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Fixed some mismatched weights by adjusting tolerance. The mismatch ironically made the models better lol

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This commit is contained in:
Jaret Burkett
2023-08-29 15:20:03 -06:00
parent 14ff51ceb4
commit 836fee47a6
8 changed files with 265 additions and 163 deletions
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27
jobs/process/BaseSDTrainProcess.py
View File

@@ -481,8 +481,33 @@ class BaseSDTrainProcess(BaseTrainProcess):
params = self.embedding.get_trainable_params() params = self.embedding.get_trainable_params()
else: else:
# set them to train or not
if self.train_config.train_unet:
self.sd.unet.requires_grad_(True)
self.sd.unet.train()
else:
self.sd.unet.requires_grad_(False)
self.sd.unet.eval()
if self.train_config.train_text_encoder:
if isinstance(self.sd.text_encoder, list):
for te in self.sd.text_encoder:
te.requires_grad_(True)
te.train()
else:
self.sd.text_encoder.requires_grad_(True)
self.sd.text_encoder.train()
else:
if isinstance(self.sd.text_encoder, list):
for te in self.sd.text_encoder:
te.requires_grad_(False)
te.eval()
else:
self.sd.text_encoder.requires_grad_(False)
self.sd.text_encoder.eval()
# will only return savable weights and ones with grad
params = self.sd.prepare_optimizer_params( params = self.sd.prepare_optimizer_params(
vae=False,
unet=self.train_config.train_unet, unet=self.train_config.train_unet,
text_encoder=self.train_config.train_text_encoder, text_encoder=self.train_config.train_text_encoder,
text_encoder_lr=self.train_config.lr, text_encoder_lr=self.train_config.lr,

2
testing/generate_weight_mappings.py
View File

@@ -93,7 +93,7 @@ total_keys = len(ldm_dict_keys)
matched_ldm_keys = [] matched_ldm_keys = []
matched_diffusers_keys = [] matched_diffusers_keys = []
error_margin = 1e-4 error_margin = 1e-6
tmp_merge_key = "TMP___MERGE" tmp_merge_key = "TMP___MERGE"

31
testing/test_model_load_save.py
View File

@@ -3,6 +3,8 @@ import os
# add project root to sys path # add project root to sys path
import sys import sys
from tqdm import tqdm
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch import torch
@@ -20,6 +22,8 @@ from toolkit.stable_diffusion_model import StableDiffusion
# you probably wont need this. Unless they change them.... again... again # you probably wont need this. Unless they change them.... again... again
# on second thought, you probably will # on second thought, you probably will
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
device = torch.device('cpu') device = torch.device('cpu')
dtype = torch.float32 dtype = torch.float32
@@ -109,7 +113,26 @@ keys_in_both.sort()
if len(keys_not_in_state_dict_2) == 0 and len(keys_not_in_state_dict_1) == 0: if len(keys_not_in_state_dict_2) == 0 and len(keys_not_in_state_dict_1) == 0:
print("All keys match!") print("All keys match!")
exit(0) print("Checking values...")
mismatch_keys = []
loss = torch.nn.MSELoss()
tolerance = 1e-6
for key in tqdm(keys_in_both):
if loss(state_dict_file_1[key], state_dict_file_2[key]) > tolerance:
print(f"Values for key {key} don't match!")
print(f"Loss: {loss(state_dict_file_1[key], state_dict_file_2[key])}")
mismatch_keys.append(key)
if len(mismatch_keys) == 0:
print("All values match!")
else:
print("Some valued font match!")
print(mismatch_keys)
mismatched_path = os.path.join(project_root, 'config', 'mismatch.json')
with open(mismatched_path, 'w') as f:
f.write(json.dumps(mismatch_keys, indent=4))
exit(0)
else: else:
print("Keys don't match!, generating info...") print("Keys don't match!, generating info...")
@@ -132,17 +155,17 @@ for key in keys_not_in_state_dict_2:
# print(json_data) # print(json_data)
project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
json_save_path = os.path.join(project_root, 'config', 'keys.json') json_save_path = os.path.join(project_root, 'config', 'keys.json')
json_matched_save_path = os.path.join(project_root, 'config', 'matched.json') json_matched_save_path = os.path.join(project_root, 'config', 'matched.json')
json_duped_save_path = os.path.join(project_root, 'config', 'duped.json') json_duped_save_path = os.path.join(project_root, 'config', 'duped.json')
state_dict_1_filename = os.path.basename(args.file_1[0]) state_dict_1_filename = os.path.basename(args.file_1[0])
state_dict_2_filename = os.path.basename(args.file_2[0]) # state_dict_2_filename = os.path.basename(args.file_2[0])
# save key names for each in own file # save key names for each in own file
with open(os.path.join(project_root, 'config', f'{state_dict_1_filename}.json'), 'w') as f: with open(os.path.join(project_root, 'config', f'{state_dict_1_filename}.json'), 'w') as f:
f.write(json.dumps(state_dict_1_keys, indent=4)) f.write(json.dumps(state_dict_1_keys, indent=4))
with open(os.path.join(project_root, 'config', f'{state_dict_2_filename}.json'), 'w') as f: with open(os.path.join(project_root, 'config', f'{state_dict_1_filename}_loop.json'), 'w') as f:
f.write(json.dumps(state_dict_2_keys, indent=4)) f.write(json.dumps(state_dict_2_keys, indent=4))
with open(json_save_path, 'w') as f: with open(json_save_path, 'w') as f:

