File size: 1,989 Bytes
6d2db08 09548eb 6d2db08 | 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | from transformers.configuration_utils import PretrainedConfig
class TinyImageGenConfig(PretrainedConfig):
model_type = "tinyimagegen"
def __init__(
self,
image_size: int = 32,
in_channels: int = 3,
patch_size: int = 4,
hidden_size: int = 32,
num_hidden_layers: int = 6,
num_attention_heads: int = 4,
num_key_value_heads: int = 2,
intermediate_size: int = 48,
swiglu_interval: int = 3,
num_lanes: int = 4,
use_xsa: bool = False,
use_per_head_gating: bool = False,
rope_theta: float = 2500.0,
rms_norm_eps: float = 1e-5,
initializer_range: float = 0.02,
**kwargs,
):
self.image_size = image_size
self.in_channels = in_channels
self.patch_size = patch_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.intermediate_size = intermediate_size
self.swiglu_interval = swiglu_interval
self.num_lanes = num_lanes
self.use_xsa = use_xsa
self.use_per_head_gating = use_per_head_gating
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
self.initializer_range = initializer_range
self.head_dim = hidden_size // num_attention_heads
self.num_patches_side = image_size // patch_size
self.num_patches = self.num_patches_side ** 2
self.patch_dim = in_channels * (patch_size ** 2)
self.auto_map = {
"AutoConfig": "configuration_tinyimagegen.TinyImageGenConfig",
"AutoModel": "modeling_tinyimagegen.TinyImageGenModelForImageDiffusion",
"AutoModelForImageDiffusion": "modeling_tinyimagegen.TinyImageGenModelForImageDiffusion",
}
super().__init__(**kwargs)
|