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Merge latest sd-script updates

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bmaltais 2023-04-01 07:14:25 -04:00
parent d37aa6efad
commit 2eddd64b90
10 changed files with 2607 additions and 1408 deletions
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12
README.md
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@ -192,8 +192,20 @@ This will store your a backup file with your current locally installed pip packa
## Change History
* 2023/04/01 (v21.4.0)
- Fix an issue that `merge_lora.py` does not work with the latest version.
- Fix an issue that `merge_lora.py` does not merge Conv2d3x3 weights.
- Fix an issue that the VRAM usage temporarily increases when loading a model in `train_network.py`.
- Fix an issue that an error occurs when loading a `.safetensors` model in `train_network.py`. [#354](https://github.com/kohya-ss/sd-scripts/issues/354)
- Support [P+](https://prompt-plus.github.io/) training. Thank you jakaline-dev!
- See [#327](https://github.com/kohya-ss/sd-scripts/pull/327) for details.
- Use `train_textual_inversion_XTI.py` for training. The usage is almost the same as `train_textual_inversion.py`. However, sample image generation during training is not supported.
- Use `gen_img_diffusers.py` for image generation (I think Web UI is not supported). Specify the embedding with `--XTI_embeddings` option.
- Reduce RAM usage at startup in `train_network.py`. [#332](https://github.com/kohya-ss/sd-scripts/pull/332) Thank you guaneec!
- Support pre-merge for LoRA in `gen_img_diffusers.py`. Specify `--network_merge` option. Note that the `--am` option of the prompt option is no longer available with this option.
* 2023/04/01 (v21.3.9)
- Update how setup is done on Windows by introducing a setup.bat script. This will make it easier to install/re-install on Windows if needed. Many thanks to @missionfloyd for his PR: https://github.com/bmaltais/kohya_ss/pull/496
- Fix issue with WD14 caption script by applying a custom fix to kohya_ss code.
* 2023/03/30 (v21.3.8)
- Fix issue with LyCORIS version not being found: https://github.com/bmaltais/kohya_ss/issues/481
* 2023/03/29 (v21.3.7)

209
XTI_hijack.py Normal file
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@ -0,0 +1,209 @@
import torch
from typing import Union, List, Optional, Dict, Any, Tuple
from diffusers.models.unet_2d_condition import UNet2DConditionOutput
def unet_forward_XTI(self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor,
class_labels: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[UNet2DConditionOutput, Tuple]:
r"""
Args:
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
Returns:
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is the sample tensor.
"""
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=self.dtype)
emb = self.time_embedding(t_emb)
if self.config.num_class_embeds is not None:
if class_labels is None:
raise ValueError("class_labels should be provided when num_class_embeds > 0")
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
emb = emb + class_emb
# 2. pre-process
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
down_i = 0
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states[down_i:down_i+2],
)
down_i += 2
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states[6])
# 5. up
up_i = 7
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=encoder_hidden_states[up_i:up_i+3],
upsample_size=upsample_size,
)
up_i += 3
else:
sample = upsample_block(
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
)
# 6. post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if not return_dict:
return (sample,)
return UNet2DConditionOutput(sample=sample)
def downblock_forward_XTI(
self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None
):
output_states = ()
i = 0
for resnet, attn in zip(self.resnets, self.attentions):
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i]
)[0]
else:
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample
output_states += (hidden_states,)
i += 1
if self.downsamplers is not None:
for downsampler in self.downsamplers:
hidden_states = downsampler(hidden_states)
output_states += (hidden_states,)
return hidden_states, output_states
def upblock_forward_XTI(
self,
hidden_states,
res_hidden_states_tuple,
temb=None,
encoder_hidden_states=None,
upsample_size=None,
):
i = 0
for resnet, attn in zip(self.resnets, self.attentions):
# pop res hidden states
res_hidden_states = res_hidden_states_tuple[-1]
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
if self.training and self.gradient_checkpointing:
def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
if return_dict is not None:
return module(*inputs, return_dict=return_dict)
else:
return module(*inputs)
return custom_forward
hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states[i]
)[0]
else:
hidden_states = resnet(hidden_states, temb)
hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states[i]).sample
i += 1
if self.upsamplers is not None:
for upsampler in self.upsamplers:
hidden_states = upsampler(hidden_states, upsample_size)
return hidden_states

