JNGOO19 commited on
Commit
f7228e2
1 Parent(s): 90de236

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +1 -1
README.md CHANGED
@@ -3,7 +3,7 @@ license: mit
3
  ---
4
  ### rl pkmn TEST on Stable Diffusion
5
  This is a test for a game inspired style, and I am still learning how to set these things up. Below is a cherry picked result from the prompt
6
- '<rl-pkmn> styled Portrait of charizard, an orange dragon anthro intimidating pokemon, digital art by eugene de blaas, ross tran, <rl-pkmn> and nasreddine dinet, vibrant color scheme, intricately detailed, in the style of romanticism, cinematic, artstation, greg rutkowski'
7
  ![<rl-pkmn> 0=9](https://i.ibb.co/gZZFWmT/image-2022-09-14-045500477.png)
8
  This is the `<rl-pkmn>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
9
 
 
3
  ---
4
  ### rl pkmn TEST on Stable Diffusion
5
  This is a test for a game inspired style, and I am still learning how to set these things up. Below is a cherry picked result from the prompt
6
+ '<rl-pkmn> styled Portrait of charizard,intimidating pokemon, digital art by eugene de blaas, ross tran, <rl-pkmn> and nasreddine dinet, vibrant color scheme, intricately detailed, in the style of romanticism, cinematic, artstation, greg rutkowski'
7
  ![<rl-pkmn> 0=9](https://i.ibb.co/gZZFWmT/image-2022-09-14-045500477.png)
8
  This is the `<rl-pkmn>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
9