Instructions to use michaelriedl/MonsterForge-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelriedl/MonsterForge-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="michaelriedl/MonsterForge-small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("michaelriedl/MonsterForge-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 919 Bytes
002ca81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | from transformers import PretrainedConfig
class MonsterForgeSmallConfig(PretrainedConfig):
model_type = "lightweight-gan"
def __init__(
self,
image_size=64,
latent_dim=256,
fmap_max=512,
fmap_inverse_coef=12,
transparent=False,
greyscale=False,
attn_res_layers=[32],
freq_chan_attn=False,
syncbatchnorm=False,
antialias=False,
**kwargs,
):
self.image_size = image_size
self.latent_dim = latent_dim
self.fmap_max = fmap_max
self.fmap_inverse_coef = fmap_inverse_coef
self.transparent = transparent
self.greyscale = greyscale
self.attn_res_layers = attn_res_layers
self.freq_chan_attn = freq_chan_attn
self.syncbatchnorm = syncbatchnorm
self.antialias = antialias
super().__init__(**kwargs)
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