Face Upscaling and Seamless Embedding (FUSE)

FUSE is an All-in-One Face Fix KSampler for ComfyUI that provides seamless face upscaling and embedding capabilities using YOLO face detection and SAM (Segment Anything Model) for precise masking.

Before and After Examples

• https://imgsli.com/Mzk5NzU1
• https://imgsli.com/Mzk5NzUx
• https://imgsli.com/Mzk5NzUz

Installation

Navigate to your ComfyUI's custom nodes

cd custom_nodes

Clone FUSE nodes.

git clone https://github.com/WASasquatch/face-upscaling-and-seamless-embedding

Navigate to the FUSE directory

cd face-upscaling-and-seamless-embedding

Install dependencies to your Python Environment used by ComfyUI

pip install -r requirements.txt

Models

YOLO Face Models

You'll need to download some YOLO Face models. You can find some from:

• https://github.com/akanametov/yolo-face
• https://github.com/Fuyucch1/yolov8_animeface/releases/tag/v1 (Anime Face)

Models should be placed in the models/yolo-face/ directory.

YOLO Models

You'll need to download some YOLO models. You can find some from:

• https://github.com/akanametov/yolo-face
• https://github.com/Fuyucch1/yolov8_animeface/releases/tag/v1 (Anime Face)

Models should be placed in the models/yolo/ directory.

SAM Models

You'll also need to download some SAM models. You can find official models from:

• https://github.com/facebookresearch/segment-anything?tab=readme-ov-file#model-checkpoints

Models should be placed in the models/sams/ directory.

Nodes

FUSEKSampler

Advanced face-aware sampling node that combines YOLO face detection, SAM segmentation, and seamless blending. Features include:

• Intelligent face detection and cropping with adjustable padding and size
• Optional square crop enforcement for consistent face processing
• Multiple blending modes (box/radial) with configurable strength
• SAM-based precise face masking
• Color preservation options using various transfer methods (LAB, PDF, mean-std)
• Internal caching system for faster workflow iteration
• Support for processing specific faces using face index

Parameter	Description	Type	Default	Value Range
model	Base model for sampling	MODEL	Required	N/A
vae	VAE model for the sampler	VAE	Required	N/A
images	Input image batch for face detection and upscaling	IMAGE	Required	N/A
positive	Positive conditioning for the sampler	CONDITIONING	Required	N/A
negative	Negative conditioning for the sampler	CONDITIONING	Required	N/A
use_cache	Use internal caching to speed up workflow iteration	BOOLEAN	True	True/False
seed	Seed for deterministic results	INT	0	≥ 0
steps	Number of steps for the sampler	INT	20	≥ 1
cfg	Classifier-Free Guidance scale	FLOAT	8.0	Any float value
sampler_name	Name of the sampler	STRING	(from KSampler)	Available KSampler options
scheduler	Scheduler type	STRING	(from KSampler)	Available KSampler schedulers
denoise	Denoising strength (lower values adhere more to input face)	FLOAT	0.5	0.0 - 1.0
yolo_detector	YOLO model for face detection	STRING	Required	Available YOLO face models
yolo_confidence	YOLO confidence threshold (higher values detect only more confident faces)	FLOAT	0.25	0.0 - 1.0
yolo_nms_iou	YOLO NMS IoU threshold (lower values remove more overlapping detections)	FLOAT	0.5	0.0 - 1.0
sam_segmenter	SAM model for face segmentation	STRING	Required	Available SAM models
sam_model_type	SAM model type	STRING	"vit_b"	"vit_b", "vit_l", "vit_h"
face_id	Index of the face to process (0 is first face, -1 is all faces)	INT	0	≥ -1
face_order	Order to process detected faces	STRING	"linear"	"linear", "linear_reverse", "largest_bbox", "smallest_bbox"
face_size	Resolution to sample the face crop at	INT	512	512, 768, 1024, 1280, 1536
face_padding	Padding in pixels around face crop	INT	20	0 - nodes.MAX_RESOLUTION (Currently 16384)
force_square	Force 1:1 square face crops	BOOLEAN	True	True/False
blend_amount	Amount of blending for face embedding	FLOAT	0.3	0.0 - 1.0
blend_mode	Feathering mode for blending	STRING	"box"	"box", "radial"
use_sam_mask	Use SAM face mask for blending	BOOLEAN	False	True/False
face_color_transfer	Color transfer mode	STRING	"none"	"none", "lab", "pdf", "mean_std"

FUSESamplerMaskOptions

Additional mask processing options for the FUSEKSampler.