184
gen_img_diffusers.py
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@ -95,6 +95,8 @@ import library.train_util as train_util
import tools.original_control_net as original_control_net
from tools.original_control_net import ControlNetInfo
from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
# Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う
TOKENIZER_PATH = "openai/clip-vit-large-patch14"
V2_STABLE_DIFFUSION_PATH = "stabilityai/stable-diffusion-2" # ここからtokenizerだけ使う
@ -491,6 +493,9 @@ class PipelineLike:
# Textual Inversion
self.token_replacements = {}
# XTI
self.token_replacements_XTI = {}
# CLIP guidance
self.clip_guidance_scale = clip_guidance_scale
self.clip_image_guidance_scale = clip_image_guidance_scale
@ -514,15 +519,26 @@ class PipelineLike:
def add_token_replacement(self, target_token_id, rep_token_ids):
self.token_replacements[target_token_id] = rep_token_ids
def replace_token(self, tokens):
def replace_token(self, tokens, layer=None):
new_tokens = []
for token in tokens:
if token in self.token_replacements:
new_tokens.extend(self.token_replacements[token])
replacer_ = self.token_replacements[token]
if layer:
replacer = []
for r in replacer_:
if r in self.token_replacements_XTI:
replacer.append(self.token_replacements_XTI[r][layer])
else:
replacer = replacer_
new_tokens.extend(replacer)
else:
new_tokens.append(token)
return new_tokens
def add_token_replacement_XTI(self, target_token_id, rep_token_ids):
self.token_replacements_XTI[target_token_id] = rep_token_ids
def set_control_nets(self, ctrl_nets):
self.control_nets = ctrl_nets
@ -744,14 +760,15 @@ class PipelineLike:
" the batch size of `prompt`."
)
text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
clip_skip=self.clip_skip,
**kwargs,
)
if not self.token_replacements_XTI:
text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
clip_skip=self.clip_skip,
**kwargs,
)
if negative_scale is not None:
_, real_uncond_embeddings, _ = get_weighted_text_embeddings(
@ -763,11 +780,47 @@ class PipelineLike:
**kwargs,
)
if do_classifier_free_guidance:
if negative_scale is None:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
else:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])
if self.token_replacements_XTI:
text_embeddings_concat = []
for layer in [
"IN01",
"IN02",
"IN04",
"IN05",
"IN07",
"IN08",
"MID",
"OUT03",
"OUT04",
"OUT05",
"OUT06",
"OUT07",
"OUT08",
"OUT09",
"OUT10",
"OUT11",
]:
text_embeddings, uncond_embeddings, prompt_tokens = get_weighted_text_embeddings(
pipe=self,
prompt=prompt,
uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
max_embeddings_multiples=max_embeddings_multiples,
clip_skip=self.clip_skip,
layer=layer,
**kwargs,
)
if do_classifier_free_guidance:
if negative_scale is None:
text_embeddings_concat.append(torch.cat([uncond_embeddings, text_embeddings]))
else:
text_embeddings_concat.append(torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings]))
text_embeddings = torch.stack(text_embeddings_concat)
else:
if do_classifier_free_guidance:
if negative_scale is None:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
else:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])
# CLIP guidanceで使用するembeddingsを取得する
if self.clip_guidance_scale > 0:
@ -1675,7 +1728,7 @@ def parse_prompt_attention(text):
return res
def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length: int):
def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length: int, layer=None):
r"""
Tokenize a list of prompts and return its tokens with weights of each token.
No padding, starting or ending token is included.
@ -1691,7 +1744,7 @@ def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length:
# tokenize and discard the starting and the ending token
token = pipe.tokenizer(word).input_ids[1:-1]
token = pipe.replace_token(token)
token = pipe.replace_token(token, layer=layer)
text_token += token
# copy the weight by length of token
@ -1807,6 +1860,7 @@ def get_weighted_text_embeddings(
skip_parsing: Optional[bool] = False,
skip_weighting: Optional[bool] = False,
clip_skip=None,
layer=None,
**kwargs,
):
r"""
@ -1837,11 +1891,11 @@ def get_weighted_text_embeddings(
prompt = [prompt]
if not skip_parsing:
prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2, layer=layer)
if uncond_prompt is not None:
if isinstance(uncond_prompt, str):
uncond_prompt = [uncond_prompt]
uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2, layer=layer)
else:
prompt_tokens = [token[1:-1] for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True).input_ids]
prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
@ -2229,13 +2283,17 @@ def main(args):
if network is None:
return
network.apply_to(text_encoder, unet)
if not args.network_merge:
network.apply_to(text_encoder, unet)
if args.opt_channels_last:
network.to(memory_format=torch.channels_last)
network.to(dtype).to(device)
if args.opt_channels_last:
network.to(memory_format=torch.channels_last)
network.to(dtype).to(device)
networks.append(network)
else:
network.merge_to(text_encoder, unet, dtype, device)
networks.append(network)
else:
networks = []
@ -2289,6 +2347,11 @@ def main(args):
if args.diffusers_xformers:
pipe.enable_xformers_memory_efficient_attention()
if args.XTI_embeddings:
diffusers.models.UNet2DConditionModel.forward = unet_forward_XTI
diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D.forward = downblock_forward_XTI
diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D.forward = upblock_forward_XTI
# Textual Inversionを処理する
if args.textual_inversion_embeddings:
token_ids_embeds = []
@ -2335,6 +2398,71 @@ def main(args):
for token_id, embed in zip(token_ids, embeds):
token_embeds[token_id] = embed
if args.XTI_embeddings:
XTI_layers = [
"IN01",
"IN02",
"IN04",
"IN05",
"IN07",
"IN08",
"MID",
"OUT03",
"OUT04",
"OUT05",
"OUT06",
"OUT07",
"OUT08",
"OUT09",
"OUT10",
"OUT11",
]
token_ids_embeds_XTI = []
for embeds_file in args.XTI_embeddings:
if model_util.is_safetensors(embeds_file):
from safetensors.torch import load_file
data = load_file(embeds_file)
else:
data = torch.load(embeds_file, map_location="cpu")
if set(data.keys()) != set(XTI_layers):
raise ValueError("NOT XTI")
embeds = torch.concat(list(data.values()))
num_vectors_per_token = data["MID"].size()[0]
token_string = os.path.splitext(os.path.basename(embeds_file))[0]
token_strings = [token_string] + [f"{token_string}{i+1}" for i in range(num_vectors_per_token - 1)]
# add new word to tokenizer, count is num_vectors_per_token
num_added_tokens = tokenizer.add_tokens(token_strings)
assert (
num_added_tokens == num_vectors_per_token
), f"tokenizer has same word to token string (filename). please rename the file / 指定した名前（ファイル名）のトークンが既に存在します。ファイルをリネームしてください: {embeds_file}"
token_ids = tokenizer.convert_tokens_to_ids(token_strings)
print(f"XTI embeddings `{token_string}` loaded. Tokens are added: {token_ids}")
# if num_vectors_per_token > 1:
pipe.add_token_replacement(token_ids[0], token_ids)
token_strings_XTI = []
for layer_name in XTI_layers:
token_strings_XTI += [f"{t}_{layer_name}" for t in token_strings]
tokenizer.add_tokens(token_strings_XTI)
token_ids_XTI = tokenizer.convert_tokens_to_ids(token_strings_XTI)
token_ids_embeds_XTI.append((token_ids_XTI, embeds))
for t in token_ids:
t_XTI_dic = {}
for i, layer_name in enumerate(XTI_layers):
t_XTI_dic[layer_name] = t + (i + 1) * num_added_tokens
pipe.add_token_replacement_XTI(t, t_XTI_dic)
text_encoder.resize_token_embeddings(len(tokenizer))
token_embeds = text_encoder.get_input_embeddings().weight.data
for token_ids, embeds in token_ids_embeds_XTI:
for token_id, embed in zip(token_ids, embeds):
token_embeds[token_id] = embed
# promptを取得する
if args.from_file is not None:
print(f"reading prompts from {args.from_file}")
@ -2983,6 +3111,7 @@ def setup_parser() -> argparse.ArgumentParser:
"--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数"
)
parser.add_argument("--network_show_meta", action="store_true", help="show metadata of network model / ネットワークモデルのメタデータを表示する")
parser.add_argument("--network_merge", action="store_true", help="merge network weights to original model / ネットワークの重みをマージする")
parser.add_argument(
"--textual_inversion_embeddings",
type=str,
@ -2990,6 +3119,13 @@ def setup_parser() -> argparse.ArgumentParser:
nargs="*",
help="Embeddings files of Textual Inversion / Textual Inversionのembeddings",
)
parser.add_argument(
"--XTI_embeddings",
type=str,
default=None,
nargs="*",
help="Embeddings files of Extended Textual Inversion / Extended Textual Inversionのembeddings",
)
parser.add_argument("--clip_skip", type=int, default=None, help="layer number from bottom to use in CLIP / CLIPの後ろからn層目の出力を使う")
parser.add_argument(
"--max_embeddings_multiples",

1781
library/model_util.py
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File diff suppressed because it is too large Load Diff

32
library/train_util.py
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@ -404,6 +404,8 @@ class BaseDataset(torch.utils.data.Dataset):
self.token_padding_disabled = False
self.tag_frequency = {}
self.XTI_layers = None
self.token_strings = None
self.enable_bucket = False
self.bucket_manager: BucketManager = None # not initialized
@ -464,6 +466,10 @@ class BaseDataset(torch.utils.data.Dataset):
def disable_token_padding(self):
self.token_padding_disabled = True
def enable_XTI(self, layers=None, token_strings=None):
self.XTI_layers = layers
self.token_strings = token_strings
def add_replacement(self, str_from, str_to):
self.replacements[str_from] = str_to
@ -909,9 +915,22 @@ class BaseDataset(torch.utils.data.Dataset):
latents_list.append(latents)
caption = self.process_caption(subset, image_info.caption)
captions.append(caption)
if self.XTI_layers:
caption_layer = []
for layer in self.XTI_layers:
token_strings_from = " ".join(self.token_strings)
token_strings_to = " ".join([f"{x}_{layer}" for x in self.token_strings])
caption_ = caption.replace(token_strings_from, token_strings_to)
caption_layer.append(caption_)
captions.append(caption_layer)
else:
captions.append(caption)
if not self.token_padding_disabled: # this option might be omitted in future
input_ids_list.append(self.get_input_ids(caption))
if self.XTI_layers:
token_caption = self.get_input_ids(caption_layer)
else:
token_caption = self.get_input_ids(caption)
input_ids_list.append(token_caption)
example = {}
example["loss_weights"] = torch.FloatTensor(loss_weights)
@ -1314,6 +1333,10 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
# for dataset in self.datasets:
# dataset.make_buckets()
def enable_XTI(self, *args, **kwargs):
for dataset in self.datasets:
dataset.enable_XTI(*args, **kwargs)
def cache_latents(self, vae, vae_batch_size=1):
for i, dataset in enumerate(self.datasets):
print(f"[Dataset {i}]")
@ -2617,14 +2640,15 @@ def prepare_dtype(args: argparse.Namespace):
return weight_dtype, save_dtype
def load_target_model(args: argparse.Namespace, weight_dtype):
def load_target_model(args: argparse.Namespace, weight_dtype, device='cpu'):
name_or_path = args.pretrained_model_name_or_path
name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path
load_stable_diffusion_format = os.path.isfile(name_or_path) # determine SD or Diffusers
if load_stable_diffusion_format:
print("load StableDiffusion checkpoint")
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, name_or_path)
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, name_or_path, device)
else:
# Diffusers model is loaded to CPU
print("load Diffusers pretrained models")
try:
pipe = StableDiffusionPipeline.from_pretrained(name_or_path, tokenizer=None, safety_checker=None)

