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Unverified Commit 35f45ecd authored by Linoy Tsaban's avatar Linoy Tsaban Committed by GitHub
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[Advanced dreambooth lora] adjustments to align with canonical script (#8406) 



* minor changes

* minor changes

* minor changes

* minor changes

* minor changes

* minor changes

* minor changes

* fix

* fix

* aligning with blora script

* aligning with blora script

* aligning with blora script

* aligning with blora script

* aligning with blora script

* remove prints

* style

* default val

* license

* move save_model_card to outside push_to_hub

* Update train_dreambooth_lora_sdxl_advanced.py

---------
Co-authored-by: default avatarSayak Paul <spsayakpaul@gmail.com>
parent d5dd8df3
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Showing with 165 additions and 177 deletions
examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
View file @ 35f45ecd
......@@ -31,8 +31,6 @@ from typing import List, Optional
import numpy as np
import torch
import torch.nn.functional as F
# imports of the TokenEmbeddingsHandler class
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
......@@ -77,6 +75,9 @@ from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import is_compiled_module
if is_wandb_available():
import wandb
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.30.0.dev0")
......@@ -101,12 +102,12 @@ def save_model_card(
repo_id: str,
use_dora: bool,
images=None,
base_model=str,
base_model: str = None,
train_text_encoder=False,
train_text_encoder_ti=False,
token_abstraction_dict=None,
instance_prompt=str,
validation_prompt=str,
instance_prompt: str = None,
validation_prompt: str = None,
repo_folder=None,
vae_path=None,
):
......@@ -135,6 +136,14 @@ def save_model_card(
diffusers_imports_pivotal = ""
diffusers_example_pivotal = ""
webui_example_pivotal = ""
license = ""
if "playground" in base_model:
license = """\n
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic/blob/main/LICENSE.md).
"""
if train_text_encoder_ti:
trigger_str = (
"To trigger image generation of trained concept(or concepts) replace each concept identifier "
......@@ -223,11 +232,75 @@ Pivotal tuning was enabled: {train_text_encoder_ti}.
Special VAE used for training: {vae_path}.
{license}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def log_validation(
pipeline,
args,
accelerator,
pipeline_args,
epoch,
is_final_validation=False,
):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
scheduler_args = {}
if not args.do_edm_style_training:
if "variance_type" in pipeline.scheduler.config:
variance_type = pipeline.scheduler.config.variance_type
if variance_type in ["learned", "learned_range"]:
variance_type = "fixed_small"
scheduler_args["variance_type"] = variance_type
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
# Currently the context determination is a bit hand-wavy. We can improve it in the future if there's a better
# way to condition it. Reference: https://github.com/huggingface/diffusers/pull/7126#issuecomment-1968523051
if torch.backends.mps.is_available() or "playground" in args.pretrained_model_name_or_path:
autocast_ctx = nullcontext()
else:
autocast_ctx = torch.autocast(accelerator.device.type)
with autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
phase_name: [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
]
}
)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
return images
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
......@@ -390,6 +463,7 @@ def parse_args(input_args=None):
)
parser.add_argument(
"--do_edm_style_training",
default=False,
action="store_true",
help="Flag to conduct training using the EDM formulation as introduced in https://arxiv.org/abs/2206.00364.",
)
......@@ -571,7 +645,7 @@ def parse_args(input_args=None):
parser.add_argument(
"--optimizer",
type=str,
default="adamW",
default="AdamW",
help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
)
......@@ -906,11 +980,6 @@ class DreamBoothDataset(Dataset):
instance_data_root,
instance_prompt,
class_prompt,
dataset_name,
dataset_config_name,
cache_dir,
image_column,
caption_column,
train_text_encoder_ti,
class_data_root=None,
class_num=None,
......@@ -929,7 +998,7 @@ class DreamBoothDataset(Dataset):
self.train_text_encoder_ti = train_text_encoder_ti
# if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,
# we load the training data using load_dataset
if dataset_name is not None:
if args.dataset_name is not None:
try:
from datasets import load_dataset
except ImportError:
......@@ -942,25 +1011,26 @@ class DreamBoothDataset(Dataset):
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
dataset = load_dataset(
dataset_name,
dataset_config_name,
cache_dir=cache_dir,
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
# Preprocessing the datasets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
if image_column is None:
if args.image_column is None:
image_column = column_names[0]
logger.info(f"image column defaulting to {image_column}")
