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Merge pull request #1655 from kohya-ss/sdxl-ctrl-net

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ControlNet training for SDXL
This commit is contained in:
Kohya S.
2024-10-11 22:27:53 +09:00
committed by GitHub
parent 83e3048cb0 035c4a8552
commit 43bfeea600
19 changed files with 1566 additions and 230 deletions
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10
README.md
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@@ -11,6 +11,16 @@ The command to install PyTorch is as follows:
### Recent Updates
Oct 11, 2024:
- ControlNet training for SDXL has been implemented in this branch. Please use `sdxl_train_control_net.py`.
- For details on defining the dataset, see [here](docs/train_lllite_README.md#creating-a-dataset-configuration-file).
- The learning rate for the copy part of the U-Net is specified by `--learning_rate`. The learning rate for the added modules in ControlNet is specified by `--control_net_lr`. The optimal value is still unknown, but try around U-Net `1e-5` and ControlNet `1e-4`.
- If you want to generate sample images, specify the control image as `--cn path/to/control/image`.
- The trained weights are automatically converted and saved in Diffusers format. It should be available in ComfyUI.
- Weighting of prompts (captions) during training in SDXL is now supported (e.g., `(some text)`, `[some text]`, `(some text:1.4)`, etc.). The function is enabled by specifying `--weighted_captions`.
- The default is `False`. It is same as before, and the parentheses are used as normal text.
- If `--weighted_captions` is specified, please use `\` to escape the parentheses in the prompt. For example, `\(some text:1.4\)`.
Oct 6, 2024:
- In FLUX.1 LoRA training and fine-tuning, the specified weight file (*.safetensors) is automatically determined to be dev or schnell. This allows schnell models to be loaded correctly. Note that LoRA training with schnell models and fine-tuning with schnell models are unverified.
- FLUX.1 LoRA training and fine-tuning can now load weights in Diffusers format in addition to BFL format (a single *.safetensors file). Please specify the parent directory of `transformer` or `diffusion_pytorch_model-00001-of-00003.safetensors` with the full path. However, Diffusers format CLIP/T5XXL is not supported. Saving is supported only in BFL format.

2
docs/train_lllite_README.md
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@@ -185,7 +185,7 @@ for img_file in img_files:
### Creating a dataset configuration file
You can use the command line arguments of `sdxl_train_control_net_lllite.py` to specify the conditioning image directory. However, if you want to use a `.toml` file, specify the conditioning image directory in `conditioning_data_dir`.
You can use the command line argument `--conditioning_data_dir` of `sdxl_train_control_net_lllite.py` to specify the conditioning image directory. However, if you want to use a `.toml` file, specify the conditioning image directory in `conditioning_data_dir`.
```toml
[general]

17
fine_tune.py
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@@ -366,22 +366,17 @@ def train(args):
with torch.set_grad_enabled(args.train_text_encoder):
# Get the text embedding for conditioning
if args.weighted_captions:
# TODO move to strategy_sd.py
encoder_hidden_states = get_weighted_text_embeddings(
tokenize_strategy.tokenizer,
text_encoder,
batch["captions"],
accelerator.device,
args.max_token_length // 75 if args.max_token_length else 1,
clip_skip=args.clip_skip,
)
input_ids_list, weights_list = tokenize_strategy.tokenize_with_weights(batch["captions"])
encoder_hidden_states = text_encoding_strategy.encode_tokens_with_weights(
tokenize_strategy, [text_encoder], input_ids_list, weights_list
)[0]
else:
input_ids = batch["input_ids_list"][0].to(accelerator.device)
encoder_hidden_states = text_encoding_strategy.encode_tokens(
tokenize_strategy, [text_encoder], [input_ids]
)[0]
if args.full_fp16:
encoder_hidden_states = encoder_hidden_states.to(weight_dtype)
if args.full_fp16:
encoder_hidden_states = encoder_hidden_states.to(weight_dtype)
# Sample noise, sample a random timestep for each image, and add noise to the latents,
# with noise offset and/or multires noise if specified

89
gen_img.py
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@@ -43,8 +43,8 @@ from diffusers import (
)
from einops import rearrange
from tqdm import tqdm
from torchvision import transforms
from transformers import CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, CLIPImageProcessor
from accelerate import init_empty_weights
import PIL
from PIL import Image
from PIL.PngImagePlugin import PngInfo
@@ -58,6 +58,7 @@ import tools.original_control_net as original_control_net
from tools.original_control_net import ControlNetInfo
from library.original_unet import UNet2DConditionModel, InferUNet2DConditionModel
from library.sdxl_original_unet import InferSdxlUNet2DConditionModel
from library.sdxl_original_control_net import SdxlControlNet
from library.original_unet import FlashAttentionFunction
from networks.control_net_lllite import ControlNetLLLite
from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL
@@ -352,8 +353,8 @@ class PipelineLike:
self.token_replacements_list.append({})
# ControlNet
self.control_nets: List[ControlNetInfo] = [] # only for SD 1.5
self.control_net_lllites: List[ControlNetLLLite] = []
self.control_nets: List[Union[ControlNetInfo, Tuple[SdxlControlNet, float]]] = []
self.control_net_lllites: List[Tuple[ControlNetLLLite, float]] = []
self.control_net_enabled = True # control_netsが空ならTrueでもFalseでもControlNetは動作しない
self.gradual_latent: GradualLatent = None
@@ -542,7 +543,7 @@ class PipelineLike:
else:
text_embeddings = torch.cat([uncond_embeddings, text_embeddings, real_uncond_embeddings])
if self.control_net_lllites:
if self.control_net_lllites or (self.control_nets and self.is_sdxl):
# ControlNetのhintにguide imageを流用する。ControlNetの場合はControlNet側で行う
if isinstance(clip_guide_images, PIL.Image.Image):
clip_guide_images = [clip_guide_images]
@@ -731,7 +732,12 @@ class PipelineLike:
num_latent_input = (3 if negative_scale is not None else 2) if do_classifier_free_guidance else 1
if self.control_nets:
guided_hints = original_control_net.get_guided_hints(self.control_nets, num_latent_input, batch_size, clip_guide_images)
if not self.is_sdxl:
guided_hints = original_control_net.get_guided_hints(
self.control_nets, num_latent_input, batch_size, clip_guide_images
)
else:
clip_guide_images = clip_guide_images * 0.5 + 0.5 # [-1, 1] => [0, 1]
each_control_net_enabled = [self.control_net_enabled] * len(self.control_nets)
if self.control_net_lllites:
@@ -793,7 +799,7 @@ class PipelineLike:
latent_model_input = latents.repeat((num_latent_input, 1, 1, 1))
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# disable ControlNet-LLLite if ratio is set. ControlNet is disabled in ControlNetInfo
# disable ControlNet-LLLite or SDXL ControlNet if ratio is set. ControlNet is disabled in ControlNetInfo
if self.control_net_lllites:
for j, ((control_net, ratio), enabled) in enumerate(zip(self.control_net_lllites, each_control_net_enabled)):
if not enabled or ratio >= 1.0:
@@ -802,9 +808,16 @@ class PipelineLike:
logger.info(f"ControlNetLLLite {j} is disabled (ratio={ratio} at {i} / {len(timesteps)})")
control_net.set_cond_image(None)
each_control_net_enabled[j] = False
if self.control_nets and self.is_sdxl:
for j, ((control_net, ratio), enabled) in enumerate(zip(self.control_nets, each_control_net_enabled)):
if not enabled or ratio >= 1.0:
continue
if ratio < i / len(timesteps):
logger.info(f"ControlNet {j} is disabled (ratio={ratio} at {i} / {len(timesteps)})")
each_control_net_enabled[j] = False
# predict the noise residual
if self.control_nets and self.control_net_enabled:
if self.control_nets and self.control_net_enabled and not self.is_sdxl:
if regional_network:
num_sub_and_neg_prompts = len(text_embeddings) // batch_size
text_emb_last = text_embeddings[num_sub_and_neg_prompts - 2 :: num_sub_and_neg_prompts] # last subprompt
@@ -823,6 +836,31 @@ class PipelineLike:
text_embeddings,
text_emb_last,
).sample
elif self.control_nets:
input_resi_add_list = []
mid_add_list = []
for (control_net, _), enbld in zip(self.control_nets, each_control_net_enabled):
if not enbld:
continue
input_resi_add, mid_add = control_net(
latent_model_input, t, text_embeddings, vector_embeddings, clip_guide_images
)
input_resi_add_list.append(input_resi_add)
mid_add_list.append(mid_add)
if len(input_resi_add_list) == 0:
noise_pred = self.unet(latent_model_input, t, text_embeddings, vector_embeddings)
else:
if len(input_resi_add_list) > 1:
# get mean of input_resi_add_list and mid_add_list
input_resi_add_mean = []
for i in range(len(input_resi_add_list[0])):
input_resi_add_mean.append(
torch.mean(torch.stack([input_resi_add_list[j][i] for j in range(len(input_resi_add_list))], dim=0))
)
input_resi_add = input_resi_add_mean
mid_add = torch.mean(torch.stack(mid_add_list), dim=0)
noise_pred = self.unet(latent_model_input, t, text_embeddings, vector_embeddings, input_resi_add, mid_add)
elif self.is_sdxl:
noise_pred = self.unet(latent_model_input, t, text_embeddings, vector_embeddings)
else:
@@ -1827,16 +1865,37 @@ def main(args):
upscaler.to(dtype).to(device)
# ControlNetの処理
control_nets: List[ControlNetInfo] = []