679
networks/lora.py
View File

@ -13,386 +13,471 @@ from library import train_util
class LoRAModule(torch.nn.Module):
"""
replaces forward method of the original Linear, instead of replacing the original Linear module.
"""
"""
replaces forward method of the original Linear, instead of replacing the original Linear module.
"""
def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1):
""" if alpha == 0 or None, alpha is rank (no scaling). """
super().__init__()
self.lora_name = lora_name
def __init__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1):
"""if alpha == 0 or None, alpha is rank (no scaling)."""
super().__init__()
self.lora_name = lora_name
if org_module.__class__.__name__ == 'Conv2d':
in_dim = org_module.in_channels
out_dim = org_module.out_channels
else:
in_dim = org_module.in_features
out_dim = org_module.out_features
if org_module.__class__.__name__ == "Conv2d":
in_dim = org_module.in_channels
out_dim = org_module.out_channels
else:
in_dim = org_module.in_features
out_dim = org_module.out_features
# if limit_rank:
# self.lora_dim = min(lora_dim, in_dim, out_dim)
# if self.lora_dim != lora_dim:
# print(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
# else:
self.lora_dim = lora_dim
# if limit_rank:
# self.lora_dim = min(lora_dim, in_dim, out_dim)
# if self.lora_dim != lora_dim:
# print(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
# else:
self.lora_dim = lora_dim
if org_module.__class__.__name__ == 'Conv2d':
kernel_size = org_module.kernel_size
stride = org_module.stride
padding = org_module.padding
self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
else:
self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
if org_module.__class__.__name__ == "Conv2d":
kernel_size = org_module.kernel_size
stride = org_module.stride
padding = org_module.padding
self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
else:
self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
if type(alpha) == torch.Tensor:
alpha = alpha.detach().float().numpy() # without casting, bf16 causes error
alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
self.scale = alpha / self.lora_dim
self.register_buffer('alpha', torch.tensor(alpha)) # 定数として扱える
if type(alpha) == torch.Tensor:
alpha = alpha.detach().float().numpy() # without casting, bf16 causes error
alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
self.scale = alpha / self.lora_dim
self.register_buffer("alpha", torch.tensor(alpha)) # 定数として扱える
# same as microsoft's
torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
torch.nn.init.zeros_(self.lora_up.weight)
# same as microsoft's
torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
torch.nn.init.zeros_(self.lora_up.weight)
self.multiplier = multiplier
self.org_module = org_module # remove in applying
self.region = None
self.region_mask = None
self.multiplier = multiplier
self.org_module = org_module # remove in applying
self.region = None
self.region_mask = None
def apply_to(self):
self.org_forward = self.org_module.forward
self.org_module.forward = self.forward
del self.org_module
def apply_to(self):
self.org_forward = self.org_module.forward
self.org_module.forward = self.forward
del self.org_module
def set_region(self, region):
self.region = region
self.region_mask = None
def merge_to(self, sd, dtype, device):
# get up/down weight
up_weight = sd["lora_up.weight"].to(torch.float).to(device)
down_weight = sd["lora_down.weight"].to(torch.float).to(device)
def forward(self, x):
if self.region is None:
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
# extract weight from org_module
org_sd = self.org_module.state_dict()
weight = org_sd["weight"].to(torch.float)
# regional LoRA FIXME same as additional-network extension
if x.size()[1] % 77 == 0:
# print(f"LoRA for context: {self.lora_name}")
self.region = None
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
# merge weight
if len(weight.size()) == 2:
# linear
weight = weight + self.multiplier * (up_weight @ down_weight) * self.scale
elif down_weight.size()[2:4] == (1, 1):
# conv2d 1x1
weight = (
weight
+ self.multiplier
* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
* self.scale
)
else:
# conv2d 3x3
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
# print(conved.size(), weight.size(), module.stride, module.padding)
weight = weight + self.multiplier * conved * self.scale
# calculate region mask first time
if self.region_mask is None:
if len(x.size()) == 4:
h, w = x.size()[2:4]
else:
seq_len = x.size()[1]
ratio = math.sqrt((self.region.size()[0] * self.region.size()[1]) / seq_len)
h = int(self.region.size()[0] / ratio + .5)
w = seq_len // h
# set weight to org_module
org_sd["weight"] = weight.to(dtype)
self.org_module.load_state_dict(org_sd)
r = self.region.to(x.device)
if r.dtype == torch.bfloat16:
r = r.to(torch.float)
r = r.unsqueeze(0).unsqueeze(1)
# print(self.lora_name, self.region.size(), x.size(), r.size(), h, w)
r = torch.nn.functional.interpolate(r, (h, w), mode='bilinear')
r = r.to(x.dtype)
def set_region(self, region):
self.region = region
self.region_mask = None
if len(x.size()) == 3:
r = torch.reshape(r, (1, x.size()[1], -1))
def forward(self, x):
if self.region is None:
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
self.region_mask = r
# regional LoRA FIXME same as additional-network extension
if x.size()[1] % 77 == 0:
# print(f"LoRA for context: {self.lora_name}")
self.region = None
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale * self.region_mask
# calculate region mask first time
if self.region_mask is None:
if len(x.size()) == 4:
h, w = x.size()[2:4]
else:
seq_len = x.size()[1]
ratio = math.sqrt((self.region.size()[0] * self.region.size()[1]) / seq_len)
h = int(self.region.size()[0] / ratio + 0.5)
w = seq_len // h
r = self.region.to(x.device)
if r.dtype == torch.bfloat16:
r = r.to(torch.float)
r = r.unsqueeze(0).unsqueeze(1)
# print(self.lora_name, self.region.size(), x.size(), r.size(), h, w)
r = torch.nn.functional.interpolate(r, (h, w), mode="bilinear")
r = r.to(x.dtype)
if len(x.size()) == 3:
r = torch.reshape(r, (1, x.size()[1], -1))
self.region_mask = r
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale * self.region_mask
def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
if network_dim is None:
network_dim = 4 # default
if network_dim is None:
network_dim = 4 # default
# extract dim/alpha for conv2d, and block dim
conv_dim = kwargs.get('conv_dim', None)
conv_alpha = kwargs.get('conv_alpha', None)
if conv_dim is not None:
conv_dim = int(conv_dim)
if conv_alpha is None:
conv_alpha = 1.0
else:
conv_alpha = float(conv_alpha)
# extract dim/alpha for conv2d, and block dim
conv_dim = kwargs.get("conv_dim", None)
conv_alpha = kwargs.get("conv_alpha", None)
if conv_dim is not None:
conv_dim = int(conv_dim)
if conv_alpha is None:
conv_alpha = 1.0
else:
conv_alpha = float(conv_alpha)
"""
block_dims = kwargs.get("block_dims")
block_alphas = None
"""
block_dims = kwargs.get("block_dims")
block_alphas = None
if block_dims is not None:
if block_dims is not None:
block_dims = [int(d) for d in block_dims.split(',')]
assert len(block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
block_alphas = kwargs.get("block_alphas")
if block_alphas is None:
block_alphas = [1] * len(block_dims)
block_alphas = [1] * len(block_dims)
else:
block_alphas = [int(a) for a in block_alphas(',')]
block_alphas = [int(a) for a in block_alphas(',')]
assert len(block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
conv_block_dims = kwargs.get("conv_block_dims")
conv_block_alphas = None
conv_block_dims = kwargs.get("conv_block_dims")
conv_block_alphas = None
if conv_block_dims is not None:
