diff options
Diffstat (limited to 'extensions-builtin/Lora/lora.py')
| -rw-r--r-- | extensions-builtin/Lora/lora.py | 55 |
1 files changed, 50 insertions, 5 deletions
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 6f246921..83c1c6fd 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -4,7 +4,7 @@ import re import torch
from typing import Union
-from modules import shared, devices, sd_models, errors
+from modules import shared, devices, sd_models, errors, scripts
metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
@@ -93,6 +93,7 @@ class LoraOnDisk: self.metadata = m
self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text
+ self.alias = self.metadata.get('ss_output_name', self.name)
class LoraModule:
@@ -165,8 +166,10 @@ def load_lora(name, filename): module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
elif type(sd_module) == torch.nn.MultiheadAttention:
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
- elif type(sd_module) == torch.nn.Conv2d:
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
else:
print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
continue
@@ -199,11 +202,11 @@ def load_loras(names, multipliers=None): loaded_loras.clear()
- loras_on_disk = [available_loras.get(name, None) for name in names]
+ loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
if any([x is None for x in loras_on_disk]):
list_available_loras()
- loras_on_disk = [available_loras.get(name, None) for name in names]
+ loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
for i, name in enumerate(names):
lora = already_loaded.get(name, None)
@@ -232,6 +235,8 @@ def lora_calc_updown(lora, module, target): if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+ elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
+ updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
else:
updown = up @ down
@@ -343,6 +348,7 @@ def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs): def list_available_loras():
available_loras.clear()
+ available_lora_aliases.clear()
os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
@@ -356,11 +362,50 @@ def list_available_loras(): continue
name = os.path.splitext(os.path.basename(filename))[0]
+ entry = LoraOnDisk(name, filename)
- available_loras[name] = LoraOnDisk(name, filename)
+ available_loras[name] = entry
+
+ available_lora_aliases[name] = entry
+ available_lora_aliases[entry.alias] = entry
+
+
+re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
+
+
+def infotext_pasted(infotext, params):
+ if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
+ return # if the other extension is active, it will handle those fields, no need to do anything
+
+ added = []
+
+ for k, v in params.items():
+ if not k.startswith("AddNet Model "):
+ continue
+
+ num = k[13:]
+
+ if params.get("AddNet Module " + num) != "LoRA":
+ continue
+
+ name = params.get("AddNet Model " + num)
+ if name is None:
+ continue
+
+ m = re_lora_name.match(name)
+ if m:
+ name = m.group(1)
+
+ multiplier = params.get("AddNet Weight A " + num, "1.0")
+
+ added.append(f"<lora:{name}:{multiplier}>")
+
+ if added:
+ params["Prompt"] += "\n" + "".join(added)
available_loras = {}
+available_lora_aliases = {}
loaded_loras = []
list_available_loras()
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