Proposal and Observation: Segmentation-Aware Base Models for Better Quality & Future Commercial Applications

Models
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Hi, my name is Fernando, I’m from Brazil.
I’m a software engineer, 55 years old, and I have always developed marketing and sales software. This year I decided to specialize in generative AI and ComfyUI workflows for professional productions. While studying GGUF base-model quantization this week, I arrived at some observations:

  1. When using GGUF models, it becomes extremely important to rely on complete and well-structured LoRAs to help the quantized base model recover fine details lost during quantization, especially character identity and important objects in the scene.
  2. Even with strong LoRAs, GGUF models still do not deliver the quality required for professional productions.

Because of this, I started asking myself:
“What is the best solution for studios or artists working on personal computers with only 6–8 GB of VRAM?”

Here in Brazil, hardware is very expensive.
A GPU with 32 GB of VRAM costs around R$ 26.600,00 (~17.6 minimum wages), which is expensive even for businesses.

This led me to a suggestion about this:

Idea: Instead of removing fine detail through quantization, why not train base models using segmentation-focused datasets?

Models like SDXL, Flux, and Qwen already ship specialized variants such as:

  • sd_xl_base_outdoor_1.0.safetensors
  • flux1-dev-fp16_outdoor.safetensors

These versions target outdoor scenes and backgrounds, and they work well with LoRAs for specific content.

A natural next step would be to expand this specialization by training segment-focused base models, for example:

  • sd_xl_base_clothes_1.0.safetensors
  • flux1-dev-fp16_sports.safetensors
  • sd_xl_base_face_1.0.safetensors
  • flux1-dev-fp16_indoor.safetensors
  • sd_xl_base_women_1.0.safetensors
  • flux1-dev-fp16_men.safetensors
  • etc.

Each model would be trained with segmentation-aware datasets targeted to a specific type of subject or scene region.

Advantages ( Especially for Character Consistency )

Segmentation-specialized base models could significantly improve:

1. Character Consistency Across Many Frames

  • Identity preservation becomes more reliable.
  • Style and clothing remain consistent over sequential generations.
  • Body structure and proportions remain stable in animations or multi-frame workflows.
  • Perfect for storytelling, marketing, comics, and cinematic productions.

2. Better Detail Recovery on Low-VRAM Hardware

  • Instead of quantization sacrificing fidelity, segmentation keeps the model lightweight and targeted.
  • Professional-quality output becomes possible on 6-8 GB GPUs.

3. Stable Multi-Step Workflows

An image or video could be generated in structured steps:

  1. Face
  2. Clothing
  3. Pose
  4. Background
  5. Final composition

Each step would be handled by the most relevant base model, increasing stability and reducing VRAM usage.

4. Improved Compatibility With Professional LoRAs

Because each model handles only its relevant region, LoRAs become:

  • more predictable
  • less destructive
  • consistent across frames and scenes

Future Market Potential ( Base Models as Sellable Products )

In the future, base models themselves may become commercial products.

Companies, especially from tourism and real estate, could pay for custom segmentation-based base models trained on:

  • their products
  • architectural layouts
  • facilities
  • hotel rooms
  • resorts
  • beaches
  • ecological tourism sites
  • real estate developments
  • vehicles
  • retail products
  • and many more environments

This would allow studios, creators, and the general public to generate scenes featuring characters inside real locations as a form of immersive promotion.

Such models would:

  • help cities, hotels, and resorts promote tourism
  • allow real estate companies to showcase properties with virtual actors
  • let automotive companies embed realistic characters around their vehicles
  • make product placement easier and more dynamic

And even more importantly:
In the future, many influencers, actors, and communicators generated by AI will become as popular as real people.
This also creates an opportunity for real influencers to use AI avatars to present products or locations they wouldn’t normally have access to—dramatically expanding brand reach and narrative possibilities.

Segmentation-aware base models are perfectly aligned with this future ecosystem.

Conclusion

Training base models using segmentation-focused datasets, rather than relying heavily on quantization, could enable high-quality professional workflows on low-VRAM hardware. This would democratize content creation, especially in countries where hardware is expensive, and unlock a new market where base models themselves become valuable commercial products.