control_nets: List[Union[ControlNetInfo, Tuple[SdxlControlNet, float]]] = []
if args.control_net_models:
for i, model in enumerate(args.control_net_models):
prep_type = None if not args.control_net_preps or len(args.control_net_preps) <= i else args.control_net_preps[i]
weight = 1.0 if not args.control_net_weights or len(args.control_net_weights) <= i else args.control_net_weights[i]
ratio = 1.0 if not args.control_net_ratios or len(args.control_net_ratios) <= i else args.control_net_ratios[i]
if not is_sdxl:
for i, model in enumerate(args.control_net_models):
prep_type = None if not args.control_net_preps or len(args.control_net_preps) <= i else args.control_net_preps[i]
weight = 1.0 if not args.control_net_weights or len(args.control_net_weights) <= i else args.control_net_weights[i]
ratio = 1.0 if not args.control_net_ratios or len(args.control_net_ratios) <= i else args.control_net_ratios[i]
ctrl_unet, ctrl_net = original_control_net.load_control_net(args.v2, unet, model)
prep = original_control_net.load_preprocess(prep_type)
control_nets.append(ControlNetInfo(ctrl_unet, ctrl_net, prep, weight, ratio))
ctrl_unet, ctrl_net = original_control_net.load_control_net(args.v2, unet, model)
prep = original_control_net.load_preprocess(prep_type)
control_nets.append(ControlNetInfo(ctrl_unet, ctrl_net, prep, weight, ratio))
else:
for i, model_file in enumerate(args.control_net_models):
multiplier = (
1.0
if not args.control_net_multipliers or len(args.control_net_multipliers) <= i
else args.control_net_multipliers[i]
)
ratio = 1.0 if not args.control_net_ratios or len(args.control_net_ratios) <= i else args.control_net_ratios[i]
logger.info(f"loading SDXL ControlNet: {model_file}")
from safetensors.torch import load_file
state_dict = load_file(model_file)
logger.info(f"Initializing SDXL ControlNet with multiplier: {multiplier}")
with init_empty_weights():
control_net = SdxlControlNet(multiplier=multiplier)
control_net.load_state_dict(state_dict)
control_net.to(dtype).to(device)
control_nets.append((control_net, ratio))
control_net_lllites: List[Tuple[ControlNetLLLite, float]] = []
if args.control_net_lllite_models:

166
library/sdxl_lpw_stable_diffusion.py
View File

@@ -13,12 +13,20 @@ from tqdm import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from diffusers import SchedulerMixin, StableDiffusionPipeline
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
from diffusers.models import AutoencoderKL
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.utils import logging
from PIL import Image
from library import sdxl_model_util, sdxl_train_util, train_util
from library import (
sdxl_model_util,
sdxl_train_util,
strategy_base,
strategy_sdxl,
train_util,
sdxl_original_unet,
sdxl_original_control_net,
)
try:
@@ -537,7 +545,7 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
vae: AutoencoderKL,
text_encoder: List[CLIPTextModel],
tokenizer: List[CLIPTokenizer],
unet: UNet2DConditionModel,
unet: Union[sdxl_original_unet.SdxlUNet2DConditionModel, sdxl_original_control_net.SdxlControlledUNet],
scheduler: SchedulerMixin,
# clip_skip: int,
safety_checker: StableDiffusionSafetyChecker,
@@ -594,74 +602,6 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
max_embeddings_multiples,
is_sdxl_text_encoder2,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `list(int)`):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`):
whether to use classifier free guidance or not
negative_prompt (`str` or `List[str]`):
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if `guidance_scale` is less than `1`).
max_embeddings_multiples (`int`, *optional*, defaults to `3`):
The max multiple length of prompt embeddings compared to the max output length of text encoder.
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
if negative_prompt is None:
negative_prompt = [""] * batch_size
elif isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
text_embeddings, text_pool, uncond_embeddings, uncond_pool = 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,
is_sdxl_text_encoder2=is_sdxl_text_encoder2,
)
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) # ??
text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if text_pool is not None:
text_pool = text_pool.repeat(1, num_images_per_prompt)
text_pool = text_pool.view(bs_embed * num_images_per_prompt, -1)
if do_classifier_free_guidance:
bs_embed, seq_len, _ = uncond_embeddings.shape
uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
uncond_embeddings = uncond_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if uncond_pool is not None:
uncond_pool = uncond_pool.repeat(1, num_images_per_prompt)
uncond_pool = uncond_pool.view(bs_embed * num_images_per_prompt, -1)
return text_embeddings, text_pool, uncond_embeddings, uncond_pool
return text_embeddings, text_pool, None, None
def check_inputs(self, prompt, height, width, strength, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
@@ -792,7 +732,7 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
max_embeddings_multiples: Optional[int] = 3,
output_type: Optional[str] = "pil",
return_dict: bool = True,
controlnet=None,
controlnet: sdxl_original_control_net.SdxlControlNet = None,
controlnet_image=None,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
is_cancelled_callback: Optional[Callable[[], bool]] = None,
@@ -896,32 +836,24 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
# 実装を簡単にするためにtokenzer/text encoderを切り替えて二回呼び出す
# To simplify the implementation, switch the tokenzer/text encoder and call it twice
text_embeddings_list = []
text_pool = None
uncond_embeddings_list = []
uncond_pool = None
for i in range(len(self.tokenizers)):
self.tokenizer = self.tokenizers[i]
self.text_encoder = self.text_encoders[i]
tokenize_strategy: strategy_sdxl.SdxlTokenizeStrategy = strategy_base.TokenizeStrategy.get_strategy()
encoding_strategy: strategy_sdxl.SdxlTextEncodingStrategy = strategy_base.TextEncodingStrategy.get_strategy()
text_embeddings, tp1, uncond_embeddings, up1 = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
max_embeddings_multiples,
is_sdxl_text_encoder2=i == 1,
text_input_ids, text_weights = tokenize_strategy.tokenize_with_weights(prompt)
hidden_states_1, hidden_states_2, text_pool = encoding_strategy.encode_tokens_with_weights(
tokenize_strategy, self.text_encoders, text_input_ids, text_weights
)
text_embeddings = torch.cat([hidden_states_1, hidden_states_2], dim=-1)
if do_classifier_free_guidance:
input_ids, weights = tokenize_strategy.tokenize_with_weights(negative_prompt or "")
hidden_states_1, hidden_states_2, uncond_pool = encoding_strategy.encode_tokens_with_weights(
tokenize_strategy, self.text_encoders, input_ids, weights
)
text_embeddings_list.append(text_embeddings)
uncond_embeddings_list.append(uncond_embeddings)
if tp1 is not None:
text_pool = tp1
if up1 is not None:
uncond_pool = up1
uncond_embeddings = torch.cat([hidden_states_1, hidden_states_2], dim=-1)
else:
uncond_embeddings = None
uncond_pool = None
unet_dtype = self.unet.dtype
dtype = unet_dtype
@@ -970,23 +902,23 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# create size embs and concat embeddings for SDXL
orig_size = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1).to(dtype)
orig_size = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1).to(device, dtype)
crop_size = torch.zeros_like(orig_size)
target_size = orig_size
embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, device).to(dtype)
embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, device).to(device, dtype)
# make conditionings
text_pool = text_pool.to(device, dtype)
if do_classifier_free_guidance:
text_embeddings = torch.cat(text_embeddings_list, dim=2)
uncond_embeddings = torch.cat(uncond_embeddings_list, dim=2)
text_embedding = torch.cat([uncond_embeddings, text_embeddings]).to(dtype)
text_embedding = torch.cat([uncond_embeddings, text_embeddings]).to(device, dtype)
cond_vector = torch.cat([text_pool, embs], dim=1)
uncond_vector = torch.cat([uncond_pool, embs], dim=1)
vector_embedding = torch.cat([uncond_vector, cond_vector]).to(dtype)
uncond_pool = uncond_pool.to(device, dtype)
cond_vector = torch.cat([text_pool, embs], dim=1).to(dtype)
uncond_vector = torch.cat([uncond_pool, embs], dim=1).to(dtype)
vector_embedding = torch.cat([uncond_vector, cond_vector])
else:
text_embedding = torch.cat(text_embeddings_list, dim=2).to(dtype)
vector_embedding = torch.cat([text_pool, embs], dim=1).to(dtype)
text_embedding = text_embeddings.to(device, dtype)
vector_embedding = torch.cat([text_pool, embs], dim=1)
# 8. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps)):
@@ -994,22 +926,14 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
unet_additional_args = {}
if controlnet is not None:
down_block_res_samples, mid_block_res_sample = controlnet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings,
controlnet_cond=controlnet_image,
conditioning_scale=1.0,
guess_mode=False,
return_dict=False,
)
unet_additional_args["down_block_additional_residuals"] = down_block_res_samples
unet_additional_args["mid_block_additional_residual"] = mid_block_res_sample
# FIXME SD1 ControlNet is not working
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, text_embedding, vector_embedding)
if controlnet is not None:
input_resi_add, mid_add = controlnet(latent_model_input, t, text_embedding, vector_embedding, controlnet_image)
