
This paragraph discusses the challenges faced during a previous live stream and the speaker's frustration in resolving them. The focus is on using a control net and a depth map pre-processor to accurately identify and correct the hands in an image. The speaker emphasizes the importance of using a fixed seed for consistency and explains the process of creating a depth map to guide the AI in understanding the hand's layout. The paragraph also highlights the use of a mask to ensure that only the hands are redrawn, and the speaker shares a mistake made in a previous attempt, which involved using the same seed for all controls, leading to issues with the final output.
🔍 Refining the AI Image Correction Process
The speaker continues to elaborate on the process of refining the AI's image correction, particularly focusing on the hands. Various techniques are discussed, including the use of bounding boxes and mask expansion to improve the accuracy of the hand correction. The speaker advises on changing the mask from depth to tight B boxes to better handle extra fingers and ensure the hands are replaced with properly meshed ones. The paragraph concludes with a recommendation to use an upscaler for final touches and expresses gratitude to Gigabyte for their sponsorship. The speaker also mentions sharing the graph in the community area for supporters of the channel.
Mindmap
Keywords
💡fix hands
💡Gigabyte laptop
💡Juggernaut model
💡custom node
💡case sampler
💡mesh grafer
💡depth map
💡control net
💡masking
💡upscale
💡community area
Highlights
The speaker is going to demonstrate a method to fix hands in images with a success rate of about 90%.
The process works well with 1.5 models and is applicable to various models like SDXL.
Gigabyte has sponsored the channel and provided a 17x laptop equipped with a 48 card for use during live streams and video production.
The laptop's performance is noted to be faster than the older 3090 card previously used.
A basic graph is used with a Juggernaut model, but any preferred model can be utilized.
A simple prompt is used for the demonstration: 'portrait of a beautiful woman in a summer dress and a flower garden waving her hands and excited'.
The use of the word 'hands' in prompts is highlighted as a popular method to correct hand issues in images.
A custom node for the empty latent is used, and a standard case sampler with a fixed seed is employed to maintain consistency.
The importance of running enough steps to allow the model to resolve the image is emphasized to avoid redoing the case sampler.
The mesh generator is introduced as a key node for fixing hands, which identifies hand shapes using a small model.
The process involves using a control net and an advanced one is suggested for personal preference.
The depth map is utilized to guide the model in understanding the hand's layout.
A mask is created to isolate the hands for correction, using an image-to-mask node.
The importance of using different seeds for the case samplers to avoid issues with the final output is noted.
The method may not correct hand size or finger length issues accurately.
The use of tight B boxes for the mask is suggested to better handle finger length issues.
The final step involves upscaling the image to resolve any remaining issues, such as lines or artifacts.
The speaker expresses gratitude to Gigabyte for their sponsorship and the community for their support.