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Update Makefile to include examples/ #256

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Feb 7, 2025
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Update Makefile to include examples/ #256

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2 changes: 1 addition & 1 deletion Makefile
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
.PHONY: quality style

check_dirs := finetrainers tests
check_dirs := finetrainers tests examples

quality:
ruff check $(check_dirs)
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73 changes: 42 additions & 31 deletions examples/hunyuan_video/convert_diffusers_lora_to_original.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import os
import argparse
import os

import torch
from safetensors.torch import load_file, save_file

Expand All @@ -13,9 +14,9 @@ def convert_lora_sd(diffusers_lora_sd):
"ff_context.net.0.proj": "txt_mlp.0",
"ff_context.net.2": "txt_mlp.2",
}

prefix = "diffusion_model."

converted_lora_sd = {}
for key in diffusers_lora_sd.keys():
# double_blocks
Expand All @@ -26,46 +27,50 @@ def convert_lora_sd(diffusers_lora_sd):
to_q_A = diffusers_lora_sd[key]
to_k_A = diffusers_lora_sd[key.replace("to_q", "to_k")]
to_v_A = diffusers_lora_sd[key.replace("to_q", "to_v")]

to_qkv_A = torch.cat([to_q_A, to_k_A, to_v_A], dim=0)
qkv_A_key = key.replace("transformer_blocks", prefix + "double_blocks").replace("attn.to_q", "img_attn.qkv")
qkv_A_key = key.replace("transformer_blocks", prefix + "double_blocks").replace(
"attn.to_q", "img_attn.qkv"
)
converted_lora_sd[qkv_A_key] = to_qkv_A

# lora_B
to_q_B = diffusers_lora_sd[key.replace("to_q.lora_A", "to_q.lora_B")]
to_k_B = diffusers_lora_sd[key.replace("to_q.lora_A", "to_k.lora_B")]
to_v_B = diffusers_lora_sd[key.replace("to_q.lora_A", "to_v.lora_B")]

to_qkv_B = torch.block_diag(to_q_B, to_k_B, to_v_B)
qkv_B_key = qkv_A_key.replace("lora_A", "lora_B")
converted_lora_sd[qkv_B_key] = to_qkv_B

# txt_attn
elif key.endswith("add_q_proj.lora_A.weight"):
# lora_A
to_q_A = diffusers_lora_sd[key]
to_k_A = diffusers_lora_sd[key.replace("add_q_proj", "add_k_proj")]
to_v_A = diffusers_lora_sd[key.replace("add_q_proj", "add_v_proj")]

to_qkv_A = torch.cat([to_q_A, to_k_A, to_v_A], dim=0)
qkv_A_key = key.replace("transformer_blocks", prefix + "double_blocks").replace("attn.add_q_proj", "txt_attn.qkv")
qkv_A_key = key.replace("transformer_blocks", prefix + "double_blocks").replace(
"attn.add_q_proj", "txt_attn.qkv"
)
converted_lora_sd[qkv_A_key] = to_qkv_A

# lora_B
to_q_B = diffusers_lora_sd[key.replace("add_q_proj.lora_A", "add_q_proj.lora_B")]
to_k_B = diffusers_lora_sd[key.replace("add_q_proj.lora_A", "add_k_proj.lora_B")]
to_v_B = diffusers_lora_sd[key.replace("add_q_proj.lora_A", "add_v_proj.lora_B")]

to_qkv_B = torch.block_diag(to_q_B, to_k_B, to_v_B)
qkv_B_key = qkv_A_key.replace("lora_A", "lora_B")
converted_lora_sd[qkv_B_key] = to_qkv_B

# just rename
for k, v in double_block_patterns.items():
if k in key:
new_key = key.replace(k, v).replace("transformer_blocks", prefix + "double_blocks")
converted_lora_sd[new_key] = diffusers_lora_sd[key]