noise_pred = self.unet(latent_model_input, t, text_embedding, vector_embedding, input_resi_add, mid_add)
else:
noise_pred = self.unet(latent_model_input, t, text_embedding, vector_embedding)
noise_pred = noise_pred.to(dtype) # U-Net changes dtype in LoRA training
# perform guidance

2
library/sdxl_model_util.py
View File

@@ -8,7 +8,7 @@ from typing import List
from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel
from library import model_util
from library import sdxl_original_unet
from .utils import setup_logging
from library.utils import setup_logging
setup_logging()
import logging

272
library/sdxl_original_control_net.py Normal file
View File

@@ -0,0 +1,272 @@
# some parts are modified from Diffusers library (Apache License 2.0)
import math
from types import SimpleNamespace
from typing import Any, Optional
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from einops import rearrange
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
from library import sdxl_original_unet
from library.sdxl_model_util import convert_sdxl_unet_state_dict_to_diffusers, convert_diffusers_unet_state_dict_to_sdxl
class ControlNetConditioningEmbedding(nn.Module):
def __init__(self):
super().__init__()
dims = [16, 32, 96, 256]
self.conv_in = nn.Conv2d(3, dims[0], kernel_size=3, padding=1)
self.blocks = nn.ModuleList([])
for i in range(len(dims) - 1):
channel_in = dims[i]
channel_out = dims[i + 1]
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
self.conv_out = nn.Conv2d(dims[-1], 320, kernel_size=3, padding=1)
nn.init.zeros_(self.conv_out.weight) # zero module weight
nn.init.zeros_(self.conv_out.bias) # zero module bias
def forward(self, x):
x = self.conv_in(x)
x = F.silu(x)
for block in self.blocks:
x = block(x)
x = F.silu(x)
x = self.conv_out(x)
return x
class SdxlControlNet(sdxl_original_unet.SdxlUNet2DConditionModel):
def __init__(self, multiplier: Optional[float] = None, **kwargs):
super().__init__(**kwargs)
self.multiplier = multiplier
# remove unet layers
self.output_blocks = nn.ModuleList([])
del self.out
self.controlnet_cond_embedding = ControlNetConditioningEmbedding()
dims = [320, 320, 320, 320, 640, 640, 640, 1280, 1280]
self.controlnet_down_blocks = nn.ModuleList([])
for dim in dims:
self.controlnet_down_blocks.append(nn.Conv2d(dim, dim, kernel_size=1))
nn.init.zeros_(self.controlnet_down_blocks[-1].weight) # zero module weight
nn.init.zeros_(self.controlnet_down_blocks[-1].bias) # zero module bias
self.controlnet_mid_block = nn.Conv2d(1280, 1280, kernel_size=1)
nn.init.zeros_(self.controlnet_mid_block.weight) # zero module weight
nn.init.zeros_(self.controlnet_mid_block.bias) # zero module bias
def init_from_unet(self, unet: sdxl_original_unet.SdxlUNet2DConditionModel):
unet_sd = unet.state_dict()
unet_sd = {k: v for k, v in unet_sd.items() if not k.startswith("out")}
sd = super().state_dict()
sd.update(unet_sd)
info = super().load_state_dict(sd, strict=True, assign=True)
return info
def load_state_dict(self, state_dict: dict, strict: bool = True, assign: bool = True) -> Any:
# convert state_dict to SAI format
unet_sd = {}
for k in list(state_dict.keys()):
if not k.startswith("controlnet_"):
unet_sd[k] = state_dict.pop(k)
unet_sd = convert_diffusers_unet_state_dict_to_sdxl(unet_sd)
state_dict.update(unet_sd)
super().load_state_dict(state_dict, strict=strict, assign=assign)
def state_dict(self, destination=None, prefix="", keep_vars=False):
# convert state_dict to Diffusers format
state_dict = super().state_dict(destination, prefix, keep_vars)
control_net_sd = {}
for k in list(state_dict.keys()):
if k.startswith("controlnet_"):
control_net_sd[k] = state_dict.pop(k)
state_dict = convert_sdxl_unet_state_dict_to_diffusers(state_dict)
state_dict.update(control_net_sd)
return state_dict
def forward(
self,
x: torch.Tensor,
timesteps: Optional[torch.Tensor] = None,
context: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
cond_image: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
# broadcast timesteps to batch dimension
timesteps = timesteps.expand(x.shape[0])
t_emb = sdxl_original_unet.get_timestep_embedding(timesteps, self.model_channels, downscale_freq_shift=0)
t_emb = t_emb.to(x.dtype)
emb = self.time_embed(t_emb)
assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}"
assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}"
emb = emb + self.label_emb(y)
def call_module(module, h, emb, context):
x = h
for layer in module:
if isinstance(layer, sdxl_original_unet.ResnetBlock2D):
x = layer(x, emb)
elif isinstance(layer, sdxl_original_unet.Transformer2DModel):
x = layer(x, context)
else:
x = layer(x)
return x
h = x
multiplier = self.multiplier if self.multiplier is not None else 1.0
hs = []
for i, module in enumerate(self.input_blocks):
h = call_module(module, h, emb, context)
if i == 0:
h = self.controlnet_cond_embedding(cond_image) + h
hs.append(self.controlnet_down_blocks[i](h) * multiplier)
h = call_module(self.middle_block, h, emb, context)
h = self.controlnet_mid_block(h) * multiplier
return hs, h
class SdxlControlledUNet(sdxl_original_unet.SdxlUNet2DConditionModel):
"""
This class is for training purpose only.
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
def forward(self, x, timesteps=None, context=None, y=None, input_resi_add=None, mid_add=None, **kwargs):
# broadcast timesteps to batch dimension
timesteps = timesteps.expand(x.shape[0])
hs = []
t_emb = sdxl_original_unet.get_timestep_embedding(timesteps, self.model_channels, downscale_freq_shift=0)
t_emb = t_emb.to(x.dtype)
emb = self.time_embed(t_emb)
assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}"
assert x.dtype == y.dtype, f"dtype mismatch: {x.dtype} != {y.dtype}"
emb = emb + self.label_emb(y)
def call_module(module, h, emb, context):
x = h
for layer in module:
if isinstance(layer, sdxl_original_unet.ResnetBlock2D):
x = layer(x, emb)
elif isinstance(layer, sdxl_original_unet.Transformer2DModel):
x = layer(x, context)
else:
x = layer(x)
return x
h = x
for module in self.input_blocks:
h = call_module(module, h, emb, context)
hs.append(h)
h = call_module(self.middle_block, h, emb, context)
h = h + mid_add
for module in self.output_blocks:
resi = hs.pop() + input_resi_add.pop()
h = torch.cat([h, resi], dim=1)
h = call_module(module, h, emb, context)
h = h.type(x.dtype)
h = call_module(self.out, h, emb, context)
return h
if __name__ == "__main__":
import time
logger.info("create unet")
unet = SdxlControlledUNet()
unet.to("cuda", torch.bfloat16)
unet.set_use_sdpa(True)
unet.set_gradient_checkpointing(True)
unet.train()
logger.info("create control_net")
control_net = SdxlControlNet()
control_net.to("cuda")
control_net.set_use_sdpa(True)
control_net.set_gradient_checkpointing(True)
control_net.train()
logger.info("Initialize control_net from unet")
control_net.init_from_unet(unet)
unet.requires_grad_(False)
control_net.requires_grad_(True)
# 使用メモリ量確認用の疑似学習ループ
logger.info("preparing optimizer")
# optimizer = torch.optim.SGD(unet.parameters(), lr=1e-3, nesterov=True, momentum=0.9) # not working
import bitsandbytes
optimizer = bitsandbytes.adam.Adam8bit(control_net.parameters(), lr=1e-3) # not working
# optimizer = bitsandbytes.optim.RMSprop8bit(unet.parameters(), lr=1e-3) # working at 23.5 GB with torch2
# optimizer=bitsandbytes.optim.Adagrad8bit(unet.parameters(), lr=1e-3) # working at 23.5 GB with torch2
# import transformers
# optimizer = transformers.optimization.Adafactor(unet.parameters(), relative_step=True) # working at 22.2GB with torch2
scaler = torch.cuda.amp.GradScaler(enabled=True)
logger.info("start training")
steps = 10
batch_size = 1
for step in range(steps):
logger.info(f"step {step}")
if step == 1:
time_start = time.perf_counter()
x = torch.randn(batch_size, 4, 128, 128).cuda() # 1024x1024
t = torch.randint(low=0, high=1000, size=(batch_size,), device="cuda")
txt = torch.randn(batch_size, 77, 2048).cuda()
vector = torch.randn(batch_size, sdxl_original_unet.ADM_IN_CHANNELS).cuda()
cond_img = torch.rand(batch_size, 3, 1024, 1024).cuda()
with torch.cuda.amp.autocast(enabled=True, dtype=torch.bfloat16):
input_resi_add, mid_add = control_net(x, t, txt, vector, cond_img)
output = unet(x, t, txt, vector, input_resi_add, mid_add)
target = torch.randn_like(output)
loss = torch.nn.functional.mse_loss(output, target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
time_end = time.perf_counter()
logger.info(f"elapsed time: {time_end - time_start} [sec] for last {steps - 1} steps")
logger.info("finish training")
sd = control_net.state_dict()
from safetensors.torch import save_file
save_file(sd, r"E:\Work\SD\Tmp\sdxl\ctrl\control_net.safetensors")

14
library/sdxl_original_unet.py
View File

@@ -30,7 +30,7 @@ import torch.utils.checkpoint
from torch import nn
from torch.nn import functional as F
from einops import rearrange
from .utils import setup_logging
from library.utils import setup_logging
setup_logging()
import logging
@@ -1156,9 +1156,9 @@ class InferSdxlUNet2DConditionModel:
self.ds_timesteps_2 = ds_timesteps_2 if ds_timesteps_2 is not None else 1000
self.ds_ratio = ds_ratio
def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
def forward(self, x, timesteps=None, context=None, y=None, input_resi_add=None, mid_add=None, **kwargs):
r"""
current implementation is a copy of `SdxlUNet2DConditionModel.forward()` with Deep Shrink.
current implementation is a copy of `SdxlUNet2DConditionModel.forward()` with Deep Shrink and ControlNet.
