readme: ---
  license: creativeml-openrail-m
  ---
  
  This is the beta version of the yama-no-susume character model (ヤマノススメ, aka encouragement of climb in English).
  Unlike most of the models out there, 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 it is possible to get **as many as 5 characters** right in one shot, and otherwise, you can always rely on inpainting.
  Here are two examples:
  
  With inpainting
  ![00014-225925947](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/with_inpaint/00014-225925947.png)
  
  Without inpainting
  ![00293-2955569580](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/without_inpaint/00293-2955569580.png)
  
  
  ### Characters
  
  The model knows 18 characters from yama no susume and for Aoi and Hinata it knows two hair styles for each of them.
  The ressemblance with a character can be improved by a better description of their appearance.
  
  ![xy_grid-0024-24](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/grids/xy_grid-0024-24.jpg)
  ![xy_grid-0027-24](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/grids/xy_grid-0027-24.jpg)
  ![xy_grid-0023-24](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/grids/xy_grid-0023-24.jpg)
  ![xy_grid-0022-24](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/grids/xy_grid-0022-24.jpg)
  ![xy_grid-0020-24](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/grids/xy_grid-0020-24.jpg)
  
  ### Dataset description
  
  The dataset contains around 40K images with the following composition
  - 11423 anime screenshots from the four seasons of the anime
  - 726 fan arts
  - ~30K 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, 0.2, and 0.5.
  Each category is itself split into many sub-categories in a hierarchical way.
  For more detail on the data preparation process please refer to https://github.com/cyber-meow/anime_screenshot_pipeline
  
  
  ### Training Details
  
  #### Trainer
  The model was 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 was trained on runpod with an A6000 and cost me around 80 dollors.
  However, I estimate a model of similar quality can be trained with fewer than 20 dollars on runpod.
  
  #### Hyperparameter specification
  
  - The model was first trained for 18000 steps, at batch size 8, lr 1e-6, resolution 640, and conditional dropping rate of 15%.
  - After this, I modified a little the captions and trained the model for another 22000 steps, at batch size 8, lr 1e-6, reslution 704, and conditional dropping rate of 15%.
  
  (Intermediate checkpoints can be found in the branch `all`)
  
  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 400K~600K images (some images are used multiple times) in an epoch,
  and therefore I did not even finish an entire epoch with the 40000 steps at batch size 8.
  
  ### Failures
  
  I tried several things in this model (this is why I trained for so long), but I failed most of them.
  
  - I put the number of people at the beginning of the captions, but at the end of 40000 steps the model still cannot count
  (it can generate like 3~5 people when we prompt 3people).
  - I use some tokens to describe the face position within a 5x5 grid but the model did not learn anything about these tokens.
  I think this is either due to 1) face position being too abstract to learn, 2) data imbalance as I did not balance my training for this, or 3) captions not enough focused on these concepts (it is much longer and contains other information).
  - As mentioned, the model can generate multi-character scenes but the success rate becomes lower and lower as we increase the number of characters in the scene.
  Character bleeding is always a hard problem to solve.
  - The model is trained with 5% weight for hand images, but I doubt it helps in any kind.
  
  Actually, I have a doubt whether the last 22000 steps really improved the model.
  This is how I get my 20$ estimate taking into account that we can simply train at resolution 512 on 3090 (and also ED2 will be more efficient).
  
  
  ### More Example Generations
  
  With inpainting
  ![00214-705765787](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/with_inpaint/00214-705765787.png)
  ![00292-111866839](https://huggingface.co/alea31415/yama-no-susume/resolve/main/examples/with_inpaint/0