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hf_public_repos/diffusers/examples
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hf_public_repos/diffusers/examples/text_to_image/requirements_flax.txt
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transformers>=4.25.1
datasets
flax
optax
torch
torchvision
ftfy
tensorboard
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hf_public_repos/diffusers/examples
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hf_public_repos/diffusers/examples/text_to_image/requirements.txt
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accelerate>=0.16.0
torchvision
transformers>=4.25.1
datasets
ftfy
tensorboard
Jinja2
| 0
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hf_public_repos/diffusers/examples
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hf_public_repos/diffusers/examples/text_to_image/train_text_to_image_sdxl.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fine-tuning script for Stable Diffusion XL for text2image."""
import argparse
import functools
import gc
import logging
import math
import os
import random
import shutil
from pathlib import Path
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from torchvision import transforms
from torchvision.transforms.functional import crop
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
StableDiffusionXLPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel, compute_snr
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.24.0.dev0")
logger = get_logger(__name__)
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def save_model_card(
repo_id: str,
images=None,
validation_prompt=None,
base_model=str,
dataset_name=str,
repo_folder=None,
vae_path=None,
):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
dataset: {dataset_name}
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
"""
model_card = f"""
# Text-to-image finetuning - {repo_id}
This pipeline was finetuned from **{base_model}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: {validation_prompt}: \n
{img_str}
Special VAE used for training: {vae_path}.
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=1,
help=(
"Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--proportion_empty_prompts",
type=float,
default=0,
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
)
parser.add_argument(
"--output_dir",
type=str,
default="sdxl-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--timestep_bias_strategy",
type=str,
default="none",
choices=["earlier", "later", "range", "none"],
help=(
"The timestep bias strategy, which may help direct the model toward learning low or high frequency details."
" Choices: ['earlier', 'later', 'range', 'none']."
" The default is 'none', which means no bias is applied, and training proceeds normally."
" The value of 'later' will increase the frequency of the model's final training timesteps."
),
)
parser.add_argument(
"--timestep_bias_multiplier",
type=float,
default=1.0,
help=(
"The multiplier for the bias. Defaults to 1.0, which means no bias is applied."
" A value of 2.0 will double the weight of the bias, and a value of 0.5 will halve it."
),
)
parser.add_argument(
"--timestep_bias_begin",
type=int,
default=0,
help=(
"When using `--timestep_bias_strategy=range`, the beginning (inclusive) timestep to bias."
" Defaults to zero, which equates to having no specific bias."
),
)
parser.add_argument(
"--timestep_bias_end",
type=int,
default=1000,
help=(
"When using `--timestep_bias_strategy=range`, the final timestep (inclusive) to bias."
" Defaults to 1000, which is the number of timesteps that Stable Diffusion is trained on."
),
)
parser.add_argument(
"--timestep_bias_portion",
type=float,
default=0.25,
help=(
"The portion of timesteps to bias. Defaults to 0.25, which 25% of timesteps will be biased."
" A value of 0.5 will bias one half of the timesteps. The value provided for `--timestep_bias_strategy` determines"
" whether the biased portions are in the earlier or later timesteps."
),
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--prediction_type",
type=str,
default=None,
help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
)
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
return args
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(batch, text_encoders, tokenizers, proportion_empty_prompts, caption_column, is_train=True):
prompt_embeds_list = []
prompt_batch = batch[caption_column]
captions = []
for caption in prompt_batch:
if random.random() < proportion_empty_prompts:
captions.append("")
elif isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
with torch.no_grad():
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
captions,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
)
# 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]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return {"prompt_embeds": prompt_embeds.cpu(), "pooled_prompt_embeds": pooled_prompt_embeds.cpu()}
def compute_vae_encodings(batch, vae):
images = batch.pop("pixel_values")
pixel_values = torch.stack(list(images))
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
pixel_values = pixel_values.to(vae.device, dtype=vae.dtype)
with torch.no_grad():
model_input = vae.encode(pixel_values).latent_dist.sample()
model_input = model_input * vae.config.scaling_factor
return {"model_input": model_input.cpu()}
def generate_timestep_weights(args, num_timesteps):
weights = torch.ones(num_timesteps)
# Determine the indices to bias
num_to_bias = int(args.timestep_bias_portion * num_timesteps)
if args.timestep_bias_strategy == "later":
bias_indices = slice(-num_to_bias, None)
elif args.timestep_bias_strategy == "earlier":
bias_indices = slice(0, num_to_bias)
elif args.timestep_bias_strategy == "range":
# Out of the possible 1000 timesteps, we might want to focus on eg. 200-500.
range_begin = args.timestep_bias_begin
range_end = args.timestep_bias_end
if range_begin < 0:
raise ValueError(
"When using the range strategy for timestep bias, you must provide a beginning timestep greater or equal to zero."
)
if range_end > num_timesteps:
raise ValueError(
"When using the range strategy for timestep bias, you must provide an ending timestep smaller than the number of timesteps."
)
bias_indices = slice(range_begin, range_end)
else: # 'none' or any other string
return weights
if args.timestep_bias_multiplier <= 0:
return ValueError(
"The parameter --timestep_bias_multiplier is not intended to be used to disable the training of specific timesteps."
" If it was intended to disable timestep bias, use `--timestep_bias_strategy none` instead."
" A timestep bias multiplier less than or equal to 0 is not allowed."
)
# Apply the bias
weights[bias_indices] *= args.timestep_bias_multiplier
# Normalize
weights /= weights.sum()
return weights
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
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(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizers
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=args.revision,
use_fast=False,
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
# Check for terminal SNR in combination with SNR Gamma
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
)
vae_path = (
args.pretrained_model_name_or_path
if args.pretrained_vae_model_name_or_path is None
else args.pretrained_vae_model_name_or_path
)
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,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
# Freeze vae and text encoders.
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
# Set unet as trainable.
unet.train()
# For mixed precision training we cast all non-trainable weigths to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
# The VAE is in float32 to avoid NaN losses.
vae.to(accelerator.device, dtype=torch.float32)
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
# Create EMA for the unet.
if args.use_ema:
ema_unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
if args.use_ema:
ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(output_dir, "unet"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if args.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
ema_unet.load_state_dict(load_model.state_dict())
ema_unet.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = unet.parameters()
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)
train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)
train_flip = transforms.RandomHorizontalFlip(p=1.0)
train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
# image aug
original_sizes = []
all_images = []
crop_top_lefts = []
for image in images:
original_sizes.append((image.height, image.width))
image = train_resize(image)
if args.center_crop:
y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
image = train_crop(image)
else:
y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
image = crop(image, y1, x1, h, w)
if args.random_flip and random.random() < 0.5:
# flip
x1 = image.width - x1
image = train_flip(image)
crop_top_left = (y1, x1)
crop_top_lefts.append(crop_top_left)
image = train_transforms(image)
all_images.append(image)
examples["original_sizes"] = original_sizes
examples["crop_top_lefts"] = crop_top_lefts
examples["pixel_values"] = all_images
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
# Let's first compute all the embeddings so that we can free up the text encoders
# from memory. We will pre-compute the VAE encodings too.
text_encoders = [text_encoder_one, text_encoder_two]
tokenizers = [tokenizer_one, tokenizer_two]
compute_embeddings_fn = functools.partial(
encode_prompt,
text_encoders=text_encoders,
tokenizers=tokenizers,
proportion_empty_prompts=args.proportion_empty_prompts,
caption_column=args.caption_column,
)
compute_vae_encodings_fn = functools.partial(compute_vae_encodings, vae=vae)
with accelerator.main_process_first():
from datasets.fingerprint import Hasher
# fingerprint used by the cache for the other processes to load the result
# details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
new_fingerprint = Hasher.hash(args)
new_fingerprint_for_vae = Hasher.hash("vae")
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
train_dataset = train_dataset.map(
compute_vae_encodings_fn,
batched=True,
batch_size=args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps,
new_fingerprint=new_fingerprint_for_vae,
)
del text_encoders, tokenizers, vae
gc.collect()
torch.cuda.empty_cache()
def collate_fn(examples):
model_input = torch.stack([torch.tensor(example["model_input"]) for example in examples])
original_sizes = [example["original_sizes"] for example in examples]
crop_top_lefts = [example["crop_top_lefts"] for example in examples]
prompt_embeds = torch.stack([torch.tensor(example["prompt_embeds"]) for example in examples])
pooled_prompt_embeds = torch.stack([torch.tensor(example["pooled_prompt_embeds"]) for example in examples])
