Dual Checkpoint Image Generation with Face Detailer & Upscaling

ComfyUI workflow featuring dual checkpoint architecture, multi-LoRA management, and progressive enhancement pipeline

This workflow uses a three-stage processing approach: base generation, face enhancement, and neural upscaling.

🚀 Quick Installation Guide

Required Custom Node Packs

ComfyUI-Impact-Pack
- Provides: FaceDetailer, UltralyticsDetectorProvider
ComfyUI-LoraManager
- Provides: Lora Loader, Debug Metadata, TriggerWord Toggle
rgthree-comfy
- Provides: Fast Groups Muter, Power Prompt - Simple
ComfyUI-Custom-Scripts
- Provides: ShowText|pysssss, CheckpointLoader|pysssss
ComfyUI-KJNodes
- Provides: JoinStringMulti, ImageResizeKJv2
ComfyUI-Studio-Nodes (Optional)
- Provides: AspectRatioImageSize

Required Models

Detection Model:

bbox/face_yolov8m.pt - Face detection model
Auto-downloads to: ComfyUI/models/ultralytics/bbox/

Upscale Model:

4x-AnimeSharp.pth or 4x-UltraSharp.pth
Download from: OpenModelDB or Upscale Wiki
Place in: ComfyUI/models/upscale_models/

Checkpoint Models: (User provided)

Base checkpoint(s)
Refinement checkpoint(s)

LoRA Models: (User provided)

LoRAs as needed for your generation

Installation Methods

Option 1: ComfyUI Manager (Recommended)

Install ComfyUI-Manager
Load the workflow in ComfyUI
Use "Install Missing Custom Nodes" button
Restart ComfyUI

Option 2: Manual Installation

cd ComfyUI/custom_nodes

git clone https://github.com/ltdrdata/ComfyUI-Impact-Pack.git
git clone https://github.com/Suzie1/ComfyUI-LoraManager.git
git clone https://github.com/rgthree/rgthree-comfy.git
git clone https://github.com/pythongosssss/ComfyUI-Custom-Scripts.git
git clone https://github.com/kijai/ComfyUI-KJNodes.git
git clone https://github.com/comfyuistudio/ComfyUI-Studio-nodes.git

cd ComfyUI-Impact-Pack
python install.py

Additional Python Dependencies

Some nodes may require additional Python packages:

Impact Pack: ultralytics, segment-anything, mmdet
KJNodes: May need numba for some operations

Notes:

Core ComfyUI nodes are included with base ComfyUI
Some nodes install their own dependencies on first run
The workflow will show red/missing nodes if dependencies are missing

🏗️ Workflow Architecture

Three-Stage Processing Pipeline

Stage 1: Base Generation

Initial image generation
Dual checkpoint support
Multi-LoRA management
Prompt processing and conditioning

Stage 2: Face Enhancement

Face detection using YOLOv8
Targeted inpainting and refinement
Uses secondary checkpoint
Adaptive denoising

Stage 3: Neural Upscaling

AI model-based upscaling
Tile-based processing
Edge preservation
Multiple save points with metadata

📦 Main Node Types Used

Model Loading

CheckpointLoader|pysssss

Loads checkpoint models
Outputs: MODEL, CLIP, VAE
Enhanced checkpoint loader with metadata features

VAELoader

Loads VAE models separately
Allows VAE selection independent of checkpoint

Prompt Processing

Power Prompt - Simple (rgthree)

Prompt input and processing
Supports prompt weighting syntax
Outputs: CONDITIONING and TEXT

CLIPTextEncode

Converts text prompts to CLIP embeddings
Separate nodes for positive and negative prompts

JoinStringMulti

Combines multiple text strings
Used for merging trigger words with prompts

ShowText|pysssss

Displays text output
Useful for debugging prompts

LoRA Management

Lora Loader (LoraManager)

Manages multiple LoRA models
Individual strength controls for each LoRA
Separate model and CLIP strength settings
Automatically extracts trigger words
Toggle system to enable/disable LoRAs

