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Update model_loading.py

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kijai 2024-11-21 03:05:51 +02:00
parent 276a045a57
commit 895d3b83a4
1 changed files with 25 additions and 20 deletions
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model_loading.py
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@ -717,35 +717,40 @@ class CogVideoXModelLoader:
#LoRAs
if lora is not None:
from .lora_utils import merge_lora#, load_lora_into_transformer
if "fun" in model.lower():
for l in lora:
log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
transformer = merge_lora(transformer, l["path"], l["strength"])
else:
dimensionx_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
dimensionx_lora = False
adapter_list = []
adapter_weights = []
for l in lora:
if any(item in l["path"].lower() for item in dimensionx_loras):
dimensionx_lora = True
fuse = True if l["fuse_lora"] else False
lora_sd = load_torch_file(l["path"])
lora_rank = None
for key, val in lora_sd.items():
if "lora_B" in key:
lora_rank = val.shape[1]
break
if lora_rank is not None:
log.info(f"Merging rank {lora_rank} LoRA weights from {l['path']} with strength {l['strength']}")
adapter_name = l['path'].split("/")[-1].split(".")[0]
adapter_weight = l['strength']
pipe.load_lora_weights(l['path'], weight_name=l['path'].split("/")[-1], lora_rank=lora_rank, adapter_name=adapter_name)
#transformer = load_lora_into_transformer(lora, transformer)
adapter_list.append(adapter_name)
adapter_weights.append(adapter_weight)
for l in lora:
else:
try: #Fun trainer LoRAs are loaded differently
from .lora_utils import merge_lora
log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
pipe.transformer = merge_lora(pipe.transformer, l["path"], l["strength"], device=transformer_load_device, state_dict=lora_sd)
except:
raise ValueError(f"Can't recognize LoRA {l['path']}")
if adapter_list:
pipe.set_adapters(adapter_list, adapter_weights=adapter_weights)
if fuse:
lora_scale = 1
dimension_loras = ["orbit", "dimensionx"] # for now dimensionx loras need scaling
if any(item in lora[-1]["path"].lower() for item in dimension_loras):
if dimensionx_lora:
lora_scale = lora_scale / lora_rank
pipe.fuse_lora(lora_scale=lora_scale, components=["transformer"])