"""
_self = self.delegate
@@ -1209,6 +1209,8 @@ class InferSdxlUNet2DConditionModel:
hs.append(h)
h = call_module(_self.middle_block, h, emb, context)
if mid_add is not None:
h = h + mid_add
for module in _self.output_blocks:
# Deep Shrink
@@ -1217,7 +1219,11 @@ class InferSdxlUNet2DConditionModel:
# print("upsample", h.shape, hs[-1].shape)
h = resize_like(h, hs[-1])
h = torch.cat([h, hs.pop()], dim=1)
resi = hs.pop()
if input_resi_add is not None:
resi = resi + input_resi_add.pop()
h = torch.cat([h, resi], dim=1)
h = call_module(module, h, emb, context)
# Deep Shrink: in case of depth 0

9
library/sdxl_train_util.py
View File

@@ -12,7 +12,6 @@ from accelerate import init_empty_weights
from tqdm import tqdm
from transformers import CLIPTokenizer
from library import model_util, sdxl_model_util, train_util, sdxl_original_unet
from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline
from .utils import setup_logging
setup_logging()
@@ -364,9 +363,9 @@ def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCachin
# )
# logger.info(f"noise_offset is set to {args.noise_offset} / noise_offsetが{args.noise_offset}に設定されました")
assert (
not hasattr(args, "weighted_captions") or not args.weighted_captions
), "weighted_captions cannot be enabled in SDXL training currently / SDXL学習では今のところweighted_captionsを有効にすることはできません"
# assert (
# not hasattr(args, "weighted_captions") or not args.weighted_captions
# ), "weighted_captions cannot be enabled in SDXL training currently / SDXL学習では今のところweighted_captionsを有効にすることはできません"
if supportTextEncoderCaching:
if args.cache_text_encoder_outputs_to_disk and not args.cache_text_encoder_outputs:
@@ -378,4 +377,6 @@ def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCachin
def sample_images(*args, **kwargs):
from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline
return train_util.sample_images_common(SdxlStableDiffusionLongPromptWeightingPipeline, *args, **kwargs)

207
library/strategy_base.py
View File

@@ -1,6 +1,7 @@
# base class for platform strategies. this file defines the interface for strategies
import os
import re
from typing import Any, List, Optional, Tuple, Union
import numpy as np
@@ -22,6 +23,24 @@ logger = logging.getLogger(__name__)
class TokenizeStrategy:
_strategy = None # strategy instance: actual strategy class
_re_attention = re.compile(
r"""\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""",
re.X,
)
@classmethod
def set_strategy(cls, strategy):
if cls._strategy is not None:
@@ -54,7 +73,154 @@ class TokenizeStrategy:
def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]:
raise NotImplementedError
def _get_input_ids(self, tokenizer: CLIPTokenizer, text: str, max_length: Optional[int] = None) -> torch.Tensor:
def tokenize_with_weights(self, text: Union[str, List[str]]) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
"""
returns: [tokens1, tokens2, ...], [weights1, weights2, ...]
"""
raise NotImplementedError
def _get_weighted_input_ids(
self, tokenizer: CLIPTokenizer, text: str, max_length: Optional[int] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
max_length includes starting and ending tokens.
"""
def parse_prompt_attention(text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
[abc] - decreases attention to abc by a multiplier of 1.1
\( - literal character '('
\[ - literal character '['
\) - literal character ')'
\] - literal character ']'
\\ - literal character '\'
anything else - just text
>>> parse_prompt_attention('normal text')
[['normal text', 1.0]]
>>> parse_prompt_attention('an (important) word')
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
>>> parse_prompt_attention('(unbalanced')
[['unbalanced', 1.1]]
>>> parse_prompt_attention('\(literal\]')
[['(literal]', 1.0]]
>>> parse_prompt_attention('(unnecessary)(parens)')
[['unnecessaryparens', 1.1]]
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
[['a ', 1.0],
['house', 1.5730000000000004],
[' ', 1.1],
['on', 1.0],
[' a ', 1.1],
['hill', 0.55],
[', sun, ', 1.1],
['sky', 1.4641000000000006],
['.', 1.1]]
"""
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in TokenizeStrategy._re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith("\\"):
res.append([text[1:], 1.0])
elif text == "(":
round_brackets.append(len(res))
elif text == "[":
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ")" and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == "]" and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
res.append([text, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
if len(res) == 0:
res = [["", 1.0]]
# merge runs of identical weights
i = 0
while i + 1 < len(res):
if res[i][1] == res[i + 1][1]:
res[i][0] += res[i + 1][0]
res.pop(i + 1)
else:
i += 1
return res
def get_prompts_with_weights(text: str, max_length: int):
r"""
Tokenize a list of prompts and return its tokens with weights of each token. max_length does not include starting and ending token.
No padding, starting or ending token is included.
"""
truncated = False
texts_and_weights = parse_prompt_attention(text)
tokens = []
weights = []
for word, weight in texts_and_weights:
# tokenize and discard the starting and the ending token
token = tokenizer(word).input_ids[1:-1]
tokens += token
# copy the weight by length of token
weights += [weight] * len(token)
# stop if the text is too long (longer than truncation limit)
if len(tokens) > max_length:
truncated = True
break
# truncate
if len(tokens) > max_length:
truncated = True
tokens = tokens[:max_length]
weights = weights[:max_length]
if truncated:
logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
return tokens, weights
def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad):
r"""
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
"""
tokens = [bos] + tokens + [eos] + [pad] * (max_length - 2 - len(tokens))
weights = [1.0] + weights + [1.0] * (max_length - 1 - len(weights))
return tokens, weights
if max_length is None:
max_length = tokenizer.model_max_length
tokens, weights = get_prompts_with_weights(text, max_length - 2)
tokens, weights = pad_tokens_and_weights(
tokens, weights, max_length, tokenizer.bos_token_id, tokenizer.eos_token_id, tokenizer.pad_token_id
)
return torch.tensor(tokens).unsqueeze(0), torch.tensor(weights).unsqueeze(0)
def _get_input_ids(
self, tokenizer: CLIPTokenizer, text: str, max_length: Optional[int] = None, weighted: bool = False
) -> torch.Tensor:
"""
for SD1.5/2.0/SDXL
TODO support batch input
@@ -62,7 +228,10 @@ class TokenizeStrategy:
if max_length is None:
max_length = tokenizer.model_max_length - 2
input_ids = tokenizer(text, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt").input_ids
if weighted:
input_ids, weights = self._get_weighted_input_ids(tokenizer, text, max_length)
else:
input_ids = tokenizer(text, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt").input_ids
if max_length > tokenizer.model_max_length:
input_ids = input_ids.squeeze(0)
@@ -101,6 +270,17 @@ class TokenizeStrategy:
iids_list.append(ids_chunk)
input_ids = torch.stack(iids_list) # 3,77
if weighted:
weights = weights.squeeze(0)
new_weights = torch.ones(input_ids.shape)
for i in range(1, max_length - tokenizer.model_max_length + 2, tokenizer.model_max_length - 2):
b = i // (tokenizer.model_max_length - 2)
new_weights[b, 1 : 1 + tokenizer.model_max_length - 2] = weights[i : i + tokenizer.model_max_length - 2]
weights = new_weights
if weighted:
return input_ids, weights
return input_ids
@@ -127,17 +307,34 @@ class TextEncodingStrategy:
"""
raise NotImplementedError
def encode_tokens_with_weights(
self, tokenize_strategy: TokenizeStrategy, models: List[Any], tokens: List[torch.Tensor], weights: List[torch.Tensor]
) -> List[torch.Tensor]:
"""
Encode tokens into embeddings and outputs.
:param tokens: list of token tensors for each TextModel
:param weights: list of weight tensors for each TextModel
:return: list of output embeddings for each architecture
"""
raise NotImplementedError
class TextEncoderOutputsCachingStrategy:
_strategy = None # strategy instance: actual strategy class
def __init__(
self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool, is_partial: bool = False
self,
cache_to_disk: bool,
batch_size: int,
skip_disk_cache_validity_check: bool,
is_partial: bool = False,
is_weighted: bool = False,
) -> None:
self._cache_to_disk = cache_to_disk
self._batch_size = batch_size
self.skip_disk_cache_validity_check = skip_disk_cache_validity_check
self._is_partial = is_partial
self._is_weighted = is_weighted
@classmethod
def set_strategy(cls, strategy):
@@ -161,6 +358,10 @@ class TextEncoderOutputsCachingStrategy:
def is_partial(self):
return self._is_partial
@property
def is_weighted(self):
return self._is_weighted
def get_outputs_npz_path(self, image_abs_path: str) -> str:
raise NotImplementedError

36
library/strategy_sd.py
View File

@@ -40,6 +40,16 @@ class SdTokenizeStrategy(TokenizeStrategy):
text = [text] if isinstance(text, str) else text
return [torch.stack([self._get_input_ids(self.tokenizer, t, self.max_length) for t in text], dim=0)]
def tokenize_with_weights(self, text: str | List[str]) -> Tuple[List[torch.Tensor]]:
text = [text] if isinstance(text, str) else text
tokens_list = []
weights_list = []
for t in text:
tokens, weights = self._get_input_ids(self.tokenizer, t, self.max_length, weighted=True)
tokens_list.append(tokens)
weights_list.append(weights)
return [torch.stack(tokens_list, dim=0)], [torch.stack(weights_list, dim=0)]
class SdTextEncodingStrategy(TextEncodingStrategy):
def __init__(self, clip_skip: Optional[int] = None) -> None:
@@ -58,6 +68,8 @@ class SdTextEncodingStrategy(TextEncodingStrategy):
model_max_length = sd_tokenize_strategy.tokenizer.model_max_length
tokens = tokens.reshape((-1, model_max_length)) # batch_size*3, 77
tokens = tokens.to(text_encoder.device)
if self.clip_skip is None:
encoder_hidden_states = text_encoder(tokens)[0]
else:
@@ -93,6 +105,30 @@ class SdTextEncodingStrategy(TextEncodingStrategy):
return [encoder_hidden_states]
def encode_tokens_with_weights(
self,
tokenize_strategy: TokenizeStrategy,
models: List[Any],
tokens_list: List[torch.Tensor],
weights_list: List[torch.Tensor],
) -> List[torch.Tensor]:
encoder_hidden_states = self.encode_tokens(tokenize_strategy, models, tokens_list)[0]
weights = weights_list[0].to(encoder_hidden_states.device)
# apply weights
if weights.shape[1] == 1: # no max_token_length
# weights: ((b, 1, 77), (b, 1, 77)), hidden_states: (b, 77, 768), (b, 77, 768)
encoder_hidden_states = encoder_hidden_states * weights.squeeze(1).unsqueeze(2)
else:
# weights: ((b, n, 77), (b, n, 77)), hidden_states: (b, n*75+2, 768), (b, n*75+2, 768)
for i in range(weights.shape[1]):
encoder_hidden_states[:, i * 75 + 1 : i * 75 + 76] = encoder_hidden_states[:, i * 75 + 1 : i * 75 + 76] * weights[
:, i, 1:-1
].unsqueeze(-1)
return [encoder_hidden_states]
class SdSdxlLatentsCachingStrategy(LatentsCachingStrategy):
