ComfyUI - Hands are finally FIXED! This solution works with all models!
TLDRIn this video, the creator discusses a method to improve the depiction of hands in images using AI, overcoming previous challenges. They introduce a sponsored Gigabyte laptop, which aids in demonstrating the process. The creator uses a depth map preprocessor to identify and correct hand shapes, employing a control net and case sampler to refine the images. They emphasize the importance of using different seeds for variable consistency and provide tips for refining the hand correction process. The video concludes with a recommendation to upscale the corrected images for further improvement.
Takeaways
- 🎥 The video is a tutorial on fixing hands in images using AI, with a success rate of around 90%.
- 💻 Gigabyte has sponsored the channel and provided a 17x laptop equipped with a 48-card for live streams and video production.
- 🖌️ The process begins with a simple prompt and uses a Juggernaut model for demonstration.
- 🌟 The video 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 crucial for identifying and correcting hand-related issues in the image.
- 🎭 A control net is utilized, specifically the advanced one, to refine the depth map and guide the model in fixing hands.
- 🚫 The script highlights a common mistake of using the same seed for all controls, which can lead to undesired results.
- 🖼️ Masking is crucial for isolating the hands and ensuring that only they are redrawn, not the entire image.
- 📈 The video suggests using 'tight B boxes' for more precise masking around the hands to correct issues like extra fingers.
- 🔄 After fixing the hands, the image is recommended to be upscaled for further refinement and to address any remaining artifacts.
Q & A
What is the main topic of the video?
-The main topic of the video is about fixing hands in images using AI, with a focus on improving the quality of the hands in a portrait of a woman in a summer dress and a flower garden.
Which company sponsored the video?
-Gigabyte sponsored the video and provided a 17x laptop for use during live streams and video production.
What specific model is used in the video for the AI image enhancement?
-The video does not explicitly mention the specific model used for the AI image enhancement, but it is implied that it is a model capable of generating high-quality images, possibly from a control net or similar AI system.
How does the video creator address the issue of extra fingers in the AI-generated images?
-The video creator uses a depth map preprocessor to identify and correct the hand layout, and then applies a control net to refine the hands. However, they note that the AI does not know the correct size of a hand and may not fix fingers that are too long.
What mistake did the video creator make during their live stream that they address in the video?
-The video creator used the same seed value for both the case sampler and the global seed, which resulted in issues with the image generation. They advise ensuring that different seeds are used to avoid such problems.
What is the purpose of the 'mask' in the AI image enhancement process?
-The mask is used to isolate the area of the image that needs enhancement, in this case, the hands. It allows the AI to focus on correcting only the specified area, leaving the rest of the image untouched.
How does the video creator suggest improving the results when fingers are too long?
-The video creator 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 to correct issues with fingers being too long as they will be masked out.
What additional step does the video creator recommend after fixing the hands?
-The video creator recommends taking the corrected image into an upscaler to improve the overall quality, particularly the face and any other areas that might need refinement.
How does the video creator share their resources and files with their audience?
-The video creator shares their resources and files, including live streams and different graphs, in the community area on YouTube for their sponsors and supporters.
What is the significance of the 'Juggernaut model' mentioned in the script?
-The Juggernaut model is likely a reference to a specific AI model or system used in the image enhancement process. The video creator does not go into detail about its specific functions but implies that it is a tool they use for their work.
What does the video creator mean by 'fetch all' or 'update all' in the context of using AI tools?
-The video creator is referring to the process of updating or retrieving the latest versions of AI tools or models. This is important because AI technology and tools are frequently updated, and using the most recent versions can ensure better results and avoid compatibility issues.
Outlines
🖌️ Introduction to Fixing Hands in Images
The speaker begins by expressing excitement about addressing the common issue of fixing hands in images, referencing a previous live stream where initial attempts were met with challenges. They have since resolved these and are ready to demonstrate a solution. The video is sponsored by Gigabyte, who provided a 17x laptop, which has been integral in the live streams and video production due to its powerful 48 card. The speaker introduces a basic graph and a simple prompt to guide the AI in creating an image of a woman with挥手 (waving hands) in a summer dress and a flower garden. The speaker emphasizes the importance of using a methodical approach to correct hands rather than relying solely on the prompt to work.
💻 Using Gigabyte Laptop and Preparing the Graph
The speaker discusses the features of the Gigabyte laptop, highlighting its portability and performance, which have been beneficial during live streams. They show appreciation for the sponsorship and move on to explain the process using a basic graph. The speaker uses the Juggernaut model and a simple prompt to set up the scenario for the AI. They discuss the absence of a negative prompt, focusing on the word 'hands' to correct any issues. The speaker explains the use of a custom node for the empty latent and a standard case sampler with a fixed seed to maintain consistency and control over variables. The goal is to resolve the image well within the initial 20 steps to avoid additional case sampling later.
🎨 Utilizing Mesh Graer for Hand Correction
The speaker dives into the technicalities of using the Mesh Graer node to identify and correct hands in the image. They explain that the Mesh Graer, part of the control net auxiliary preprocessor, helps determine the hand shape using a small model. The process involves pushing the image through the Mesh Graer to generate a depth map that highlights the hand's layout, aiding the model in understanding the hand's position. The speaker emphasizes the importance of using the correct resolution and control net for the depth map and the necessity of masking the area of interest to ensure only the hands are corrected. They also discuss common issues encountered, such as extra fingers or incorrect hand size, and provide solutions like using bounding boxes for more precise corrections.
🚀 Finalizing the Hand Correction and Upscaling
The speaker concludes the tutorial by discussing the final steps in the hand correction process. They explain how to use a control net with a depth map to refine the hands and the importance of using different seeds for the case samplers to avoid issues with the corrections. The speaker also addresses the limitations of the method, such as the inability to adjust hand size or fix overly long fingers. They suggest using an upscaler to improve the overall image quality and resolve any remaining issues. The speaker expresses gratitude to Gigabyte for their sponsorship and to the community members who support the channel. They mention that the detailed graph used in the live stream will be shared in the community area for supporters to access, along with other resources and live stream recordings.
Mindmap
Keywords
💡fix hands
💡Juggernaut model
💡prompt
💡Gigabyte
💡case sampler
💡mesh grafer
💡depth map
💡control net
💡masking
💡upscale
💡community area
Highlights
The speaker has resolved previous issues and is now presenting a method to fix hands in images with a success rate of about 90%.
The method is applicable to various models, including 1.5 and SDXL.
Gigabyte has sponsored the channel and provided a 17x laptop equipped with a 48 card for live streams and video production.
The speaker uses a basic graph and the Juggernaut model with a simple prompt 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.
A custom node for the empty latent and a standard case sampler are used, with a focus on correcting the hands in the image.
The Mesh Grafer node is introduced as a key tool for identifying and correcting hand shapes in the image.
The speaker explains the use of a control net and its role in guiding the correction process, specifically for the hands.
A mistake made during a live stream is shared, where the speaker discusses the importance of using different seeds for the case sampler to avoid errors.
The speaker suggests using a mask to focus corrections only on the hands, and explains how to apply this mask using specific nodes.
The issue of incorrect hand size is acknowledged, and the speaker notes that the method may produce hands that are too large.
The problem of long fingers not being corrected is addressed, and the speaker proposes using tight B boxes for more accurate masking.
The speaker recommends upscaling the corrected image to improve overall quality and resolve any remaining issues.
The speaker expresses gratitude to Gigabyte for their sponsorship and support of the channel.
The speaker mentions the community area on YouTube, where supporters can access files, live streams, and other resources.
The speaker concludes by encouraging supporters to explore the community area and promises to continue providing valuable content.