213 lines
6.7 KiB
Python
213 lines
6.7 KiB
Python
from logging import getLogger
|
|
from os import path
|
|
from sys import argv
|
|
from typing import List, Tuple
|
|
|
|
import onnx.checker
|
|
import torch
|
|
from numpy import ndarray
|
|
from onnx import ModelProto, TensorProto, helper, load, numpy_helper, save_model
|
|
from safetensors import safe_open
|
|
|
|
from ..utils import ConversionContext
|
|
|
|
logger = getLogger(__name__)
|
|
|
|
|
|
###
|
|
# everything in this file is still super experimental and may not produce valid ONNX models
|
|
###
|
|
|
|
|
|
def load_lora(filename: str):
|
|
model = load(filename)
|
|
|
|
for weight in model.graph.initializer:
|
|
# print(weight.name, numpy_helper.to_array(weight).shape)
|
|
pass
|
|
|
|
return model
|
|
|
|
|
|
def blend_loras(
|
|
base: ModelProto, weights: List[ModelProto], alphas: List[float]
|
|
) -> List[Tuple[TensorProto, ndarray]]:
|
|
total = 1 + sum(alphas)
|
|
|
|
results = []
|
|
|
|
for base_node in base.graph.initializer:
|
|
logger.info("blending initializer node %s", base_node.name)
|
|
base_weights = numpy_helper.to_array(base_node).copy()
|
|
|
|
for weight, alpha in zip(weights, alphas):
|
|
weight_node = next(
|
|
iter([f for f in weight.graph.initializer if f.name == base_node.name]),
|
|
None,
|
|
)
|
|
|
|
if weight_node is not None:
|
|
base_weights += numpy_helper.to_array(weight_node) * alpha
|
|
else:
|
|
logger.warning(
|
|
"missing weights: %s in %s", base_node.name, weight.doc_string
|
|
)
|
|
|
|
results.append((base_node, base_weights / total))
|
|
|
|
return results
|
|
|
|
|
|
def convert_diffusion_lora(context: ConversionContext, component: str):
|
|
lora_weights = [
|
|
f"diffusion-lora-jack/{component}/model.onnx",
|
|
f"diffusion-lora-taters/{component}/model.onnx",
|
|
]
|
|
|
|
base = load_lora(f"stable-diffusion-onnx-v1-5/{component}/model.onnx")
|
|
weights = [load_lora(f) for f in lora_weights]
|
|
alphas = [1 / len(weights)] * len(weights)
|
|
logger.info("blending LoRAs with alphas: %s, %s", weights, alphas)
|
|
|
|
result = blend_loras(base, weights, alphas)
|
|
logger.info("blended result keys: %s", len(result))
|
|
|
|
del weights
|
|
del alphas
|
|
|
|
tensors = []
|
|
for node, tensor in result:
|
|
logger.info("remaking tensor for %s", node.name)
|
|
tensors.append(helper.make_tensor(node.name, node.data_type, node.dims, tensor))
|
|
|
|
del result
|
|
|
|
graph = helper.make_graph(
|
|
base.graph.node,
|
|
base.graph.name,
|
|
base.graph.input,
|
|
base.graph.output,
|
|
tensors,
|
|
base.graph.doc_string,
|
|
base.graph.value_info,
|
|
base.graph.sparse_initializer,
|
|
)
|
|
model = helper.make_model(graph)
|
|
|
|
del model.opset_import[:]
|
|
opset = model.opset_import.add()
|
|
opset.version = 14
|
|
|
|
onnx_path = path.join(context.cache_path, f"lora-{component}.onnx")
|
|
tensor_path = path.join(context.cache_path, f"lora-{component}.tensors")
|
|
save_model(
|
|
model,
|
|
onnx_path,
|
|
save_as_external_data=True,
|
|
all_tensors_to_one_file=True,
|
|
location=tensor_path,
|
|
)
|
|
logger.info(
|
|
"saved model to %s and tensors to %s",
|
|
onnx_path,
|
|
tensor_path,
|
|
)
|
|
|
|
|
|
def fix_key(key: str):
|
|
# lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0.lora_down.weight
|
|
# lora, unet, up_block.3.attentions.2.transformer_blocks.0.attn2.to_out.0
|
|
return key.replace(".", "_")
|
|
|
|
|
|
def merge_lora():
|
|
base_name = argv[1]
|
|
lora_name = argv[2]
|
|
|
|
base_model = load(base_name)
|
|
lora_model = safe_open(lora_name, framework="pt")
|
|
|
|
lora_nodes = []
|
|
for base_node in base_model.graph.initializer:
|
|
base_key = fix_key(base_node.name)
|
|
|
|
for key in lora_model.keys():
|
|
if "lora_down" in key:
|
|
lora_key = key[: key.index("lora_down")].replace("lora_unet_", "")
|
|
if lora_key.startswith(base_key):
|
|
print("down for key:", base_key, lora_key)
|
|
|
|
up_key = key.replace("lora_down", "lora_up")
|
|
alpha_key = key[: key.index("lora_down")] + "alpha"
|
|
|
|
down_weight = lora_model.get_tensor(key).to(dtype=torch.float32)
|
|
up_weight = lora_model.get_tensor(up_key).to(dtype=torch.float32)
|
|
|
|
dim = down_weight.size()[0]
|
|
alpha = lora_model.get(alpha_key).numpy() or dim
|
|
|
|
np_vals = numpy_helper.to_array(base_node)
|
|
print(np_vals.shape, up_weight.shape, down_weight.shape)
|
|
|
|
squoze = (
|
|
(
|
|
up_weight.squeeze(3).squeeze(2)
|
|
@ down_weight.squeeze(3).squeeze(2)
|
|
)
|
|
.unsqueeze(2)
|
|
.unsqueeze(3)
|
|
)
|
|
print(squoze.shape)
|
|
|
|
np_vals = np_vals + (alpha * squoze.numpy())
|
|
|
|
try:
|
|
if len(up_weight.size()) == 2:
|
|
squoze = up_weight @ down_weight
|
|
print(squoze.shape)
|
|
np_vals = np_vals + (squoze.numpy() * (alpha / dim))
|
|
else:
|
|
squoze = (
|
|
(
|
|
up_weight.squeeze(3).squeeze(2)
|
|
@ down_weight.squeeze(3).squeeze(2)
|
|
)
|
|
.unsqueeze(2)
|
|
.unsqueeze(3)
|
|
)
|
|
print(squoze.shape)
|
|
np_vals = np_vals + (alpha * squoze.numpy())
|
|
|
|
# retensor = numpy_helper.from_array(np_vals, base_node.name)
|
|
retensor = helper.make_tensor(
|
|
base_node.name,
|
|
base_node.data_type,
|
|
base_node.dim,
|
|
np_vals,
|
|
raw=True,
|
|
)
|
|
print(retensor)
|
|
|
|
# TypeError: does not support assignment
|
|
lora_nodes.append(retensor)
|
|
|
|
break
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
if retensor is None:
|
|
print("no lora found for key", base_key)
|
|
lora_nodes.append(base_node)
|
|
|
|
print(len(lora_nodes), len(base_model.graph.initializer))
|
|
del base_model.graph.initializer[:]
|
|
base_model.graph.initializer.extend(lora_nodes)
|
|
|
|
onnx.checker.check_model(base_model)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
context = ConversionContext.from_environ()
|
|
convert_diffusion_lora(context, "unet")
|
|
convert_diffusion_lora(context, "text_encoder")
|