import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("ana-tamais/2d-art-sprites", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]2d-art-sprites Dreambooth model trained by ana-tamais with TheLastBen's fast-DreamBooth notebook
You can test this model using this Colab Notebook for Inference
Sample pictures of 2dart concept:
We saved the training data in dataset.zip, and some generated results in results.zip.
Some recommendations
We recommend to set the:
- prompt as:
"[some wizard, paladin, healer, etc.], in the style of 2dart, white background, no background, full body" - negative prompt as:
"deformed, mutilated limbs, background, multiple people" - guidance scale between
9 and 10 - sampling method as
Euler - sampling steps as
60 - batch size as
1- avoid high batch size, unless you have a high memory GPU
This set of hyperparameters lead us to stable and good results.
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.
You need at least a Tesla T4 to be able to run the inference step using the given notebook.
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