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[Enhance] Add rank in dreambooth #4112

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sayakpaul merged 1 commit into from
Jul 18, 2023
Merged

[Enhance] Add rank in dreambooth #4112

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8 changes: 5 additions & 3 deletions examples/dreambooth/train_dreambooth_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -872,7 +872,9 @@ def main(args):
LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
)

module = lora_attn_processor_class(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
module = lora_attn_processor_class(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=args.rank
)
unet_lora_attn_procs[name] = module
unet_lora_parameters.extend(module.parameters())

Expand All @@ -882,7 +884,7 @@ def main(args):
# So, instead, we monkey-patch the forward calls of its attention-blocks.
if args.train_text_encoder:
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
text_lora_parameters = LoraLoaderMixin._modify_text_encoder(text_encoder, dtype=torch.float32)
text_lora_parameters = LoraLoaderMixin._modify_text_encoder(text_encoder, dtype=torch.float32, rank=args.rank)

# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
Expand Down Expand Up @@ -1364,7 +1366,7 @@ def compute_text_embeddings(prompt):
pipeline = pipeline.to(accelerator.device)

# load attention processors
pipeline.load_lora_weights(args.output_dir)
pipeline.load_lora_weights(args.output_dir, weight_name="pytorch_lora_weights.bin")
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@sayakpaul sayakpaul Jul 18, 2023

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"pytorch_lora_weights.bin" is the default:

https://github.com/huggingface/diffusers/blob/8b18cd8e7f6be0cf2904dfec4285d4ba98c5586f/src/diffusers/loaders.py#L1215C1-L1219C47

So, safe to not add it explicitly.


# run inference
images = []
Expand Down