return {
"model_input": model_input,
"prompt_embeds": prompt_embeds,
"pooled_prompt_embeds": pooled_prompt_embeds,
"original_sizes": original_sizes,
"crop_top_lefts": crop_top_lefts,
}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
# Prepare everything with our `accelerator`.
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("text2image-fine-tune-sdxl", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# Sample noise that we'll add to the latents
model_input = batch["model_input"].to(accelerator.device)
noise = torch.randn_like(model_input)
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn(
(model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device
)
bsz = model_input.shape[0]
if args.timestep_bias_strategy == "none":
# Sample a random timestep for each image without bias.
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
)
else:
# Sample a random timestep for each image, potentially biased by the timestep weights.
# Biasing the timestep weights allows us to spend less time training irrelevant timesteps.
weights = generate_timestep_weights(args, noise_scheduler.config.num_train_timesteps).to(
model_input.device
)
timesteps = torch.multinomial(weights, bsz, replacement=True).long()
# Add noise to the model input according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
# time ids
def compute_time_ids(original_size, crops_coords_top_left):
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
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])
add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)
return add_time_ids
add_time_ids = torch.cat(
[compute_time_ids(s, c) for s, c in zip(batch["original_sizes"], batch["crop_top_lefts"])]
)
# Predict the noise residual
unet_added_conditions = {"time_ids": add_time_ids}
prompt_embeds = batch["prompt_embeds"].to(accelerator.device)
pooled_prompt_embeds = batch["pooled_prompt_embeds"].to(accelerator.device)
unet_added_conditions.update({"text_embeds": pooled_prompt_embeds})
model_pred = unet(
noisy_model_input, timesteps, prompt_embeds, added_cond_kwargs=unet_added_conditions
).sample
# Get the target for loss depending on the prediction type
if args.prediction_type is not None:
# set prediction_type of scheduler if defined
noise_scheduler.register_to_config(prediction_type=args.prediction_type)
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(model_input, noise, timesteps)
elif noise_scheduler.config.prediction_type == "sample":
# We set the target to latents here, but the model_pred will return the noise sample prediction.
target = model_input
# We will have to subtract the noise residual from the prediction to get the target sample.
model_pred = model_pred - noise
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective requires that we add one to SNR values before we divide by them.
snr = snr + 1
mse_loss_weights = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = unet.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
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}."
)
if args.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
ema_unet.store(unet.parameters())
ema_unet.copy_to(unet.parameters())
# create 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,
)
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
unet=accelerator.unwrap_model(unet),
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
if args.prediction_type is not None:
scheduler_args = {"prediction_type": args.prediction_type}
pipeline.scheduler = pipeline.scheduler.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}
with torch.cuda.amp.autocast():
images = [
pipeline(**pipeline_args, generator=generator, num_inference_steps=25).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()
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
if args.use_ema:
ema_unet.copy_to(unet.parameters())
# Serialize 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,
unet=unet,
vae=vae,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
if args.prediction_type is not None:
scheduler_args = {"prediction_type": args.prediction_type}
pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)
pipeline.save_pretrained(args.output_dir)
# run inference
images = []
if args.validation_prompt and args.num_validation_images > 0:
pipeline = pipeline.to(accelerator.device)
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
with torch.cuda.amp.autocast():
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)
]
}
)
if args.push_to_hub:
save_model_card(
repo_id=repo_id,
images=images,
validation_prompt=args.validation_prompt,
base_model=args.pretrained_model_name_or_path,
dataset_name=args.dataset_name,
repo_folder=args.output_dir,
vae_path=args.pretrained_vae_model_name_or_path,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| 0
|
hf_public_repos/diffusers/examples
|
hf_public_repos/diffusers/examples/text_to_image/train_text_to_image_lora_sdxl.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fine-tuning script for Stable Diffusion XL for text2image with support for LoRA."""
import argparse
import itertools
import logging
import math
import os
import random
import shutil
from pathlib import Path
from typing import Dict
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from torchvision import transforms
from torchvision.transforms.functional import crop
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
StableDiffusionXLPipeline,
UNet2DConditionModel,
)
from diffusers.loaders import LoraLoaderMixin
from diffusers.models.lora import LoRALinearLayer
from diffusers.optimization import get_scheduler
from diffusers.training_utils import compute_snr
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.24.0.dev0")
logger = get_logger(__name__)
# TODO: This function should be removed once training scripts are rewritten in PEFT
def text_encoder_lora_state_dict(text_encoder):
state_dict = {}
def text_encoder_attn_modules(text_encoder):
from transformers import CLIPTextModel, CLIPTextModelWithProjection
attn_modules = []
if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
for i, layer in enumerate(text_encoder.text_model.encoder.layers):
name = f"text_model.encoder.layers.{i}.self_attn"
mod = layer.self_attn
attn_modules.append((name, mod))
return attn_modules
for name, module in text_encoder_attn_modules(text_encoder):
for k, v in module.q_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
for k, v in module.k_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
for k, v in module.v_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
for k, v in module.out_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
return state_dict
def save_model_card(
repo_id: str,
images=None,
base_model=str,
dataset_name=str,
train_text_encoder=False,
repo_folder=None,
vae_path=None,
):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
dataset: {dataset_name}
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
"""
model_card = f"""
# LoRA text2image fine-tuning - {repo_id}
These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n
{img_str}
LoRA for the text encoder was enabled: {train_text_encoder}.
Special VAE used for training: {vae_path}.
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=1,
help=(
"Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_text_encoder",
action="store_true",
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--prediction_type",
type=str,
default=None,
help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
)
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def unet_attn_processors_state_dict(unet) -> Dict[str, torch.tensor]:
"""
Returns:
a state dict containing just the attention processor parameters.