2
toolkit/config_modules.py
View File

@@ -99,6 +99,8 @@ class ModelConfig:
self.use_text_encoder_1: bool = kwargs.get('use_text_encoder_1', True) self.use_text_encoder_1: bool = kwargs.get('use_text_encoder_1', True)
self.use_text_encoder_2: bool = kwargs.get('use_text_encoder_2', True) self.use_text_encoder_2: bool = kwargs.get('use_text_encoder_2', True)
self.experimental_xl: bool = kwargs.get('experimental_xl', False)
if self.name_or_path is None: if self.name_or_path is None:
raise ValueError('name_or_path must be specified') raise ValueError('name_or_path must be specified')

116
toolkit/keymaps/stable_diffusion_sd1.json
View File

@@ -58,7 +58,7 @@
"cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "te_text_model.encoder.layers.11.mlp.fc1.weight", "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "te_text_model.encoder.layers.11.mlp.fc1.weight",
"cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "te_text_model.encoder.layers.11.mlp.fc2.bias", "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "te_text_model.encoder.layers.11.mlp.fc2.bias",
"cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "te_text_model.encoder.layers.11.mlp.fc2.weight", "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "te_text_model.encoder.layers.11.mlp.fc2.weight",
"cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "te_text_model.encoder.layers.2.self_attn.k_proj.bias", "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "te_text_model.encoder.layers.11.self_attn.k_proj.bias",
"cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "te_text_model.encoder.layers.11.self_attn.k_proj.weight", "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "te_text_model.encoder.layers.11.self_attn.k_proj.weight",
"cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "te_text_model.encoder.layers.11.self_attn.out_proj.bias", "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "te_text_model.encoder.layers.11.self_attn.out_proj.bias",
"cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.weight": "te_text_model.encoder.layers.11.self_attn.out_proj.weight", "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj.weight": "te_text_model.encoder.layers.11.self_attn.out_proj.weight",
@@ -74,7 +74,7 @@
"cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.weight": "te_text_model.encoder.layers.2.mlp.fc1.weight", "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1.weight": "te_text_model.encoder.layers.2.mlp.fc1.weight",
"cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.bias": "te_text_model.encoder.layers.2.mlp.fc2.bias", "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.bias": "te_text_model.encoder.layers.2.mlp.fc2.bias",
"cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.weight": "te_text_model.encoder.layers.2.mlp.fc2.weight", "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2.weight": "te_text_model.encoder.layers.2.mlp.fc2.weight",
"cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.bias": "te_text_model.encoder.layers.3.self_attn.k_proj.bias", "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.bias": "te_text_model.encoder.layers.2.self_attn.k_proj.bias",
"cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.weight": "te_text_model.encoder.layers.2.self_attn.k_proj.weight", "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj.weight": "te_text_model.encoder.layers.2.self_attn.k_proj.weight",
"cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.bias": "te_text_model.encoder.layers.2.self_attn.out_proj.bias", "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.bias": "te_text_model.encoder.layers.2.self_attn.out_proj.bias",
"cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.weight": "te_text_model.encoder.layers.2.self_attn.out_proj.weight", "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj.weight": "te_text_model.encoder.layers.2.self_attn.out_proj.weight",
@@ -90,7 +90,7 @@
"cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.weight": "te_text_model.encoder.layers.3.mlp.fc1.weight", "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1.weight": "te_text_model.encoder.layers.3.mlp.fc1.weight",
"cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.bias": "te_text_model.encoder.layers.3.mlp.fc2.bias", "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.bias": "te_text_model.encoder.layers.3.mlp.fc2.bias",
"cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.weight": "te_text_model.encoder.layers.3.mlp.fc2.weight", "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2.weight": "te_text_model.encoder.layers.3.mlp.fc2.weight",
"cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.bias": "te_text_model.encoder.layers.11.self_attn.k_proj.bias", "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.bias": "te_text_model.encoder.layers.3.self_attn.k_proj.bias",
"cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.weight": "te_text_model.encoder.layers.3.self_attn.k_proj.weight", "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj.weight": "te_text_model.encoder.layers.3.self_attn.k_proj.weight",
"cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.bias": "te_text_model.encoder.layers.3.self_attn.out_proj.bias", "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.bias": "te_text_model.encoder.layers.3.self_attn.out_proj.bias",
"cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.weight": "te_text_model.encoder.layers.3.self_attn.out_proj.weight", "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj.weight": "te_text_model.encoder.layers.3.self_attn.out_proj.weight",
@@ -446,11 +446,11 @@
"first_stage_model.quant_conv.weight": "vae_quant_conv.weight", "first_stage_model.quant_conv.weight": "vae_quant_conv.weight",
"model.diffusion_model.input_blocks.0.0.bias": "unet_conv_in.bias", "model.diffusion_model.input_blocks.0.0.bias": "unet_conv_in.bias",
"model.diffusion_model.input_blocks.0.0.weight": "unet_conv_in.weight", "model.diffusion_model.input_blocks.0.0.weight": "unet_conv_in.weight",
"model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "unet_down_blocks.0.resnets.0.conv1.bias", "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "unet_down_blocks.0.resnets.0.time_emb_proj.bias",
"model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "unet_down_blocks.0.resnets.0.time_emb_proj.weight", "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "unet_down_blocks.0.resnets.0.time_emb_proj.weight",
"model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "unet_down_blocks.0.resnets.0.norm1.bias", "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "unet_down_blocks.0.resnets.0.norm1.bias",
"model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "unet_down_blocks.0.resnets.0.norm1.weight", "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "unet_down_blocks.0.resnets.0.norm1.weight",
"model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "unet_down_blocks.0.resnets.0.time_emb_proj.bias", "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "unet_down_blocks.0.resnets.0.conv1.bias",
"model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "unet_down_blocks.0.resnets.0.conv1.weight", "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "unet_down_blocks.0.resnets.0.conv1.weight",
"model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "unet_down_blocks.0.resnets.0.norm2.bias", "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "unet_down_blocks.0.resnets.0.norm2.bias",
"model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "unet_down_blocks.0.resnets.0.norm2.weight", "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "unet_down_blocks.0.resnets.0.norm2.weight",
@@ -482,31 +482,31 @@
"model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "unet_down_blocks.0.attentions.0.transformer_blocks.0.norm2.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "unet_down_blocks.0.attentions.0.transformer_blocks.0.norm2.weight",
"model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "unet_down_blocks.0.attentions.0.transformer_blocks.0.norm3.bias", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "unet_down_blocks.0.attentions.0.transformer_blocks.0.norm3.bias",
"model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "unet_down_blocks.0.attentions.0.transformer_blocks.0.norm3.weight", "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "unet_down_blocks.0.attentions.0.transformer_blocks.0.norm3.weight",
"model.diffusion_model.input_blocks.10.0.emb_layers.1.bias": "unet_down_blocks.3.resnets.0.conv1.bias", "model.diffusion_model.input_blocks.10.0.emb_layers.1.bias": "unet_down_blocks.3.resnets.0.time_emb_proj.bias",
"model.diffusion_model.input_blocks.10.0.emb_layers.1.weight": "unet_time_embedding.linear_2.weight", "model.diffusion_model.input_blocks.10.0.emb_layers.1.weight": "unet_down_blocks.3.resnets.0.time_emb_proj.weight",
"model.diffusion_model.input_blocks.10.0.in_layers.0.bias": "unet_down_blocks.3.resnets.0.norm1.bias", "model.diffusion_model.input_blocks.10.0.in_layers.0.bias": "unet_down_blocks.3.resnets.0.norm1.bias",
"model.diffusion_model.input_blocks.10.0.in_layers.0.weight": "unet_down_blocks.3.resnets.0.norm1.weight", "model.diffusion_model.input_blocks.10.0.in_layers.0.weight": "unet_down_blocks.3.resnets.0.norm1.weight",
"model.diffusion_model.input_blocks.10.0.in_layers.2.bias": "unet_down_blocks.3.resnets.0.time_emb_proj.bias", "model.diffusion_model.input_blocks.10.0.in_layers.2.bias": "unet_down_blocks.3.resnets.0.conv1.bias",
"model.diffusion_model.input_blocks.10.0.in_layers.2.weight": "unet_down_blocks.3.resnets.0.conv1.weight", "model.diffusion_model.input_blocks.10.0.in_layers.2.weight": "unet_down_blocks.3.resnets.0.conv1.weight",
"model.diffusion_model.input_blocks.10.0.out_layers.0.bias": "unet_down_blocks.3.resnets.0.norm2.bias", "model.diffusion_model.input_blocks.10.0.out_layers.0.bias": "unet_down_blocks.3.resnets.0.norm2.bias",
"model.diffusion_model.input_blocks.10.0.out_layers.0.weight": "unet_down_blocks.3.resnets.0.norm2.weight", "model.diffusion_model.input_blocks.10.0.out_layers.0.weight": "unet_down_blocks.3.resnets.0.norm2.weight",
"model.diffusion_model.input_blocks.10.0.out_layers.3.bias": "unet_down_blocks.3.resnets.0.conv2.bias", "model.diffusion_model.input_blocks.10.0.out_layers.3.bias": "unet_down_blocks.3.resnets.0.conv2.bias",
"model.diffusion_model.input_blocks.10.0.out_layers.3.weight": "unet_down_blocks.3.resnets.0.conv2.weight", "model.diffusion_model.input_blocks.10.0.out_layers.3.weight": "unet_down_blocks.3.resnets.0.conv2.weight",
"model.diffusion_model.input_blocks.11.0.emb_layers.1.bias": "unet_down_blocks.3.resnets.1.conv1.bias", "model.diffusion_model.input_blocks.11.0.emb_layers.1.bias": "unet_down_blocks.3.resnets.1.time_emb_proj.bias",
"model.diffusion_model.input_blocks.11.0.emb_layers.1.weight": "unet_down_blocks.3.resnets.0.time_emb_proj.weight", "model.diffusion_model.input_blocks.11.0.emb_layers.1.weight": "unet_down_blocks.3.resnets.1.time_emb_proj.weight",
"model.diffusion_model.input_blocks.11.0.in_layers.0.bias": "unet_down_blocks.3.resnets.1.norm1.bias", "model.diffusion_model.input_blocks.11.0.in_layers.0.bias": "unet_down_blocks.3.resnets.1.norm1.bias",
"model.diffusion_model.input_blocks.11.0.in_layers.0.weight": "unet_down_blocks.3.resnets.1.norm1.weight", "model.diffusion_model.input_blocks.11.0.in_layers.0.weight": "unet_down_blocks.3.resnets.1.norm1.weight",
"model.diffusion_model.input_blocks.11.0.in_layers.2.bias": "unet_down_blocks.3.resnets.1.time_emb_proj.bias", "model.diffusion_model.input_blocks.11.0.in_layers.2.bias": "unet_down_blocks.3.resnets.1.conv1.bias",
"model.diffusion_model.input_blocks.11.0.in_layers.2.weight": "unet_down_blocks.3.resnets.1.conv1.weight", "model.diffusion_model.input_blocks.11.0.in_layers.2.weight": "unet_down_blocks.3.resnets.1.conv1.weight",
"model.diffusion_model.input_blocks.11.0.out_layers.0.bias": "unet_down_blocks.3.resnets.1.norm2.bias", "model.diffusion_model.input_blocks.11.0.out_layers.0.bias": "unet_down_blocks.3.resnets.1.norm2.bias",
"model.diffusion_model.input_blocks.11.0.out_layers.0.weight": "unet_down_blocks.3.resnets.1.norm2.weight", "model.diffusion_model.input_blocks.11.0.out_layers.0.weight": "unet_down_blocks.3.resnets.1.norm2.weight",
"model.diffusion_model.input_blocks.11.0.out_layers.3.bias": "unet_down_blocks.3.resnets.1.conv2.bias", "model.diffusion_model.input_blocks.11.0.out_layers.3.bias": "unet_down_blocks.3.resnets.1.conv2.bias",
"model.diffusion_model.input_blocks.11.0.out_layers.3.weight": "unet_down_blocks.3.resnets.1.conv2.weight", "model.diffusion_model.input_blocks.11.0.out_layers.3.weight": "unet_down_blocks.3.resnets.1.conv2.weight",
"model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "unet_down_blocks.0.resnets.1.conv1.bias", "model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "unet_down_blocks.0.resnets.1.time_emb_proj.bias",
"model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "unet_down_blocks.0.resnets.1.time_emb_proj.weight", "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "unet_down_blocks.0.resnets.1.time_emb_proj.weight",
"model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "unet_down_blocks.0.resnets.1.norm1.bias", "model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "unet_down_blocks.0.resnets.1.norm1.bias",
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104
toolkit/keymaps/stable_diffusion_sd2.json
View File

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70
toolkit/keymaps/stable_diffusion_sdxl.json
View File

@@ -58,7 +58,7 @@
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@@ -231,7 +231,7 @@
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@@ -241,7 +241,7 @@
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@@ -251,7 +251,7 @@
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@@ -461,7 +461,7 @@
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@@ -471,7 +471,7 @@
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@@ -481,7 +481,7 @@
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@@ -491,7 +491,7 @@
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@@ -501,7 +501,7 @@
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@@ -511,7 +511,7 @@
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@@ -1013,7 +1013,7 @@
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@@ -2197,7 +2197,7 @@
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"model.diffusion_model.output_blocks.2.1.transformer_blocks.9.ff.net.0.proj.bias": "unet_up_blocks.0.attentions.2.transformer_blocks.9.ff.net.0.proj.bias", "model.diffusion_model.output_blocks.2.1.transformer_blocks.9.ff.net.0.proj.bias": "unet_up_blocks.0.attentions.2.transformer_blocks.9.ff.net.0.proj.bias",

76
toolkit/stable_diffusion_model.py
View File

@@ -1,13 +1,14 @@
import gc import gc
import json import json
import typing import typing
from typing import Union, List, Tuple from typing import Union, List, Tuple, Iterator
import sys import sys
import os import os
from collections import OrderedDict from collections import OrderedDict
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import rescale_noise_cfg from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import rescale_noise_cfg
from safetensors.torch import save_file from safetensors.torch import save_file, load_file
from torch.nn import Parameter
from tqdm import tqdm from tqdm import tqdm
from torchvision.transforms import Resize from torchvision.transforms import Resize
@@ -31,12 +32,12 @@ import diffusers
# tell it to shut up # tell it to shut up
diffusers.logging.set_verbosity(diffusers.logging.ERROR) diffusers.logging.set_verbosity(diffusers.logging.ERROR)
VAE_PREFIX_UNET = "vae" SD_PREFIX_VAE = "vae"
SD_PREFIX_UNET = "unet" SD_PREFIX_UNET = "unet"
SD_PREFIX_TEXT_ENCODER = "te" SD_PREFIX_TEXT_ENCODER = "te"
SD_PREFIX_TEXT_ENCODER1 = "te1" SD_PREFIX_TEXT_ENCODER1 = "te0"
SD_PREFIX_TEXT_ENCODER2 = "te2" SD_PREFIX_TEXT_ENCODER2 = "te1"
# prefixed diffusers keys # prefixed diffusers keys
DO_NOT_TRAIN_WEIGHTS = [ DO_NOT_TRAIN_WEIGHTS = [
@@ -184,6 +185,21 @@ class StableDiffusion:
text_encoder.requires_grad_(False) text_encoder.requires_grad_(False)
text_encoder.eval() text_encoder.eval()
text_encoder = text_encoders text_encoder = text_encoders
if self.model_config.experimental_xl:
print("Experimental XL mode enabled")
print("Loading and injecting alt weights")
# load the mismatched weight and force it in
raw_state_dict = load_file(model_path)
replacement_weight = raw_state_dict['conditioner.embedders.1.model.text_projection'].clone()
del raw_state_dict
# get state dict for for 2nd text encoder
te1_state_dict = text_encoders[1].state_dict()
# replace weight with mismatched weight
te1_state_dict['text_projection.weight'] = replacement_weight.to(self.device_torch, dtype=dtype)
flush()
print("Injecting alt weights")
else: else:
if self.custom_pipeline is not None: if self.custom_pipeline is not None:
pipln = self.custom_pipeline pipln = self.custom_pipeline
@@ -707,7 +723,7 @@ class StableDiffusion:
state_dict = OrderedDict() state_dict = OrderedDict()
if vae: if vae:
for k, v in self.vae.state_dict().items(): for k, v in self.vae.state_dict().items():
new_key = k if k.startswith(f"{VAE_PREFIX_UNET}") else f"{VAE_PREFIX_UNET}_{k}" new_key = k if k.startswith(f"{SD_PREFIX_VAE}") else f"{SD_PREFIX_VAE}_{k}"
state_dict[new_key] = v state_dict[new_key] = v
if text_encoder: if text_encoder:
if isinstance(self.text_encoder, list): if isinstance(self.text_encoder, list):
@@ -726,6 +742,35 @@ class StableDiffusion:
state_dict[new_key] = v state_dict[new_key] = v
return state_dict return state_dict
def named_parameters(self, vae=True, text_encoder=True, unet=True, state_dict_keys=False) -> OrderedDict[str, Parameter]:
named_params: OrderedDict[str, Parameter] = OrderedDict()
if vae:
for name, param in self.vae.named_parameters(recurse=True, prefix=f"{SD_PREFIX_VAE}"):
named_params[name] = param
if text_encoder:
if isinstance(self.text_encoder, list):
for i, encoder in enumerate(self.text_encoder):
for name, param in encoder.named_parameters(recurse=True, prefix=f"{SD_PREFIX_TEXT_ENCODER}{i}"):
named_params[name] = param
else:
for name, param in self.text_encoder.named_parameters(recurse=True, prefix=f"{SD_PREFIX_TEXT_ENCODER}"):
named_params[name] = param
if unet:
for name, param in self.unet.named_parameters(recurse=True, prefix=f"{SD_PREFIX_UNET}"):
named_params[name] = param
# convert to state dict keys, jsut replace . with _ on keys
if state_dict_keys:
new_named_params = OrderedDict()
for k, v in named_params.items():
# replace only the first . with an _
new_key = k.replace('.', '_', 1)
new_named_params[new_key] = v
named_params = new_named_params
return named_params
def save(self, output_file: str, meta: OrderedDict, save_dtype=get_torch_dtype('fp16'), logit_scale=None): def save(self, output_file: str, meta: OrderedDict, save_dtype=get_torch_dtype('fp16'), logit_scale=None):
version_string = '1' version_string = '1'
if self.is_v2: if self.is_v2:
@@ -764,24 +809,31 @@ class StableDiffusion:
trainable_parameters = [] trainable_parameters = []
# we use state dict to find params
if unet: if unet:
state_dict = self.state_dict(vae=False, unet=unet, text_encoder=False) named_params = self.named_parameters(vae=False, unet=unet, text_encoder=False, state_dict_keys=True)
unet_lr = unet_lr if unet_lr is not None else default_lr unet_lr = unet_lr if unet_lr is not None else default_lr
params = [] params = []
for key, diffusers_key in ldm_diffusers_keymap.items(): for key, diffusers_key in ldm_diffusers_keymap.items():
if diffusers_key in state_dict and diffusers_key not in DO_NOT_TRAIN_WEIGHTS: if diffusers_key in named_params and diffusers_key not in DO_NOT_TRAIN_WEIGHTS:
params.append(state_dict[diffusers_key]) if named_params[diffusers_key].requires_grad:
params.append(named_params[diffusers_key])
param_data = {"params": params, "lr": unet_lr} param_data = {"params": params, "lr": unet_lr}
trainable_parameters.append(param_data) trainable_parameters.append(param_data)
print(f"Found {len(params)} trainable parameter in unet")
if text_encoder: if text_encoder:
state_dict = self.state_dict(vae=False, unet=unet, text_encoder=text_encoder) named_params = self.named_parameters(vae=False, unet=False, text_encoder=text_encoder, state_dict_keys=True)
text_encoder_lr = text_encoder_lr if text_encoder_lr is not None else default_lr text_encoder_lr = text_encoder_lr if text_encoder_lr is not None else default_lr
params = [] params = []
for key, diffusers_key in ldm_diffusers_keymap.items(): for key, diffusers_key in ldm_diffusers_keymap.items():
if diffusers_key in state_dict and diffusers_key not in DO_NOT_TRAIN_WEIGHTS: if diffusers_key in named_params and diffusers_key not in DO_NOT_TRAIN_WEIGHTS:
params.append(state_dict[diffusers_key]) if named_params[diffusers_key].requires_grad:
params.append(named_params[diffusers_key])
param_data = {"params": params, "lr": text_encoder_lr} param_data = {"params": params, "lr": text_encoder_lr}
trainable_parameters.append(param_data) trainable_parameters.append(param_data)
print(f"Found {len(params)} trainable parameter in text encoder")
return trainable_parameters return trainable_parameters