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Update README.md

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@@ -80,11 +80,21 @@ with anxious excitement, his famous bald spot sweatily glistening under warm lig
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  shot on a cell phone in a Los Angeles apartment kitchen
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  output:
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  url: samples/1729711603742__000004000_3.jpg
 
 
 
 
 
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  - text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
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  regions of white fur, playing a piano and singing, amateur 2004 photograph
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  shot on a cell phone in a Los Angeles apartment kitchen
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  output:
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  url: samples/1729701550832__000001800_3.jpg
 
 
 
 
 
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  base_model: stabilityai/stable-diffusion-3.5-large
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  license: creativeml-openrail-m
@@ -101,7 +111,7 @@ Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
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  'HST style autochrome photo'
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  ## Config Parameters
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- Using the Google Colab Notebook Version of #ai-toolkit#.
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  I've used A100 via Colab Pro.
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  But training SD3.5 may potentially work with Free Colab or lower Vram in general, especially if one used lower rank (try 4 or 8), dataset size (in terms of caching/bucketing/pre-loading impacts), 1 batch size, Adamw8bit optimizer, 512 resolution, maybe adding the "lowvram, true" argument, and plausibly specifying alternate quantization variants!
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  Generally, VRAM expenditures tend to be lower than for Flux during training. So, try it! I certainly will.
 
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  shot on a cell phone in a Los Angeles apartment kitchen
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  output:
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  url: samples/1729711603742__000004000_3.jpg
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+ - text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
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+ regions of white fur, playing a piano and singing, amateur 2004 photograph
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+ shot on a cell phone in a Los Angeles apartment kitchen
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+ output:
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+ url: samples/1729707029633__000003000_3.jpg
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  - text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
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  regions of white fur, playing a piano and singing, amateur 2004 photograph
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  shot on a cell phone in a Los Angeles apartment kitchen
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  output:
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  url: samples/1729701550832__000001800_3.jpg
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+ - text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
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+ regions of white fur, playing a piano and singing, amateur 2004 photograph
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+ shot on a cell phone in a Los Angeles apartment kitchen
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+ output:
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+ url: samples/1729697887015__000001000_3.jpg
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  base_model: stabilityai/stable-diffusion-3.5-large
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  license: creativeml-openrail-m
 
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  'HST style autochrome photo'
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  ## Config Parameters
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+ Using the Google Colab Notebook Version of ai-toolkit.
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  I've used A100 via Colab Pro.
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  But training SD3.5 may potentially work with Free Colab or lower Vram in general, especially if one used lower rank (try 4 or 8), dataset size (in terms of caching/bucketing/pre-loading impacts), 1 batch size, Adamw8bit optimizer, 512 resolution, maybe adding the "lowvram, true" argument, and plausibly specifying alternate quantization variants!
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  Generally, VRAM expenditures tend to be lower than for Flux during training. So, try it! I certainly will.