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--- |
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license: creativeml-openrail-m |
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tags: |
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- text-to-image |
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--- |
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### 2d-art-sprites Dreambooth model trained by ana-tamais with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook |
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You can test this model using this [Colab Notebook for Inference](https://colab.research.google.com/drive/1pFaEJHa7mxFruBfm2hDnR8S6aEo7sAFx?usp=sharing) |
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Sample pictures of 2dart concept: |
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<img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_1.png" width=256></img> |
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<img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_4.png" width=256></img> |
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<img src="https://huggingface.co/ana-tamais/2d-art-sprites/resolve/main/concept_images/2dart_9.png" width=256></img> |
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We saved the training data in `dataset.zip`, and some generated results in `results.zip`. |
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### Some recommendations |
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We recommend to set the: |
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- prompt as: `"[some wizard, paladin, healer, etc.], in the style of 2dart, white background, no background, full body"` |
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- negative prompt as: `"deformed, mutilated limbs, background, multiple people"` |
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- guidance scale between `9 and 10` |
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- sampling method as `Euler` |
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- sampling steps as `60` |
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- batch size as `1` - avoid high batch size, unless you have a high memory GPU |
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This set of hyperparameters lead us to stable and good results. |
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This model was trained using images which the character has some human format or is a not deformed living being. So if you try to predict something like "sword, mirror, candle, etc" (non-living things), we saw the model doesn't perform so well. |
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You need at least a Tesla T4 to be able to run the inference step using the given [notebook](https://colab.research.google.com/drive/1pFaEJHa7mxFruBfm2hDnR8S6aEo7sAFx?usp=sharing). |