"""
attn_processors = unet.attn_processors
attn_processors_state_dict = {}
for attn_processor_key, attn_processor in attn_processors.items():
for parameter_key, parameter in attn_processor.state_dict().items():
attn_processors_state_dict[f"{attn_processor_key}.{parameter_key}"] = parameter
return attn_processors_state_dict
def tokenize_prompt(tokenizer, prompt):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
return text_input_ids
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
prompt_embeds_list = []
for i, text_encoder in enumerate(text_encoders):
if tokenizers is not None:
tokenizer = tokenizers[i]
text_input_ids = tokenize_prompt(tokenizer, prompt)
else:
assert text_input_ids_list is not None
text_input_ids = text_input_ids_list[i]
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
)
# 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]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return prompt_embeds, pooled_prompt_embeds
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
kwargs_handlers=[kwargs],
)
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(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizers
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=args.revision,
use_fast=False,
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
)
vae_path = (
args.pretrained_model_name_or_path
if args.pretrained_vae_model_name_or_path is None
else args.pretrained_vae_model_name_or_path
)
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,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
# We only train the additional adapter LoRA layers
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
unet.requires_grad_(False)
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
# The VAE is in float32 to avoid NaN losses.
unet.to(accelerator.device, dtype=weight_dtype)
if args.pretrained_vae_model_name_or_path is None:
vae.to(accelerator.device, dtype=torch.float32)
else:
vae.to(accelerator.device, dtype=weight_dtype)
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# now we will add new LoRA weights to the attention layers
# Set correct lora layers
unet_lora_parameters = []
for attn_processor_name, attn_processor in unet.attn_processors.items():
# Parse the attention module.
attn_module = unet
for n in attn_processor_name.split(".")[:-1]:
attn_module = getattr(attn_module, n)
# Set the `lora_layer` attribute of the attention-related matrices.
attn_module.to_q.set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=args.rank
)
)
attn_module.to_k.set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=args.rank
)
)
attn_module.to_v.set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=args.rank
)
)
attn_module.to_out[0].set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_out[0].in_features,
out_features=attn_module.to_out[0].out_features,
rank=args.rank,
)
)
# Accumulate the LoRA params to optimize.
unet_lora_parameters.extend(attn_module.to_q.lora_layer.parameters())
unet_lora_parameters.extend(attn_module.to_k.lora_layer.parameters())
unet_lora_parameters.extend(attn_module.to_v.lora_layer.parameters())
unet_lora_parameters.extend(attn_module.to_out[0].lora_layer.parameters())
# The text encoder comes from 🤗 transformers, so we cannot directly modify it.
# So, instead, we monkey-patch the forward calls of its attention-blocks.
if args.train_text_encoder:
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
text_lora_parameters_one = LoraLoaderMixin._modify_text_encoder(
text_encoder_one, dtype=torch.float32, rank=args.rank
)
text_lora_parameters_two = LoraLoaderMixin._modify_text_encoder(
text_encoder_two, dtype=torch.float32, rank=args.rank
)
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
# there are only two options here. Either are just the unet attn processor layers
# or there are the unet and text encoder atten layers
unet_lora_layers_to_save = None
text_encoder_one_lora_layers_to_save = None
text_encoder_two_lora_layers_to_save = None
for model in models:
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_lora_layers_to_save = unet_attn_processors_state_dict(model)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
text_encoder_one_lora_layers_to_save = text_encoder_lora_state_dict(model)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
text_encoder_two_lora_layers_to_save = text_encoder_lora_state_dict(model)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
StableDiffusionXLPipeline.save_lora_weights(
output_dir,
unet_lora_layers=unet_lora_layers_to_save,
text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,
)
def load_model_hook(models, input_dir):
unet_ = None
text_encoder_one_ = None
text_encoder_two_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
text_encoder_one_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
text_encoder_two_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
text_encoder_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder." in k}
LoraLoaderMixin.load_lora_into_text_encoder(
text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_
)
text_encoder_2_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder_2." in k}
LoraLoaderMixin.load_lora_into_text_encoder(
text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_
)
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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = (
itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)
if args.train_text_encoder
else unet_lora_parameters
)
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, data_dir=args.train_data_dir
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
tokens_one = tokenize_prompt(tokenizer_one, captions)
tokens_two = tokenize_prompt(tokenizer_two, captions)
return tokens_one, tokens_two
# Preprocessing the datasets.
train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)
train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)
train_flip = transforms.RandomHorizontalFlip(p=1.0)
train_transforms = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
# image aug
original_sizes = []
all_images = []
crop_top_lefts = []
for image in images:
original_sizes.append((image.height, image.width))
image = train_resize(image)
if args.center_crop:
y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
image = train_crop(image)
else:
y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
image = crop(image, y1, x1, h, w)
if args.random_flip and random.random() < 0.5:
# flip
x1 = image.width - x1
image = train_flip(image)
crop_top_left = (y1, x1)
crop_top_lefts.append(crop_top_left)
image = train_transforms(image)
all_images.append(image)
examples["original_sizes"] = original_sizes
examples["crop_top_lefts"] = crop_top_lefts
examples["pixel_values"] = all_images
tokens_one, tokens_two = tokenize_captions(examples)
examples["input_ids_one"] = tokens_one
examples["input_ids_two"] = tokens_two
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
original_sizes = [example["original_sizes"] for example in examples]
crop_top_lefts = [example["crop_top_lefts"] for example in examples]
input_ids_one = torch.stack([example["input_ids_one"] for example in examples])
input_ids_two = torch.stack([example["input_ids_two"] for example in examples])
return {
"pixel_values": pixel_values,
"input_ids_one": input_ids_one,
"input_ids_two": input_ids_two,
"original_sizes": original_sizes,
"crop_top_lefts": crop_top_lefts,
}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("text2image-fine-tune", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder_one.train()
text_encoder_two.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# Convert images to latent space
if args.pretrained_vae_model_name_or_path is not None:
pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
else:
pixel_values = batch["pixel_values"]
model_input = vae.encode(pixel_values).latent_dist.sample()
model_input = model_input * vae.config.scaling_factor
if args.pretrained_vae_model_name_or_path is None:
model_input = model_input.to(weight_dtype)
# Sample noise that we'll add to the latents
noise = torch.randn_like(model_input)
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn(
(model_input.shape[0], model_input.shape[1], 1, 1), device=model_input.device
)
bsz = model_input.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
)
timesteps = timesteps.long()
# Add noise to the model input according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
# time ids
def compute_time_ids(original_size, crops_coords_top_left):
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
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])
add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)
return add_time_ids
add_time_ids = torch.cat(
[compute_time_ids(s, c) for s, c in zip(batch["original_sizes"], batch["crop_top_lefts"])]
)
# Predict the noise residual
unet_added_conditions = {"time_ids": add_time_ids}
prompt_embeds, pooled_prompt_embeds = encode_prompt(
text_encoders=[text_encoder_one, text_encoder_two],
tokenizers=None,
prompt=None,
text_input_ids_list=[batch["input_ids_one"], batch["input_ids_two"]],
)
unet_added_conditions.update({"text_embeds": pooled_prompt_embeds})
model_pred = unet(
noisy_model_input, timesteps, prompt_embeds, added_cond_kwargs=unet_added_conditions
).sample
# Get the target for loss depending on the prediction type
if args.prediction_type is not None:
# set prediction_type of scheduler if defined
noise_scheduler.register_to_config(prediction_type=args.prediction_type)
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(model_input, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective requires that we add one to SNR values before we divide by them.
