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license: creativeml-openrail-m |
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This is the beta version of the yama-no-susume character model (ヤマノススメ, aka encouragement of climb in English). |
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Unlike most of the models out there, this model is capable of generating **multi-character scenes** beyond images of a single character. |
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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. |
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Here are two examples: |
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With inpainting |
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Without inpainting |
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### Characters |
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The model knows 18 characters from yama no susume and for Aoi and Hinata it knows two hair styles for each of them. |
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The ressemblance with a character can be improved by a better description of their appearance. |
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### Dataset description |
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The dataset contains around 40K images with the following composition |
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- 11423 anime screenshots from the four seasons of the anime |
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- 726 fan arts |
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- ~30K customized regularization images |
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The model is trained with a specific weighting scheme to balance between different concepts. |
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For example, the above three categories have weights respectively 0.3, 0.2, and 0.5. |
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Each category is itself split into many sub-categories in a hierarchical way. |
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For more detail on the data preparation process please refer to https://github.com/cyber-meow/anime_screenshot_pipeline |
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### Training Details |
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#### Trainer |
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The model was trained using [EveryDream1](https://github.com/victorchall/EveryDream-trainer) as |
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EveryDream seems to be the only trainer out there that supports sample weighting (through the use of `multiply.txt`). |
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Note that for future training it makes sense to migrate to [EveryDream2](https://github.com/victorchall/EveryDream2trainer). |
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#### Hardware and cost |
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The model was trained on runpod with an A6000 and cost me around 80 dollors. |
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However, I estimate a model of similar quality can be trained with fewer than 20 dollars on runpod. |
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#### Hyperparameter specification |
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- The model was first trained for 18000 steps, at batch size 8, lr 1e-6, resolution 640, and conditional dropping rate of 15%. |
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- 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%. |
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(Intermediate checkpoints can be found in the branch `all`) |
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Note that as a consequence of the weighting scheme which translates into a number of different multiply for each image, |
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the count of repeat and epoch has a quite different meaning here. |
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For example, depending on the weighting, I have 400K~600K images (some images are used multiple times) in an epoch, |
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and therefore I did not even finish an entire epoch with the 40000 steps at batch size 8. |
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### Failures |
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I tried several things in this model (this is why I trained for so long), but I failed most of them. |
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- I put the number of people at the beginning of the captions, but at the end of 40000 steps the model still cannot count |
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(it can generate like 3~5 people when we prompt 3people). |
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- I use some tokens to describe the face position within a 5x5 grid but the model did not learn anything about these tokens. |
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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). |
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- 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. |
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Character bleeding is always a hard problem to solve. |
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- The model is trained with 5% weight for hand images, but I doubt it helps in any kind. |
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Actually, I have a doubt whether the last 22000 steps really improved the model. |
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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). |
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### More Example Generations |
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With inpainting |
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Without inpainting |
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Some failure cases |
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