# sd and sdxl share the same strategy. we can make them separate, but the difference is only the suffix.

77
library/strategy_sdxl.py
View File

@@ -37,6 +37,22 @@ class SdxlTokenizeStrategy(TokenizeStrategy):
torch.stack([self._get_input_ids(self.tokenizer2, t, self.max_length) for t in text], dim=0),
)
def tokenize_with_weights(self, text: str | List[str]) -> Tuple[List[torch.Tensor]]:
text = [text] if isinstance(text, str) else text
tokens1_list, tokens2_list = [], []
weights1_list, weights2_list = [], []
for t in text:
tokens1, weights1 = self._get_input_ids(self.tokenizer1, t, self.max_length, weighted=True)
tokens2, weights2 = self._get_input_ids(self.tokenizer2, t, self.max_length, weighted=True)
tokens1_list.append(tokens1)
tokens2_list.append(tokens2)
weights1_list.append(weights1)
weights2_list.append(weights2)
return [torch.stack(tokens1_list, dim=0), torch.stack(tokens2_list, dim=0)], [
torch.stack(weights1_list, dim=0),
torch.stack(weights2_list, dim=0),
]
class SdxlTextEncodingStrategy(TextEncodingStrategy):
def __init__(self) -> None:
@@ -98,7 +114,10 @@ class SdxlTextEncodingStrategy(TextEncodingStrategy):
):
# input_ids: b,n,77 -> b*n, 77
b_size = input_ids1.size()[0]
max_token_length = input_ids1.size()[1] * input_ids1.size()[2]
if input_ids1.size()[1] == 1:
max_token_length = None
else:
max_token_length = input_ids1.size()[1] * input_ids1.size()[2]
input_ids1 = input_ids1.reshape((-1, tokenizer1.model_max_length)) # batch_size*n, 77
input_ids2 = input_ids2.reshape((-1, tokenizer2.model_max_length)) # batch_size*n, 77
input_ids1 = input_ids1.to(text_encoder1.device)
@@ -155,7 +174,8 @@ class SdxlTextEncodingStrategy(TextEncodingStrategy):
"""
Args:
tokenize_strategy: TokenizeStrategy
models: List of models, [text_encoder1, text_encoder2, unwrapped text_encoder2 (optional)]
models: List of models, [text_encoder1, text_encoder2, unwrapped text_encoder2 (optional)].
If text_encoder2 is wrapped by accelerate, unwrapped_text_encoder2 is required
tokens: List of tokens, for text_encoder1 and text_encoder2
"""
if len(models) == 2:
@@ -172,14 +192,45 @@ class SdxlTextEncodingStrategy(TextEncodingStrategy):
)
return [hidden_states1, hidden_states2, pool2]
def encode_tokens_with_weights(
self,
tokenize_strategy: TokenizeStrategy,
models: List[Any],
tokens_list: List[torch.Tensor],
weights_list: List[torch.Tensor],
) -> List[torch.Tensor]:
hidden_states1, hidden_states2, pool2 = self.encode_tokens(tokenize_strategy, models, tokens_list)
weights_list = [weights.to(hidden_states1.device) for weights in weights_list]
# apply weights
if weights_list[0].shape[1] == 1: # no max_token_length
# weights: ((b, 1, 77), (b, 1, 77)), hidden_states: (b, 77, 768), (b, 77, 768)
hidden_states1 = hidden_states1 * weights_list[0].squeeze(1).unsqueeze(2)
hidden_states2 = hidden_states2 * weights_list[1].squeeze(1).unsqueeze(2)
else:
# weights: ((b, n, 77), (b, n, 77)), hidden_states: (b, n*75+2, 768), (b, n*75+2, 768)
for weight, hidden_states in zip(weights_list, [hidden_states1, hidden_states2]):
for i in range(weight.shape[1]):
hidden_states[:, i * 75 + 1 : i * 75 + 76] = hidden_states[:, i * 75 + 1 : i * 75 + 76] * weight[
:, i, 1:-1
].unsqueeze(-1)
return [hidden_states1, hidden_states2, pool2]
class SdxlTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy):
SDXL_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX = "_te_outputs.npz"
def __init__(
self, cache_to_disk: bool, batch_size: int, skip_disk_cache_validity_check: bool, is_partial: bool = False
self,
cache_to_disk: bool,
batch_size: int,
skip_disk_cache_validity_check: bool,
is_partial: bool = False,
is_weighted: bool = False,
) -> None:
super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial)
super().__init__(cache_to_disk, batch_size, skip_disk_cache_validity_check, is_partial, is_weighted)
def get_outputs_npz_path(self, image_abs_path: str) -> str:
return os.path.splitext(image_abs_path)[0] + SdxlTextEncoderOutputsCachingStrategy.SDXL_TEXT_ENCODER_OUTPUTS_NPZ_SUFFIX
@@ -215,11 +266,19 @@ class SdxlTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy):
sdxl_text_encoding_strategy = text_encoding_strategy # type: SdxlTextEncodingStrategy
captions = [info.caption for info in infos]
tokens1, tokens2 = tokenize_strategy.tokenize(captions)
with torch.no_grad():
hidden_state1, hidden_state2, pool2 = sdxl_text_encoding_strategy.encode_tokens(
tokenize_strategy, models, [tokens1, tokens2]
)
if self.is_weighted:
tokens_list, weights_list = tokenize_strategy.tokenize_with_weights(captions)
with torch.no_grad():
hidden_state1, hidden_state2, pool2 = sdxl_text_encoding_strategy.encode_tokens_with_weights(
tokenize_strategy, models, tokens_list, weights_list
)
else:
tokens1, tokens2 = tokenize_strategy.tokenize(captions)
with torch.no_grad():
hidden_state1, hidden_state2, pool2 = sdxl_text_encoding_strategy.encode_tokens(
tokenize_strategy, models, [tokens1, tokens2]
)
if hidden_state1.dtype == torch.bfloat16:
hidden_state1 = hidden_state1.float()
if hidden_state2.dtype == torch.bfloat16:

55
library/train_util.py
View File

@@ -31,6 +31,7 @@ import hashlib
import subprocess
from io import BytesIO
import toml
# from concurrent.futures import ThreadPoolExecutor, as_completed
from tqdm import tqdm
@@ -74,6 +75,7 @@ import imagesize
import cv2
import safetensors.torch
from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline
from library.sdxl_lpw_stable_diffusion import SdxlStableDiffusionLongPromptWeightingPipeline
import library.model_util as model_util
import library.huggingface_util as huggingface_util
import library.sai_model_spec as sai_model_spec
@@ -911,6 +913,23 @@ class BaseDataset(torch.utils.data.Dataset):
if info.image_size is None:
info.image_size = self.get_image_size(info.absolute_path)
# # run in parallel
# max_workers = min(os.cpu_count(), len(self.image_data)) # TODO consider multi-gpu (processes)
# with ThreadPoolExecutor(max_workers) as executor:
# futures = []
# for info in tqdm(self.image_data.values(), desc="loading image sizes"):
# if info.image_size is None:
# def get_and_set_image_size(info):
# info.image_size = self.get_image_size(info.absolute_path)
# futures.append(executor.submit(get_and_set_image_size, info))
# # consume futures to reduce memory usage and prevent Ctrl-C hang
# if len(futures) >= max_workers:
# for future in futures:
# future.result()
# futures = []
# for future in futures:
# future.result()
if self.enable_bucket:
logger.info("make buckets")
else:
@@ -1825,7 +1844,7 @@ class DreamBoothDataset(BaseDataset):
# 画像ファイルごとにプロンプトを読み込み、もしあればそちらを使う
captions = []
missing_captions = []
for img_path in img_paths:
for img_path in tqdm(img_paths, desc="read caption"):
cap_for_img = read_caption(img_path, subset.caption_extension, subset.enable_wildcard)
if cap_for_img is None and subset.class_tokens is None:
logger.warning(
@@ -3581,7 +3600,20 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
# available backends:
# https://github.com/huggingface/accelerate/blob/d1abd59114ada8ba673e1214218cb2878c13b82d/src/accelerate/utils/dataclasses.py#L376-L388C5
# https://pytorch.org/docs/stable/torch.compiler.html
choices=["eager", "aot_eager", "inductor", "aot_ts_nvfuser", "nvprims_nvfuser", "cudagraphs", "ofi", "fx2trt", "onnxrt"],
choices=[
"eager",
"aot_eager",
"inductor",
"aot_ts_nvfuser",
"nvprims_nvfuser",
"cudagraphs",
"ofi",
"fx2trt",
"onnxrt",
"tensort",
"ipex",
"tvm",
],
help="dynamo backend type (default is inductor) / dynamoのbackendの種類（デフォルトは inductor）",
)
parser.add_argument("--xformers", action="store_true", help="use xformers for CrossAttention / CrossAttentionにxformersを使う")
@@ -5850,8 +5882,8 @@ def sample_images_common(
pipe_class,
accelerator: Accelerator,
args: argparse.Namespace,
epoch,
steps,
epoch: int,
steps: int,
device,
vae,
tokenizer,
@@ -5910,11 +5942,7 @@ def sample_images_common(
with open(args.sample_prompts, "r", encoding="utf-8") as f:
prompts = json.load(f)
# schedulers: dict = {} cannot find where this is used
default_scheduler = get_my_scheduler(
sample_sampler=args.sample_sampler,
v_parameterization=args.v_parameterization,
)
default_scheduler = get_my_scheduler(sample_sampler=args.sample_sampler, v_parameterization=args.v_parameterization)
pipeline = pipe_class(
text_encoder=text_encoder,
@@ -5975,21 +6003,18 @@ def sample_images_common(
# clear pipeline and cache to reduce vram usage
del pipeline
# I'm not sure which of these is the correct way to clear the memory, but accelerator's device is used in the pipeline, so I'm using it here.
