Solutions
AI Image Generation Solutions
We build image generation pipelines and applications that produce on-brand visuals at scale, with clear model licensing, content provenance and safety controls built in from the start.
Generate On-Brand Images at Scale, Responsibly
Image generation models such as Stable Diffusion, FLUX, OpenAI’s image models, Google Imagen and Adobe Firefly can now produce product shots, lifestyle scenes, illustrations and design variations in seconds. For businesses, the opportunity is not a single impressive image but a reliable pipeline: consistent brand style, accurate products, controlled composition, human review and outputs that can be used commercially without legal or reputational surprises.
Hanoi Technology builds AI image generation solutions for ecommerce, marketing, real estate, gaming and media teams. We select models with suitable licenses, fine-tune them on images you own, build controllable workflows with tools such as ComfyUI, ControlNet and LoRA adapters, and integrate generation into your content systems. Safety filters, content credentials and approval workflows are part of every delivery.
The Challenge
What Holds Back AI Image Adoption
Businesses face practical, legal and quality hurdles when moving beyond experiments.
Inconsistent Brand and Product Accuracy
General models drift in color, style and product details from one image to the next. Logos, packaging and text are often distorted, which makes outputs unusable without heavy editing.
Unclear Rights and Licensing
Model licenses differ: some open-weight models restrict commercial use or apply revenue thresholds, and copyright protection for purely AI-generated images is limited in several jurisdictions. Teams need clarity before publishing.
Safety and Reputational Risk
Without filters, models can generate inappropriate content, misuse real people’s likeness or imitate protected characters and trademarks. A single public mistake can harm the brand.
No Path From Demo to Production
Prompting in a web tool does not scale to thousands of catalog images. Teams need batch processing, quality checks, asset management and integration with existing workflows.
Business Value
What a Production Image Pipeline Delivers
On-Brand Output
Fine-tuned adapters and style references keep colors, lighting and composition consistent. Images look like they belong to the same campaign.
Faster Content Production
Variations for channels, markets and seasons are generated in minutes rather than weeks of shoots and edits. Creative teams focus on direction and selection.
Lower Cost per Asset
Background replacement, scene generation and resizing reduce the need for repeated photo shoots. Spending shifts to the hero assets that need them.
Controlled Composition
ControlNet, inpainting and reference conditioning let teams control pose, layout and product placement precisely. Results are predictable enough for production.
Traceable Provenance
C2PA content credentials and invisible watermarks record how images were made. Transparency obligations for synthetic content can be met.
Built-In Safety and Review
Prompt and output filters, restricted subjects and human approval steps prevent harmful or off-brand images from being published.
Rights and Safety Come Before Pixels
Before any image is generated, we document which models are used and under what license, which training images you own or have rights to, how outputs are labeled and who approves publication. Getting these decisions right protects the brand and makes AI imagery something legal and marketing teams can support confidently.
Our Services
Hanoi Technology’s End-to-End AI Image Generation Services
We deliver image generation capabilities from assessment to production operations.
Use Case and Rights Assessment
We identify where generated imagery adds value, review model licenses and data rights with your legal team, and define usage and labeling policies.
- Use case prioritization
- Model license review
- Usage and labeling policy
Model Selection and Fine-Tuning
We compare open-weight and API models on your use cases and fine-tune LoRA adapters on images you own to capture products, characters or brand style.
- Model benchmark
- Fine-tuned adapters
- Style and product reference sets
Controllable Generation Workflows
We build workflows in ComfyUI or with the Diffusers library combining ControlNet, inpainting, upscaling and background replacement for repeatable results.
- Production workflows
- Prompt and parameter templates
- Quality check steps
Image Generation Applications and APIs
We build web tools and APIs that let marketers, merchandisers or customers generate and edit images within approved boundaries, integrated with DAM, PIM or ecommerce systems.
- Web generation tool
- Generation API
- DAM and PIM integration
Safety, Provenance and Compliance
We implement prompt filters, NSFW and likeness checks, restricted subject lists, C2PA content credentials and audit logs, supporting obligations such as the EU AI Act transparency rules for synthetic content.
- Safety filter configuration
- Content credentials
- Generation audit log
GPU Infrastructure and Operations
We deploy models on cloud GPUs or managed endpoints with autoscaling, queueing and cost monitoring, and maintain models as new versions appear.
- GPU deployment
- Autoscaling and queueing
- Cost and usage dashboards
Our Approach
How We Deliver AI Image Generation
We combine creative quality with engineering discipline and clear governance.
- 01
Define Use Cases and Policies
We agree which images will be generated, for which channels, and the rules on licensing, labeling, subjects and approval.
