Removed all submodules. Submodule free now, yay.

toolkit/custom_adapter.py

@@ -19,16 +19,12 @@ from toolkit.models.te_adapter import TEAdapter
 from toolkit.models.te_aug_adapter import TEAugAdapter
 from toolkit.models.vd_adapter import VisionDirectAdapter
 from toolkit.models.redux import ReduxImageEncoder
-from toolkit.paths import REPOS_ROOT
 from toolkit.photomaker import PhotoMakerIDEncoder, FuseModule, PhotoMakerCLIPEncoder
 from toolkit.saving import load_ip_adapter_model, load_custom_adapter_model
 from toolkit.train_tools import get_torch_dtype
 from toolkit.models.pixtral_vision import PixtralVisionEncoderCompatible, PixtralVisionImagePreprocessorCompatible
 import random
-
 from toolkit.util.mask import generate_random_mask
-
-sys.path.append(REPOS_ROOT)
 from typing import TYPE_CHECKING, Union, Iterator, Mapping, Any, Tuple, List, Optional, Dict
 from collections import OrderedDict
 from toolkit.config_modules import AdapterConfig, AdapterTypes, TrainConfig

toolkit/lora_special.py

@@ -14,16 +14,11 @@ from toolkit.models.lokr import LokrModule
 from .config_modules import NetworkConfig
 from .lorm import count_parameters
 from .network_mixins import ToolkitNetworkMixin, ToolkitModuleMixin, ExtractableModuleMixin
-from .paths import SD_SCRIPTS_ROOT
 
-sys.path.append(SD_SCRIPTS_ROOT)
-
-from networks.lora import LoRANetwork, get_block_index
+from toolkit.kohya_lora import LoRANetwork
 from toolkit.models.DoRA import DoRAModule
 from typing import TYPE_CHECKING
 
-from torch.utils.checkpoint import checkpoint
-
 if TYPE_CHECKING:
     from toolkit.stable_diffusion_model import StableDiffusion
 
@@ -389,15 +384,6 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
                                 if lora_name in modules_dim:
                                     dim = modules_dim[lora_name]
                                     alpha = modules_alpha[lora_name]
-                            elif is_unet and block_dims is not None:
-                                # U-Netでblock_dims指定あり
-                                block_idx = get_block_index(lora_name)
-                                if is_linear or is_conv2d_1x1:
-                                    dim = block_dims[block_idx]
-                                    alpha = block_alphas[block_idx]
-                                elif conv_block_dims is not None:
-                                    dim = conv_block_dims[block_idx]
-                                    alpha = conv_block_alphas[block_idx]
                             else:
                                 # 通常、すべて対象とする
                                 if is_linear or is_conv2d_1x1:

toolkit/models/single_value_adapter.py

@@ -7,9 +7,6 @@ import weakref
 from typing import Union, TYPE_CHECKING
 
 from diffusers import Transformer2DModel
-from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer, CLIPVisionModelWithProjection
-from toolkit.paths import REPOS_ROOT
-sys.path.append(REPOS_ROOT)
 
 
 if TYPE_CHECKING:

toolkit/models/wan21/wan21.py

@@ -8,7 +8,6 @@ from toolkit.config_modules import GenerateImageConfig, ModelConfig
 from toolkit.dequantize import patch_dequantization_on_save
 from toolkit.models.base_model import BaseModel
 from toolkit.prompt_utils import PromptEmbeds
-from toolkit.paths import REPOS_ROOT
 from transformers import AutoTokenizer, UMT5EncoderModel
 from diffusers import AutoencoderKLWan, WanPipeline, WanTransformer3DModel
 import os
@@ -34,7 +33,6 @@ from diffusers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler
 from typing import TYPE_CHECKING, List
 from toolkit.accelerator import unwrap_model
 from toolkit.samplers.custom_flowmatch_sampler import CustomFlowMatchEulerDiscreteScheduler
-from torchvision.transforms import Resize, ToPILImage
 from tqdm import tqdm
 import torch.nn.functional as F
 from diffusers.pipelines.wan.pipeline_output import WanPipelineOutput

toolkit/models/wan21/wan21_i2v.py

@@ -6,7 +6,6 @@ from toolkit.accelerator import unwrap_model
 from toolkit.basic import flush
 from toolkit.config_modules import GenerateImageConfig, ModelConfig
 from toolkit.prompt_utils import PromptEmbeds
-from toolkit.paths import REPOS_ROOT
 from transformers import AutoTokenizer, UMT5EncoderModel
 from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, WanTransformer3DModel
 import os

toolkit/paths.py

@@ -2,8 +2,6 @@ import os
 
 TOOLKIT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 CONFIG_ROOT = os.path.join(TOOLKIT_ROOT, 'config')
-SD_SCRIPTS_ROOT = os.path.join(TOOLKIT_ROOT, "repositories", "sd-scripts")
-REPOS_ROOT = os.path.join(TOOLKIT_ROOT, "repositories")
 KEYMAPS_ROOT = os.path.join(TOOLKIT_ROOT, "toolkit", "keymaps")
 ORIG_CONFIGS_ROOT = os.path.join(TOOLKIT_ROOT, "toolkit", "orig_configs")
 DIFFUSERS_CONFIGS_ROOT = os.path.join(TOOLKIT_ROOT, "toolkit", "diffusers_configs")

toolkit/reference_adapter.py

@@ -8,11 +8,8 @@ from torch.nn import Parameter
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from toolkit.basic import adain
-from toolkit.paths import REPOS_ROOT
 from toolkit.saving import load_ip_adapter_model
 from toolkit.train_tools import get_torch_dtype
-
-sys.path.append(REPOS_ROOT)
 from typing import TYPE_CHECKING, Union, Iterator, Mapping, Any, Tuple, List, Optional, Dict
 from collections import OrderedDict
 from toolkit.config_modules import AdapterConfig

toolkit/stable_diffusion_model.py

@@ -26,13 +26,12 @@ from toolkit.clip_vision_adapter import ClipVisionAdapter
 from toolkit.custom_adapter import CustomAdapter
 from toolkit.dequantize import patch_dequantization_on_save
 from toolkit.ip_adapter import IPAdapter
-from library.model_util import convert_unet_state_dict_to_sd, convert_text_encoder_state_dict_to_sd_v2, \
-    convert_vae_state_dict, load_vae
+from toolkit.util.vae import load_vae
 from toolkit import train_tools
 from toolkit.config_modules import ModelConfig, GenerateImageConfig, ModelArch
 from toolkit.metadata import get_meta_for_safetensors
 from toolkit.models.decorator import Decorator
-from toolkit.paths import REPOS_ROOT, KEYMAPS_ROOT
+from toolkit.paths import KEYMAPS_ROOT
 from toolkit.prompt_utils import inject_trigger_into_prompt, PromptEmbeds, concat_prompt_embeds
 from toolkit.reference_adapter import ReferenceAdapter
 from toolkit.sampler import get_sampler

toolkit/train_tools.py

@@ -6,11 +6,6 @@ import time
 from typing import TYPE_CHECKING, Union, List
 import sys
 
-from torch.cuda.amp import GradScaler
-
-from toolkit.paths import SD_SCRIPTS_ROOT
-
-sys.path.append(SD_SCRIPTS_ROOT)
 
 from diffusers import (
     DDPMScheduler,

toolkit/util/vae.py

@@ -0,0 +1,20 @@
+from diffusers import AutoencoderKL
+
+
+def load_vae(vae_path, dtype):
+    try:
+        vae = AutoencoderKL.from_pretrained(
+            vae_path,
+            torch_dtype=dtype,
+        )
+    except Exception as e:
+        try:
+            vae = AutoencoderKL.from_pretrained(
+                vae_path.vae_path,
+                subfolder="vae",
+                torch_dtype=dtype,
+            )
+        except Exception as e:
+            raise ValueError(f"Failed to load VAE from {vae_path}: {e}")
+    vae.to(dtype)
+    return vae