Update README.md

README.md

@@ -1,3 +1,76 @@
 ---
 license: creativeml-openrail-m
 ---
+
+---
+license: creativeml-openrail-m
+---
+
+This is a low-quality bocchi-the-rock (ぼっち・ざ・ろっく!) character model.
+Similar to my [yama-no-susume model](https://huggingface.co/alea31415/yama-no-susume), this model is capable of generating **multi-character scenes** beyond images of a single character.
+Of course, the result is still hit-or-miss, but I think the success rate of getting the **entire Kessoku Band** right in one shot is already quite high,
+and otherwise, you can always rely on inpainting.
+Here are two examples:
+
+With inpainting
+*Coming soon*
+
+Without inpainting
+*Coming soon*
+
+
+### Characters
+
+The model knows 12 characters from bocchi the rock.
+The ressemblance with a character can be improved by a better description of their appearance.
+
+*Coming soon*
+
+### Dataset description
+
+The dataset contains around 27K images with the following composition
+- 7024 anime screenshots
+- 1630 fan arts
+- 18519 customized regularization images
+
+The model is trained with a specific weighting scheme to balance between different concepts.
+For example, the above three categories have weights respectively 0.3, 0.25, and 0.45.
+Each category is itself split into many sub-categories in a hierarchical way.
+For more details on the data preparation process please refer to https://github.com/cyber-meow/anime_screenshot_pipeline
+
+
+### Training Details
+
+#### Trainer
+The model is trained using [EveryDream1](https://github.com/victorchall/EveryDream-trainer) as
+EveryDream seems to be the only trainer out there that supports sample weighting (through the use of `multiply.txt`).
+Note that for future training it makes sense to migrate to [EveryDream2](https://github.com/victorchall/EveryDream2trainer).
+
+#### Hardware and cost
+The model is trained on runpod using 3090 and cost me around 15 dollors.
+
+#### Hyperparameter specification
+
+- The model is trained for 48000 steps, at batch size 4, lr 1e-6, resolution 512, and conditional dropping rate of 10%.
+
+Note that as a consequence of the weighting scheme which translates into a number of different multiply for each image,
+the count of repeat and epoch has a quite different meaning here.
+For example, depending on the weighting, I have around 300K images (some images are used multiple times) in an epoch,
+and therefore I did not even finish an entire epoch with the 48000 steps at batch size 4.
+
+### Failures
+
+- For the first 24000 steps I use the trigger words `Bfan1` and `Bfan2` for the two fans of Bocchi.
+  However, these two words are too similar and the model fails to different characters for these. Therefore I changed Bfan2 to Bofa2 at step 24000.
+
+
+### More Example Generations
+
+With inpainting
+*Coming soon*
+
+Without inpainting
+*Coming soon*
+
+Some failure cases
+*Coming soon*