AlekseyCalvin
commited on
Commit
•
c63b771
1
Parent(s):
8fd3f06
Update README.md
Browse files
README.md
CHANGED
@@ -80,11 +80,21 @@ with anxious excitement, his famous bald spot sweatily glistening under warm lig
|
|
80 |
shot on a cell phone in a Los Angeles apartment kitchen
|
81 |
output:
|
82 |
url: samples/1729711603742__000004000_3.jpg
|
|
|
|
|
|
|
|
|
|
|
83 |
- text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
|
84 |
regions of white fur, playing a piano and singing, amateur 2004 photograph
|
85 |
shot on a cell phone in a Los Angeles apartment kitchen
|
86 |
output:
|
87 |
url: samples/1729701550832__000001800_3.jpg
|
|
|
|
|
|
|
|
|
|
|
88 |
base_model: stabilityai/stable-diffusion-3.5-large
|
89 |
|
90 |
license: creativeml-openrail-m
|
@@ -101,7 +111,7 @@ Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
|
|
101 |
'HST style autochrome photo'
|
102 |
|
103 |
## Config Parameters
|
104 |
-
Using the Google Colab Notebook Version of
|
105 |
I've used A100 via Colab Pro.
|
106 |
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!
|
107 |
Generally, VRAM expenditures tend to be lower than for Flux during training. So, try it! I certainly will.
|
|
|
80 |
shot on a cell phone in a Los Angeles apartment kitchen
|
81 |
output:
|
82 |
url: samples/1729711603742__000004000_3.jpg
|
83 |
+
- text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
|
84 |
+
regions of white fur, playing a piano and singing, amateur 2004 photograph
|
85 |
+
shot on a cell phone in a Los Angeles apartment kitchen
|
86 |
+
output:
|
87 |
+
url: samples/1729707029633__000003000_3.jpg
|
88 |
- text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
|
89 |
regions of white fur, playing a piano and singing, amateur 2004 photograph
|
90 |
shot on a cell phone in a Los Angeles apartment kitchen
|
91 |
output:
|
92 |
url: samples/1729701550832__000001800_3.jpg
|
93 |
+
- text: /@step 1800 weights:/ HST style autochrome photo of realistic green-eyed black cat, with prominent
|
94 |
+
regions of white fur, playing a piano and singing, amateur 2004 photograph
|
95 |
+
shot on a cell phone in a Los Angeles apartment kitchen
|
96 |
+
output:
|
97 |
+
url: samples/1729697887015__000001000_3.jpg
|
98 |
base_model: stabilityai/stable-diffusion-3.5-large
|
99 |
|
100 |
license: creativeml-openrail-m
|
|
|
111 |
'HST style autochrome photo'
|
112 |
|
113 |
## Config Parameters
|
114 |
+
Using the Google Colab Notebook Version of ai-toolkit.
|
115 |
I've used A100 via Colab Pro.
|
116 |
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!
|
117 |
Generally, VRAM expenditures tend to be lower than for Flux during training. So, try it! I certainly will.
|