Better Face Swap = FaceDetailer + InstantID + IP-Adapter (ComfyUI Tutorial)
TLDRIn this tutorial, the host Way dives into a common issue with face swapping in Confy UI using Instant ID. The challenge is that the output often matches the composition of the reference image, resulting in incomplete body images. Way introduces a workflow using SDXL to generate a portrait, then leveraging Instant ID and IP Adapter to extract detailed facial features for a more accurate swap. The background can be sourced from various options, including personal photos. The video guides viewers through setting up the workflow, including the installation of necessary nodes and models, and adjusting parameters for better facial similarity. It also suggests using IP painting and tweaking settings in Face Detailer for fine-tuning. For projects requiring higher accuracy, training a ControlNet model is recommended, with a link provided for a detailed tutorial on this process. The host encourages viewers to follow for more updates and provides links to related content in the description.
Takeaways
- ๐ The issue with instant ID in Confy UI is that it tends to maintain the same composition as the reference image, resulting in face-only or half-body images even when a full-body prompt is requested.
- ๐ ๏ธ A workflow is introduced to swap the face in a photo with any reference image desired, using tools like SDXL, Instant ID, and IP Adapter.
- ๐ผ๏ธ For the background of the face swap, one can use an image from M Journey or a personal photo that aligns with the vision.
- ๐ Efficiency nodes must be installed in the Conf Manager for the workflow to function properly.
- ๐ SDXL is used to generate a crisp portrait photo, which is then fed into Instant ID and IP Adapter to extract detailed facial features.
- ๐ฑ The Impact Pack node package is necessary for the Face Detailer note, which is used to paint and correct faces and automatically recognizes the face area.
- ๐ Before using Face Detailer, ensure that the necessary node packages are installed and connected correctly.
- ๐งฉ Instant ID node divided by Cubic and the Instant ID model are required for the face swapping process.
- ๐ Overfitting can be adjusted by tweaking the CFG and step count in the Instant ID node.
- ๐ The IP Adapter can be used to boost the resemblance in the face swap by choosing the appropriate Face ID preset.
- ๐จ Further fine-tuning can be done by adjusting the weights in both Instant ID and IP Adapter nodes, and using inpainting for small issues.
- ๐ Training a ControlNet model specifically for the project can significantly improve the likeness in face swaps, with a tutorial available for this process.
Q & A
What is the common issue with instant ID in Confy UI when attempting a face swap?
-The common issue is that it tends to keep the same composition as the reference image, resulting in an incomplete body image even when a full body image is requested.
What is the workflow suggested by the speaker to overcome the limitations of instant ID for face swapping?
-The suggested workflow involves using SDXL to generate a portrait photo, feeding reference images into Instant ID and IP Adapter, and then swapping the face in the photo with any reference image desired.
What tools are used to extract detailed facial features for a solid face swap?
-The tools used are Instant ID and IP Adapter, which help in pulling out detailed facial features necessary for a high-quality face swap.
What are the options for the background of a face swap?
-The options for the background include using an image from M Journey or a personal photo that fits the vision of the project.
What is the role of the Face Detailer node in the face swapping process?
-The Face Detailer node is used to paint and correct disfigured faces, and it automatically recognizes the face area, eliminating the need to draw a face mask by hand.
How does the speaker suggest improving the resemblance in a face swap if it's not quite right?
-The speaker suggests using the IP Adapter with the Face ID plus V2 preset, which automatically configures the best version of Face ID for the swap. Additionally, adjusting the weights in both Instant ID and the IP adapter can help fine-tune the resemblance.
What is the purpose of the ControlNet model in the face swapping process?
-The ControlNet model works alongside Instant ID to recognize visual features and is necessary for the face swapping process to function effectively.
How can one further enhance the similarity in face swaps using Instant ID and the IP adapter?
-One can train a ControlNet model specifically for their project, which can significantly boost the likeness in face swaps. The speaker also provides a tutorial on how to do this.
What is the significance of the CFG parameter in refining the face swap?
-The CFG parameter controls the level of detail in the face swap. Turning it down slightly and increasing the step count can provide more refinement and improve the face swap.