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"`--image_column` value '{image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
instance_images = dataset["train"][image_column]
if caption_column is None:
if args.caption_column is None:
logger.info(
"No caption column provided, defaulting to instance_prompt for all images. If your dataset "
"contains captions/prompts for the images, make sure to specify the "
......@@ -968,11 +1038,11 @@ class DreamBoothDataset(Dataset):
)
self.custom_instance_prompts = None
else:
if caption_column not in column_names:
if args.caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
custom_instance_prompts = dataset["train"][caption_column]
custom_instance_prompts = dataset["train"][args.caption_column]
# create final list of captions according to --repeats
self.custom_instance_prompts = []
for caption in custom_instance_prompts:
......@@ -1178,13 +1248,12 @@ def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
text_input_ids = text_input_ids_list[i]
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False
)
# We are only ALWAYS interested in the pooled output of the final text encoder
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
prompt_embeds = prompt_embeds[-1][-2]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
......@@ -1200,9 +1269,16 @@ def main(args):
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `huggingface-cli login` to authenticate with the Hub."
)
if args.do_edm_style_training and args.snr_gamma is not None:
raise ValueError("Min-SNR formulation is not supported when conducting EDM-style training.")
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
......@@ -1215,10 +1291,13 @@ def main(args):
kwargs_handlers=[kwargs],
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
import wandb
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
......@@ -1246,7 +1325,8 @@ def main(args):
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()
torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
......@@ -1404,6 +1484,12 @@ def main(args):
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
......@@ -1508,15 +1594,13 @@ def main(args):
if isinstance(model, type(unwrap_model(unet))):
unet_lora_layers_to_save = convert_state_dict_to_diffusers(get_peft_model_state_dict(model))
elif isinstance(model, type(unwrap_model(text_encoder_one))):
if args.train_text_encoder:
text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
text_encoder_one_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
elif isinstance(model, type(unwrap_model(text_encoder_two))):
if args.train_text_encoder:
text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
text_encoder_two_lora_layers_to_save = convert_state_dict_to_diffusers(
get_peft_model_state_dict(model)
)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
......@@ -1564,6 +1648,7 @@ def main(args):
)
if args.train_text_encoder:
# Do we need to call `scale_lora_layers()` here?
_set_state_dict_into_text_encoder(lora_state_dict, prefix="text_encoder.", text_encoder=text_encoder_one_)
_set_state_dict_into_text_encoder(
......@@ -1578,14 +1663,14 @@ def main(args):
if args.train_text_encoder:
models.extend([text_encoder_one_, text_encoder_two_])
# only upcast trainable parameters (LoRA) into fp32
cast_training_params(models)
cast_training_params(models)
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
......@@ -1711,12 +1796,7 @@ def main(args):
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_prompt=args.class_prompt,
dataset_name=args.dataset_name,
dataset_config_name=args.dataset_config_name,
cache_dir=args.cache_dir,
image_column=args.image_column,
train_text_encoder_ti=args.train_text_encoder_ti,
caption_column=args.caption_column,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
token_abstraction_dict=token_abstraction_dict if args.train_text_encoder_ti else None,
class_num=args.num_class_images,
......@@ -1740,8 +1820,6 @@ def main(args):
def compute_time_ids(crops_coords_top_left, original_size=None):
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
if original_size is None:
original_size = (args.resolution, args.resolution)
target_size = (args.resolution, args.resolution)
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_time_ids = torch.tensor([add_time_ids])
......@@ -1778,7 +1856,8 @@ def main(args):
if freeze_text_encoder and not train_dataset.custom_instance_prompts:
del tokenizers, text_encoders
gc.collect()
torch.cuda.empty_cache()
if torch.cuda.is_available():
torch.cuda.empty_cache()
# If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
# pack the statically computed variables appropriately here. This is so that we don't
......@@ -1946,8 +2025,8 @@ def main(args):
text_encoder_two.train()
# set top parameter requires_grad = True for gradient checkpointing works
if args.train_text_encoder:
text_encoder_one.text_model.embeddings.requires_grad_(True)
text_encoder_two.text_model.embeddings.requires_grad_(True)