Parameter	Description	Type	Default	Value Range
mask_dilation	Amount to dilate the mask	INT	0	0 - nodes.MAX_RESOLUTION (Currently 16384)
mask_erosion	Amount to erode the mask	INT	0	0 - nodes.MAX_RESOLUTION (Currently 16384)
mask_smoothing	Smoothing factor for mask edges	FLOAT	0.0	0.0 - 1024.0
mask_fill_holes	Fill holes in the mask	BOOL	True	True/False
blend_sampling	Resampling method for resizing operations (face/mask crop and paste back)	STRING	"bilinear"	"bilinear", "lanczos", "nearest", "bicubic", "box", "hamming"

FUSEKSampler (Generic)

A generic version of the FUSEKSampler designed for use with any YOLO detection model, not just face-specific ones. This node provides the same sampling capabilities as the original FUSEKSampler but with more generic parameter naming to avoid confusion when working with non-face detection models.

Use Cases:

• Hand detection and enhancement
• General object isolation and processing
• Any scenario where you need to detect, crop, and enhance specific objects in images

Parameter	Description	Type	Default	Value Range
model	Base model for sampling	MODEL	Required	N/A
vae	VAE model for the sampler	VAE	Required	N/A
images	Input image batch for mask detection and upscaling	IMAGE	Required	N/A
positive	Positive conditioning for the sampler	CONDITIONING	Required	N/A
negative	Negative conditioning for the sampler	CONDITIONING	Required	N/A
use_cache	Use internal caching to speed up workflow iteration	BOOLEAN	True	True/False
seed	Seed for deterministic results	INT	0	≥ 0
steps	Number of steps for the sampler	INT	20	≥ 1
cfg	Classifier-Free Guidance scale	FLOAT	8.0	Any float value
sampler_name	Name of the sampler	STRING	(from KSampler)	Available KSampler options
scheduler	Scheduler type	STRING	(from KSampler)	Available KSampler schedulers
denoise	Denoising strength (lower values adhere more to input mask)	FLOAT	0.5	0.0 - 1.0
yolo_detector	YOLO model for mask detection	STRING	Required	Available YOLO models
yolo_confidence	YOLO confidence threshold (higher values detect only more confident objects)	FLOAT	0.25	0.0 - 1.0
yolo_nms_iou	YOLO NMS IoU threshold (lower values remove more overlapping detections)	FLOAT	0.5	0.0 - 1.0
sam_segmenter	SAM model for mask segmentation	STRING	Required	Available SAM models
sam_model_type	SAM model type	STRING	"vit_b"	"vit_b", "vit_l", "vit_h"
mask_id	Index of the mask to process (0 is first mask found, -1 is all masks)	INT	0	≥ -1
mask_order	Order to process detected masks	STRING	"linear"	"linear", "linear_reverse", "largest_bbox", "smallest_bbox"
mask_size	Resolution to sample the mask crop at	INT	512	512, 768, 1024, 1280, 1536
mask_padding	Padding in pixels around mask crop	INT	20	0 - nodes.MAX_RESOLUTION (Currently 16384)
force_square	Force 1:1 square mask crops	BOOLEAN	True	True/False
blend_amount	Amount of blending for mask embedding	FLOAT	0.3	0.0 - 1.0
blend_mode	Feathering mode for blending	STRING	"box"	"box", "radial"
use_sam_mask	Use SAM mask for blending	BOOLEAN	False	True/False
mask_color_transfer	Color transfer mode	STRING	"none"	"none", "lab", "pdf", "mean_std"

Workflows

You can find example workflows in the workflows directory.

Known Issues

Torch 2.6+ Compatibility

Torch 2.6+ versions will not allow loading pickles with Ultralytics due to security restrictions. This may cause issues when loading YOLO models. A pull request has been submitted to address this issue:

• Issue: Ultralytics pickle loading incompatibility with Torch 2.6+
• Status: PR submitted to Ultralytics: ultralytics/ultralytics#21260
• Workaround: Consider using Torch versions < 2.6 if you encounter pickle loading errors.

Requirements

• Python >= 3.10
• torch
• numpy
• Pillow
• scipy
• ultralytics (YOLO)
• segment-anything (SAM)

License

MIT License

Credits

Created by WASasquatch (https://github.com/WASasquatch)