mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-03-15 14:09:36 +00:00
Move lora code to comfy/lora.py
This commit is contained in:
parent
ec96f6d03a
commit
15a7716fa6
186
comfy/lora.py
Normal file
186
comfy/lora.py
Normal file
@ -0,0 +1,186 @@
|
|||||||
|
import comfy.utils
|
||||||
|
|
||||||
|
LORA_CLIP_MAP = {
|
||||||
|
"mlp.fc1": "mlp_fc1",
|
||||||
|
"mlp.fc2": "mlp_fc2",
|
||||||
|
"self_attn.k_proj": "self_attn_k_proj",
|
||||||
|
"self_attn.q_proj": "self_attn_q_proj",
|
||||||
|
"self_attn.v_proj": "self_attn_v_proj",
|
||||||
|
"self_attn.out_proj": "self_attn_out_proj",
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def load_lora(lora, to_load):
|
||||||
|
patch_dict = {}
|
||||||
|
loaded_keys = set()
|
||||||
|
for x in to_load:
|
||||||
|
alpha_name = "{}.alpha".format(x)
|
||||||
|
alpha = None
|
||||||
|
if alpha_name in lora.keys():
|
||||||
|
alpha = lora[alpha_name].item()
|
||||||
|
loaded_keys.add(alpha_name)
|
||||||
|
|
||||||
|
regular_lora = "{}.lora_up.weight".format(x)
|
||||||
|
diffusers_lora = "{}_lora.up.weight".format(x)
|
||||||
|
transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
|
||||||
|
A_name = None
|
||||||
|
|
||||||
|
if regular_lora in lora.keys():
|
||||||
|
A_name = regular_lora
|
||||||
|
B_name = "{}.lora_down.weight".format(x)
|
||||||
|
mid_name = "{}.lora_mid.weight".format(x)
|
||||||
|
elif diffusers_lora in lora.keys():
|
||||||
|
A_name = diffusers_lora
|
||||||
|
B_name = "{}_lora.down.weight".format(x)
|
||||||
|
mid_name = None
|
||||||
|
elif transformers_lora in lora.keys():
|
||||||
|
A_name = transformers_lora
|
||||||
|
B_name ="{}.lora_linear_layer.down.weight".format(x)
|
||||||
|
mid_name = None
|
||||||
|
|
||||||
|
if A_name is not None:
|
||||||
|
mid = None
|
||||||
|
if mid_name is not None and mid_name in lora.keys():
|
||||||
|
mid = lora[mid_name]
|
||||||
|
loaded_keys.add(mid_name)
|
||||||
|
patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
|
||||||
|
loaded_keys.add(A_name)
|
||||||
|
loaded_keys.add(B_name)
|
||||||
|
|
||||||
|
|
||||||
|
######## loha
|
||||||
|
hada_w1_a_name = "{}.hada_w1_a".format(x)
|
||||||
|
hada_w1_b_name = "{}.hada_w1_b".format(x)
|
||||||
|
hada_w2_a_name = "{}.hada_w2_a".format(x)
|
||||||
|
hada_w2_b_name = "{}.hada_w2_b".format(x)
|
||||||
|
hada_t1_name = "{}.hada_t1".format(x)
|
||||||
|
hada_t2_name = "{}.hada_t2".format(x)
|
||||||
|
if hada_w1_a_name in lora.keys():
|
||||||
|
hada_t1 = None
|
||||||
|
hada_t2 = None
|
||||||
|
if hada_t1_name in lora.keys():
|
||||||
|
hada_t1 = lora[hada_t1_name]
|
||||||
|
hada_t2 = lora[hada_t2_name]
|
||||||
|
loaded_keys.add(hada_t1_name)
|
||||||
|
loaded_keys.add(hada_t2_name)
|
||||||
|
|
||||||
|
patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)
|
||||||
|
loaded_keys.add(hada_w1_a_name)
|
||||||
|
loaded_keys.add(hada_w1_b_name)
|
||||||
|
loaded_keys.add(hada_w2_a_name)
|
||||||
|
loaded_keys.add(hada_w2_b_name)
|
||||||
|
|
||||||
|
|
||||||
|
######## lokr
|
||||||
|
lokr_w1_name = "{}.lokr_w1".format(x)
|
||||||
|
lokr_w2_name = "{}.lokr_w2".format(x)
|
||||||
|
lokr_w1_a_name = "{}.lokr_w1_a".format(x)
|
||||||
|
lokr_w1_b_name = "{}.lokr_w1_b".format(x)
|
||||||
|
lokr_t2_name = "{}.lokr_t2".format(x)
|
||||||
|
lokr_w2_a_name = "{}.lokr_w2_a".format(x)
|
||||||
|
lokr_w2_b_name = "{}.lokr_w2_b".format(x)
|
||||||
|
|
||||||
|
lokr_w1 = None
|
||||||
|
if lokr_w1_name in lora.keys():
|
||||||
|
lokr_w1 = lora[lokr_w1_name]
|
||||||
|
loaded_keys.add(lokr_w1_name)
|
||||||
|
|
||||||
|
lokr_w2 = None
|
||||||
|
