Generative AI
Models that create, edit, and operate imagination.
Vision Models & Vector Quantization
Generative AI goes beyond image generation — a creative system that selects, edits, and iterates outputs in line with brand tone and production purpose.
AX Lab has extended Vision Models, VQGAN/SCQ, and Latent Diffusion research into image generation, editing, banner production, and multimodal content pipelines.
— Section 1
Three technology axes extend the model.
01 Vision Models
Models that understand images as combinations of scenes, objects, styles, and context — not raw pixels. They turn production intent into visuals, then analyze outputs to suggest the next edit.
02 VQGAN / SCQ
Compress and reconstruct images into meaningful vector units. Structuring visual information becomes the basis for style transfer, image search, quality evaluation, and brand consistency review.
03 Latent Diffusion
Generate and tune high-resolution images in latent space. Combining text, reference images, masks, and style conditions creates more precise production workflows.
— Section 2
We reshape production with tools we already built.
01 AI Image Generation & Editing Platform
FLUX, DALL·E, Gemini, ComfyUI, Qwen Image Edit, and background removal — bundled into one production flow. Generate, edit, vary, process backgrounds, and save — all on the same screen. Possibility: Cut iteration time for designers; combine brand-specific presets with review criteria to stabilize mass-production quality.
02 Banner Auto-generation System
Input product image, copy, aspect ratio, and campaign purpose; output banner drafts across formats automatically. Generative imagery meets layout automation. Possibility: In e-commerce, promotion, and SNS content — where many variations are needed — secure speed and consistency at once.
03 Multimodal Story-verse Platform
Connect text, image, video, and character settings to produce story-based content. Generative models are treated as worlds and scenes, not single images. Possibility: For brand campaigns, character IP, education content, and short-form video — extend into a creation pipeline from planning to visualization.
04 Conversational Multimodal Media Production
A media-production pipeline where voice, video, and text are integrated and the user steers content direction through conversation. Possibility: Shift creators from operating tools to negotiating intent with AI to improve the work.
— Section 3
Mapping generative AI tags to project structure.
Vision Models
Models that understand and generate images and scenes. The image platform and banner-automation projects let vision models read brand assets, product images, and layout conditions, then produce results. Related projects: AI Image Platform, Banner Automation
VQGAN / SCQ
Vector quantization that compresses and reconstructs visual information. VQGAN/SCQ research handles latent representations of images and video, becoming the basis for style consistency, quality control, and lower iteration cost. Related projects: Multimodal Story-verse Platform, Generation Quality Control
Latent Diffusion
A generation pipeline that drafts and edits quickly in latent space. Conversational Multimodal Media Production extends into a production style that combines text, image, and video conditions in latent space — enabling fast drafts and iterative edits. Related projects: Conversational Multimodal Media, Campaign Visual Generation
— Timeline
Generation is the start; operations is what follows.
Near · Faster production
Iterate drafts, variations, background work, and banner mock-ups fast.
Next · Brand consistency
Bake style, tone, forbidden terms, and image-quality criteria into the model workflow.
Future · Creative OS
Build a production operating layer where outcomes from generation, edit, review, and release feed back into learning.