You will get Identity-consistent synthetic human outputs using SDXL workflows
Project details
This project is designed for teams that need the same synthetic human to remain visually consistent across multiple prompts, scenes, and compositions using an SDXL workflow.
Most text-to-image systems generate different faces every time a prompt changes. This service focuses specifically on preserving identity so the same character can be reused across variations instead of restarting from scratch.
This type of identity consistency is useful for campaign concepts, synthetic model testing, product demonstrations, and early-stage virtual character workflows.
The result is a structured set of identity-consistent outputs that demonstrate how a synthetic character behaves across prompts, lighting setups, and scene variations instead of isolated one-off generations.
Most text-to-image systems generate different faces every time a prompt changes. This service focuses specifically on preserving identity so the same character can be reused across variations instead of restarting from scratch.
This type of identity consistency is useful for campaign concepts, synthetic model testing, product demonstrations, and early-stage virtual character workflows.
The result is a structured set of identity-consistent outputs that demonstrate how a synthetic character behaves across prompts, lighting setups, and scene variations instead of isolated one-off generations.
AI Algorithms
Convolutional Neural Network, CycleGAN, Generative Adversarial Network, StyleGAN, Transformer Model, Variational AutoencoderAI Applications
AI Text-to-Image, AI-Generated Art, Facial Recognition, Image Analysis, Image Processing, Image Recognition, Image Upscaling, Image-to-Image Translation, Neural Style Transfer, Synthetic Data GenerationAI Development Language
PythonAI Tools
Gradio, Hugging Face, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
DALL-E, GPT-4, LLaMA, Midjourney AI, Stable DiffusionWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional identity variation set
(+ 1 Day)
+$120Frequently asked questions
About Stan
SDXL LoRA Engineer | Synthetic Humans | Identity Consistency Pipelines
Aadorp, Netherlands - 9:34 am local time
Most AI image workflows can create impressive one-off visuals.
The harder problem is generating the same synthetic person reliably across different scenes, outfits, lighting conditions, camera angles, and campaign concepts.
That is the gap I focus on.
I help agencies, visual production teams, and AI media companies build reproducible systems for synthetic human generation. My work is not focused on random prompt experiments or generic AI automation. I build structured pipelines that can be trained, tested, repeated, and extended.
Typical systems I work on include:
• SDXL-based image generation workflows
• LoRA training pipelines for synthetic identities
• dataset preparation and captioning structures
• identity-consistency workflows across multiple outputs
• character persistence across scenes and campaigns
• realism-focused generation pipelines
• inference workflows for repeatable production output
• internal demo environments for testing generated results
My focus is turning a visual AI concept into a usable production workflow.
That usually means helping a team move from “we can generate nice images sometimes” to “we can generate this specific person, in this specific style, across many usable variations.”
I have built and tested private end-to-end systems where datasets are prepared, models are trained, generations are triggered, and outputs are reviewed inside a structured workspace environment. This includes work around synthetic human consistency, campaign-style generation, and reusable identity workflows.
I am usually a strong fit for:
• AI agencies building synthetic model or virtual influencer pipelines
• visual studios that need repeatable character generation
• marketing teams using generative humans in campaigns
• founders building AI image products
• teams that need SDXL / LoRA workflow structure
• companies moving beyond Midjourney-style one-off prompting
I am not the right fit for generic ChatGPT automation, Zapier workflows, basic prompt writing, or no-code AI tasks.
My value is in the technical layer behind the output: dataset structure, LoRA training logic, generation workflow design, identity stability, and production-ready execution.
If you need a custom generative pipeline that can create consistent synthetic humans instead of random one-off images, I can help you build it.
Steps for completing your project
After purchasing the project, send requirements so Stan can start the project.
Delivery time starts when Stan receives requirements from you.
Stan works on your project following the steps below.
Revisions may occur after the delivery date.
Review references and target identity style
Confirm reference images or define the intended synthetic character appearance.
Consistency-oriented prompt configuration
Structure prompts and workflow setup to preserve identity across variations.