if lokr_w2_name in lora.keys():
|
||||||
|
lokr_w2 = lora[lokr_w2_name]
|
||||||
|
loaded_keys.add(lokr_w2_name)
|
||||||
|
|
||||||
|
lokr_w1_a = None
|
||||||
|
if lokr_w1_a_name in lora.keys():
|
||||||
|
lokr_w1_a = lora[lokr_w1_a_name]
|
||||||
|
loaded_keys.add(lokr_w1_a_name)
|
||||||
|
|
||||||
|
lokr_w1_b = None
|
||||||
|
if lokr_w1_b_name in lora.keys():
|
||||||
|
lokr_w1_b = lora[lokr_w1_b_name]
|
||||||
|
loaded_keys.add(lokr_w1_b_name)
|
||||||
|
|
||||||
|
lokr_w2_a = None
|
||||||
|
if lokr_w2_a_name in lora.keys():
|
||||||
|
lokr_w2_a = lora[lokr_w2_a_name]
|
||||||
|
loaded_keys.add(lokr_w2_a_name)
|
||||||
|
|
||||||
|
lokr_w2_b = None
|
||||||
|
if lokr_w2_b_name in lora.keys():
|
||||||
|
lokr_w2_b = lora[lokr_w2_b_name]
|
||||||
|
loaded_keys.add(lokr_w2_b_name)
|
||||||
|
|
||||||
|
lokr_t2 = None
|
||||||
|
if lokr_t2_name in lora.keys():
|
||||||
|
lokr_t2 = lora[lokr_t2_name]
|
||||||
|
loaded_keys.add(lokr_t2_name)
|
||||||
|
|
||||||
|
if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
|
||||||
|
patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)
|
||||||
|
|
||||||
|
for x in lora.keys():
|
||||||
|
if x not in loaded_keys:
|
||||||
|
print("lora key not loaded", x)
|
||||||
|
return patch_dict
|
||||||
|
|
||||||
|
def model_lora_keys_clip(model, key_map={}):
|
||||||
|
sdk = model.state_dict().keys()
|
||||||
|
|
||||||
|
text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
|
||||||
|
clip_l_present = False
|
||||||
|
for b in range(32):
|
||||||
|
for c in LORA_CLIP_MAP:
|
||||||
|
k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
|
||||||
|
if k in sdk:
|
||||||
|
lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
|
||||||
|
key_map[lora_key] = k
|
||||||
|
lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c])
|
||||||
|
key_map[lora_key] = k
|
||||||
|
lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
||||||
|
key_map[lora_key] = k
|
||||||
|
|
||||||
|
k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
|
||||||
|
if k in sdk:
|
||||||
|
lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
|
||||||
|
key_map[lora_key] = k
|
||||||
|
clip_l_present = True
|
||||||
|
lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
||||||
|
key_map[lora_key] = k
|
||||||
|
|
||||||
|
k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
|
||||||
|
if k in sdk:
|
||||||
|
if clip_l_present:
|
||||||
|
lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
|
||||||
|
key_map[lora_key] = k
|
||||||
|
lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
||||||
|
key_map[lora_key] = k
|
||||||
|
else:
|
||||||
|
lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
|
||||||
|
key_map[lora_key] = k
|
||||||
|
lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
||||||
|
key_map[lora_key] = k
|
||||||
|
|
||||||
|
return key_map
|
||||||
|
|
||||||
|
def model_lora_keys_unet(model, key_map={}):
|
||||||
|
sdk = model.state_dict().keys()
|
||||||
|
|
||||||
|
for k in sdk:
|
||||||
|
if k.startswith("diffusion_model.") and k.endswith(".weight"):
|
||||||
|
key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
|
||||||
|
key_map["lora_unet_{}".format(key_lora)] = k
|
||||||
|
|
||||||
|
diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config)
|
||||||
|
for k in diffusers_keys:
|
||||||
|
if k.endswith(".weight"):
|
||||||
|
unet_key = "diffusion_model.{}".format(diffusers_keys[k])
|
||||||
|
key_lora = k[:-len(".weight")].replace(".", "_")
|
||||||
|
key_map["lora_unet_{}".format(key_lora)] = unet_key
|
||||||
|
|
||||||
|
diffusers_lora_prefix = ["", "unet."]
