Update README.md

README.md

@@ -9,7 +9,7 @@ massive thank you to [@silveroxides](https://huggingface.co/silveroxides) for ph
 > [!IMPORTANT]
 > # MIR (Machine Intelligence Resource)
 
-MIR is a naming standard, a proposed schema for ML work.<br>
+MIR is a naming standard, a proposed schema for AIGC/ML work.<br>
 In its current incarnation, it looks like this:
 
 > [!NOTE]
@@ -34,7 +34,28 @@ and by the super-resolution registry code from the [Spandrel](https://github.com
 - Efficient and reliable compatability checks
 - Organized hyperparameter management
 
-## Work to be done
-- Ensure compatability with [OECD AI Classifications](https://oecd.ai/en/classification)
-- Ensure compatability with [NIST AI 200-1 NIST Trustworthy and Responsible AI](https://www.nist.gov/publications/ai-use-taxonomy-human-centered-approach)
 
+> <details> <summary>Why not `diffusion`, `transformer`, `sgm`, `ldm`, `text`, hf.co folder-structure, (brand-specific trade word)</summary>
+>      
+> - Exact frameworks (SGM/LDM/RectifiedFlow) includes too few
+> - Diffusion/Transformer are too broad, share and overlap resources
+> - Multimodal models complicate content terms (Text/Image/Vision/etc)
+> - HF.CO names do all of this, inconsistent across folders/files
+> - Impartiality
+> </details>
+
+> <details><summary>Why `unet`, `dit`, `lora` over alternatives</summary>
+>    
+> - UNET/DiT are shared enough to be a genre but not too narrowly specific
+> - Very similar technical process on this level
+> - Functional and efficient for random lookups
+> </details>
+
+> <details><summary>Roadmap</summary>
+> 
+> - Decide on `@` (like @8cfg for an indistinguishable 8 step lora that requires cfg)
+> -- crucial spec element, or an optional, MIR app-determined feature?
+> - Proof of concept generative model registry 
+> - Ensure compatability/integration/cross-pollenation with [OECD AI Classifications](https://oecd.ai/en/classification)
+> - Ensure compatability/integration/cross-pollenation with [NIST AI 200-1 NIST Trustworthy and Responsible AI](https://www.nist.gov/publications/ai-use-taxonomy-human-centered-approach)
+> </details>