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-rw-r--r--extensions-builtin/Lora/lora.py200
-rw-r--r--extensions-builtin/Lora/scripts/lora_script.py22
-rw-r--r--extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js15
3 files changed, 192 insertions, 45 deletions
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py
index 8937b585..696be8ea 100644
--- a/extensions-builtin/Lora/lora.py
+++ b/extensions-builtin/Lora/lora.py
@@ -2,20 +2,34 @@ import glob
import os
import re
import torch
+from typing import Union
from modules import shared, devices, sd_models, errors
metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
re_digits = re.compile(r"\d+")
-re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
-re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+ "attentions": {},
+ "resnets": {
+ "conv1": "in_layers_2",
+ "conv2": "out_layers_3",
+ "time_emb_proj": "emb_layers_1",
+ "conv_shortcut": "skip_connection",
+ }
+}
+
+
+def convert_diffusers_name_to_compvis(key, is_sd2):
+ def match(match_list, regex_text):
+ regex = re_compiled.get(regex_text)
+ if regex is None:
+ regex = re.compile(regex_text)
+ re_compiled[regex_text] = regex
-
-def convert_diffusers_name_to_compvis(key):
- def match(match_list, regex):
r = re.match(regex, key)
if not r:
return False
@@ -26,16 +40,33 @@ def convert_diffusers_name_to_compvis(key):
m = []
- if match(m, re_unet_down_blocks):
- return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
+ 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}"
+
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
- if match(m, re_unet_mid_blocks):
- return f"diffusion_model_middle_block_1_{m[1]}"
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
- if match(m, re_unet_up_blocks):
- return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+ if is_sd2:
+ if 'mlp_fc1' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+ elif 'mlp_fc2' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+ else:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
- if match(m, re_text_block):
return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
return key
@@ -101,15 +132,22 @@ def load_lora(name, filename):
sd = sd_models.read_state_dict(filename)
- keys_failed_to_match = []
+ keys_failed_to_match = {}
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
for key_diffusers, weight in sd.items():
- fullkey = convert_diffusers_name_to_compvis(key_diffusers)
- key, lora_key = fullkey.split(".", 1)
+ key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
+ key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
+
if sd_module is None:
- keys_failed_to_match.append(key_diffusers)
+ m = re_x_proj.match(key)
+ if m:
+ sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
+
+ if sd_module is None:
+ keys_failed_to_match[key_diffusers] = key
continue
lora_module = lora.modules.get(key, None)
@@ -123,15 +161,21 @@ def load_lora(name, filename):
if type(sd_module) == torch.nn.Linear:
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+ elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
+ 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:
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
else:
+ print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
+ continue
assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
with torch.no_grad():
module.weight.copy_(weight)
- module.to(device=devices.device, dtype=devices.dtype)
+ module.to(device=devices.cpu, dtype=devices.dtype)
if lora_key == "lora_up.weight":
lora_module.up = module
@@ -177,28 +221,120 @@ def load_loras(names, multipliers=None):
loaded_loras.append(lora)
-def lora_forward(module, input, res):
- if len(loaded_loras) == 0:
- return res
+def lora_calc_updown(lora, module, target):
+ with torch.no_grad():
+ up = module.up.weight.to(target.device, dtype=target.dtype)
+ down = module.down.weight.to(target.device, dtype=target.dtype)
- lora_layer_name = getattr(module, 'lora_layer_name', None)
- for lora in loaded_loras:
- module = lora.modules.get(lora_layer_name, None)
- if module is not None:
- if shared.opts.lora_apply_to_outputs and res.shape == input.shape:
- res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+ 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)
+ else:
+ updown = up @ down
+
+ updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+ return updown
+
+
+def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+ """
+ Applies the currently selected set of Loras to the weights of torch layer self.
+ If weights already have this particular set of loras applied, does nothing.
+ If not, restores orginal weights from backup and alters weights according to loras.
+ """
+
+ lora_layer_name = getattr(self, 'lora_layer_name', None)
+ if lora_layer_name is None:
+ return
+
+ current_names = getattr(self, "lora_current_names", ())
+ wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
+
+ weights_backup = getattr(self, "lora_weights_backup", None)
+ if weights_backup is None:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
+ else:
+ weights_backup = self.weight.to(devices.cpu, copy=True)
+
+ self.lora_weights_backup = weights_backup
+
+ if current_names != wanted_names:
+ if weights_backup is not None:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ self.in_proj_weight.copy_(weights_backup[0])
+ self.out_proj.weight.copy_(weights_backup[1])
else:
- res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+ self.weight.copy_(weights_backup)
- return res
+ for lora in loaded_loras:
+ module = lora.modules.get(lora_layer_name, None)
+ if module is not None and hasattr(self, 'weight'):
+ self.weight += lora_calc_updown(lora, module, self.weight)
+ continue
+
+ module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
+ module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
+ module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
+ module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
+
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
+ updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
+ updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
+ updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+
+ self.in_proj_weight += updown_qkv
+ self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
+ continue
+
+ if module is None:
+ continue
+
+ print(f'failed to calculate lora weights for layer {lora_layer_name}')
+
+ setattr(self, "lora_current_names", wanted_names)
+
+
+def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+ setattr(self, "lora_current_names", ())
+ setattr(self, "lora_weights_backup", None)
def lora_Linear_forward(self, input):
- return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Linear_forward_before_lora(self, input)
+
+
+def lora_Linear_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
def lora_Conv2d_forward(self, input):
- return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Conv2d_forward_before_lora(self, input)
+
+
+def lora_Conv2d_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_forward(self, *args, **kwargs):
+ lora_apply_weights(self)
+
+ return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
def list_available_loras():
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index 2e860160..0adab225 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -9,7 +9,11 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared
def unload():
torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
+ torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+ torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
+ torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
+ torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
def before_ui():
@@ -20,11 +24,27 @@ def before_ui():
if not hasattr(torch.nn, 'Linear_forward_before_lora'):
torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
+ torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+
if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
+ torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
+ torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
+ torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+
torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
+torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
@@ -33,6 +53,4 @@ script_callbacks.on_before_ui(before_ui)
shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
- "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
-
}))
diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
index 4a85c8eb..f0918e26 100644
--- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
+++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -89,22 +89,15 @@ function checkBrackets(evt, textArea, counterElt) {
function setupBracketChecking(id_prompt, id_counter){
var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
var counter = gradioApp().getElementById(id_counter)
+
textarea.addEventListener("input", function(evt){
checkBrackets(evt, textarea, counter)
});
}
-var shadowRootLoaded = setInterval(function() {
- var shadowRoot = document.querySelector('gradio-app').shadowRoot;
- if(! shadowRoot) return false;
-
- var shadowTextArea = shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
- if(shadowTextArea.length < 1) return false;
-
- clearInterval(shadowRootLoaded);
-
+onUiLoaded(function(){
setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
- setupBracketChecking('img2img_prompt', 'imgimg_token_counter')
+ setupBracketChecking('img2img_prompt', 'img2img_token_counter')
setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
-}, 1000);
+}) \ No newline at end of file
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 12:52:02 +0000