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Handle the "medium" and "small" SDXL controlnet models

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Lincoln Stein
2023-08-17 15:33:00 -04:00
parent 89b82b3dc4
commit e805021cd3
1 changed files with 5 additions and 0 deletions
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5
invokeai/backend/util/hotfixes.py
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@ -98,6 +98,7 @@ class ControlNetModel(ModelMixin, ConfigMixin):
norm_num_groups: Optional[int] = 32,
norm_eps: float = 1e-5,
cross_attention_dim: int = 1280,
transformer_layers_per_block: Union[int, Tuple[int]] = 1,
attention_head_dim: Union[int, Tuple[int]] = 8,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
use_linear_projection: bool = False,
@ -135,6 +136,8 @@ class ControlNetModel(ModelMixin, ConfigMixin):
raise ValueError(
f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
)
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
# input
conv_in_kernel = 3
@ -212,6 +215,7 @@ class ControlNetModel(ModelMixin, ConfigMixin):
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
transformer_layers_per_block=transformer_layers_per_block[i],
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
@ -248,6 +252,7 @@ class ControlNetModel(ModelMixin, ConfigMixin):
self.controlnet_mid_block = controlnet_block
self.mid_block = UNetMidBlock2DCrossAttn(
transformer_layers_per_block=transformer_layers_per_block[-1],
in_channels=mid_block_channel,
temb_channels=time_embed_dim,
resnet_eps=norm_eps,