|
||||||
|
for p in diffusers_lora_prefix:
|
||||||
|
diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_"))
|
||||||
|
if diffusers_lora_key.endswith(".to_out.0"):
|
||||||
|
diffusers_lora_key = diffusers_lora_key[:-2]
|
||||||
|
key_map[diffusers_lora_key] = unet_key
|
||||||
|
return key_map
|
191
comfy/sd.py
191
comfy/sd.py
@ -22,6 +22,8 @@ from . import sd1_clip
|
|||||||
from . import sd2_clip
|
from . import sd2_clip
|
||||||
from . import sdxl_clip
|
from . import sdxl_clip
|
||||||
|
|
||||||
|
import comfy.lora
|
||||||
|
|
||||||
def load_model_weights(model, sd):
|
def load_model_weights(model, sd):
|
||||||
m, u = model.load_state_dict(sd, strict=False)
|
m, u = model.load_state_dict(sd, strict=False)
|
||||||
m = set(m)
|
m = set(m)
|
||||||
@ -51,191 +53,8 @@ def load_clip_weights(model, sd):
|
|||||||
sd = utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
|
sd = utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
|
||||||
return load_model_weights(model, sd)
|
return load_model_weights(model, sd)
|
||||||
|
|
||||||
LORA_CLIP_MAP = {
|
|
||||||
"mlp.fc1": "mlp_fc1",
|
|
||||||
"mlp.fc2": "mlp_fc2",
|
|
||||||
"self_attn.k_proj": "self_attn_k_proj",
|
|
||||||
"self_attn.q_proj": "self_attn_q_proj",
|
|
||||||
"self_attn.v_proj": "self_attn_v_proj",
|
|
||||||
"self_attn.out_proj": "self_attn_out_proj",
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
def load_lora(lora, to_load):
|
|
||||||
patch_dict = {}
|
|
||||||
loaded_keys = set()
|
|
||||||
for x in to_load:
|
|
||||||
alpha_name = "{}.alpha".format(x)
|
|
||||||
alpha = None
|
|
||||||
if alpha_name in lora.keys():
|
|
||||||
alpha = lora[alpha_name].item()
|
|
||||||
loaded_keys.add(alpha_name)
|
|
||||||
|
|
||||||
regular_lora = "{}.lora_up.weight".format(x)
|
|
||||||
diffusers_lora = "{}_lora.up.weight".format(x)
|
|
||||||
transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
|
|
||||||
A_name = None
|
|
||||||
|
|
||||||
if regular_lora in lora.keys():
|
|
||||||
A_name = regular_lora
|
|
||||||
B_name = "{}.lora_down.weight".format(x)
|
|
||||||
mid_name = "{}.lora_mid.weight".format(x)
|
|
||||||
elif diffusers_lora in lora.keys():
|
|
||||||
A_name = diffusers_lora
|
|
||||||
B_name = "{}_lora.down.weight".format(x)
|
|
||||||
mid_name = None
|
|
||||||
elif transformers_lora in lora.keys():
|
|
||||||
A_name = transformers_lora
|
|
||||||
B_name ="{}.lora_linear_layer.down.weight".format(x)
|
|
||||||
mid_name = None
|
|
||||||
|
|
||||||
if A_name is not None:
|
|
||||||
mid = None
|
|
||||||
if mid_name is not None and mid_name in lora.keys():
|
|
||||||
mid = lora[mid_name]
|
|
||||||
loaded_keys.add(mid_name)
|
|
||||||
patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
|
|
||||||
loaded_keys.add(A_name)
|
|
||||||
loaded_keys.add(B_name)
|
|
||||||
|
|
||||||
|
|
||||||
######## loha
|
|
||||||
hada_w1_a_name = "{}.hada_w1_a".format(x)
|
|
||||||
hada_w1_b_name = "{}.hada_w1_b".format(x)
|
|
||||||
hada_w2_a_name = "{}.hada_w2_a".format(x)
|
|
||||||
hada_w2_b_name = "{}.hada_w2_b".format(x)
|
|
||||||
hada_t1_name = "{}.hada_t1".format(x)
