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Add 'rank' parameter to Dreambooth LoRA training script #3945

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merged 1 commit into from
Jul 7, 2023
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Add 'rank' parameter to Dreambooth LoRA training script #3945

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14 changes: 12 additions & 2 deletions examples/dreambooth/train_dreambooth_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -436,6 +436,12 @@ def parse_args(input_args=None):
default=None,
help="The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.",
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)

if input_args is not None:
args = parser.parse_args(input_args)
Expand Down Expand Up @@ -845,7 +851,9 @@ def main(args):
LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
)
unet_lora_attn_procs[name] = lora_attn_processor_class(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
rank=args.rank,
)

unet.set_attn_processor(unet_lora_attn_procs)
Expand All @@ -860,7 +868,9 @@ def main(args):
for name, module in text_encoder.named_modules():
if name.endswith(TEXT_ENCODER_ATTN_MODULE):
text_lora_attn_procs[name] = LoRAAttnProcessor(
hidden_size=module.out_proj.out_features, cross_attention_dim=None
hidden_size=module.out_proj.out_features,
cross_attention_dim=None,
rank=args.rank,
)
text_encoder_lora_layers = AttnProcsLayers(text_lora_attn_procs)
temp_pipeline = DiffusionPipeline.from_pretrained(
Expand Down