# single_blocks
elif key.startswith("single_transformer_blocks"):
if key.endswith("to_q.lora_A.weight"):
Expand All @@ -74,72 +79,78 @@ def convert_lora_sd(diffusers_lora_sd):
to_k_A = diffusers_lora_sd[key.replace("to_q", "to_k")]
to_v_A = diffusers_lora_sd[key.replace("to_q", "to_v")]
proj_mlp_A = diffusers_lora_sd[key.replace("attn.to_q", "proj_mlp")]

linear1_A = torch.cat([to_q_A, to_k_A, to_v_A, proj_mlp_A], dim=0)
linear1_A_key = key.replace("single_transformer_blocks", prefix + "single_blocks").replace("attn.to_q", "linear1")
linear1_A_key = key.replace("single_transformer_blocks", prefix + "single_blocks").replace(
"attn.to_q", "linear1"
)
converted_lora_sd[linear1_A_key] = linear1_A

# lora_B
to_q_B = diffusers_lora_sd[key.replace("to_q.lora_A", "to_q.lora_B")]
to_k_B = diffusers_lora_sd[key.replace("to_q.lora_A", "to_k.lora_B")]
to_v_B = diffusers_lora_sd[key.replace("to_q.lora_A", "to_v.lora_B")]
proj_mlp_B = diffusers_lora_sd[key.replace("attn.to_q.lora_A", "proj_mlp.lora_B")]

linear1_B = torch.block_diag(to_q_B, to_k_B, to_v_B, proj_mlp_B)
linear1_B_key = linear1_A_key.replace("lora_A", "lora_B")
converted_lora_sd[linear1_B_key] = linear1_B

elif "proj_out" in key:
new_key = key.replace("proj_out", "linear2").replace("single_transformer_blocks", prefix + "single_blocks")
new_key = key.replace("proj_out", "linear2").replace(
"single_transformer_blocks", prefix + "single_blocks"
)
converted_lora_sd[new_key] = diffusers_lora_sd[key]

else:
print(f"unknown or not implemented: {key}")

return converted_lora_sd


def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input_lora", type=str, required=True, help="Path to LoRA .safetensors")
parser.add_argument("--alpha", type=float, default=None, help="Optional alpha value, defaults to rank")
parser.add_argument("--dtype", type=str, default=None, help="Optional dtype (bfloat16, float16, float32), defaults to input dtype")
parser.add_argument(
"--dtype", type=str, default=None, help="Optional dtype (bfloat16, float16, float32), defaults to input dtype"
)
parser.add_argument("--debug", action="store_true", help="Print converted keys instead of saving")
return parser.parse_args()


if __name__ == "__main__":
args = get_args()

converted_lora_sd = convert_lora_sd(load_file(args.input_lora))

if args.alpha is not None:
for key in list(converted_lora_sd.keys()):
if "lora_A" in key:
alpha_name = key.replace(".lora_A.weight", ".alpha")
converted_lora_sd[alpha_name] = torch.tensor([args.alpha], dtype=converted_lora_sd[key].dtype)

dtype = None
if args.dtype == "bfloat16":
dtype = torch.bfloat16
elif args.dtype == "float16":
dtype = torch.float16
elif args.dtype == "float32":
dtype = torch.float32

if dtype is not None:
dtype_min = torch.finfo(dtype).min
dtype_max = torch.finfo(dtype).max
for key in converted_lora_sd.keys():
if converted_lora_sd[key].min() < dtype_min or converted_lora_sd[key].max() > dtype_max:
print(f"warning: {key} has values outside of {dtype} {dtype_min} {dtype_max} range")
converted_lora_sd[key] = converted_lora_sd[key].to(dtype)

if args.debug:
for key in sorted(list(converted_lora_sd.keys())):
for key in sorted(converted_lora_sd.keys()):
print(key, converted_lora_sd[key].shape, converted_lora_sd[key].dtype)
exit()

output_path = os.path.splitext(args.input_lora)[0] + "_converted.safetensors"
save_file(converted_lora_sd, output_path)
print(f"saved to {output_path}")
print(f"saved to {output_path}")