snr = snr + 1
mse_loss_weights = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)
if args.train_text_encoder
else unet_lora_parameters
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
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
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
text_encoder=accelerator.unwrap_model(text_encoder_one),
text_encoder_2=accelerator.unwrap_model(text_encoder_two),
unet=accelerator.unwrap_model(unet),
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
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}
with torch.cuda.amp.autocast():
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()
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet_lora_layers = unet_attn_processors_state_dict(unet)
if args.train_text_encoder:
text_encoder_one = accelerator.unwrap_model(text_encoder_one)
text_encoder_lora_layers = text_encoder_lora_state_dict(text_encoder_one)
text_encoder_two = accelerator.unwrap_model(text_encoder_two)
text_encoder_2_lora_layers = text_encoder_lora_state_dict(text_encoder_two)
else:
text_encoder_lora_layers = None
text_encoder_2_lora_layers = None
StableDiffusionXLPipeline.save_lora_weights(
save_directory=args.output_dir,
unet_lora_layers=unet_lora_layers,
text_encoder_lora_layers=text_encoder_lora_layers,
text_encoder_2_lora_layers=text_encoder_2_lora_layers,
)
del unet
del text_encoder_one
del text_encoder_two
del text_encoder_lora_layers
del text_encoder_2_lora_layers
torch.cuda.empty_cache()
# Final inference
# Load previous pipeline
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
# load attention processors
pipeline.load_lora_weights(args.output_dir)
# run inference
images = []
if args.validation_prompt and args.num_validation_images > 0:
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)
]
}
)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
dataset_name=args.dataset_name,
train_text_encoder=args.train_text_encoder,
repo_folder=args.output_dir,
vae_path=args.pretrained_vae_model_name_or_path,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| 0
|
hf_public_repos/diffusers/examples
|
hf_public_repos/diffusers/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import shutil
import warnings
from pathlib import Path
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
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from PIL.ImageOps import exif_transpose
from safetensors.torch import save_file
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DPMSolverMultistepScheduler,
StableDiffusionXLPipeline,
UNet2DConditionModel,
)
from diffusers.loaders import LoraLoaderMixin
from diffusers.models.lora import LoRALinearLayer, text_encoder_lora_state_dict
from diffusers.optimization import get_scheduler
from diffusers.training_utils import compute_snr, unet_lora_state_dict
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.24.0.dev0")
logger = get_logger(__name__)
def save_model_card(
repo_id: str,
images=None,
base_model=str,
train_text_encoder=False,
instance_prompt=str,
validation_prompt=str,
repo_folder=None,
vae_path=None,
):
img_str = "widget:\n" if images else ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"""
- text: '{validation_prompt if validation_prompt else ' ' }'
output:
url: >-
"image_{i}.png"
"""
yaml = f"""
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
{img_str}
---
base_model: {base_model}
instance_prompt: {instance_prompt}
license: openrail++
---
"""
model_card = f"""
# SDXL LoRA DreamBooth - {repo_id}
<Gallery />
## Model description
These are {repo_id} LoRA adaption weights for {base_model}.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: {train_text_encoder}.
Special VAE used for training: {vae_path}.
## Trigger words
You should use {instance_prompt} to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download]({repo_id}/tree/main) them in the Files & versions tab.
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
help=("A folder containing the training data. "),
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--image_column",
type=str,
default="image",
help="The column of the dataset containing the target image. By "
"default, the standard Image Dataset maps out 'file_name' "
"to 'image'.",
)
parser.add_argument(
"--caption_column",
type=str,
default=None,
help="The column of the dataset containing the instance prompt for each image",
)
parser.add_argument("--repeats", type=int, default=1, help="How many times to repeat the training data.")
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
)
parser.add_argument(
"--token_abstraction",
default="TOK",
help="identifier specifying the instance(or instances) as used in instance_prompt, validation prompt, "
"captions - e.g. TOK",
)
parser.add_argument(
"--num_new_tokens_per_abstraction",
default=2,
help="number of new tokens inserted to the tokenizers per token_abstraction value when "
"--train_text_encoder_ti = True. By default, each --token_abstraction (e.g. TOK) is mapped to 2 new "
"tokens - <si><si+1> ",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help=(
"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="lora-dreambooth-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--crops_coords_top_left_h",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--crops_coords_top_left_w",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--train_text_encoder",
action="store_true",
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--text_encoder_lr",
type=float,
default=5e-6,
help="Text encoder learning rate to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--train_text_encoder_ti",
action="store_true",
help=("Whether to use textual inversion"),
)
parser.add_argument(
"--train_text_encoder_ti_frac",
type=float,
default=0.5,
help=("The percentage of epochs to perform textual inversion"),
)
parser.add_argument(
"--train_text_encoder_frac",
type=float,
default=0.5,
help=("The percentage of epochs to perform text encoder tuning"),
)
parser.add_argument(
"--optimizer",
type=str,
default="adamW",
help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
)
parser.add_argument(
"--use_8bit_adam",
action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
)
parser.add_argument(
"--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--prodigy_beta3",
type=float,
default=None,
help="coefficients for computing the Prodidy stepsize using running averages. If set to None, "
"uses the value of square root of beta2. Ignored if optimizer is adamW",
)
parser.add_argument("--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay")
parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
parser.add_argument(
"--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
)
parser.add_argument(
"--adam_epsilon",
type=float,
default=1e-08,
help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
)
parser.add_argument(
"--prodigy_use_bias_correction",
type=bool,
default=True,
help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW",
)
parser.add_argument(
"--prodigy_safeguard_warmup",
type=bool,
default=True,
help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. "
"Ignored if optimizer is adamW",
)
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if args.dataset_name is None and args.instance_data_dir is None:
raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`")
if args.dataset_name is not None and args.instance_data_dir is not None:
raise ValueError("Specify only one of `--dataset_name` or `--instance_data_dir`")
if args.train_text_encoder and args.train_text_encoder_ti:
raise ValueError(
"Specify only one of `--train_text_encoder` or `--train_text_encoder_ti. "
"For full LoRA text encoder training check --train_text_encoder, for textual "
"inversion training check `--train_text_encoder_ti`"
)
if args.train_text_encoder_ti:
if isinstance(args.token_abstraction, str):
args.token_abstraction = [args.token_abstraction]
elif isinstance(args.token_abstraction, List):
args.token_abstraction = args.token_abstraction
else:
raise ValueError(
f"Unsupported type for --args.token_abstraction: {type(args.token_abstraction)}. "
f"Supported types are: str (for a single instance identifier) or List[str] (for multiple concepts)"
)
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
# Taken from https://github.com/replicate/cog-sdxl/blob/main/dataset_and_utils.py
class TokenEmbeddingsHandler:
def __init__(self, text_encoders, tokenizers):
self.text_encoders = text_encoders
self.tokenizers = tokenizers
self.train_ids: Optional[torch.Tensor] = None
self.inserting_toks: Optional[List[str]] = None
self.embeddings_settings = {}
def initialize_new_tokens(self, inserting_toks: List[str]):
idx = 0
for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
assert isinstance(inserting_toks, list), "inserting_toks should be a list of strings."
assert all(
isinstance(tok, str) for tok in inserting_toks
), "All elements in inserting_toks should be strings."
self.inserting_toks = inserting_toks
special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
tokenizer.add_special_tokens(special_tokens_dict)
text_encoder.resize_token_embeddings(len(tokenizer))
self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
# random initialization of new tokens
std_token_embedding = text_encoder.text_model.embeddings.token_embedding.weight.data.std()
print(f"{idx} text encodedr's std_token_embedding: {std_token_embedding}")
text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids] = (
torch.randn(len(self.train_ids), text_encoder.text_model.config.hidden_size)
.to(device=self.device)
.to(dtype=self.dtype)
* std_token_embedding
)
self.embeddings_settings[
f"original_embeddings_{idx}"
] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
inu = torch.ones((len(tokenizer),), dtype=torch.bool)
inu[self.train_ids] = False
self.embeddings_settings[f"index_no_updates_{idx}"] = inu
print(self.embeddings_settings[f"index_no_updates_{idx}"].shape)
idx += 1
def save_embeddings(self, file_path: str):
assert self.train_ids is not None, "Initialize new tokens before saving embeddings."
tensors = {}
for idx, text_encoder in enumerate(self.text_encoders):
assert text_encoder.text_model.embeddings.token_embedding.weight.data.shape[0] == len(
self.tokenizers[0]
), "Tokenizers should be the same."
