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support specifying te and unet weights separately

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update lora code
support full module
This commit is contained in:
AUTOMATIC1111
2023-07-17 09:00:47 +03:00
parent 46466f09d0
commit 238adeaffb
10 changed files with 151 additions and 78 deletions
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22
extensions-builtin/Lora/networks.py
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@@ -6,6 +6,7 @@ import network_lora
import network_hada
import network_ia3
import network_lokr
import network_full
import torch
from typing import Union
@@ -17,6 +18,7 @@ module_types = [
network_hada.ModuleTypeHada(),
network_ia3.ModuleTypeIa3(),
network_lokr.ModuleTypeLokr(),
network_full.ModuleTypeFull(),
]
@@ -52,6 +54,15 @@ def convert_diffusers_name_to_compvis(key, is_sd2):
m = []
if match(m, r"lora_unet_conv_in(.*)"):
return f'diffusion_model_input_blocks_0_0{m[0]}'
if match(m, r"lora_unet_conv_out(.*)"):
return f'diffusion_model_out_2{m[0]}'
if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
@@ -179,7 +190,7 @@ def load_network(name, network_on_disk):
return net
def load_networks(names, multipliers=None):
def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
already_loaded = {}
for net in loaded_networks:
@@ -218,7 +229,9 @@ def load_networks(names, multipliers=None):
print(f"Couldn't find network with name {name}")
continue
net.multiplier = multipliers[i] if multipliers else 1.0
net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
loaded_networks.append(net)
if failed_to_load_networks:
@@ -250,7 +263,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
return
current_names = getattr(self, "network_current_names", ())
wanted_names = tuple((x.name, x.multiplier) for x in loaded_networks)
wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
weights_backup = getattr(self, "network_weights_backup", None)
if weights_backup is None:
@@ -288,9 +301,10 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
updown_k = module_k.calc_updown(self.in_proj_weight)
updown_v = module_v.calc_updown(self.in_proj_weight)
updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
updown_out = module_out.calc_updown(self.out_proj.weight)
self.in_proj_weight += updown_qkv
self.out_proj.weight += module_out.calc_updown(self.out_proj.weight)
self.out_proj.weight += updown_out
continue
if module is None: