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README.md
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license: creativeml-openrail-m
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SD 1.5 dreambooth models
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Textual inversion embedding versions can be found [here](https://huggingface.co/922-CA/gfl-ddlc-TI-tests) (trained off the same or similar datasets).
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# nes2-1350 (Negev)
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* Keyword: negebave
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* First attempt
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# ma1-850 (FMG9)
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* Keyword: magmgbg
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* First attempt
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# ca1-1260 (KAC-PDW)
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* Keyword: kcpdweh
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* First attempt
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# p3-1800 (P90)
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* Keyword: hrstlleds
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* Third attempt
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# pn1-1350 (P90)
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* Keyword: hrstlleds
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* Fourth attempt, may get best results with TI
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* First attempt, may get best results with TI
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MTBA (previews, any future loras or models trained off better bases- hopefully some SDXL too)
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license: creativeml-openrail-m
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---
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SD 1.5 dreambooth models fine-tuned on Anythingv3. Checkpoint files trained on (number of steps divided by 90) images scraped from boorus, with text encoder trained for around (number of images times 12) steps. Focused on ckpts before moving on to safetensors and lora. (~11/2022)
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Textual inversion embedding versions can be found [here](https://huggingface.co/922-CA/gfl-ddlc-TI-tests) (trained off the same or similar datasets).
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# nes2-1350 (Negev)
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* Fine-tuned 12/05/2022
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* Keyword: negebave
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* First attempt
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# ma1-850 (FMG9)
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* Fine-tuned ~12/2022
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* Keyword: magmgbg
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* First attempt
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# ca1-1260 (KAC-PDW)
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* Trained 12/06/2022
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* Keyword: kcpdweh
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* First attempt
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# p3-1800 (P90)
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* Trained ~12/2022
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* Keyword: hrstlleds
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* Third attempt
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# pn1-1350 (P90)
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* Trained ~12/2022
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* Keyword: hrstlleds
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* Fourth attempt, may get best results with TI
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# pr2 (Persicaria)
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* Trained 12/06/2022
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* Keyword: prsheheas
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* First attempt, may get best results with TI
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MTBA (previews, any future loras or models trained off better bases- hopefully some SDXL too)
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