|
|
||||||
hada_t2_name = "{}.hada_t2".format(x)
|
|
||||||
if hada_w1_a_name in lora.keys():
|
|
||||||
hada_t1 = None
|
|
||||||
hada_t2 = None
|
|
||||||
if hada_t1_name in lora.keys():
|
|
||||||
hada_t1 = lora[hada_t1_name]
|
|
||||||
hada_t2 = lora[hada_t2_name]
|
|
||||||
loaded_keys.add(hada_t1_name)
|
|
||||||
loaded_keys.add(hada_t2_name)
|
|
||||||
|
|
||||||
patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)
|
|
||||||
loaded_keys.add(hada_w1_a_name)
|
|
||||||
loaded_keys.add(hada_w1_b_name)
|
|
||||||
loaded_keys.add(hada_w2_a_name)
|
|
||||||
loaded_keys.add(hada_w2_b_name)
|
|
||||||
|
|
||||||
|
|
||||||
######## lokr
|
|
||||||
lokr_w1_name = "{}.lokr_w1".format(x)
|
|
||||||
lokr_w2_name = "{}.lokr_w2".format(x)
|
|
||||||
lokr_w1_a_name = "{}.lokr_w1_a".format(x)
|
|
||||||
lokr_w1_b_name = "{}.lokr_w1_b".format(x)
|
|
||||||
lokr_t2_name = "{}.lokr_t2".format(x)
|
|
||||||
lokr_w2_a_name = "{}.lokr_w2_a".format(x)
|
|
||||||
lokr_w2_b_name = "{}.lokr_w2_b".format(x)
|
|
||||||
|
|
||||||
lokr_w1 = None
|
|
||||||
if lokr_w1_name in lora.keys():
|
|
||||||
lokr_w1 = lora[lokr_w1_name]
|
|
||||||
loaded_keys.add(lokr_w1_name)
|
|
||||||
|
|
||||||
lokr_w2 = None
|
|
||||||
if lokr_w2_name in lora.keys():
|
|
||||||
lokr_w2 = lora[lokr_w2_name]
|
|
||||||
loaded_keys.add(lokr_w2_name)
|
|
||||||
|
|
||||||
lokr_w1_a = None
|
|
||||||
if lokr_w1_a_name in lora.keys():
|
|
||||||
lokr_w1_a = lora[lokr_w1_a_name]
|
|
||||||
loaded_keys.add(lokr_w1_a_name)
|
|
||||||
|
|
||||||
lokr_w1_b = None
|
|
||||||
if lokr_w1_b_name in lora.keys():
|
|
||||||
lokr_w1_b = lora[lokr_w1_b_name]
|
|
||||||
loaded_keys.add(lokr_w1_b_name)
|
|
||||||
|
|
||||||
lokr_w2_a = None
|
|
||||||
if lokr_w2_a_name in lora.keys():
|
|
||||||
lokr_w2_a = lora[lokr_w2_a_name]
|
|
||||||
loaded_keys.add(lokr_w2_a_name)
|
|
||||||
|
|
||||||
lokr_w2_b = None
|
|
||||||
if lokr_w2_b_name in lora.keys():
|
|
||||||
lokr_w2_b = lora[lokr_w2_b_name]
|
|
||||||
loaded_keys.add(lokr_w2_b_name)
|
|
||||||
|
|
||||||
lokr_t2 = None
|
|
||||||
if lokr_t2_name in lora.keys():
|
|
||||||
lokr_t2 = lora[lokr_t2_name]
|
|
||||||
loaded_keys.add(lokr_t2_name)
|
|
||||||
|
|
||||||
if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
|
|
||||||
patch_dict[to_load[x]] = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)
|
|
||||||
|
|
||||||
for x in lora.keys():
|
|
||||||
if x not in loaded_keys:
|
|
||||||
print("lora key not loaded", x)
|
|
||||||
return patch_dict
|
|
||||||
|
|
||||||
def model_lora_keys_clip(model, key_map={}):
|
|
||||||
sdk = model.state_dict().keys()
|
|
||||||
|
|
||||||
text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
|
|
||||||
clip_l_present = False
|
|
||||||
for b in range(32):
|
|
||||||
for c in LORA_CLIP_MAP:
|
|
||||||
k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
|
|
||||||
if k in sdk:
|
|
||||||
lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
|
|
||||||
key_map[lora_key] = k
|
|
||||||
lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c])
|
|
||||||
key_map[lora_key] = k
|
|
||||||
lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
|
||||||
key_map[lora_key] = k
|
|
||||||
|
|
||||||
k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
|
|
||||||
if k in sdk:
|
|
||||||
lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
|
|
||||||
key_map[lora_key] = k
|
|
||||||
clip_l_present = True
|
|
||||||
lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
|
||||||
key_map[lora_key] = k
|
|
||||||
|
|
||||||
k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
|
|
||||||
if k in sdk:
|
|
||||||
if clip_l_present:
|
|
||||||
lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
|
|
||||||
key_map[lora_key] = k
|
|
||||||
lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
|
||||||
key_map[lora_key] = k
|
|
||||||
else:
|
|
||||||
lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
|
|
||||||
key_map[lora_key] = k
|
|
||||||
lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
|
|
||||||
key_map[lora_key] = k
|
|
||||||
|
|
||||||
return key_map
|
|
||||||
|
|
||||||
def model_lora_keys_unet(model, key_map={}):
|
|
||||||
sdk = model.state_dict().keys()
|
|
||||||
|
|
||||||
for k in sdk:
|
|
||||||
if k.startswith("diffusion_model.") and k.endswith(".weight"):
|
|
||||||
key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
|
|
||||||
key_map["lora_unet_{}".format(key_lora)] = k
|
|
||||||
|
|
||||||
diffusers_keys = utils.unet_to_diffusers(model.model_config.unet_config)
|
|
||||||
for k in diffusers_keys:
|
|
||||||
if k.endswith(".weight"):
|
|
||||||
unet_key = "diffusion_model.{}".format(diffusers_keys[k])
|
|
||||||
key_lora = k[:-len(".weight")].replace(".", "_")
|
|
||||||
key_map["lora_unet_{}".format(key_lora)] = unet_key
|
|
||||||
|
|
||||||
diffusers_lora_prefix = ["", "unet."]
|
|
||||||
for p in diffusers_lora_prefix:
|
|
||||||
diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_"))
|
|
||||||
if diffusers_lora_key.endswith(".to_out.0"):
|
|
||||||
diffusers_lora_key = diffusers_lora_key[:-2]
|
|
||||||
key_map[diffusers_lora_key] = unet_key
|
|
||||||
return key_map
|
|
||||||
|
|
||||||
def set_attr(obj, attr, value):
|
def set_attr(obj, attr, value):
|
||||||
attrs = attr.split(".")
|
attrs = attr.split(".")
|
||||||
for name in attrs[:-1]:
|
for name in attrs[:-1]:
|
||||||
@ -518,9 +337,9 @@ class ModelPatcher:
|
|||||||
|
|
||||||
|
|
||||||
def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
|
def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
|
||||||
key_map = model_lora_keys_unet(model.model)
|
key_map = comfy.lora.model_lora_keys_unet(model.model)
|
||||||
key_map = model_lora_keys_clip(clip.cond_stage_model, key_map)
|
key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
|
||||||
loaded = load_lora(lora, key_map)
|
loaded = comfy.lora.load_lora(lora, key_map)
|
||||||
new_modelpatcher = model.clone()
|
new_modelpatcher = model.clone()
|
||||||
k = new_modelpatcher.add_patches(loaded, strength_model)
|
k = new_modelpatcher.add_patches(loaded, strength_model)
|
||||||
new_clip = clip.clone()
|
new_clip = clip.clone()
|
||||||
|
Loading…
Reference in New Issue
Block a user