Deliverables: Use case brief · Governance policy
- 02
Benchmark and Prototype
Candidate models and workflows are tested on real briefs, and results are reviewed with your creative team against quality criteria.
Deliverables: Benchmark results · Prototype outputs
- 03
Fine-Tune and Build the Pipeline
We train adapters on approved images, build the generation workflow and add safety and quality checks.
Deliverables: Fine-tuned models · Production pipeline
- 04
Integrate and Pilot
The pipeline is connected to your tools and used by a pilot team on live work, with human review on every output.
Deliverables: Integrated tool · Pilot feedback
- 05
Scale and Operate
We scale infrastructure, monitor quality and cost and update models and workflows as needs evolve.
Deliverables: Operations runbook · Improvement roadmap
Use Cases
AI Image Generation Use Cases
Examples of image generation delivering business value.
Ecommerce Product Scenes
Product photos shot on a plain background are placed into lifestyle scenes generated for each season or market. The product itself remains unchanged and accurate.
Marketing Campaign Variations
A campaign’s key visual is adapted into many formats, backgrounds and audiences while keeping the brand style. Designers review and select the final set.
Real Estate Virtual Staging
Photos of empty rooms are staged with furniture in different styles, clearly labeled as virtually staged. Listings attract more interest without physical staging costs.
Game and Concept Art Exploration
Artists generate concept variations from sketches and style references to explore ideas quickly. Final assets are refined by the art team.
Personalized Visual Content
Customers create personalized cards, posters or product previews within templates and safety boundaries. Outputs are moderated before printing or sharing.
Catalog Image Enhancement
Thousands of supplier images are cleaned, background-standardized and upscaled in batch. The catalog looks consistent across categories.
Technology
Image Generation Technologies We Use
We work with leading open-weight and hosted image models and tooling.
- Stable Diffusion 3.5
- SDXL
- FLUX
- OpenAI image models
- Google Imagen
- Adobe Firefly API
- Amazon Bedrock
- ComfyUI
- Hugging Face Diffusers
- ControlNet
- LoRA
- IP-Adapter
- C2PA Content Credentials
- PyTorch
- NVIDIA GPUs
- Python
Platforms
Deliver AI Image Generation on Your Preferred Platform
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · E-commerce and retail
Personalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Read the Full StoryMore Success Stories
Real estate
+38%lead conversion rateProperty discovery and lead conversion for a US real estate marketplace
A US marketplace connecting buyers, sellers and agents had plenty of traffic but few enquiries. We built personalized property discovery, AI valuation and a one-tap route to contact an agent.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Create Visual Content Faster and Safely
Book a free consultation to discuss your imagery needs, brand requirements and rights questions. We reply within 24 hours and can help plan a focused pilot.
AI Image Generation FAQ
Can we use AI-generated images commercially?
Often yes, but it depends on the model license and your use. Some models allow commercial use freely, some only below a revenue threshold or under a paid license, and some prohibit commercial use entirely. Copyright protection for purely AI-generated images is also limited in several jurisdictions, so meaningful human creative input matters. We document the license of every model we use and recommend confirming your position with legal counsel.
Can you train a model on our products or brand style?
Yes. We fine-tune lightweight adapters such as LoRA on images you own or have rights to use, such as product photography or brand illustrations. This teaches the model your products and style while keeping the base model unchanged. We keep a record of the training images used, so the provenance of every fine-tuned model is documented.
How do you prevent harmful or inappropriate images?
We combine prompt filtering, restricted subject lists, safety classifiers on generated images and checks to avoid generating real people without consent or imitating protected characters and logos. Sensitive use cases include mandatory human review before publication. All generations are logged, so issues can be investigated and filters improved over time.
Do generated images need to be labeled?
Increasingly, yes. The EU AI Act includes transparency obligations, applicable from August 2026, requiring providers to mark synthetic content in a machine-readable way and deployers to disclose certain deepfakes. Platforms and advertising rules may impose their own requirements. We implement C2PA content credentials and visible labels where appropriate and recommend confirming obligations with your legal team.
Should we use open-weight models or hosted APIs?
Hosted APIs are quick to start, require no GPU infrastructure and often include safety systems. Open-weight models such as Stable Diffusion or FLUX variants offer more control, fine-tuning and on-premises deployment, which suits privacy-sensitive or high-volume work, but require infrastructure and careful license review. Many solutions combine both, choosing the best model per task.
What does an image generation pipeline cost to run?
Costs depend on image volume, resolution, model choice and whether you use APIs or self-hosted GPUs. APIs charge per image, while self-hosting involves GPU time and operations effort that becomes cost-effective at higher volumes. We estimate costs during the pilot using your real workloads and design autoscaling so GPUs run only when needed.