Instructions to use ControlNet/marlin_vit_large_ytf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ControlNet/marlin_vit_large_ytf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ControlNet/marlin_vit_large_ytf", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ControlNet/marlin_vit_large_ytf", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class MarlinConfig(PretrainedConfig): | |
| model_type = "marlin" | |
| def __init__(self, **kwargs): | |
| self.img_size = kwargs.pop("img_size", None) | |
| self.patch_size = kwargs.pop("patch_size", None) | |
| self.n_frames = kwargs.pop("n_frames", None) | |
| self.encoder_embed_dim = kwargs.pop("encoder_embed_dim", None) | |
| self.encoder_depth = kwargs.pop("encoder_depth", None) | |
| self.encoder_num_heads = kwargs.pop("encoder_num_heads", None) | |
| self.decoder_embed_dim = kwargs.pop("decoder_embed_dim", None) | |
| self.decoder_depth = kwargs.pop("decoder_depth", None) | |
| self.decoder_num_heads = kwargs.pop("decoder_num_heads", None) | |
| self.mlp_ratio = kwargs.pop("mlp_ratio", None) | |
| self.qkv_bias = kwargs.pop("qkv_bias", None) | |
| self.qk_scale = kwargs.pop("qk_scale", None) | |
| self.drop_rate = kwargs.pop("drop_rate", None) | |
| self.attn_drop_rate = kwargs.pop("attn_drop_rate", None) | |
| self.norm_layer = kwargs.pop("norm_layer", None) | |
| self.init_values = kwargs.pop("init_values", None) | |
| self.tubelet_size = kwargs.pop("tubelet_size", None) | |
| self.as_feature_extractor = kwargs.pop("as_feature_extractor", True) | |
| super().__init__(**kwargs) | |