What is the recommended approach if the face swap still isn't hitting the mark on similarity?
-If the face swap isn't achieving the desired level of similarity, one can use impainting to address small issues or tweak the settings inside the Face Detailer for better results.
What additional resources does the speaker provide for those interested in similar techniques?
-The speaker provides links to other tutorials in the description, including one on face swapping using Instant ID and IP Adapter in Web UI and another on training a ControlNet model for a specific project.
How can viewers stay updated with the speaker's future tutorials?
-Viewers can hit the like button and follow the speaker's channel for more updates and to catch the next tutorial.
Outlines
๐ Introduction to Face Swapping with Instant ID
The speaker introduces the common issue of maintaining the composition of a reference image when performing a face swap with Instant ID. They explain that regardless of the body length requested, the output often matches the reference image's composition. To overcome this, the speaker presents a workflow that allows swapping faces with any desired reference image. They mention using tools like sdxl for generating portrait photos and instant ID and IP adapter for detailed facial features. The background for the face swap can be sourced from various options, including personal photos or images from M Journey. The workflow is detailed with instructions on using efficiency nodes, key sampler for sdxl, and the face detailer node package for correcting facial features. The speaker also provides links to additional resources and tutorials in the description.
๐ Advanced Face Swapping Techniques
The paragraph details the process of setting up a face swap using Instant ID and an inside face model to recognize visual features. It also involves using a control net model known as 'well'. The speaker guides through uploading a reference image and connecting various nodes for the face swap. They discuss the issue of overfitting and how to adjust the CFG and step count for refinement. The use of the IP adapter is introduced to enhance the resemblance in the face swap. The speaker explains how to connect the IP adapter node and model inputs and outputs for optimal results. They also mention the possibility of training a model specifically for the project to improve likeness and provide a link to a tutorial on this topic. The paragraph concludes with a discussion on fine-tuning the face swap using inpainting and tweaking settings within the face detailer node.
๐ Conclusion and Additional Resources
The speaker concludes the tutorial by thanking the viewers and encouraging them to like and follow for more updates. They summarize the process of integrating a trained model into Confy to enhance face changes and improve similarity in face swaps. Links to previous tutorials on similar techniques are provided for further learning. The speaker emphasizes the noticeable improvement in similarity when using these advanced techniques.
Mindmap
Keywords
๐กFace Swap
๐กInstant ID
๐กIP-Adapter
๐กConfy UI
๐กEfficiency Nodes
๐กSDXL
๐กFace Detailer
๐กControl Net
๐กUnified Loader
๐กCFG and Step Count
๐กFace ID
Highlights
The video addresses a common issue with instant ID in Confy UI when attempting face swaps, where the composition often remains the same as the reference image.
The creator introduces a workflow that allows swapping the face in a photo with any reference image desired.
SDXL is used to generate a crisp portrait photo, which is the first step in the face swapping process.
Reference images are fed into Instant ID and IP Adapter to extract detailed facial features necessary for a solid swap.
For the background of the face swap, one can use an image from M Journey or a personal photo that aligns with their vision.
Efficiency nodes must be installed in the Conf manager to use the workflow effectively.
The video demonstrates how to connect nodes in the workflow, including the Key Sampler for SDXL and the Face Detailer node.
The Face Detailer node is particularly useful for painting and correcting disfigured faces and automatically recognizes the face area.
Instant ID node and model are required for the face swapping process, along with a ControlNet model for visual feature recognition.
The IP Adapter is introduced to boost the resemblance in the face swap by automatically configuring the best version of Face ID.
Adjustments to the CFG and step count can refine the face swap and reduce overfitting.
If the face swap similarity isn't ideal, tweaking the weights in both Instant ID and IP Adapter can help achieve better results.
Imp painting and adjusting settings in Face Detailer can address minor issues like ear or forehead discrepancies.
Training a model specifically for the project can significantly enhance the likeness in face swaps.
The video provides links to additional tutorials on how to train a model for face swaps and integrate it into Confy UI.
The creator encourages viewers to like the video and follow for more updates on similar techniques.