# with torch.cuda.device(torch.cuda.current_device()):
# torch.cuda.empty_cache()
clean_memory_on_device(accelerator.device)
torch.set_rng_state(rng_state)
if torch.cuda.is_available() and cuda_rng_state is not None:
torch.cuda.set_rng_state(cuda_rng_state)
vae.to(org_vae_device)
clean_memory_on_device(accelerator.device)
def sample_image_inference(
accelerator: Accelerator,
args: argparse.Namespace,
pipeline,
pipeline: Union[StableDiffusionLongPromptWeightingPipeline, SdxlStableDiffusionLongPromptWeightingPipeline],
save_dir,
prompt_dict,
epoch,

40
sdxl_train.py
View File

@@ -104,8 +104,8 @@ def train(args):
setup_logging(args, reset=True)
assert (
not args.weighted_captions
), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません"
not args.weighted_captions or not args.cache_text_encoder_outputs
), "weighted_captions is not supported when caching text encoder outputs / cache_text_encoder_outputsを使うときはweighted_captionsはサポートされていません"
assert (
not args.train_text_encoder or not args.cache_text_encoder_outputs
), "cache_text_encoder_outputs is not supported when training text encoder / text encoderを学習するときはcache_text_encoder_outputsはサポートされていません"
@@ -321,7 +321,7 @@ def train(args):
if args.cache_text_encoder_outputs:
# Text Encodes are eval and no grad
text_encoder_output_caching_strategy = strategy_sdxl.SdxlTextEncoderOutputsCachingStrategy(
args.cache_text_encoder_outputs_to_disk, None, False
args.cache_text_encoder_outputs_to_disk, None, False, is_weighted=args.weighted_captions
)
strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_output_caching_strategy)
@@ -660,22 +660,24 @@ def train(args):
input_ids1, input_ids2 = batch["input_ids_list"]
with torch.set_grad_enabled(args.train_text_encoder):
# Get the text embedding for conditioning
# TODO support weighted captions
# if args.weighted_captions:
# encoder_hidden_states = get_weighted_text_embeddings(
# tokenizer,
# text_encoder,
# batch["captions"],
# accelerator.device,
# args.max_token_length // 75 if args.max_token_length else 1,
# clip_skip=args.clip_skip,
# )
# else:
input_ids1 = input_ids1.to(accelerator.device)
input_ids2 = input_ids2.to(accelerator.device)
encoder_hidden_states1, encoder_hidden_states2, pool2 = text_encoding_strategy.encode_tokens(
tokenize_strategy, [text_encoder1, text_encoder2], [input_ids1, input_ids2]
)
if args.weighted_captions:
input_ids_list, weights_list = tokenize_strategy.tokenize_with_weights(batch["captions"])
encoder_hidden_states1, encoder_hidden_states2, pool2 = (
text_encoding_strategy.encode_tokens_with_weights(
tokenize_strategy,
[text_encoder1, text_encoder2, accelerator.unwrap_model(text_encoder2)],
input_ids_list,
weights_list,
)
)
else:
input_ids1 = input_ids1.to(accelerator.device)
input_ids2 = input_ids2.to(accelerator.device)
encoder_hidden_states1, encoder_hidden_states2, pool2 = text_encoding_strategy.encode_tokens(
tokenize_strategy,
[text_encoder1, text_encoder2, accelerator.unwrap_model(text_encoder2)],
[input_ids1, input_ids2],
)
if args.full_fp16:
encoder_hidden_states1 = encoder_hidden_states1.to(weight_dtype)
encoder_hidden_states2 = encoder_hidden_states2.to(weight_dtype)

722
sdxl_train_control_net.py Normal file
View File

@@ -0,0 +1,722 @@
import argparse
import math
import os
import random
from multiprocessing import Value
import toml
from tqdm import tqdm
import torch
from library.device_utils import init_ipex, clean_memory_on_device
init_ipex()
from accelerate.utils import set_seed
from accelerate import init_empty_weights
from diffusers import DDPMScheduler
from diffusers.utils.torch_utils import is_compiled_module
from safetensors.torch import load_file
from library import (
deepspeed_utils,
sai_model_spec,
sdxl_model_util,
sdxl_train_util,
strategy_base,
strategy_sd,
strategy_sdxl,
)
import library.train_util as train_util
import library.config_util as config_util
from library.config_util import (
ConfigSanitizer,
BlueprintGenerator,
)
import library.huggingface_util as huggingface_util
import library.custom_train_functions as custom_train_functions
from library.custom_train_functions import (
add_v_prediction_like_loss,
apply_snr_weight,
prepare_scheduler_for_custom_training,
scale_v_prediction_loss_like_noise_prediction,
apply_debiased_estimation,
)
from library.sdxl_original_control_net import SdxlControlNet, SdxlControlledUNet
from library.utils import setup_logging, add_logging_arguments
setup_logging()
import logging
logger = logging.getLogger(__name__)
# TODO 他のスクリプトと共通化する
def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
logs = {
"loss/current": current_loss,
"loss/average": avr_loss,
"lr": lr_scheduler.get_last_lr()[0],
}
if args.optimizer_type.lower().startswith("DAdapt".lower()):
logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
return logs
def train(args):
train_util.verify_training_args(args)
train_util.prepare_dataset_args(args, True)
sdxl_train_util.verify_sdxl_training_args(args)
setup_logging(args, reset=True)
cache_latents = args.cache_latents
use_user_config = args.dataset_config is not None
if args.seed is None:
args.seed = random.randint(0, 2**32)
set_seed(args.seed)
tokenize_strategy = strategy_sdxl.SdxlTokenizeStrategy(args.max_token_length, args.tokenizer_cache_dir)
strategy_base.TokenizeStrategy.set_strategy(tokenize_strategy)
tokenizer1, tokenizer2 = tokenize_strategy.tokenizer1, tokenize_strategy.tokenizer2 # this is used for sampling images
# prepare caching strategy: this must be set before preparing dataset. because dataset may use this strategy for initialization.