TriggerWord Toggle (LoraManager)

Manages trigger words from active LoRAs
Filters based on enabled LoRAs
Group mode for batch management

Dimension Management

AspectRatioImageSize

Calculates dimensions for generation
Preset aspect ratios available
Ensures VAE-compatible dimensions (divisible by 8)

EmptyLatentImage

Creates initial latent tensor
Supports batch generation

Sampling

KSampler

Core generation node
Configurable samplers (DPM++, Euler, DDIM, etc.)
Configurable schedulers (Karras, exponential, simple, etc.)
Adjustable steps, CFG scale, and denoise strength

Face Detection and Enhancement

UltralyticsDetectorProvider

Provides YOLOv8 face detection model
Generates bounding boxes for detected faces

FaceDetailer

Enhances detected face regions
Performs targeted inpainting
Uses separate model for face processing
Configurable denoise, crop factor, feathering
Supports SAM model integration
Processes faces at higher resolution

Image Processing

VAEEncode

Converts pixel images to latent space

VAEDecode

Converts latent tensors to pixel images

ImageResizeKJv2

Resizes images with multiple interpolation methods
Maintains aspect ratios
Divisibility enforcement for model compatibility

UpscaleModelLoader

Loads neural upscaling models (ESRGAN, etc.)

ImageUpscaleWithModel

Applies neural upscaling to images
Tile-based processing for large images

Utilities

LazySwitchKJ

Routes connections based on boolean switch
Used for conditional workflow paths

WildcardPromptFromString

Processes wildcard syntax in prompts

Debug Metadata (LoraManager)

Tracks generation parameters
Outputs metadata for documentation

SaveImageWithMetaData

Saves images with embedded metadata
Configurable file naming and organization
Multiple instances for different pipeline stages

🔧 Workflow Structure

The workflow uses:

2 Checkpoint Loaders - Dual checkpoint architecture
2 VAE Loaders - Separate VAE selection
2 KSamplers - Base generation and refinement
1 LoRA Loader - Multi-LoRA management
1 FaceDetailer - Face enhancement
2 Upscale nodes - Neural upscaling
4 Save nodes - Multiple output points
4 Debug Metadata nodes - Parameter tracking

Total: 36 nodes in the workflow

⚙️ Key Features

Dual Checkpoint Support

Load different checkpoint models for base generation and face refinement, allowing specialized models for different stages.

Multi-LoRA Management

LoraManager system allows:

Loading multiple LoRAs simultaneously
Individual strength control per LoRA
Toggle activation without reloading
Automatic trigger word extraction and filtering

Face Enhancement Pipeline

YOLOv8 detection → FaceDetailer inpainting:

Automatic face detection
Higher resolution processing for faces
Separate model for face refinement
Configurable enhancement strength

Progressive Enhancement

Three-stage approach:

Generate base image
Enhance detected faces
Upscale final result

Metadata Tracking

Debug Metadata nodes throughout pipeline track:

Generation parameters
LoRA configurations
Model settings
For reproducibility and documentation

📋 Workflow Groups

The workflow organizes nodes into functional groups:

Prompt Creation
Model Loaders - Base Image
Model Loaders - Face Detail
Base Image generation and save
Face Detailer settings and save
Core Image Upscale and save
Final Upscale and save
Subdirectory configuration
Base Resolution settings

💾 Output Management

Multiple save points capture different stages:

Post-base generation
Post-face enhancement
Post-first upscale
Post-final upscale

Each save node:

Embeds metadata
Customizable file naming
Subdirectory organization
Configurable output format

🎯 Usage

Load checkpoint models
Load LoRA models (optional)
Configure prompts (positive and negative)
Set generation parameters (steps, CFG, sampler, scheduler)
Set resolution via AspectRatioImageSize
Configure face enhancement settings
Select upscale model
Queue and generate

The workflow saves outputs at multiple stages, allowing comparison of results throughout the pipeline.

This workflow provides a complete pipeline from initial generation through face enhancement to final upscaling with comprehensive parameter control and metadata tracking.

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