if conv_block_dims is not None:
conv_block_dims = [int(d) for d in conv_block_dims.split(',')]
assert len(conv_block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
conv_block_alphas = kwargs.get("conv_block_alphas")
if conv_block_alphas is None:
conv_block_alphas = [1] * len(conv_block_dims)
conv_block_alphas = [1] * len(conv_block_dims)
else:
conv_block_alphas = [int(a) for a in conv_block_alphas(',')]
conv_block_alphas = [int(a) for a in conv_block_alphas(',')]
assert len(conv_block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
"""
network = LoRANetwork(text_encoder, unet, multiplier=multiplier, lora_dim=network_dim,
alpha=network_alpha, conv_lora_dim=conv_dim, conv_alpha=conv_alpha)
return network
network = LoRANetwork(
text_encoder,
unet,
multiplier=multiplier,
lora_dim=network_dim,
alpha=network_alpha,
conv_lora_dim=conv_dim,
conv_alpha=conv_alpha,
)
return network
def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, **kwargs):
if weights_sd is None:
if os.path.splitext(file)[1] == '.safetensors':
from safetensors.torch import load_file, safe_open
weights_sd = load_file(file)
else:
weights_sd = torch.load(file, map_location='cpu')
if weights_sd is None:
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import load_file, safe_open
# get dim/alpha mapping
modules_dim = {}
modules_alpha = {}
for key, value in weights_sd.items():
if '.' not in key:
continue
weights_sd = load_file(file)
else:
weights_sd = torch.load(file, map_location="cpu")
lora_name = key.split('.')[0]
if 'alpha' in key:
modules_alpha[lora_name] = value
elif 'lora_down' in key:
dim = value.size()[0]
modules_dim[lora_name] = dim
# print(lora_name, value.size(), dim)
# get dim/alpha mapping
modules_dim = {}
modules_alpha = {}
for key, value in weights_sd.items():
if "." not in key:
continue
# support old LoRA without alpha
for key in modules_dim.keys():
if key not in modules_alpha:
modules_alpha = modules_dim[key]
lora_name = key.split(".")[0]
if "alpha" in key:
modules_alpha[lora_name] = value
elif "lora_down" in key:
dim = value.size()[0]
modules_dim[lora_name] = dim
# print(lora_name, value.size(), dim)
network = LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
network.weights_sd = weights_sd
return network
# support old LoRA without alpha
for key in modules_dim.keys():
if key not in modules_alpha:
modules_alpha = modules_dim[key]
network = LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
network.weights_sd = weights_sd
return network
class LoRANetwork(torch.nn.Module):
# is it possible to apply conv_in and conv_out?
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = 'lora_unet'
LORA_PREFIX_TEXT_ENCODER = 'lora_te'
# is it possible to apply conv_in and conv_out?
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te"
def __init__(self, text_encoder, unet, multiplier=1.0, lora_dim=4, alpha=1, conv_lora_dim=None, conv_alpha=None, modules_dim=None, modules_alpha=None) -> None:
super().__init__()
self.multiplier = multiplier
def __init__(
self,
text_encoder,
unet,
multiplier=1.0,
lora_dim=4,
alpha=1,
conv_lora_dim=None,
conv_alpha=None,
modules_dim=None,
modules_alpha=None,
) -> None:
super().__init__()
self.multiplier = multiplier
self.lora_dim = lora_dim
self.alpha = alpha
self.conv_lora_dim = conv_lora_dim
self.conv_alpha = conv_alpha
self.lora_dim = lora_dim
self.alpha = alpha
self.conv_lora_dim = conv_lora_dim
self.conv_alpha = conv_alpha
if modules_dim is not None:
print(f"create LoRA network from weights")
else:
print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
if modules_dim is not None:
print(f"create LoRA network from weights")
else:
print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
self.apply_to_conv2d_3x3 = self.conv_lora_dim is not None
if self.apply_to_conv2d_3x3:
if self.conv_alpha is None:
self.conv_alpha = self.alpha
print(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
self.apply_to_conv2d_3x3 = self.conv_lora_dim is not None
if self.apply_to_conv2d_3x3:
if self.conv_alpha is None:
self.conv_alpha = self.alpha
print(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
# create module instances
def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
loras = []
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
# TODO get block index here
for child_name, child_module in module.named_modules():
is_linear = child_module.__class__.__name__ == "Linear"
is_conv2d = child_module.__class__.__name__ == "Conv2d"
is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
if is_linear or is_conv2d:
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
# create module instances
def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
loras = []
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
# TODO get block index here
for child_name, child_module in module.named_modules():
is_linear = child_module.__class__.__name__ == "Linear"
is_conv2d = child_module.__class__.__name__ == "Conv2d"
is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
if is_linear or is_conv2d:
lora_name = prefix + "." + name + "." + child_name
lora_name = lora_name.replace(".", "_")
if modules_dim is not None:
if lora_name not in modules_dim:
continue # no LoRA module in this weights file
dim = modules_dim[lora_name]
alpha = modules_alpha[lora_name]
else:
if is_linear or is_conv2d_1x1:
dim = self.lora_dim
alpha = self.alpha
elif self.apply_to_conv2d_3x3:
dim = self.conv_lora_dim
alpha = self.conv_alpha
else:
continue
if modules_dim is not None:
if lora_name not in modules_dim:
continue # no LoRA module in this weights file
dim = modules_dim[lora_name]
alpha = modules_alpha[lora_name]
else:
if is_linear or is_conv2d_1x1:
dim = self.lora_dim
alpha = self.alpha
elif self.apply_to_conv2d_3x3:
dim = self.conv_lora_dim
alpha = self.conv_alpha
else:
continue
lora = LoRAModule(lora_name, child_module, self.multiplier, dim, alpha)
loras.append(lora)
return loras
lora = LoRAModule(lora_name, child_module, self.multiplier, dim, alpha)
loras.append(lora)
return loras
self.text_encoder_loras = create_modules(LoRANetwork.LORA_PREFIX_TEXT_ENCODER,
text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
self.text_encoder_loras = create_modules(
LoRANetwork.LORA_PREFIX_TEXT_ENCODER, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
)
print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
# extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE
if modules_dim is not None or self.conv_lora_dim is not None:
target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
# extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE
if modules_dim is not None or self.conv_lora_dim is not None:
target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, target_modules)
print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, target_modules)
print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
self.weights_sd = None
self.weights_sd = None
# assertion
names = set()
for lora in self.text_encoder_loras + self.unet_loras:
assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
names.add(lora.lora_name)
# assertion
names = set()
for lora in self.text_encoder_loras + self.unet_loras:
assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
names.add(lora.lora_name)
def set_multiplier(self, multiplier):
self.multiplier = multiplier
for lora in self.text_encoder_loras + self.unet_loras:
lora.multiplier = self.multiplier
def set_multiplier(self, multiplier):
self.multiplier = multiplier
for lora in self.text_encoder_loras + self.unet_loras:
lora.multiplier = self.multiplier
def load_weights(self, file):
if os.path.splitext(file)[1] == '.safetensors':
from safetensors.torch import load_file, safe_open
self.weights_sd = load_file(file)
else:
