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--- |
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language: |
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- en |
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library_name: mir |
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--- |
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<div align="center"><img src="https://github.com/darkshapes/MIR/raw/main/img_src/mir300_dark.png" width="25%"></div> |
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# |
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> [!IMPORTANT] |
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> # MIR (Machine Intelligence Resource)<br><br>A naming schema for AIGC/ML work. |
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The MIR classification format seeks to standardize and complete a hyperlinked network of model information, improving accessibility and reproducibility across the AI community.<br> |
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The work is inspired by: |
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- [AIR-URN](https://github.com/civitai/civitai/wiki/AIR-%E2%80%90-Uniform-Resource-Names-for-AI) project by [CivitAI](https://civitai.com/) |
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- [Spandrel](https://github.com/chaiNNer-org/spandrel/blob/main/libs/spandrel/spandrel/__helpers/registry.py) library's super-resolution registry |
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Example: |
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> [!NOTE] |
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> # mir : model . transformer . clip-l : stable-diffusion-xl |
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``` |
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mir : model . lora . hyper : flux-1 |
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↑ ↑ ↑ ↑ ↑ |
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[URI]:[Domain].[Architecture].[Series]:[Compatibility] |
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``` |
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Code for this project can be found at [darkshapes/MIR on GitHub](https://github.com/darkshapes/MIR) |
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## Definitions: |
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Like other URI schema, the order of the identifiers roughly indicates their specificity from left (broad) to right (narrow) |
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### Domains |
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- `dev`: Varying local neural network layers, in-training, pre-release, items under evaluation, likely in unexpected formats<br> |
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- `model`: Static local neural network layers. Publicly released machine learning models with an identifier in the database<br> |
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- `operations`: Varying global neural network attributes, algorithms, optimizations and procedures on models<br> |
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- `info`: Static global neural network attributes, metadata with an identifier in the database<br> |
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### Architecture |
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Broad and general terms for system architectures. |
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- `dit`: Diffusion transformer, typically Vision Synthesis |
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- `unet`: Unet diffusion structure |
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- `art` : Autoregressive transformer, typically LLMs |
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- `lora`: Low-Rank Adapter (may work with dit or transformer) |
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- `vae`: Variational Autoencoder |
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etc |
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### Series |
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Foundational network and technique types. |
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### Compatibility |
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Implementation details based on version-breaking changes, configuration inconsistencies, or other conflicting indicators that have practical application. |
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### Goals |
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- Standard identification scheme for **ALL** fields of ML-related development |
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- Simplification of code for model-related logistics |
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- Rapid retrieval of resources and metadata |
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- Efficient and reliable compatibility checks |
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- Organized hyperparameter management |
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> <details> <summary>Why not use `diffusion`/`sgm`, `ldm`/`text`/hf.co folder-structure/brand or trade name/preprint paper/development house/algorithm</summary> |
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> |
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> - The format here isnt finalized, but overlapping resource definitions or complicated categories that are difficult to narrow have been pruned |
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> - Likewise, definitions that are too specific have also been trimmed |
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> - HF.CO become inconsistent across folders/files and often the metadata enforcement of many important developments is neglected |
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> - Development credit often shared, [Paper heredity tree](https://www.connectedpapers.com/search?q=generative%20diffusion), super complicated |
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> - Algorithms (esp application) are less common knowledge, vague, ~~and I'm too smooth-brain.~~ |
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> - Overall an attempt at impartiality and neutrality with regards to brand/territory origins |
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> </details> |
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> <details><summary>Why `unet`, `dit`, `lora` over alternatives</summary> |
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> |
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> - UNET/DiT/Transformer are shared enough to be genre-ish but not too narrowly specific |
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> - Very similar technical process on this level |
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> - Functional and efficient for random lookups |
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> - Short to type |
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> </details> |
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> <details><summary>Roadmap</summary> |
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> |
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> - Decide on `@` or `:` delimiters (like @8cfg for an indistinguishable 8 step lora that requires cfg) |
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> - crucial spec element, or an optional, MIR app-determined feature? |
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> - Proof of concept generative model registry |
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> - Ensure compatability/integration/cross-pollenation with [OECD AI Classifications](https://oecd.ai/en/classification) |
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> - 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) |
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> </details> |
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massive thank you to [@silveroxides](https://huggingface.co/silveroxides) for phenomenal work collecting pristine state dicts and related information |
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