accelerator.unwrap_model(text_encoder_one).text_model.embeddings.requires_grad_(True)
accelerator.unwrap_model(text_encoder_two).text_model.embeddings.requires_grad_(True)
for step, batch in enumerate(train_dataloader):
if pivoted:
......@@ -2040,7 +2119,6 @@ def main(args):
if freeze_text_encoder:
unet_added_conditions = {
"time_ids": add_time_ids,
# "time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1),
"text_embeds": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),
}
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
......@@ -2220,10 +2298,6 @@ def main(args):
if accelerator.is_main_process:
if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline
if freeze_text_encoder:
text_encoder_one = text_encoder_cls_one.from_pretrained(
......@@ -2250,70 +2324,29 @@ def main(args):
variant=args.variant,
torch_dtype=weight_dtype,
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
scheduler_args = {}
if not args.do_edm_style_training:
if "variance_type" in pipeline.scheduler.config:
variance_type = pipeline.scheduler.config.variance_type
if variance_type in ["learned", "learned_range"]:
variance_type = "fixed_small"
scheduler_args["variance_type"] = variance_type
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
pipeline.scheduler.config, **scheduler_args
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
pipeline_args = {"prompt": args.validation_prompt}
if torch.backends.mps.is_available() or "playground" in args.pretrained_model_name_or_path:
autocast_ctx = nullcontext()
else:
autocast_ctx = torch.autocast(accelerator.device.type)
with autocast_ctx:
images = [
pipeline(**pipeline_args, generator=generator).images[0]
for _ in range(args.num_validation_images)
]
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
del pipeline
torch.cuda.empty_cache()
images = log_validation(
pipeline,
args,
accelerator,
pipeline_args,
epoch,
)
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet = unwrap_model(unet)
unet = unet.to(torch.float32)
unet_lora_layers = convert_state_dict_to_diffusers(get_peft_model_state_dict(unet))
if args.train_text_encoder:
text_encoder_one = accelerator.unwrap_model(text_encoder_one)
text_encoder_one = unwrap_model(text_encoder_one)
text_encoder_lora_layers = convert_state_dict_to_diffusers(
get_peft_model_state_dict(text_encoder_one.to(torch.float32))
)
text_encoder_two = accelerator.unwrap_model(text_encoder_two)
text_encoder_two = unwrap_model(text_encoder_two)
text_encoder_2_lora_layers = convert_state_dict_to_diffusers(
get_peft_model_state_dict(text_encoder_two.to(torch.float32))
)
......@@ -2332,85 +2365,39 @@ def main(args):
embeddings_path = f"{args.output_dir}/{args.output_dir}_emb.safetensors"
embedding_handler.save_embeddings(embeddings_path)
# Final inference
# Load previous pipeline
vae = AutoencoderKL.from_pretrained(
vae_path,
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
# load attention processors
pipeline.load_lora_weights(args.output_dir)
# run inference
images = []
if args.validation_prompt and args.num_validation_images > 0:
# Final inference
# Load previous pipeline
vae = AutoencoderKL.from_pretrained(
vae_path,
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
pipeline_args = {"prompt": args.validation_prompt, "num_inference_steps": 25}
images = log_validation(
pipeline,
args,
accelerator,
pipeline_args,
epoch,
is_final_validation=True,
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
scheduler_args = {}
if not args.do_edm_style_training:
if "variance_type" in pipeline.scheduler.config:
variance_type = pipeline.scheduler.config.variance_type
if variance_type in ["learned", "learned_range"]:
variance_type = "fixed_small"
scheduler_args["variance_type"] = variance_type
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
pipeline.scheduler.config, **scheduler_args
)
# load attention processors
pipeline.load_lora_weights(args.output_dir)
# load new tokens
if args.train_text_encoder_ti:
state_dict = load_file(embeddings_path)
all_new_tokens = []
for key, value in token_abstraction_dict.items():
all_new_tokens.extend(value)
pipeline.load_textual_inversion(
state_dict["clip_l"],
token=all_new_tokens,
text_encoder=pipeline.text_encoder,
tokenizer=pipeline.tokenizer,
)
pipeline.load_textual_inversion(
state_dict["clip_g"],
token=all_new_tokens,
text_encoder=pipeline.text_encoder_2,
tokenizer=pipeline.tokenizer_2,
)
# run inference
pipeline = pipeline.to(accelerator.device)
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
for _ in range(args.num_validation_images)
]
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"test": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
# Convert to WebUI format
lora_state_dict = load_file(f"{args.output_dir}/pytorch_lora_weights.safetensors")
peft_state_dict = convert_all_state_dict_to_peft(lora_state_dict)
......@@ -2430,6 +2417,7 @@ def main(args):
repo_folder=args.output_dir,
vae_path=args.pretrained_vae_model_name_or_path,
)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
......
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