new_token_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[self.train_ids]
tensors[f"text_encoders_{idx}"] = new_token_embeddings
save_file(tensors, file_path)
@property
def dtype(self):
return self.text_encoders[0].dtype
@property
def device(self):
return self.text_encoders[0].device
# def _load_embeddings(self, loaded_embeddings, tokenizer, text_encoder):
# # Assuming new tokens are of the format <s_i>
# self.inserting_toks = [f"<s{i}>" for i in range(loaded_embeddings.shape[0])]
# special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
# tokenizer.add_special_tokens(special_tokens_dict)
# text_encoder.resize_token_embeddings(len(tokenizer))
#
# self.train_ids = tokenizer.convert_tokens_to_ids(self.inserting_toks)
# assert self.train_ids is not None, "New tokens could not be converted to IDs."
# text_encoder.text_model.embeddings.token_embedding.weight.data[
# self.train_ids
# ] = loaded_embeddings.to(device=self.device).to(dtype=self.dtype)
@torch.no_grad()
def retract_embeddings(self):
for idx, text_encoder in enumerate(self.text_encoders):
index_no_updates = self.embeddings_settings[f"index_no_updates_{idx}"]
text_encoder.text_model.embeddings.token_embedding.weight.data[index_no_updates] = (
self.embeddings_settings[f"original_embeddings_{idx}"][index_no_updates]
.to(device=text_encoder.device)
.to(dtype=text_encoder.dtype)
)
# for the parts that were updated, we need to normalize them
# to have the same std as before
std_token_embedding = self.embeddings_settings[f"std_token_embedding_{idx}"]
index_updates = ~index_no_updates
new_embeddings = text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates]
off_ratio = std_token_embedding / new_embeddings.std()
new_embeddings = new_embeddings * (off_ratio**0.1)
text_encoder.text_model.embeddings.token_embedding.weight.data[index_updates] = new_embeddings
# def load_embeddings(self, file_path: str):
# with safe_open(file_path, framework="pt", device=self.device.type) as f:
# for idx in range(len(self.text_encoders)):
# text_encoder = self.text_encoders[idx]
# tokenizer = self.tokenizers[idx]
#
# loaded_embeddings = f.get_tensor(f"text_encoders_{idx}")
# self._load_embeddings(loaded_embeddings, tokenizer, text_encoder)
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
class_prompt,
class_data_root=None,
class_num=None,
token_abstraction_dict=None, # token mapping for textual inversion
size=1024,
repeats=1,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.instance_prompt = instance_prompt
self.custom_instance_prompts = None
self.class_prompt = class_prompt
self.token_abstraction_dict = token_abstraction_dict
# 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 args.dataset_name is not None:
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"You are trying to load your data using the datasets library. If you wish to train using custom "
"captions please install the datasets library: `pip install datasets`. If you wish to load a "
"local folder containing images only, specify --instance_data_dir instead."
)
# Downloading and loading a dataset from the hub.
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
dataset = load_dataset(
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 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 '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
instance_images = dataset["train"][image_column]
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 "
"column as --caption_column"
)
self.custom_instance_prompts = None
else:
if args.caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
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:
self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))
else:
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]
self.custom_instance_prompts = None
self.instance_images = []
for img in instance_images:
self.instance_images.extend(itertools.repeat(img, repeats))
self.num_instance_images = len(self.instance_images)
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
if class_num is not None:
self.num_class_images = min(len(self.class_images_path), class_num)
else:
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = self.instance_images[index % self.num_instance_images]
instance_image = exif_transpose(instance_image)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
if self.custom_instance_prompts:
caption = self.custom_instance_prompts[index % self.num_instance_images]
if caption:
if args.train_text_encoder_ti:
# replace instances of --token_abstraction in caption with the new tokens: "<si><si+1>" etc.
for token_abs, token_replacement in self.token_abstraction_dict.items():
caption = caption.replace(token_abs, "".join(token_replacement))
example["instance_prompt"] = caption
else:
example["instance_prompt"] = self.instance_prompt
else: # costum prompts were provided, but length does not match size of image dataset
example["instance_prompt"] = self.instance_prompt
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
class_image = exif_transpose(class_image)
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt"] = self.class_prompt
return example
def collate_fn(examples, with_prior_preservation=False):
pixel_values = [example["instance_images"] for example in examples]
prompts = [example["instance_prompt"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
pixel_values += [example["class_images"] for example in examples]
prompts += [example["class_prompt"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
batch = {"pixel_values": pixel_values, "prompts": prompts}
return batch
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def tokenize_prompt(tokenizer, prompt, add_special_tokens=False):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
add_special_tokens=add_special_tokens,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
return text_input_ids
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
prompt_embeds_list = []
for i, text_encoder in enumerate(text_encoders):
if tokenizers is not None:
tokenizer = tokenizers[i]
text_input_ids = tokenize_prompt(tokenizer, prompt)
else:
assert text_input_ids_list is not None
text_input_ids = text_input_ids_list[i]
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
)
# 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]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return prompt_embeds, pooled_prompt_embeds
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
kwargs_handlers=[kwargs],
)
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(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
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
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizers
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
)
vae_path = (
args.pretrained_model_name_or_path
if args.pretrained_vae_model_name_or_path is None
else args.pretrained_vae_model_name_or_path
)
vae = AutoencoderKL.from_pretrained(
vae_path, subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None, revision=args.revision
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
if args.train_text_encoder_ti:
token_abstraction_dict = {}
token_idx = 0
for i, token in enumerate(args.token_abstraction):
token_abstraction_dict[token] = [
f"<s{token_idx + i + j}>" for j in range(args.num_new_tokens_per_abstraction)
]
token_idx += args.num_new_tokens_per_abstraction - 1
# replace instances of --token_abstraction in --instance_prompt with the new tokens: "<si><si+1>" etc.
for token_abs, token_replacement in token_abstraction_dict.items():
args.instance_prompt = args.instance_prompt.replace(token_abs, "".join(token_replacement))
if args.with_prior_preservation:
args.class_prompt = args.class_prompt.replace(token_abs, "".join(token_replacement))
# initialize the new tokens for textual inversion
embedding_handler = TokenEmbeddingsHandler(
[text_encoder_one, text_encoder_two], [tokenizer_one, tokenizer_two]
)
inserting_toks = []
for new_tok in token_abstraction_dict.values():
inserting_toks.extend(new_tok)
embedding_handler.initialize_new_tokens(inserting_toks=inserting_toks)
# We only train the additional adapter LoRA layers
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
unet.requires_grad_(False)
# For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
# The VAE is always in float32 to avoid NaN losses.
vae.to(accelerator.device, dtype=torch.float32)
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, "
"please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder_one.gradient_checkpointing_enable()
text_encoder_two.gradient_checkpointing_enable()
# now we will add new LoRA weights to the attention layers
# Set correct lora layers
unet_lora_parameters = []
for attn_processor_name, attn_processor in unet.attn_processors.items():
# Parse the attention module.
attn_module = unet
for n in attn_processor_name.split(".")[:-1]:
attn_module = getattr(attn_module, n)
# Set the `lora_layer` attribute of the attention-related matrices.
attn_module.to_q.set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_q.in_features, out_features=attn_module.to_q.out_features, rank=args.rank
)
)
attn_module.to_k.set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_k.in_features, out_features=attn_module.to_k.out_features, rank=args.rank
)
)
attn_module.to_v.set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_v.in_features, out_features=attn_module.to_v.out_features, rank=args.rank
)
)
attn_module.to_out[0].set_lora_layer(
LoRALinearLayer(
in_features=attn_module.to_out[0].in_features,
out_features=attn_module.to_out[0].out_features,
rank=args.rank,
)
)
# Accumulate the LoRA params to optimize.
unet_lora_parameters.extend(attn_module.to_q.lora_layer.parameters())
unet_lora_parameters.extend(attn_module.to_k.lora_layer.parameters())
unet_lora_parameters.extend(attn_module.to_v.lora_layer.parameters())
unet_lora_parameters.extend(attn_module.to_out[0].lora_layer.parameters())
# The text encoder comes from 🤗 transformers, so we cannot directly modify it.
# So, instead, we monkey-patch the forward calls of its attention-blocks.
if args.train_text_encoder:
# ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
text_lora_parameters_one = LoraLoaderMixin._modify_text_encoder(
text_encoder_one, dtype=torch.float32, rank=args.rank
)
text_lora_parameters_two = LoraLoaderMixin._modify_text_encoder(
text_encoder_two, dtype=torch.float32, rank=args.rank
)
# if we use textual inversion, we freeze all parameters except for the token embeddings
# in text encoder
elif args.train_text_encoder_ti:
text_lora_parameters_one = []
for name, param in text_encoder_one.named_parameters():
if "token_embedding" in name:
param.requires_grad = True
text_lora_parameters_one.append(param)
else:
param.requires_grad = False
text_lora_parameters_two = []
for name, param in text_encoder_two.named_parameters():
if "token_embedding" in name:
param.requires_grad = True
text_lora_parameters_two.append(param)
else:
param.requires_grad = False
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
# there are only two options here. Either are just the unet attn processor layers
# or there are the unet and text encoder atten layers
unet_lora_layers_to_save = None
text_encoder_one_lora_layers_to_save = None
text_encoder_two_lora_layers_to_save = None
for model in models:
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_lora_layers_to_save = unet_lora_state_dict(model)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
text_encoder_one_lora_layers_to_save = text_encoder_lora_state_dict(model)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
text_encoder_two_lora_layers_to_save = text_encoder_lora_state_dict(model)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
StableDiffusionXLPipeline.save_lora_weights(
output_dir,
unet_lora_layers=unet_lora_layers_to_save,
text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,
)
def load_model_hook(models, input_dir):
unet_ = None
text_encoder_one_ = None
text_encoder_two_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
text_encoder_one_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
text_encoder_two_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
text_encoder_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder." in k}
LoraLoaderMixin.load_lora_into_text_encoder(
text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_
)
text_encoder_2_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder_2." in k}
LoraLoaderMixin.load_lora_into_text_encoder(
text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_
)
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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# If neither --train_text_encoder nor --train_text_encoder_ti, text_encoders remain frozen during training
freeze_text_encoder = not (args.train_text_encoder or args.train_text_encoder_ti)
# Optimization parameters
unet_lora_parameters_with_lr = {"params": unet_lora_parameters, "lr": args.learning_rate}
if not freeze_text_encoder:
# different learning rate for text encoder and unet
text_lora_parameters_one_with_lr = {
"params": text_lora_parameters_one,
"weight_decay": args.adam_weight_decay_text_encoder,
"lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
}
text_lora_parameters_two_with_lr = {
"params": text_lora_parameters_two,
"weight_decay": args.adam_weight_decay_text_encoder,
"lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
}
params_to_optimize = [
unet_lora_parameters_with_lr,
text_lora_parameters_one_with_lr,
text_lora_parameters_two_with_lr,
]
else:
params_to_optimize = [unet_lora_parameters_with_lr]
# Optimizer creation
if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
logger.warn(
f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
"Defaulting to adamW"
)
args.optimizer = "adamw"
if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
logger.warn(
f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
f"set to {args.optimizer.lower()}"
)
if args.optimizer.lower() == "adamw":
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
if args.optimizer.lower() == "prodigy":
try:
import prodigyopt
except ImportError:
raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
optimizer_class = prodigyopt.Prodigy
if args.learning_rate <= 0.1:
logger.warn(
"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
)
if args.train_text_encoder and args.text_encoder_lr:
logger.warn(
f"Learning rates were provided both for the unet and the text encoder- e.g. text_encoder_lr:"
f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. "
f"When using prodigy only learning_rate is used as the initial learning rate."
)
# changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be
# --learning_rate
params_to_optimize[1]["lr"] = args.learning_rate
params_to_optimize[2]["lr"] = args.learning_rate
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
beta3=args.prodigy_beta3,
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
decouple=args.prodigy_decouple,
use_bias_correction=args.prodigy_use_bias_correction,
safeguard_warmup=args.prodigy_safeguard_warmup,
)
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_prompt=args.class_prompt,
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,
size=args.resolution,
repeats=args.repeats,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.dataloader_num_workers,
)
# Computes additional embeddings/ids required by the SDXL UNet.
# regular text embeddings (when `train_text_encoder` is not True)
# pooled text embeddings
# time ids
def compute_time_ids():
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
original_size = (args.resolution, args.resolution)
target_size = (args.resolution, args.resolution)
crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_time_ids = torch.tensor([add_time_ids])
add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)
return add_time_ids
if not args.train_text_encoder:
tokenizers = [tokenizer_one, tokenizer_two]
text_encoders = [text_encoder_one, text_encoder_two]
def compute_text_embeddings(prompt, text_encoders, tokenizers):
with torch.no_grad():
prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)
prompt_embeds = prompt_embeds.to(accelerator.device)
pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)
return prompt_embeds, pooled_prompt_embeds
# Handle instance prompt.
instance_time_ids = compute_time_ids()
# If no type of tuning is done on the text_encoder and custom instance prompts are NOT
# provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
# the redundant encoding.
if freeze_text_encoder and not train_dataset.custom_instance_prompts:
instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(
args.instance_prompt, text_encoders, tokenizers
)
# Handle class prompt for prior-preservation.
if args.with_prior_preservation:
class_time_ids = compute_time_ids()
if freeze_text_encoder:
class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(
args.class_prompt, text_encoders, tokenizers
)
# Clear the memory here
if freeze_text_encoder and not train_dataset.custom_instance_prompts:
del tokenizers, text_encoders
gc.collect()
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
# have to pass them to the dataloader.
add_time_ids = instance_time_ids
if args.with_prior_preservation:
add_time_ids = torch.cat([add_time_ids, class_time_ids], dim=0)
# if --train_text_encoder_ti we need add_special_tokens to be True fo textual inversion
add_special_tokens = True if args.train_text_encoder_ti else False
if not train_dataset.custom_instance_prompts:
if freeze_text_encoder:
prompt_embeds = instance_prompt_hidden_states
unet_add_text_embeds = instance_pooled_prompt_embeds
if args.with_prior_preservation:
prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)
# if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the
# batch prompts on all training steps
else:
tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt, add_special_tokens)
tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt, add_special_tokens)
if args.with_prior_preservation:
class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, add_special_tokens)
class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt, add_special_tokens)
tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if not freeze_text_encoder:
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth-lora-sd-xl", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
if args.train_text_encoder:
num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs)
elif args.train_text_encoder_ti: # args.train_text_encoder_ti
num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs)
for epoch in range(first_epoch, args.num_train_epochs):
# if performing any kind of optimization of text_encoder params
if args.train_text_encoder or args.train_text_encoder_ti:
if epoch == num_train_epochs_text_encoder:
print("PIVOT HALFWAY", epoch)
# stopping optimization of text_encoder params
params_to_optimize = params_to_optimize[:1]
# reinitializing the optimizer to optimize only on unet params
if args.optimizer.lower() == "prodigy":
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
beta3=args.prodigy_beta3,
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
decouple=args.prodigy_decouple,
use_bias_correction=args.prodigy_use_bias_correction,
safeguard_warmup=args.prodigy_safeguard_warmup,
)
else: # AdamW or 8-bit-AdamW
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
else:
# still optimizng the text encoder
text_encoder_one.train()
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)
unet.train()
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
prompts = batch["prompts"]
print(prompts)
# encode batch prompts when custom prompts are provided for each image -
if train_dataset.custom_instance_prompts:
if freeze_text_encoder:
prompt_embeds, unet_add_text_embeds = compute_text_embeddings(
prompts, text_encoders, tokenizers
)
else:
tokens_one = tokenize_prompt(tokenizer_one, prompts, add_special_tokens)
tokens_two = tokenize_prompt(tokenizer_two, prompts, add_special_tokens)
# Convert images to latent space
model_input = vae.encode(pixel_values).latent_dist.sample()
model_input = model_input * vae.config.scaling_factor
if args.pretrained_vae_model_name_or_path is None:
model_input = model_input.to(weight_dtype)
# Sample noise that we'll add to the latents
noise = torch.randn_like(model_input)
bsz = model_input.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
)
timesteps = timesteps.long()
# Add noise to the model input according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
# Calculate the elements to repeat depending on the use of prior-preservation and custom captions.
if not train_dataset.custom_instance_prompts:
elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz
elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
else:
elems_to_repeat_text_embeds = 1
elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
# Predict the noise residual
if freeze_text_encoder:
unet_added_conditions = {
"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)
model_pred = unet(
noisy_model_input,
timesteps,
prompt_embeds_input,
added_cond_kwargs=unet_added_conditions,
).sample
else:
unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1)}
prompt_embeds, pooled_prompt_embeds = encode_prompt(
text_encoders=[text_encoder_one, text_encoder_two],
tokenizers=None,
prompt=None,
text_input_ids_list=[tokens_one, tokens_two],
)
unet_added_conditions.update(
{"text_embeds": pooled_prompt_embeds.repeat(elems_to_repeat_text_embeds, 1)}
)
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
model_pred = unet(
noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions
).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(model_input, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
base_weight = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective needs to be floored to an SNR weight of one.
mse_loss_weights = base_weight + 1
else:
# Epsilon and sample both use the same loss weights.
mse_loss_weights = base_weight
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
if args.with_prior_preservation:
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)
if (args.train_text_encoder or args.train_text_encoder_ti)
else unet_lora_parameters
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# every step, we reset the embeddings to the original embeddings.
if args.train_text_encoder_ti:
for idx, text_encoder in enumerate(text_encoders):
embedding_handler.retract_embeddings()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
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 not args.train_text_encoder:
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
)
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
text_encoder=accelerator.unwrap_model(text_encoder_one),
text_encoder_2=accelerator.unwrap_model(text_encoder_two),
unet=accelerator.unwrap_model(unet),
revision=args.revision,
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 "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}
with torch.cuda.amp.autocast():
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()
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet = unet.to(torch.float32)
unet_lora_layers = unet_lora_state_dict(unet)
if args.train_text_encoder:
text_encoder_one = accelerator.unwrap_model(text_encoder_one)
text_encoder_lora_layers = text_encoder_lora_state_dict(text_encoder_one.to(torch.float32))
text_encoder_two = accelerator.unwrap_model(text_encoder_two)
text_encoder_2_lora_layers = text_encoder_lora_state_dict(text_encoder_two.to(torch.float32))
else:
text_encoder_lora_layers = None
text_encoder_2_lora_layers = None
StableDiffusionXLPipeline.save_lora_weights(
save_directory=args.output_dir,
unet_lora_layers=unet_lora_layers,
text_encoder_lora_layers=text_encoder_lora_layers,
text_encoder_2_lora_layers=text_encoder_2_lora_layers,
)
# 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,
torch_dtype=weight_dtype,
)
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path, vae=vae, revision=args.revision, 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 "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)
# run inference
images = []
if args.validation_prompt and args.num_validation_images > 0:
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)
]
}
)
if args.push_to_hub:
if args.train_text_encoder_ti:
embedding_handler.save_embeddings(
f"{args.output_dir}/embeddings.safetensors",
)
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
train_text_encoder=args.train_text_encoder,
instance_prompt=args.instance_prompt,
validation_prompt=args.validation_prompt,
repo_folder=args.output_dir,
vae_path=args.pretrained_vae_model_name_or_path,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| 0
|
hf_public_repos/diffusers/examples
|
hf_public_repos/diffusers/examples/research_projects/README.md
|
# Research projects
This folder contains various research projects using 🧨 Diffusers.
They are not really maintained by the core maintainers of this library and often require a specific version of Diffusers that is indicated in the requirements file of each folder.
Updating them to the most recent version of the library will require some work.
To use any of them, just run the command
```
pip install -r requirements.txt
```
inside the folder of your choice.
If you need help with any of those, please open an issue where you directly ping the author(s), as indicated at the top of the README of each folder.
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/controlnet/train_controlnet_webdataset.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import functools
import gc
import itertools
import json
import logging
import math
import os
import random
import shutil
from pathlib import Path
from typing import List, Optional, Union
import accelerate
import cv2
import numpy as np
import torch
import torch.utils.checkpoint
import transformers
import webdataset as wds
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from braceexpand import braceexpand
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from torch.utils.data import default_collate
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, DPTFeatureExtractor, DPTForDepthEstimation, PretrainedConfig
from webdataset.tariterators import (
base_plus_ext,
tar_file_expander,
url_opener,
valid_sample,
)
import diffusers
from diffusers import (
AutoencoderKL,
ControlNetModel,
EulerDiscreteScheduler,
StableDiffusionXLControlNetPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
MAX_SEQ_LENGTH = 77
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("0.18.0.dev0")
logger = get_logger(__name__)
def filter_keys(key_set):
def _f(dictionary):
return {k: v for k, v in dictionary.items() if k in key_set}
return _f
def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
"""Return function over iterator that groups key, value pairs into samples.