latents_caching_strategy = strategy_sd.SdSdxlLatentsCachingStrategy(
False, args.cache_latents_to_disk, args.vae_batch_size, False
)
strategy_base.LatentsCachingStrategy.set_strategy(latents_caching_strategy)
# データセットを準備する
blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, False, True, True))
if use_user_config:
logger.info(f"Load dataset config from {args.dataset_config}")
user_config = config_util.load_user_config(args.dataset_config)
ignored = ["train_data_dir", "conditioning_data_dir"]
if any(getattr(args, attr) is not None for attr in ignored):
logger.warning(
"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:
user_config = {
"datasets": [
{
"subsets": config_util.generate_controlnet_subsets_config_by_subdirs(
args.train_data_dir,
args.conditioning_data_dir,
args.caption_extension,
)
}
]
}
blueprint = blueprint_generator.generate(user_config, args)
train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
current_epoch = Value("i", 0)
current_step = Value("i", 0)
ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
train_dataset_group.verify_bucket_reso_steps(32)
if args.debug_dataset:
train_dataset_group.set_current_strategies() # dasaset needs to know the strategies explicitly
train_util.debug_dataset(train_dataset_group)
return
if len(train_dataset_group) == 0:
logger.error(
"No data found. Please verify arguments (train_data_dir must be the parent of folders with images) / 画像がありません。引数指定を確認してください（train_data_dirには画像があるフォルダではなく、画像があるフォルダの親フォルダを指定する必要があります）"
)
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"
else:
logger.warning(
"WARNING: random_crop is not supported yet for ControlNet training / ControlNetの学習ではrandom_cropはまだサポートされていません"
)
if args.cache_text_encoder_outputs:
assert (
train_dataset_group.is_text_encoder_output_cacheable()
), "when caching Text Encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / Text Encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません"
# acceleratorを準備する
logger.info("prepare accelerator")
accelerator = train_util.prepare_accelerator(args)
is_main_process = accelerator.is_main_process
def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model
# 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)
vae_dtype = torch.float32 if args.no_half_vae else weight_dtype
# モデルを読み込む
(
load_stable_diffusion_format,
text_encoder1,
text_encoder2,
vae,
unet,
logit_scale,
ckpt_info,
) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype)
unet.to(accelerator.device) # reduce main memory usage
# convert U-Net to Controlled U-Net
logger.info("convert U-Net to Controlled U-Net")
unet_sd = unet.state_dict()
with init_empty_weights():
unet = SdxlControlledUNet()
unet.load_state_dict(unet_sd, strict=True, assign=True)
del unet_sd
# make control net
logger.info("make ControlNet")
if args.controlnet_model_path:
with init_empty_weights():
control_net = SdxlControlNet()
logger.info(f"load ControlNet from {args.controlnet_model_path}")
filename = args.controlnet_model_path
if os.path.splitext(filename)[1] == ".safetensors":
state_dict = load_file(filename)
else:
state_dict = torch.load(filename)
info = control_net.load_state_dict(state_dict, strict=True, assign=True)
logger.info(f"ControlNet loaded from {filename}: {info}")
else:
control_net = SdxlControlNet()
logger.info("initialize ControlNet from U-Net")
info = control_net.init_from_unet(unet)
logger.info(f"ControlNet initialized from U-Net: {info}")
# 学習を準備する
if cache_latents:
vae.to(accelerator.device, dtype=vae_dtype)
vae.requires_grad_(False)
vae.eval()
train_dataset_group.new_cache_latents(vae, accelerator.is_main_process)
vae.to("cpu")
clean_memory_on_device(accelerator.device)
accelerator.wait_for_everyone()
text_encoding_strategy = strategy_sdxl.SdxlTextEncodingStrategy()
strategy_base.TextEncodingStrategy.set_strategy(text_encoding_strategy)
# TextEncoderの出力をキャッシュする
if args.cache_text_encoder_outputs:
# Text Encodes are eval and no grad
text_encoder_output_caching_strategy = strategy_sdxl.SdxlTextEncoderOutputsCachingStrategy(
args.cache_text_encoder_outputs_to_disk, None, False
)
strategy_base.TextEncoderOutputsCachingStrategy.set_strategy(text_encoder_output_caching_strategy)
text_encoder1.to(accelerator.device)
text_encoder2.to(accelerator.device)
with accelerator.autocast():
train_dataset_group.new_cache_text_encoder_outputs([text_encoder1, text_encoder2], accelerator.is_main_process)
accelerator.wait_for_everyone()
# 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, args.sdpa)
if args.xformers:
unet.set_use_memory_efficient_attention(True, False)
control_net.set_use_memory_efficient_attention(True, False)
elif args.sdpa:
unet.set_use_sdpa(True)
control_net.set_use_sdpa(True)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
control_net.enable_gradient_checkpointing()
# 学習に必要なクラスを準備する
accelerator.print("prepare optimizer, data loader etc.")
trainable_params = []
ctrlnet_params = []
unet_params = []
for name, param in control_net.named_parameters():
if name.startswith("controlnet_"):
ctrlnet_params.append(param)
else:
unet_params.append(param)
trainable_params.append({"params": ctrlnet_params, "lr": args.control_net_lr})
trainable_params.append({"params": unet_params, "lr": args.learning_rate})
all_params = ctrlnet_params + unet_params
logger.info(f"trainable params count: {len(all_params)}")
logger.info(f"number of trainable parameters: {sum(p.numel() for p in all_params)}")
_, _, optimizer = train_util.get_optimizer(args, trainable_params)
# prepare dataloader
# strategies are set here because they cannot be referenced in another process. Copy them with the dataset
# some strategies can be None
train_dataset_group.set_current_strategies()
# DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers
train_dataloader = torch.utils.data.DataLoader(
train_dataset_group,
batch_size=1,
shuffle=True,
collate_fn=collator,
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
)
accelerator.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)
# 実験的機能：勾配も含めたfp16/bf16学習を行う　モデル全体をfp16/bf16にする
if args.full_fp16:
assert (
args.mixed_precision == "fp16"
), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
accelerator.print("enable full fp16 training.")
control_net.to(weight_dtype)
elif args.full_bf16:
assert (
args.mixed_precision == "bf16"
), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。"
accelerator.print("enable full bf16 training.")
control_net.to(weight_dtype)
# acceleratorがなんかよろしくやってくれるらしい
control_net, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
control_net, optimizer, train_dataloader, lr_scheduler
)
if args.fused_backward_pass:
# use fused optimizer for backward pass: other optimizers will be supported in the future
import library.adafactor_fused
library.adafactor_fused.patch_adafactor_fused(optimizer)
for param_group in optimizer.param_groups:
for parameter in param_group["params"]:
if parameter.requires_grad:
def __grad_hook(tensor: torch.Tensor, param_group=param_group):
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
optimizer.step_param(tensor, param_group)
tensor.grad = None
parameter.register_post_accumulate_grad_hook(__grad_hook)
unet.requires_grad_(False)
text_encoder1.requires_grad_(False)
text_encoder2.requires_grad_(False)
unet.to(accelerator.device, dtype=weight_dtype)
unet.eval()
control_net.train()
# TextEncoderの出力をキャッシュするときにはCPUへ移動する
if args.cache_text_encoder_outputs:
# move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
text_encoder1.to("cpu", dtype=torch.float32)
text_encoder2.to("cpu", dtype=torch.float32)
clean_memory_on_device(accelerator.device)
else:
# make sure Text Encoders are on GPU
text_encoder1.to(accelerator.device)
text_encoder2.to(accelerator.device)
if not cache_latents:
vae.requires_grad_(False)
vae.eval()
vae.to(accelerator.device, dtype=vae_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)
# resumeする
train_util.resume_from_local_or_hf_if_specified(accelerator, args)
# 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
# TODO: find a way to handle total batch size when there are multiple datasets
accelerator.print("running training / 学習開始")
accelerator.print(f" num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
accelerator.print(f" num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
accelerator.print(f" num epochs / epoch数: {num_train_epochs}")
accelerator.print(
f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
)
# logger.info(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}")
accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
accelerator.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
)
prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device)
if args.zero_terminal_snr:
custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler)
if accelerator.is_main_process:
init_kwargs = {}
if args.wandb_run_name:
init_kwargs["wandb"] = {"name": args.wandb_run_name}
if args.log_tracker_config is not None:
init_kwargs = toml.load(args.log_tracker_config)
accelerator.init_trackers(
("sdxl_control_net_train" if args.log_tracker_name is None else args.log_tracker_name),
config=train_util.get_sanitized_config_or_none(args),
init_kwargs=init_kwargs,
)
loss_recorder = train_util.LossRecorder()
del train_dataset_group
# function for saving/removing
def save_model(ckpt_name, model, force_sync_upload=False):
os.makedirs(args.output_dir, exist_ok=True)
ckpt_file = os.path.join(args.output_dir, ckpt_name)
accelerator.print(f"\nsaving checkpoint: {ckpt_file}")
sai_metadata = train_util.get_sai_model_spec(None, args, True, True, False)
sai_metadata["modelspec.architecture"] = sai_model_spec.ARCH_SD_XL_V1_BASE + "/controlnet"
state_dict = model.state_dict()
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(ckpt_file)[1] == ".safetensors":
from safetensors.torch import save_file
save_file(state_dict, ckpt_file, sai_metadata)
else:
torch.save(state_dict, ckpt_file)
if args.huggingface_repo_id is not None:
huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
def remove_model(old_ckpt_name):
old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
if os.path.exists(old_ckpt_file):
accelerator.print(f"removing old checkpoint: {old_ckpt_file}")
os.remove(old_ckpt_file)
# For --sample_at_first
sdxl_train_util.sample_images(
accelerator,
args,
0,
global_step,
accelerator.device,
vae,
[tokenizer1, tokenizer2],
[text_encoder1, text_encoder2, unwrap_model(text_encoder2)],
unet,
controlnet=control_net,
)
# training loop
for epoch in range(num_train_epochs):
accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
current_epoch.value = epoch + 1
control_net.train()
for step, batch in enumerate(train_dataloader):
current_step.value = global_step
with accelerator.accumulate(control_net):
with torch.no_grad():
if "latents" in batch and batch["latents"] is not None:
latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
else:
# latentに変換
latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype)
# NaNが含まれていれば警告を表示し0に置き換える
if torch.any(torch.isnan(latents)):
accelerator.print("NaN found in latents, replacing with zeros")
latents = torch.nan_to_num(latents, 0, out=latents)
latents = latents * sdxl_model_util.VAE_SCALE_FACTOR
text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None)
if text_encoder_outputs_list is not None:
