Upload scripts/convert_lora_to_peft_format.py with huggingface_hub

scripts/convert_lora_to_peft_format.py

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+# currently only works with flux as support is not quite there yet
+
+import argparse
+import os.path
+from collections import OrderedDict
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    'input_path',
+    type=str,
+    help='Path to original sdxl model'
+)
+parser.add_argument(
+    'output_path',
+    type=str,
+    help='output path'
+)
+args = parser.parse_args()
+args.input_path = os.path.abspath(args.input_path)
+args.output_path = os.path.abspath(args.output_path)
+
+from safetensors.torch import load_file, save_file
+
+meta = OrderedDict()
+meta['format'] = 'pt'
+
+state_dict = load_file(args.input_path)
+
+# peft doesnt have an alpha so we need to scale the weights
+alpha_keys = [
+    'lora_transformer_single_transformer_blocks_0_attn_to_q.alpha'  # flux
+]
+
+# keys where the rank is in the first dimension
+rank_idx0_keys = [
+    'lora_transformer_single_transformer_blocks_0_attn_to_q.lora_down.weight'
+    # 'transformer.single_transformer_blocks.0.attn.to_q.lora_A.weight'
+]
+
+alpha = None
+rank = None
+
+for key in rank_idx0_keys:
+    if key in state_dict:
+        rank = int(state_dict[key].shape[0])
+        break
+
+if rank is None:
+    raise ValueError(f'Could not find rank in state dict')
+
+for key in alpha_keys:
+    if key in state_dict:
+        alpha = int(state_dict[key])
+        break
+
+if alpha is None:
+    # set to rank if not found
+    alpha = rank
+
+
+up_multiplier = alpha / rank
+
+new_state_dict = {}
+
+for key, value in state_dict.items():
+    if key.endswith('.alpha'):
+        continue
+
+    orig_dtype = value.dtype
+
+    new_val = value.float() * up_multiplier
+
+    new_key = key
+    new_key = new_key.replace('lora_transformer_', 'transformer.')
+    for i in range(100):
+        new_key = new_key.replace(f'transformer_blocks_{i}_', f'transformer_blocks.{i}.')
+    new_key = new_key.replace('lora_down', 'lora_A')
+    new_key = new_key.replace('lora_up', 'lora_B')
+    new_key = new_key.replace('_lora', '.lora')
+    new_key = new_key.replace('attn_', 'attn.')
+    new_key = new_key.replace('ff_', 'ff.')
+    new_key = new_key.replace('context_net_', 'context.net.')
+    new_key = new_key.replace('0_proj', '0.proj')
+    new_key = new_key.replace('norm_linear', 'norm.linear')
+    new_key = new_key.replace('norm_out_linear', 'norm_out.linear')
+    new_key = new_key.replace('to_out_', 'to_out.')
+
+    new_state_dict[new_key] = new_val.to(orig_dtype)
+
+save_file(new_state_dict, args.output_path, meta)
+print(f'Saved to {args.output_path}')