self.weights_sd = torch.load(file, map_location='cpu')
def load_weights(self, file):
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import load_file, safe_open
def apply_to(self, text_encoder, unet, apply_text_encoder=None, apply_unet=None):
if self.weights_sd:
weights_has_text_encoder = weights_has_unet = False
for key in self.weights_sd.keys():
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
weights_has_text_encoder = True
elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
weights_has_unet = True
self.weights_sd = load_file(file)
else:
self.weights_sd = torch.load(file, map_location="cpu")
if apply_text_encoder is None:
apply_text_encoder = weights_has_text_encoder
else:
assert apply_text_encoder == weights_has_text_encoder, f"text encoder weights: {weights_has_text_encoder} but text encoder flag: {apply_text_encoder} / 重みとText Encoderのフラグが矛盾しています"
def apply_to(self, text_encoder, unet, apply_text_encoder=None, apply_unet=None):
if self.weights_sd:
weights_has_text_encoder = weights_has_unet = False
for key in self.weights_sd.keys():
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
weights_has_text_encoder = True
elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
weights_has_unet = True
if apply_unet is None:
apply_unet = weights_has_unet
else:
assert apply_unet == weights_has_unet, f"u-net weights: {weights_has_unet} but u-net flag: {apply_unet} / 重みとU-Netのフラグが矛盾しています"
else:
assert apply_text_encoder is not None and apply_unet is not None, f"internal error: flag not set"
if apply_text_encoder is None:
apply_text_encoder = weights_has_text_encoder
else:
assert (
apply_text_encoder == weights_has_text_encoder
), f"text encoder weights: {weights_has_text_encoder} but text encoder flag: {apply_text_encoder} / 重みとText Encoderのフラグが矛盾しています"
if apply_text_encoder:
print("enable LoRA for text encoder")
else:
self.text_encoder_loras = []
if apply_unet is None:
apply_unet = weights_has_unet
else:
assert (
apply_unet == weights_has_unet
), f"u-net weights: {weights_has_unet} but u-net flag: {apply_unet} / 重みとU-Netのフラグが矛盾しています"
else:
assert apply_text_encoder is not None and apply_unet is not None, f"internal error: flag not set"
if apply_unet:
print("enable LoRA for U-Net")
else:
self.unet_loras = []
if apply_text_encoder:
print("enable LoRA for text encoder")
else:
self.text_encoder_loras = []
for lora in self.text_encoder_loras + self.unet_loras:
lora.apply_to()
self.add_module(lora.lora_name, lora)
if apply_unet:
print("enable LoRA for U-Net")
else:
self.unet_loras = []
if self.weights_sd:
# if some weights are not in state dict, it is ok because initial LoRA does nothing (lora_up is initialized by zeros)
info = self.load_state_dict(self.weights_sd, False)
print(f"weights are loaded: {info}")
for lora in self.text_encoder_loras + self.unet_loras:
lora.apply_to()
self.add_module(lora.lora_name, lora)
def enable_gradient_checkpointing(self):
# not supported
pass
if self.weights_sd:
# if some weights are not in state dict, it is ok because initial LoRA does nothing (lora_up is initialized by zeros)
info = self.load_state_dict(self.weights_sd, False)
print(f"weights are loaded: {info}")
def prepare_optimizer_params(self, text_encoder_lr, unet_lr):
def enumerate_params(loras):
params = []
for lora in loras:
params.extend(lora.parameters())
return params
# TODO refactor to common function with apply_to
def merge_to(self, text_encoder, unet, dtype, device):
assert self.weights_sd is not None, "weights are not loaded"
self.requires_grad_(True)
all_params = []
apply_text_encoder = apply_unet = False
for key in self.weights_sd.keys():
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
apply_text_encoder = True
elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
apply_unet = True
if self.text_encoder_loras:
param_data = {'params': enumerate_params(self.text_encoder_loras)}
if text_encoder_lr is not None:
param_data['lr'] = text_encoder_lr
all_params.append(param_data)
if apply_text_encoder:
print("enable LoRA for text encoder")
else:
self.text_encoder_loras = []
if self.unet_loras:
param_data = {'params': enumerate_params(self.unet_loras)}
if unet_lr is not None:
param_data['lr'] = unet_lr
all_params.append(param_data)
if apply_unet:
print("enable LoRA for U-Net")
else:
self.unet_loras = []
return all_params
for lora in self.text_encoder_loras + self.unet_loras:
sd_for_lora = {}
for key in self.weights_sd.keys():
if key.startswith(lora.lora_name):
sd_for_lora[key[len(lora.lora_name) + 1 :]] = self.weights_sd[key]
lora.merge_to(sd_for_lora, dtype, device)
print(f"weights are merged")
def prepare_grad_etc(self, text_encoder, unet):
self.requires_grad_(True)
def enable_gradient_checkpointing(self):
# not supported
pass
def on_epoch_start(self, text_encoder, unet):
self.train()
def prepare_optimizer_params(self, text_encoder_lr, unet_lr):
def enumerate_params(loras):
params = []
for lora in loras:
params.extend(lora.parameters())
return params
def get_trainable_params(self):
return self.parameters()
self.requires_grad_(True)
all_params = []
def save_weights(self, file, dtype, metadata):
if metadata is not None and len(metadata) == 0:
metadata = None
if self.text_encoder_loras:
param_data = {"params": enumerate_params(self.text_encoder_loras)}
if text_encoder_lr is not None:
param_data["lr"] = text_encoder_lr
all_params.append(param_data)
state_dict = self.state_dict()
if self.unet_loras:
param_data = {"params": enumerate_params(self.unet_loras)}
if unet_lr is not None:
param_data["lr"] = unet_lr
all_params.append(param_data)
if dtype is not None:
for key in list(state_dict.keys()):
v = state_dict[key]
v = v.detach().clone().to("cpu").to(dtype)
state_dict[key] = v
return all_params
if os.path.splitext(file)[1] == '.safetensors':
from safetensors.torch import save_file
def prepare_grad_etc(self, text_encoder, unet):
self.requires_grad_(True)
# Precalculate model hashes to save time on indexing
if metadata is None:
metadata = {}
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
metadata["sshs_model_hash"] = model_hash
metadata["sshs_legacy_hash"] = legacy_hash
def on_epoch_start(self, text_encoder, unet):
self.train()
save_file(state_dict, file, metadata)
else:
torch.save(state_dict, file)
def get_trainable_params(self):
return self.parameters()
@ staticmethod
def set_regions(networks, image):
image = image.astype(np.float32) / 255.0
for i, network in enumerate(networks[:3]):
# NOTE: consider averaging overwrapping area
region = image[:, :, i]
if region.max() == 0:
continue
region = torch.tensor(region)
network.set_region(region)
def save_weights(self, file, dtype, metadata):
if metadata is not None and len(metadata) == 0:
metadata = None
def set_region(self, region):
for lora in self.unet_loras:
lora.set_region(region)
state_dict = self.state_dict()
if dtype is not None:
for key in list(state_dict.keys()):
v = state_dict[key]
v = v.detach().clone().to("cpu").to(dtype)
state_dict[key] = v
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import save_file
# Precalculate model hashes to save time on indexing
if metadata is None:
metadata = {}
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
metadata["sshs_model_hash"] = model_hash
metadata["sshs_legacy_hash"] = legacy_hash
save_file(state_dict, file, metadata)
else:
torch.save(state_dict, file)
@staticmethod
def set_regions(networks, image):
image = image.astype(np.float32) / 255.0
for i, network in enumerate(networks[:3]):
# NOTE: consider averaging overwrapping area
region = image[:, :, i]
if region.max() == 0:
continue
region = torch.tensor(region)
network.set_region(region)
def set_region(self, region):
for lora in self.unet_loras:
lora.set_region(region)