# Text Encoder outputs are cached
encoder_hidden_states1, encoder_hidden_states2, pool2 = text_encoder_outputs_list
encoder_hidden_states1 = encoder_hidden_states1.to(accelerator.device, dtype=weight_dtype)
encoder_hidden_states2 = encoder_hidden_states2.to(accelerator.device, dtype=weight_dtype)
pool2 = pool2.to(accelerator.device, dtype=weight_dtype)
else:
input_ids1, input_ids2 = batch["input_ids_list"]
with torch.no_grad():
input_ids1 = input_ids1.to(accelerator.device)
input_ids2 = input_ids2.to(accelerator.device)
encoder_hidden_states1, encoder_hidden_states2, pool2 = text_encoding_strategy.encode_tokens(
tokenize_strategy, [text_encoder1, text_encoder2, unwrap_model(text_encoder2)], [input_ids1, input_ids2]
)
if args.full_fp16:
encoder_hidden_states1 = encoder_hidden_states1.to(weight_dtype)
encoder_hidden_states2 = encoder_hidden_states2.to(weight_dtype)
pool2 = pool2.to(weight_dtype)
# get size embeddings
orig_size = batch["original_sizes_hw"]
crop_size = batch["crop_top_lefts"]
target_size = batch["target_sizes_hw"]
embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, accelerator.device).to(weight_dtype)
# concat embeddings
vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype)
text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim=2).to(weight_dtype)
# Sample noise, sample a random timestep for each image, and add noise to the latents,
# with noise offset and/or multires noise if specified
noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps(
args, noise_scheduler, latents
)
controlnet_image = batch["conditioning_images"].to(dtype=weight_dtype)
# '-1 to +1' to '0 to 1'
controlnet_image = (controlnet_image + 1) / 2
with accelerator.autocast():
input_resi_add, mid_add = control_net(
noisy_latents, timesteps, text_embedding, vector_embedding, controlnet_image
)
noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding, input_resi_add, mid_add)
if args.v_parameterization:
# v-parameterization training
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
target = noise
loss = train_util.conditional_loss(
noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c
)
loss = loss.mean([1, 2, 3])
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, args.v_parameterization)
if args.scale_v_pred_loss_like_noise_pred:
loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
if args.v_pred_like_loss:
loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
if args.debiased_estimation_loss:
loss = apply_debiased_estimation(loss, timesteps, noise_scheduler)
loss = loss.mean() # 平均なのでbatch_sizeで割る必要なし
accelerator.backward(loss)
if not args.fused_backward_pass:
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
params_to_clip = control_net.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
else:
# optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
lr_scheduler.step()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
sdxl_train_util.sample_images(
accelerator,
args,
None,
global_step,
accelerator.device,
vae,
[tokenizer1, tokenizer2],
[text_encoder1, text_encoder2, unwrap_model(text_encoder2)],
unet,
controlnet=control_net,
)
# 指定ステップごとにモデルを保存
if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
accelerator.wait_for_everyone()
if accelerator.is_main_process:
ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step)
save_model(ckpt_name, unwrap_model(control_net))
if args.save_state:
train_util.save_and_remove_state_stepwise(args, accelerator, global_step)
remove_step_no = train_util.get_remove_step_no(args, global_step)
if remove_step_no is not None:
remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no)
remove_model(remove_ckpt_name)
current_loss = loss.detach().item()
loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
avr_loss: float = loss_recorder.moving_average
logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if len(accelerator.trackers) > 0:
logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if len(accelerator.trackers) > 0:
logs = {"loss/epoch": loss_recorder.moving_average}
accelerator.log(logs, step=epoch + 1)
accelerator.wait_for_everyone()
# 指定エポックごとにモデルを保存
if args.save_every_n_epochs is not None:
saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs
if is_main_process and saving:
ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)
save_model(ckpt_name, unwrap_model(control_net))
remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1)
if remove_epoch_no is not None:
remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)
remove_model(remove_ckpt_name)
if args.save_state:
train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)
sdxl_train_util.sample_images(
accelerator,
args,
epoch + 1,
global_step,
accelerator.device,
vae,
[tokenizer1, tokenizer2],
[text_encoder1, text_encoder2, unwrap_model(text_encoder2)],
unet,
controlnet=control_net,
)
# end of epoch
if is_main_process:
control_net = unwrap_model(control_net)
accelerator.end_training()
if is_main_process and (args.save_state or args.save_state_on_train_end):
train_util.save_state_on_train_end(args, accelerator)
if is_main_process:
ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
save_model(ckpt_name, control_net, force_sync_upload=True)
logger.info("model saved.")
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
add_logging_arguments(parser)
train_util.add_sd_models_arguments(parser)
train_util.add_dataset_arguments(parser, False, True, True)
train_util.add_training_arguments(parser, False)
# train_util.add_masked_loss_arguments(parser)
deepspeed_utils.add_deepspeed_arguments(parser)
# train_util.add_sd_saving_arguments(parser)
train_util.add_optimizer_arguments(parser)
config_util.add_config_arguments(parser)
custom_train_functions.add_custom_train_arguments(parser)
sdxl_train_util.add_sdxl_training_arguments(parser)
parser.add_argument(
"--controlnet_model_path",
type=str,
default=None,
help="controlnet model name or path / controlnetのモデル名またはパス",
)
parser.add_argument(
"--conditioning_data_dir",
type=str,
default=None,
help="conditioning data directory / 条件付けデータのディレクトリ",
)
parser.add_argument(
"--save_model_as",
type=str,
default="safetensors",
choices=[None, "ckpt", "pt", "safetensors"],
help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）",
)
parser.add_argument(
"--no_half_vae",
action="store_true",
help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",
)
parser.add_argument(
"--control_net_lr",
type=float,
default=1e-4,
help="learning rate for controlnet modules / controlnetモジュールの学習率",
)
return parser
if __name__ == "__main__":
# sdxl_original_unet.USE_REENTRANT = False
parser = setup_parser()
args = parser.parse_args()
train_util.verify_command_line_training_args(args)
args = train_util.read_config_from_file(args, parser)
train(args)

4
sdxl_train_network.py
View File

@@ -79,7 +79,9 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
def get_text_encoder_outputs_caching_strategy(self, args):
if args.cache_text_encoder_outputs:
return strategy_sdxl.SdxlTextEncoderOutputsCachingStrategy(args.cache_text_encoder_outputs_to_disk, None, False)
return strategy_sdxl.SdxlTextEncoderOutputsCachingStrategy(
args.cache_text_encoder_outputs_to_disk, None, False, is_weighted=args.weighted_captions
)
else:
return None

31
train_controlnet.py
View File

@@ -254,6 +254,7 @@ def train(args):
accelerator.wait_for_everyone()
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
controlnet.enable_gradient_checkpointing()
# 学習に必要なクラスを準備する
@@ -304,6 +305,20 @@ def train(args):
controlnet, optimizer, train_dataloader, lr_scheduler
)
if args.fused_backward_pass:
import library.adafactor_fused
library.adafactor_fused.patch_adafactor_fused(optimizer)
for param_group in optimizer.param_groups:
for parameter in param_group["params"]:
if parameter.requires_grad:
def __grad_hook(tensor: torch.Tensor, param_group=param_group):
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
accelerator.clip_grad_norm_(tensor, args.max_grad_norm)
optimizer.step_param(tensor, param_group)
tensor.grad = None
parameter.register_post_accumulate_grad_hook(__grad_hook)
unet.requires_grad_(False)
text_encoder.requires_grad_(False)
unet.to(accelerator.device)
@@ -497,13 +512,17 @@ def train(args):
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 = controlnet.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
if not args.fused_backward_pass:
if accelerator.sync_gradients and args.max_grad_norm != 0.0:
params_to_clip = controlnet.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
else:
# optimizer.step() and optimizer.zero_grad() are called in the optimizer hook
lr_scheduler.step()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:

16
train_db.py
View File

@@ -356,21 +356,17 @@ def train(args):
# Get the text embedding for conditioning
with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
if args.weighted_captions:
encoder_hidden_states = get_weighted_text_embeddings(
tokenize_strategy.tokenizer,
text_encoder,
batch["captions"],
accelerator.device,
args.max_token_length // 75 if args.max_token_length else 1,
clip_skip=args.clip_skip,
)
input_ids_list, weights_list = tokenize_strategy.tokenize_with_weights(batch["captions"])
encoder_hidden_states = text_encoding_strategy.encode_tokens_with_weights(
tokenize_strategy, [text_encoder], input_ids_list, weights_list
)[0]
else:
input_ids = batch["input_ids_list"][0].to(accelerator.device)
encoder_hidden_states = text_encoding_strategy.encode_tokens(
tokenize_strategy, [text_encoder], [input_ids]
)[0]
if args.full_fp16:
encoder_hidden_states = encoder_hidden_states.to(weight_dtype)
if args.full_fp16:
encoder_hidden_states = encoder_hidden_states.to(weight_dtype)
# Sample noise, sample a random timestep for each image, and add noise to the latents,
# with noise offset and/or multires noise if specified

27
train_network.py
View File

@@ -1123,14 +1123,21 @@ class NetworkTrainer:
with torch.set_grad_enabled(train_text_encoder), accelerator.autocast():
# Get the text embedding for conditioning
if args.weighted_captions:
# SD only
encoded_text_encoder_conds = get_weighted_text_embeddings(
tokenizers[0],
text_encoder,
batch["captions"],
accelerator.device,
args.max_token_length // 75 if args.max_token_length else 1,
clip_skip=args.clip_skip,
# # SD only
# encoded_text_encoder_conds = get_weighted_text_embeddings(
# tokenizers[0],
# text_encoder,
# batch["captions"],
# accelerator.device,
# args.max_token_length // 75 if args.max_token_length else 1,
# clip_skip=args.clip_skip,
# )
input_ids_list, weights_list = tokenize_strategy.tokenize_with_weights(batch["captions"])
encoded_text_encoder_conds = text_encoding_strategy.encode_tokens_with_weights(
tokenize_strategy,
self.get_models_for_text_encoding(args, accelerator, text_encoders),
input_ids_list,
weights_list,
)
else:
input_ids = [ids.to(accelerator.device) for ids in batch["input_ids_list"]]
@@ -1139,8 +1146,8 @@ class NetworkTrainer:
self.get_models_for_text_encoding(args, accelerator, text_encoders),
input_ids,
)
if args.full_fp16:
encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
if args.full_fp16:
encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
# if text_encoder_conds is not cached, use encoded_text_encoder_conds
if len(text_encoder_conds) == 0: