forked from MinusZoneAI/ComfyUI-TrainTools-MZ
-
Notifications
You must be signed in to change notification settings - Fork 0
/
__init__.py
316 lines (253 loc) · 11 KB
/
__init__.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
import inspect
import json
import os
import folder_paths
import importlib
from .mz_train_tools_utils import Utils
from . import mz_train_tools_core
NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
}
AUTHOR_NAME = "MinusZone"
CATEGORY_NAME = f"{AUTHOR_NAME} - TrainTools"
class MZ_KohyaSSInitWorkspace:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"lora_name": ("STRING", {"default": ""}),
"branch": ("STRING", {"default": "71e2c91330a9d866ec05cdd10584bbb962896a99"}),
"source": ([
"github",
"githubfast",
"521github",
"kkgithub",
], {"default": "none"}),
"seed": ("INT", {"default": 0}),
},
}
RETURN_TYPES = ("MZ_TT_SS_WorkspaceConfig",)
RETURN_NAMES = ("workspace_config",)
FUNCTION = "start"
CATEGORY = CATEGORY_NAME
def start(self, **kwargs):
importlib.reload(mz_train_tools_core)
return mz_train_tools_core.MZ_KohyaSSInitWorkspace_call(kwargs)
NODE_CLASS_MAPPINGS["MZ_KohyaSSInitWorkspace"] = MZ_KohyaSSInitWorkspace
NODE_DISPLAY_NAME_MAPPINGS["MZ_KohyaSSInitWorkspace"] = f"{AUTHOR_NAME} - KohyaSSInitWorkspace"
class MZ_ImagesCopyWorkspace:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"workspace_config": ("MZ_TT_SS_WorkspaceConfig",),
"images": ("IMAGE",),
"enable_bucket": (["enable", "disable"], {"default": "enable"}),
"resolution": ("INT", {"default": 512}),
"num_repeats": ("INT", {"default": 1}),
"batch_size": ("INT", {"default": 1}),
# "class_name": ("STRING", {"default": "girl", "dynamicPrompts": True}),
"force_clear": (["enable", "disable"], {"default": "disable"}),
"force_clear_only_images": (["enable", "disable"], {"default": "disable"}),
"same_caption_generate": (["enable", "disable"], {"default": "disable"}),
"same_caption": ("STRING", {"default": "", "dynamicPrompts": True, "multiline": True}),
},
}
RETURN_TYPES = (f"STRING",)
RETURN_NAMES = ("workspace_images_dir",)
# OUTPUT_NODE = True
FUNCTION = "start"
CATEGORY = CATEGORY_NAME
def start(self, **kwargs):
importlib.reload(mz_train_tools_core)
return mz_train_tools_core.MZ_ImageSelecter_call(kwargs)
NODE_CLASS_MAPPINGS["MZ_ImagesCopyWorkspace"] = MZ_ImagesCopyWorkspace
NODE_DISPLAY_NAME_MAPPINGS["MZ_ImagesCopyWorkspace"] = f"{AUTHOR_NAME} - ImagesCopyWorkspace"
class MZ_KohyaSSUseConfig:
train_config_template_dir = os.path.join(
os.path.dirname(__file__), "configs", "kohya_ss_lora"
)
@classmethod
def INPUT_TYPES(s):
train_config_templates = os.listdir(s.train_config_template_dir)
# 去掉json后缀
train_config_templates = [os.path.splitext(x)[0]
for x in train_config_templates]
return {
"required": {
"workspace_config": ("MZ_TT_SS_WorkspaceConfig",),
"workspace_images_dir": ("STRING", {"forceInput": True}),
"train_config_template": (train_config_templates,),
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"max_train_steps": ("INT", {"default": 300}),
"max_train_epochs": ("INT", {"default": 0}),
"save_every_n_epochs": ("INT", {"default": 20}),
"learning_rate": ("STRING", {"default": "1e-5"}),
},
"optional": {
"save_advanced_config": ("MZ_TT_SS_AdvConfig",),
}
}
RETURN_TYPES = (f"MZ_TT_SS_TrainConfig",)
RETURN_NAMES = ("train_config",)
FUNCTION = "start"
CATEGORY = CATEGORY_NAME
def start(self, **kwargs):
importlib.reload(mz_train_tools_core)
kwargs["train_config_template_dir"] = self.train_config_template_dir
return mz_train_tools_core.MZ_KohyaSSUseConfig_call(kwargs)
NODE_CLASS_MAPPINGS["MZ_KohyaSSUseConfig"] = MZ_KohyaSSUseConfig
NODE_DISPLAY_NAME_MAPPINGS["MZ_KohyaSSUseConfig"] = f"{AUTHOR_NAME} - KohyaSSUseConfig"
class MZ_KohyaSSAdvConfig:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"xformers": (["enable", "disable"], {"default": "enable"}),
"sdpa": (["enable", "disable"], {"default": "disable"}),
"fp8_base": (["enable", "disable"], {"default": "disable"}),
"mixed_precision": (["no", "fp16", "bf16"], {"default": "fp16"}),
"cache_latents": (["enable", "disable"], {"default": "enable"}),
"cache_latents_to_disk": (["enable", "disable"], {"default": "enable"}),
"network_dim": ("INT", {"default": 16}),
"network_alpha": ("INT", {"default": 8}),
"network_module": ([
"networks.lora",
"networks.dylora",
"networks.oft",
], {"default": "networks.lora"}),
"network_train_unet_only": (["enable", "disable"], {"default": "enable"}),
# linear, cosine, cosine_with_restarts, polynomial, constant (default), constant_with_warmup, adafactor
"lr_scheduler": ([
"linear",
"cosine",
"cosine_with_restarts",
"polynomial",
"constant",
"constant_with_warmup",
"adafactor",
], {"default": "cosine"}),
"lr_scheduler_num_cycles": ("INT", {"default": 1}),
# AdamW (default), AdamW8bit, PagedAdamW, PagedAdamW8bit, PagedAdamW32bit, Lion8bit, PagedLion8bit, Lion, SGDNesterov, SGDNesterov8bit, DAdaptation(DAdaptAdamPreprint), DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptAdanIP, DAdaptLion, DAdaptSGD, AdaFactor
"optimizer_type": ([
"AdamW",
"AdamW8bit",
"PagedAdamW",
"PagedAdamW8bit",
"PagedAdamW32bit",
"Lion8bit",
"PagedLion8bit",
"Lion",
"SGDNesterov",
"SGDNesterov8bit",
"DAdaptation",
"DAdaptAdaGrad",
"DAdaptAdam",
"DAdaptAdan",
"DAdaptAdanIP",
"DAdaptLion",
"DAdaptSGD",
"AdaFactor",
], {"default": "AdamW"}),
"lr_warmup_steps": ("INT", {"default": 0}),
"unet_lr": ("STRING", {"default": ""}),
"text_encoder_lr": ("STRING", {"default": ""}),
"shuffle_caption": (["enable", "disable"], {"default": "enable"}),
"save_precision": (["float", "fp16", "bf16"], {"default": "fp16"}),
"persistent_data_loader_workers": (["enable", "disable"], {"default": "enable"}),
"no_metadata": (["enable", "disable"], {"default": "enable"}),
"noise_offset": ("FLOAT", {"default": 0.1}),
"no_half_vae": (["enable", "disable"], {"default": "enable"}),
"lowram": (["enable", "disable"], {"default": "disable"}),
},
}
RETURN_TYPES = ("MZ_TT_SS_AdvConfig",)
RETURN_NAMES = ("advanced_config",)
FUNCTION = "start"
CATEGORY = CATEGORY_NAME
def start(self, **kwargs):
return (kwargs,)
NODE_CLASS_MAPPINGS["MZ_KohyaSSAdvConfig"] = MZ_KohyaSSAdvConfig
NODE_DISPLAY_NAME_MAPPINGS["MZ_KohyaSSAdvConfig"] = f"{AUTHOR_NAME} - KohyaSSAdvConfig"
class MZ_KohyaSSTrain:
@classmethod
def INPUT_TYPES(s):
loras = [
"latest",
"empty",
]
workspaces_dir = os.path.join(
folder_paths.output_directory, "mz_train_workspaces")
# 使用walk查询所有的workspace中的所有lora模型,lora存放在每个workspace的output目录下
workspaces_loras = []
for root, dirs, files in os.walk(workspaces_dir):
if root.endswith("output"):
for file in files:
if file.endswith(".safetensors"):
workspaces_loras.append(
os.path.join(root, file)
)
# 按创建时间排序
workspaces_loras = sorted(
workspaces_loras, key=lambda x: os.path.getctime(x), reverse=True)
comfyui_full_loras = []
comfyui_loras = folder_paths.get_filename_list("loras")
for lora in comfyui_loras:
lora_path = folder_paths.get_full_path("loras", lora)
comfyui_full_loras.append(lora_path)
# 按创建时间排序
comfyui_full_loras = sorted(
comfyui_full_loras, key=lambda x: os.path.getctime(x), reverse=True)
loras = loras + workspaces_loras + comfyui_full_loras
return {
"required": {
"train_config": ("MZ_TT_SS_TrainConfig",),
"base_lora": (loras, {"default": "latest"}),
"sample_generate": (["enable", "disable"], {"default": "enable"}),
"sample_prompt": ("STRING", {"default:": "", "dynamicPrompts": True, "multiline": True}),
},
"optional": {
},
}
RETURN_TYPES = ()
RETURN_NAMES = ()
OUTPUT_NODE = True
FUNCTION = "start"
CATEGORY = CATEGORY_NAME
def start(self, **kwargs):
importlib.reload(mz_train_tools_core)
return mz_train_tools_core.MZ_KohyaSSTrain_call(kwargs)
NODE_CLASS_MAPPINGS["MZ_KohyaSSTrain"] = MZ_KohyaSSTrain
NODE_DISPLAY_NAME_MAPPINGS["MZ_KohyaSSTrain"] = f"{AUTHOR_NAME} - KohyaSSTrain"
class MZ_LoadImagesFromDirectoryPath:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"directory": ("STRING", {"default": "X://path/to/images"}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("images",)
FUNCTION = "start"
CATEGORY = CATEGORY_NAME
def start(self, **kwargs):
from PIL import Image
images = []
image_dir = kwargs["directory"]
if not os.path.exists(image_dir):
return (images,)
images = os.listdir(image_dir)
images = [x for x in images if x.endswith(".png") or x.endswith(".jpg")]
images = [os.path.join(image_dir, x) for x in images]
pil_images = []
for image in images:
pil_images.append(Image.open(image))
tensor_images = []
for pil_image in pil_images:
tensor_images.append(Utils.pil2tensor(pil_image))
return (tensor_images,)
NODE_CLASS_MAPPINGS["MZ_LoadImagesFromDirectoryPath"] = MZ_LoadImagesFromDirectoryPath
NODE_DISPLAY_NAME_MAPPINGS[
"MZ_LoadImagesFromDirectoryPath"] = f"{AUTHOR_NAME} - LoadImagesFromDirectoryPath"