363
networks/merge_lora.py
View File

@ -1,4 +1,3 @@
import math
import argparse
import os
@ -9,216 +8,236 @@ import lora
def load_state_dict(file_name, dtype):
if os.path.splitext(file_name)[1] == '.safetensors':
sd = load_file(file_name)
else:
sd = torch.load(file_name, map_location='cpu')
for key in list(sd.keys()):
if type(sd[key]) == torch.Tensor:
sd[key] = sd[key].to(dtype)
return sd
if os.path.splitext(file_name)[1] == ".safetensors":
sd = load_file(file_name)
else:
sd = torch.load(file_name, map_location="cpu")
for key in list(sd.keys()):
if type(sd[key]) == torch.Tensor:
sd[key] = sd[key].to(dtype)
return sd
def save_to_file(file_name, model, state_dict, dtype):
if dtype is not None:
for key in list(state_dict.keys()):
if type(state_dict[key]) == torch.Tensor:
state_dict[key] = state_dict[key].to(dtype)
if dtype is not None:
for key in list(state_dict.keys()):
if type(state_dict[key]) == torch.Tensor:
state_dict[key] = state_dict[key].to(dtype)
if os.path.splitext(file_name)[1] == '.safetensors':
save_file(model, file_name)
else:
torch.save(model, file_name)
if os.path.splitext(file_name)[1] == ".safetensors":
save_file(model, file_name)
else:
torch.save(model, file_name)
def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
text_encoder.to(merge_dtype)
unet.to(merge_dtype)
text_encoder.to(merge_dtype)
unet.to(merge_dtype)
# create module map
name_to_module = {}
for i, root_module in enumerate([text_encoder, unet]):
if i == 0:
prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
else:
prefix = lora.LoRANetwork.LORA_PREFIX_UNET
target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
for child_name, child_module in module.named_modules():
if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
name_to_module[lora_name] = child_module
for model, ratio in zip(models, ratios):
print(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
print(f"merging...")
for key in lora_sd.keys():
if "lora_down" in key:
up_key = key.replace("lora_down", "lora_up")
alpha_key = key[:key.index("lora_down")] + 'alpha'
# find original module for this lora
module_name = '.'.join(key.split('.')[:-2]) # remove trailing ".lora_down.weight"
if module_name not in name_to_module:
print(f"no module found for LoRA weight: {key}")
continue
module = name_to_module[module_name]
# print(f"apply {key} to {module}")
down_weight = lora_sd[key]
up_weight = lora_sd[up_key]
dim = down_weight.size()[0]
alpha = lora_sd.get(alpha_key, dim)
scale = alpha / dim
# W <- W + U * D
weight = module.weight
# print(module_name, down_weight.size(), up_weight.size())
if len(weight.size()) == 2:
# linear
weight = weight + ratio * (up_weight @ down_weight) * scale
elif down_weight.size()[2:4] == (1, 1):
# conv2d 1x1
weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)
).unsqueeze(2).unsqueeze(3) * scale
# create module map
name_to_module = {}
for i, root_module in enumerate([text_encoder, unet]):
if i == 0:
prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
else:
# conv2d 3x3
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
# print(conved.size(), weight.size(), module.stride, module.padding)
weight = weight + ratio * conved * scale
prefix = lora.LoRANetwork.LORA_PREFIX_UNET
target_replace_modules = (
lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE + lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
)
module.weight = torch.nn.Parameter(weight)
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
for child_name, child_module in module.named_modules():
if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
lora_name = prefix + "." + name + "." + child_name
lora_name = lora_name.replace(".", "_")
name_to_module[lora_name] = child_module
for model, ratio in zip(models, ratios):
print(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
print(f"merging...")
for key in lora_sd.keys():
if "lora_down" in key:
up_key = key.replace("lora_down", "lora_up")
alpha_key = key[: key.index("lora_down")] + "alpha"
# find original module for this lora
module_name = ".".join(key.split(".")[:-2]) # remove trailing ".lora_down.weight"
if module_name not in name_to_module:
print(f"no module found for LoRA weight: {key}")
continue
module = name_to_module[module_name]
# print(f"apply {key} to {module}")
down_weight = lora_sd[key]
up_weight = lora_sd[up_key]
dim = down_weight.size()[0]
alpha = lora_sd.get(alpha_key, dim)
scale = alpha / dim
# W <- W + U * D
weight = module.weight
# print(module_name, down_weight.size(), up_weight.size())
if len(weight.size()) == 2:
# linear
weight = weight + ratio * (up_weight @ down_weight) * scale
elif down_weight.size()[2:4] == (1, 1):
# conv2d 1x1
weight = (
weight
+ ratio
* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
* scale
)
else:
# conv2d 3x3
conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
# print(conved.size(), weight.size(), module.stride, module.padding)
weight = weight + ratio * conved * scale
module.weight = torch.nn.Parameter(weight)
def merge_lora_models(models, ratios, merge_dtype):
base_alphas = {} # alpha for merged model
base_dims = {}
base_alphas = {} # alpha for merged model
base_dims = {}
merged_sd = {}
for model, ratio in zip(models, ratios):
print(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
merged_sd = {}
for model, ratio in zip(models, ratios):
print(f"loading: {model}")
lora_sd = load_state_dict(model, merge_dtype)
# get alpha and dim
alphas = {} # alpha for current model
dims = {} # dims for current model
for key in lora_sd.keys():
if 'alpha' in key:
lora_module_name = key[:key.rfind(".alpha")]
alpha = float(lora_sd[key].detach().numpy())
alphas[lora_module_name] = alpha
if lora_module_name not in base_alphas:
base_alphas[lora_module_name] = alpha
elif "lora_down" in key:
lora_module_name = key[:key.rfind(".lora_down")]
dim = lora_sd[key].size()[0]
dims[lora_module_name] = dim
if lora_module_name not in base_dims:
base_dims[lora_module_name] = dim
# get alpha and dim
alphas = {} # alpha for current model
dims = {} # dims for current model
for key in lora_sd.keys():
if "alpha" in key:
lora_module_name = key[: key.rfind(".alpha")]
alpha = float(lora_sd[key].detach().numpy())
alphas[lora_module_name] = alpha
if lora_module_name not in base_alphas:
base_alphas[lora_module_name] = alpha
elif "lora_down" in key:
lora_module_name = key[: key.rfind(".lora_down")]
dim = lora_sd[key].size()[0]
dims[lora_module_name] = dim
if lora_module_name not in base_dims:
base_dims[lora_module_name] = dim
for lora_module_name in dims.keys():
if lora_module_name not in alphas:
alpha = dims[lora_module_name]
alphas[lora_module_name] = alpha
if lora_module_name not in base_alphas:
base_alphas[lora_module_name] = alpha
for lora_module_name in dims.keys():
if lora_module_name not in alphas:
alpha = dims[lora_module_name]
alphas[lora_module_name] = alpha
if lora_module_name not in base_alphas:
base_alphas[lora_module_name] = alpha
print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")
# merge
print(f"merging...")
for key in lora_sd.keys():
if 'alpha' in key:
continue
# merge
print(f"merging...")
for key in lora_sd.keys():
if "alpha" in key:
continue
lora_module_name = key[:key.rfind(".lora_")]
lora_module_name = key[: key.rfind(".lora_")]
base_alpha = base_alphas[lora_module_name]
alpha = alphas[lora_module_name]
base_alpha = base_alphas[lora_module_name]
alpha = alphas[lora_module_name]
scale = math.sqrt(alpha / base_alpha) * ratio
scale = math.sqrt(alpha / base_alpha) * ratio
if key in merged_sd:
assert merged_sd[key].size() == lora_sd[key].size(
), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
else:
merged_sd[key] = lora_sd[key] * scale
if key in merged_sd:
assert (
merged_sd[key].size() == lora_sd[key].size()
), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
else:
merged_sd[key] = lora_sd[key] * scale
# set alpha to sd
for lora_module_name, alpha in base_alphas.items():
key = lora_module_name + ".alpha"
merged_sd[key] = torch.tensor(alpha)
# set alpha to sd
for lora_module_name, alpha in base_alphas.items():
key = lora_module_name + ".alpha"
merged_sd[key] = torch.tensor(alpha)
print("merged model")
print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
print("merged model")
print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")
return merged_sd
return merged_sd
def merge(args):
assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"
def str_to_dtype(p):
if p == 'float':
return torch.float
if p == 'fp16':
return torch.float16
if p == 'bf16':
return torch.bfloat16
return None
def str_to_dtype(p):
if p == "float":
return torch.float
if p == "fp16":
return torch.float16
if p == "bf16":
return torch.bfloat16
return None
merge_dtype = str_to_dtype(args.precision)
save_dtype = str_to_dtype(args.save_precision)
if save_dtype is None:
save_dtype = merge_dtype
merge_dtype = str_to_dtype(args.precision)
save_dtype = str_to_dtype(args.save_precision)
if save_dtype is None:
save_dtype = merge_dtype
if args.sd_model is not None:
print(f"loading SD model: {args.sd_model}")
if args.sd_model is not None:
print(f"loading SD model: {args.sd_model}")
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)
merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)
print(f"saving SD model to: {args.save_to}")
model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
args.sd_model, 0, 0, save_dtype, vae)
else:
state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
print(f"saving SD model to: {args.save_to}")
model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet, args.sd_model, 0, 0, save_dtype, vae)
else:
state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)
print(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype)
print(f"saving model to: {args.save_to}")
save_to_file(args.save_to, state_dict, state_dict, save_dtype)
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument("--v2", action='store_true',
help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む')
parser.add_argument("--save_precision", type=str, default=None,
choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
parser.add_argument("--precision", type=str, default="float",
choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
parser.add_argument("--sd_model", type=str, default=None,
help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
parser.add_argument("--save_to", type=str, default=None,
help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
parser.add_argument("--models", type=str, nargs='*',
help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
parser.add_argument("--ratios", type=float, nargs='*',
help="ratios for each model / それぞれのLoRAモデルの比率")
parser = argparse.ArgumentParser()
parser.add_argument("--v2", action="store_true", help="load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む")
parser.add_argument(
"--save_precision",
type=str,
default=None,
choices=[None, "float", "fp16", "bf16"],
help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ",
)
parser.add_argument(
"--precision",
type=str,
default="float",
choices=["float", "fp16", "bf16"],
help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）",
)
parser.add_argument(
"--sd_model",
type=str,
default=None,
help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする",
)
parser.add_argument(
"--save_to", type=str, default=None, help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors"
)
parser.add_argument(
"--models", type=str, nargs="*", help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors"
)
parser.add_argument("--ratios", type=float, nargs="*", help="ratios for each model / それぞれのLoRAモデルの比率")
return parser
return parser
if __name__ == '__main__':
parser = setup_parser()
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()
merge(args)
args = parser.parse_args()
merge(args)

