Stable Diffusion Experts in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used Stable Diffusion
Muntaha Shams
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
Tobias Von Dewitz
Last position:
Managing Partner at Unwritten GmbH
- Pioneer work in personalized AI: development of a framework for “Interactive Content” (RAG) for novels, lectures, expert debriefing
- Successful launch of Einbug, the Pantopia chatbot, with media resonance (SZ interview)
- Creation of compelling AI personalities: AI blog ([link]), 100% personalized learning environments, Perry Rhodan, and others.
Puranjan Bandyopadhyaya
Last position:
Internship - Generative AI at Continental
- Gathered tire images and their feature descriptions.
- Cleaned dataset of image metadata using pandas.
- Stored image feature embeddings in Chroma vector db.
- Used image augmentations to increase dataset size.
- Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
- Used PyTorch to train and test different neural networks.
- Validated model using custom accuracy metric based on similarity search in ChromaDB.
- Visualized accuracy predictions using matplotlib.
- Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
- Created custom Docker image in Amazon Elastic Container Registry for machine learning script.
Pawan Saxena
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Ekaansh Khosla
Last position:
Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg
- Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
- Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
- Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS
Kashyap Khunt
Last position:
Master’s Thesis - Synthetic Data Generation for Quality Inspection at Schaeffler Technologies AG
- Developed a synthetic data generation framework using 3D simulation (NVIDIA Omniverse) and Generative AI (Stable Diffusion) to model and augment industrial surface defects.
- Trained and evaluated Computer Vision models (YOLO, DETR), achieving 94% detection accuracy on real-world samples and demonstrating successful simulation-to-reality transfer.
- Applied domain adaptation to improve simulation-to-reality transfer, enabling scalable Industrial AI for automated quality inspection and reducing manufacturing downtime.
Discover over 15,000 top freelancers
Statistics of experts using Stable Diffusion
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2 years
Positions per freelancer
5
Top business areas
Product Development, Research and Development, Information Technology
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
100%
Certifications per freelancer
2
Most common languages
English, German, Bangla
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Nuremberg are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Nuremberg using Stable Diffusion
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Stable Diffusion is a text-to-image model used to create and edit images from prompts. Companies use it for concept visuals, campaign assets, mockups, style exploration, and controlled image variation. It also appears in image-to-image and inpainting workflows.
Common setups
- Prompt design and prompt libraries
- SDXL and other Stable Diffusion model variants
- Image-to-image, inpainting, outpainting, and upscaling
- LoRA, fine-tuning, and custom style work
- Local or server-based inference workflows
Ecosystem skills
Strong specialists understand the model itself and the tools around it, such as ComfyUI, AUTOMATIC1111, ControlNet, and common diffusion checkpoints. They know how to choose the right workflow for speed, quality, and repeatability. They also manage GPU limits, model files, and version changes.
When to bring help
Companies bring in freelance expertise when outputs look inconsistent, prompts are hard to control, or a team needs a production-ready setup. That often happens during brand asset creation, product image testing, internal design support, or prototype work. In Nuremberg, this is especially useful for teams that want remote support with clear communication.
What good specialists do
Good professionals do more than generate nice images. They control composition, keep style consistent, reduce unwanted artifacts, and document the workflow so others can reuse it. They also understand where Stable Diffusion fits better than manual editing or fully custom generative systems.
Quality signals
- Clear prompt and workflow structure
- Practical knowledge of SDXL, ControlNet, and inpainting
- Ability to balance creativity with repeatable results
- Experience with brand-safe image generation
- Clean handoff of files, prompts, and settings
Frequently asked questions
Before you brief your next project: the most common questions about Stable Diffusion.
Stable Diffusion is used to generate and edit images from text prompts or source images. Teams use it for concept art, ad variations, product mockups, storyboards, and quick visual exploration. It is also common for inpainting, outpainting, and style transfer work.
Stable Diffusion is often chosen when teams want more control over the workflow, local execution, or custom tuning. Midjourney is usually seen as simpler for fast visual ideas, while DALL·E is often used for prompt-driven generation with a different output style. The best choice depends on how much control, repeatability, and model access the project needs.
A strong Stable Diffusion specialist usually knows prompt design, image editing basics, and model workflows such as SDXL, LoRA, and ControlNet. Familiarity with tools like ComfyUI or AUTOMATIC1111 helps a lot. For production work, version control for prompts and clear file handoff are also important.
You may not need deep custom tuning for a one-off image task, but Stable Diffusion becomes harder when you need consistency across many outputs. A freelancer helps when prompts need to be refined, a style must stay stable, or the workflow has to be documented for a team. That saves time later.
Yes, most Stable Diffusion work is remote because the workflow is digital and easy to review through shared files and prompt notes. For teams in Nuremberg, remote collaboration often works well when the freelancer can iterate quickly and communicate clearly in English or German. On-site sessions can help for brand alignment, but they are rarely required.
Look at whether the freelancer can explain why a result works, not just show finished images. A good Stable Diffusion professional can reproduce a style, reduce artifacts, and adapt the workflow to your brand or use case. Ask for examples of prompt structure, model choices, and before-and-after results.
Stable Diffusion is the broader family name, while SDXL, or Stable Diffusion XL, is a newer model variant within that family. SDXL is often preferred for stronger image quality and better prompt handling, depending on the setup. A good specialist knows when to use the base model and when to switch variants.
Before bringing in a Stable Diffusion expert, prepare example images, brand rules, target styles, and a clear list of deliverables. If you need inpainting, product visuals, or a local workflow, share the technical setup and any constraints upfront. That helps the specialist move faster and avoid wasted iterations.
The average hourly rate of freelancers in Nuremberg, Germany who have used Stable Diffusion in their recent projects is 61 €, which corresponds to a daily rate of about 488 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Stable Diffusion in their recent projects, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Stable Diffusion in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Nuremberg, Germany who have used Stable Diffusion in their recent projects are English (100%), German (83%), and Bangla (17%).
The most common industries among freelancers in Nuremberg, Germany who have used Stable Diffusion in their recent projects are Information Technology (83%), Education (50%), and Manufacturing (50%).
The most common business areas among freelancers in Nuremberg, Germany who have used Stable Diffusion in their recent projects are Product Development (100%), Research and Development (100%), and Information Technology (83%).
Main locations of FRATCH Experts, who have recently used Stable Diffusion
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Countries:
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