80
tools/merge_lycoris.py Normal file
View File

@ -0,0 +1,80 @@
import os
import sys
import argparse
import torch
from lycoris.utils import merge_loha, merge_locon
from lycoris.kohya_model_utils import (
load_models_from_stable_diffusion_checkpoint,
save_stable_diffusion_checkpoint,
load_file
)
import gradio as gr
def merge_models(base_model, lycoris_model, output_name, is_v2, device, dtype, weight):
base = load_models_from_stable_diffusion_checkpoint(is_v2, base_model)
if lycoris_model.rsplit('.', 1)[-1] == 'safetensors':
lyco = load_file(lycoris_model)
else:
lyco = torch.load(lycoris_model)
algo = None
for key in lyco:
if 'hada' in key:
algo = 'loha'
break
elif 'lora_up' in key:
algo = 'lora'
break
else:
raise NotImplementedError('Cannot find the algo for this lycoris model file.')
dtype_str = dtype.replace('fp', 'float').replace('bf', 'bfloat')
dtype = {
'float': torch.float,
'float16': torch.float16,
'float32': torch.float32,
'float64': torch.float64,
'bfloat': torch.bfloat16,
'bfloat16': torch.bfloat16,
}.get(dtype_str, None)
if dtype is None:
raise ValueError(f'Cannot Find the dtype "{dtype}"')
if algo == 'loha':
merge_loha(base, lyco, weight, device)
elif algo == 'lora':
merge_locon(base, lyco, weight, device)
save_stable_diffusion_checkpoint(
is_v2, output_name,
base[0], base[2],
None, 0, 0, dtype,
base[1]
)
return output_name
def main():
iface = gr.Interface(
fn=merge_models,
inputs=[
gr.inputs.Textbox(label="Base Model Path"),
gr.inputs.Textbox(label="Lycoris Model Path"),
gr.inputs.Textbox(label="Output Model Path", default='./out.pt'),
gr.inputs.Checkbox(label="Is base model SD V2?", default=False),
gr.inputs.Textbox(label="Device", default='cpu'),
gr.inputs.Dropdown(choices=['float', 'float16', 'float32', 'float64', 'bfloat', 'bfloat16'], label="Dtype", default='float'),
gr.inputs.Number(label="Weight", default=1.0)
],
outputs=gr.outputs.Textbox(label="Merged Model Path"),
title="Model Merger",
description="Merge Lycoris and Stable Diffusion models",
)
iface.launch()
if __name__ == '__main__':
main()

31
train_network.py
View File

@ -25,7 +25,7 @@ from library.config_util import (
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight
from library.custom_train_functions import apply_snr_weight
# TODO 他のスクリプトと共通化する
@ -127,12 +127,25 @@ def train(args):
weight_dtype, save_dtype = train_util.prepare_dtype(args)
# モデルを読み込む
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
for pi in range(accelerator.state.num_processes):
# TODO: modify other training scripts as well
if pi == accelerator.state.local_process_index:
print(f"loading model for process {accelerator.state.local_process_index}/{accelerator.state.num_processes}")
text_encoder, vae, unet, _ = train_util.load_target_model(
args, weight_dtype, accelerator.device if args.lowram else "cpu"
)
# work on low-ram device
if args.lowram:
text_encoder.to(accelerator.device)
unet.to(accelerator.device)
vae.to(accelerator.device)
gc.collect()
torch.cuda.empty_cache()
accelerator.wait_for_everyone()
# work on low-ram device
if args.lowram:
text_encoder.to("cuda")
unet.to("cuda")
# モデルに xformers とか memory efficient attention を組み込む
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
@ -189,7 +202,7 @@ def train(args):
# dataloaderを準備する
# DataLoaderのプロセス数：0はメインプロセスになる
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
train_dataloader = torch.utils.data.DataLoader(
train_dataset_group,
batch_size=1,
@ -556,9 +569,9 @@ def train(args):
loss_weights = batch["loss_weights"] # 各sampleごとのweight
loss = loss * loss_weights
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし

644
train_textual_inversion_XTI.py Normal file
View File

@ -0,0 +1,644 @@
import importlib
import argparse
import gc
import math
import os
import toml
from multiprocessing import Value
from tqdm import tqdm
import torch
from accelerate.utils import set_seed
import diffusers
from diffusers import DDPMScheduler
import library.train_util as train_util
import library.config_util as config_util
from library.config_util import (
ConfigSanitizer,
BlueprintGenerator,
)
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import apply_snr_weight
from XTI_hijack import unet_forward_XTI, downblock_forward_XTI, upblock_forward_XTI
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
def train(args):
if args.output_name is None:
args.output_name = args.token_string
use_template = args.use_object_template or args.use_style_template
train_util.verify_training_args(args)
train_util.prepare_dataset_args(args, True)
if args.sample_every_n_steps is not None or args.sample_every_n_epochs is not None:
print(
"sample_every_n_steps and sample_every_n_epochs are not supported in this script currently / sample_every_n_stepsとsample_every_n_epochsは現在このスクリプトではサポートされていません"
)
cache_latents = args.cache_latents
if args.seed is not None:
set_seed(args.seed)
tokenizer = train_util.load_tokenizer(args)
# acceleratorを準備する
print("prepare accelerator")
accelerator, unwrap_model = train_util.prepare_accelerator(args)
# mixed precisionに対応した型を用意しておき適宜castする
weight_dtype, save_dtype = train_util.prepare_dtype(args)
# モデルを読み込む
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
# Convert the init_word to token_id
if args.init_word is not None:
init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
print(
f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
)
else:
init_token_ids = None
# add new word to tokenizer, count is num_vectors_per_token
token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
num_added_tokens = tokenizer.add_tokens(token_strings)
assert (
num_added_tokens == args.num_vectors_per_token
), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
token_ids = tokenizer.convert_tokens_to_ids(token_strings)
print(f"tokens are added: {token_ids}")
assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
token_strings_XTI = []
XTI_layers = [
"IN01",
"IN02",
"IN04",
"IN05",
"IN07",
"IN08",
"MID",
"OUT03",
"OUT04",
"OUT05",
"OUT06",
"OUT07",
"OUT08",
"OUT09",
"OUT10",
"OUT11",
]
for layer_name in XTI_layers:
token_strings_XTI += [f"{t}_{layer_name}" for t in token_strings]
tokenizer.add_tokens(token_strings_XTI)
token_ids_XTI = tokenizer.convert_tokens_to_ids(token_strings_XTI)
print(f"tokens are added (XTI): {token_ids_XTI}")
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
if init_token_ids is not None:
for i, token_id in enumerate(token_ids_XTI):
token_embeds[token_id] = token_embeds[init_token_ids[(i // 16) % len(init_token_ids)]]
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
# load weights
if args.weights is not None:
embeddings = load_weights(args.weights)
assert len(token_ids) == len(
embeddings
), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
# print(token_ids, embeddings.size())
for token_id, embedding in zip(token_ids_XTI, embeddings):
token_embeds[token_id] = embedding
# print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
print(f"weighs loaded")
print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
# データセットを準備する
blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False))
if args.dataset_config is not None:
print(f"Load dataset config from {args.dataset_config}")
user_config = config_util.load_user_config(args.dataset_config)
ignored = ["train_data_dir", "reg_data_dir", "in_json"]
if any(getattr(args, attr) is not None for attr in ignored):
print(
"ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
", ".join(ignored)
)
)
else:
use_dreambooth_method = args.in_json is None
if use_dreambooth_method:
print("Use DreamBooth method.")
user_config = {
"datasets": [
{"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
]
}
else:
print("Train with captions.")
user_config = {
"datasets": [
{
"subsets": [
{
"image_dir": args.train_data_dir,
"metadata_file": args.in_json,
}
]
}
]
}
blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
train_dataset_group.enable_XTI(XTI_layers, token_strings=token_strings)
current_epoch = Value("i", 0)
current_step = Value("i", 0)
ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
# make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
if use_template:
print("use template for training captions. is object: {args.use_object_template}")
templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
replace_to = " ".join(token_strings)
captions = []
for tmpl in templates:
captions.append(tmpl.format(replace_to))
train_dataset_group.add_replacement("", captions)
if args.num_vectors_per_token > 1:
prompt_replacement = (args.token_string, replace_to)
else:
prompt_replacement = None
else:
if args.num_vectors_per_token > 1:
replace_to = " ".join(token_strings)
train_dataset_group.add_replacement(args.token_string, replace_to)
prompt_replacement = (args.token_string, replace_to)
else:
prompt_replacement = None
if args.debug_dataset:
train_util.debug_dataset(train_dataset_group, show_input_ids=True)
return
if len(train_dataset_group) == 0:
print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
return
if cache_latents:
assert (
train_dataset_group.is_latent_cacheable()
), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
# モデルに xformers とか memory efficient attention を組み込む
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
diffusers.models.UNet2DConditionModel.forward = unet_forward_XTI
diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D.forward = downblock_forward_XTI
diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D.forward = upblock_forward_XTI
# 学習を準備する
if cache_latents:
vae.to(accelerator.device, dtype=weight_dtype)
vae.requires_grad_(False)
vae.eval()
with torch.no_grad():
train_dataset_group.cache_latents(vae, args.vae_batch_size)
vae.to("cpu")
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
text_encoder.gradient_checkpointing_enable()
# 学習に必要なクラスを準備する
print("prepare optimizer, data loader etc.")
trainable_params = text_encoder.get_input_embeddings().parameters()
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
# dataloaderを準備する
# DataLoaderのプロセス数：0はメインプロセスになる
n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1) # cpu_count-1 ただし最大で指定された数まで
train_dataloader = torch.utils.data.DataLoader(
train_dataset_group,
batch_size=1,
shuffle=True,
collate_fn=collater,
num_workers=n_workers,
persistent_workers=args.persistent_data_loader_workers,
)
# 学習ステップ数を計算する
if args.max_train_epochs is not None:
args.max_train_steps = args.max_train_epochs * math.ceil(
len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
)
print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
# データセット側にも学習ステップを送信
train_dataset_group.set_max_train_steps(args.max_train_steps)
# lr schedulerを用意する
lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
# acceleratorがなんかよろしくやってくれるらしい
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, lr_scheduler
)
index_no_updates = torch.arange(len(tokenizer)) < token_ids_XTI[0]
# print(len(index_no_updates), torch.sum(index_no_updates))
orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
# Freeze all parameters except for the token embeddings in text encoder
text_encoder.requires_grad_(True)
text_encoder.text_model.encoder.requires_grad_(False)
text_encoder.text_model.final_layer_norm.requires_grad_(False)
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
# text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
unet.requires_grad_(False)
unet.to(accelerator.device, dtype=weight_dtype)
if args.gradient_checkpointing: # according to TI example in Diffusers, train is required
unet.train()
else:
unet.eval()
if not cache_latents:
vae.requires_grad_(False)
vae.eval()
vae.to(accelerator.device, dtype=weight_dtype)
# 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
if args.full_fp16:
train_util.patch_accelerator_for_fp16_training(accelerator)
text_encoder.to(weight_dtype)
# resumeする
if args.resume is not None:
print(f"resume training from state: {args.resume}")
accelerator.load_state(args.resume)
# epoch数を計算する
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
# 学習する
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
print("running training / 学習開始")
print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
print(f" num epochs / epoch数: {num_train_epochs}")
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
global_step = 0
noise_scheduler = DDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
)
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion")
for epoch in range(num_train_epochs):
print(f"epoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1
text_encoder.train()
loss_total = 0
for step, batch in enumerate(train_dataloader):
current_step.value = global_step
with accelerator.accumulate(text_encoder):
with torch.no_grad():
if "latents" in batch and batch["latents"] is not None:
latents = batch["latents"].to(accelerator.device)
else:
# latentに変換
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
b_size = latents.shape[0]
# Get the text embedding for conditioning
input_ids = batch["input_ids"].to(accelerator.device)
# weight_dtype) use float instead of fp16/bf16 because text encoder is float
encoder_hidden_states = torch.stack(
[
train_util.get_hidden_states(args, s, tokenizer, text_encoder, weight_dtype)
for s in torch.split(input_ids, 1, dim=1)
]
)
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents, device=latents.device)
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Predict the noise residual
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
if args.v_parameterization:
# v-parameterization training
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
target = noise
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
loss = loss.mean([1, 2, 3])
if args.min_snr_gamma:
loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
loss_weights = batch["loss_weights"] # 各sampleごとのweight
loss = loss * loss_weights
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
accelerator.backward(loss)
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
params_to_clip = text_encoder.get_input_embeddings().parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
# Let's make sure we don't update any embedding weights besides the newly added token
with torch.no_grad():
unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[
index_no_updates
]
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
# TODO: fix sample_images
# train_util.sample_images(
# accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
# )
current_loss = loss.detach().item()
if args.logging_dir is not None:
logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
if args.optimizer_type.lower() == "DAdaptation".lower(): # tracking d*lr value
logs["lr/d*lr"] = (
lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
)
accelerator.log(logs, step=global_step)
loss_total += current_loss
avr_loss = loss_total / (step + 1)
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if args.logging_dir is not None:
logs = {"loss/epoch": loss_total / len(train_dataloader)}
accelerator.log(logs, step=epoch + 1)
accelerator.wait_for_everyone()
updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids_XTI].data.detach().clone()
if args.save_every_n_epochs is not None:
model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
def save_func():
ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as
ckpt_file = os.path.join(args.output_dir, ckpt_name)
print(f"saving checkpoint: {ckpt_file}")
save_weights(ckpt_file, updated_embs, save_dtype)
def remove_old_func(old_epoch_no):
old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
if os.path.exists(old_ckpt_file):
print(f"removing old checkpoint: {old_ckpt_file}")
os.remove(old_ckpt_file)
saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
if saving and args.save_state:
train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
# TODO: fix sample_images
# train_util.sample_images(
# accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
# )
# end of epoch
is_main_process = accelerator.is_main_process
if is_main_process:
text_encoder = unwrap_model(text_encoder)
accelerator.end_training()
if args.save_state:
train_util.save_state_on_train_end(args, accelerator)
updated_embs = text_encoder.get_input_embeddings().weight[token_ids_XTI].data.detach().clone()
del accelerator # この後メモリを使うのでこれは消す
if is_main_process:
os.makedirs(args.output_dir, exist_ok=True)
model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
ckpt_name = model_name + "." + args.save_model_as
ckpt_file = os.path.join(args.output_dir, ckpt_name)
print(f"save trained model to {ckpt_file}")
save_weights(ckpt_file, updated_embs, save_dtype)
print("model saved.")
def save_weights(file, updated_embs, save_dtype):
updated_embs = updated_embs.reshape(16, -1, updated_embs.shape[-1])
updated_embs = updated_embs.chunk(16)
XTI_layers = [
"IN01",
"IN02",
"IN04",
"IN05",
"IN07",
"IN08",
"MID",
"OUT03",
"OUT04",
"OUT05",
"OUT06",
"OUT07",
"OUT08",
"OUT09",
"OUT10",
"OUT11",
]
state_dict = {}
for i, layer_name in enumerate(XTI_layers):
state_dict[layer_name] = updated_embs[i].squeeze(0).detach().clone().to("cpu").to(save_dtype)
# if save_dtype is not None:
# for key in list(state_dict.keys()):
# v = state_dict[key]
# v = v.detach().clone().to("cpu").to(save_dtype)
# state_dict[key] = v
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import save_file
save_file(state_dict, file)
else:
torch.save(state_dict, file) # can be loaded in Web UI
def load_weights(file):
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import load_file
data = load_file(file)
else:
raise ValueError(f"NOT XTI: {file}")
if len(data.values()) != 16:
raise ValueError(f"NOT XTI: {file}")
emb = torch.concat([x for x in data.values()])
return emb
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
train_util.add_sd_models_arguments(parser)
train_util.add_dataset_arguments(parser, True, True, False)
train_util.add_training_arguments(parser, True)
train_util.add_optimizer_arguments(parser)
config_util.add_config_arguments(parser)
custom_train_functions.add_custom_train_arguments(parser)
parser.add_argument(
"--save_model_as",
type=str,
default="pt",
choices=[None, "ckpt", "pt", "safetensors"],
help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）",
)
parser.add_argument("--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み")
parser.add_argument(
"--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数"
)
parser.add_argument(
"--token_string",
type=str,
default=None,
help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
)
parser.add_argument("--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
parser.add_argument(
"--use_object_template",
action="store_true",
help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する",
)
parser.add_argument(
"--use_style_template",
action="store_true",
help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する",
)
return parser
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()
args = train_util.read_config_from_file(args, parser)
train(args)