:param keys: function that splits the key into key and extension (base_plus_ext)
:param lcase: convert suffixes to lower case (Default value = True)
"""
current_sample = None
for filesample in data:
assert isinstance(filesample, dict)
fname, value = filesample["fname"], filesample["data"]
prefix, suffix = keys(fname)
if prefix is None:
continue
if lcase:
suffix = suffix.lower()
# FIXME webdataset version throws if suffix in current_sample, but we have a potential for
# this happening in the current LAION400m dataset if a tar ends with same prefix as the next
# begins, rare, but can happen since prefix aren't unique across tar files in that dataset
if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
if valid_sample(current_sample):
yield current_sample
current_sample = {"__key__": prefix, "__url__": filesample["__url__"]}
if suffixes is None or suffix in suffixes:
current_sample[suffix] = value
if valid_sample(current_sample):
yield current_sample
def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
# NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
streams = url_opener(src, handler=handler)
files = tar_file_expander(streams, handler=handler)
samples = group_by_keys_nothrow(files, handler=handler)
return samples
def control_transform(image):
image = np.array(image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
control_image = Image.fromarray(image)
return control_image
def canny_image_transform(example, resolution=1024):
image = example["image"]
image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image)
# get crop coordinates
c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
image = transforms.functional.crop(image, c_top, c_left, resolution, resolution)
control_image = control_transform(image)
image = transforms.ToTensor()(image)
image = transforms.Normalize([0.5], [0.5])(image)
control_image = transforms.ToTensor()(control_image)
example["image"] = image
example["control_image"] = control_image
example["crop_coords"] = (c_top, c_left)
return example
def depth_image_transform(example, feature_extractor, resolution=1024):
image = example["image"]
image = transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR)(image)
# get crop coordinates
c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
image = transforms.functional.crop(image, c_top, c_left, resolution, resolution)
control_image = feature_extractor(images=image, return_tensors="pt").pixel_values.squeeze(0)
image = transforms.ToTensor()(image)
image = transforms.Normalize([0.5], [0.5])(image)
example["image"] = image
example["control_image"] = control_image
example["crop_coords"] = (c_top, c_left)
return example
class WebdatasetFilter:
def __init__(self, min_size=1024, max_pwatermark=0.5):
self.min_size = min_size
self.max_pwatermark = max_pwatermark
def __call__(self, x):
try:
if "json" in x:
x_json = json.loads(x["json"])
filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get(
"original_height", 0
) >= self.min_size
filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark
return filter_size and filter_watermark
else:
return False
except Exception:
return False
class Text2ImageDataset:
def __init__(
self,
train_shards_path_or_url: Union[str, List[str]],
eval_shards_path_or_url: Union[str, List[str]],
num_train_examples: int,
per_gpu_batch_size: int,
global_batch_size: int,
num_workers: int,
resolution: int = 256,
center_crop: bool = True,
random_flip: bool = False,
shuffle_buffer_size: int = 1000,
pin_memory: bool = False,
persistent_workers: bool = False,
control_type: str = "canny",
feature_extractor: Optional[DPTFeatureExtractor] = None,
):
if not isinstance(train_shards_path_or_url, str):
train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
# flatten list using itertools
train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))
if not isinstance(eval_shards_path_or_url, str):
eval_shards_path_or_url = [list(braceexpand(urls)) for urls in eval_shards_path_or_url]
# flatten list using itertools
eval_shards_path_or_url = list(itertools.chain.from_iterable(eval_shards_path_or_url))
def get_orig_size(json):
return (int(json.get("original_width", 0.0)), int(json.get("original_height", 0.0)))
if control_type == "canny":
image_transform = functools.partial(canny_image_transform, resolution=resolution)
elif control_type == "depth":
image_transform = functools.partial(
depth_image_transform, feature_extractor=feature_extractor, resolution=resolution
)
processing_pipeline = [
wds.decode("pil", handler=wds.ignore_and_continue),
wds.rename(
image="jpg;png;jpeg;webp",
control_image="jpg;png;jpeg;webp",
text="text;txt;caption",
orig_size="json",
handler=wds.warn_and_continue,
),
wds.map(filter_keys({"image", "control_image", "text", "orig_size"})),
wds.map_dict(orig_size=get_orig_size),
wds.map(image_transform),
wds.to_tuple("image", "control_image", "text", "orig_size", "crop_coords"),
]
# Create train dataset and loader
pipeline = [
wds.ResampledShards(train_shards_path_or_url),
tarfile_to_samples_nothrow,
wds.select(WebdatasetFilter(min_size=512)),
wds.shuffle(shuffle_buffer_size),
*processing_pipeline,
wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
]
num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers)) # per dataloader worker
num_batches = num_worker_batches * num_workers
num_samples = num_batches * global_batch_size
# each worker is iterating over this
self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)
self._train_dataloader = wds.WebLoader(
self._train_dataset,
batch_size=None,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory,
persistent_workers=persistent_workers,
)
# add meta-data to dataloader instance for convenience
self._train_dataloader.num_batches = num_batches
self._train_dataloader.num_samples = num_samples
# Create eval dataset and loader
pipeline = [
wds.SimpleShardList(eval_shards_path_or_url),
wds.split_by_worker,
wds.tarfile_to_samples(handler=wds.ignore_and_continue),
*processing_pipeline,
wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
]
self._eval_dataset = wds.DataPipeline(*pipeline)
self._eval_dataloader = wds.WebLoader(
self._eval_dataset,
batch_size=None,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory,
persistent_workers=persistent_workers,
)
@property
def train_dataset(self):
return self._train_dataset
@property
def train_dataloader(self):
return self._train_dataloader
@property
def eval_dataset(self):
return self._eval_dataset
@property
def eval_dataloader(self):
return self._eval_dataloader
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step):
logger.info("Running validation... ")
controlnet = accelerator.unwrap_model(controlnet)
pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
unet=unet,
controlnet=controlnet,
revision=args.revision,
torch_dtype=weight_dtype,
)
# pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
if args.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
if len(args.validation_image) == len(args.validation_prompt):
validation_images = args.validation_image
validation_prompts = args.validation_prompt
elif len(args.validation_image) == 1:
validation_images = args.validation_image * len(args.validation_prompt)
validation_prompts = args.validation_prompt
elif len(args.validation_prompt) == 1:
validation_images = args.validation_image
validation_prompts = args.validation_prompt * len(args.validation_image)
else:
raise ValueError(
"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
)
image_logs = []
for validation_prompt, validation_image in zip(validation_prompts, validation_images):
validation_image = Image.open(validation_image).convert("RGB")
validation_image = validation_image.resize((args.resolution, args.resolution))
images = []
for _ in range(args.num_validation_images):
with torch.autocast("cuda"):
image = pipeline(
validation_prompt, image=validation_image, num_inference_steps=20, generator=generator
).images[0]
images.append(image)
image_logs.append(
{"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
)
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
for log in image_logs:
images = log["images"]
validation_prompt = log["validation_prompt"]
validation_image = log["validation_image"]
formatted_images = []
formatted_images.append(np.asarray(validation_image))
for image in images:
formatted_images.append(np.asarray(image))
formatted_images = np.stack(formatted_images)
tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
elif tracker.name == "wandb":
formatted_images = []
for log in image_logs:
images = log["images"]
validation_prompt = log["validation_prompt"]
validation_image = log["validation_image"]
formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
for image in images:
image = wandb.Image(image, caption=validation_prompt)
formatted_images.append(image)
tracker.log({"validation": formatted_images})
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
gc.collect()
torch.cuda.empty_cache()
return image_logs
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision, use_auth_token=True
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
img_str = ""
if image_logs is not None:
img_str = "You can find some example images below.\n"
for i, log in enumerate(image_logs):
images = log["images"]
validation_prompt = log["validation_prompt"]
validation_image = log["validation_image"]
validation_image.save(os.path.join(repo_folder, "image_control.png"))
img_str += f"prompt: {validation_prompt}\n"
images = [validation_image] + images
image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
"""
model_card = f"""
# controlnet-{repo_id}
These are controlnet weights trained on {base_model} with new type of conditioning.
{img_str}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--controlnet_model_name_or_path",
type=str,
default=None,
help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
" If not specified controlnet weights are initialized from unet.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help=(
"Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
" float32 precision."
),
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--output_dir",
type=str,
default="controlnet-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--crops_coords_top_left_h",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--crops_coords_top_left_w",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
"instructions."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=3,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=1,
help=("Number of subprocesses to use for data loading."),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--train_shards_path_or_url",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--eval_shards_path_or_url",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing the target image."
)
parser.add_argument(
"--conditioning_image_column",
type=str,
default="conditioning_image",
help="The column of the dataset containing the controlnet conditioning image.",
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--proportion_empty_prompts",
type=float,
default=0,
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
nargs="+",
help=(
"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
" Provide either a matching number of `--validation_image`s, a single `--validation_image`"
" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
),
)
parser.add_argument(
"--validation_image",
type=str,
default=None,
nargs="+",
help=(
"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
" a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
" `--validation_image` that will be used with all `--validation_prompt`s."
),
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run validation every X steps. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="sd_xl_train_controlnet",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
parser.add_argument(
"--control_type",
type=str,
default="canny",
help=("The type of controlnet conditioning image to use. One of `canny`, `depth`" " Defaults to `canny`."),
)
parser.add_argument(
"--transformer_layers_per_block",
type=str,
default=None,
help=("The number of layers per block in the transformer. If None, defaults to" " `args.transformer_layers`."),
)
parser.add_argument(
"--old_style_controlnet",
action="store_true",
default=False,
help=(
"Use the old style controlnet, which is a single transformer layer with"
" a single head. Defaults to False."
),
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
if args.validation_prompt is not None and args.validation_image is None:
raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
if args.validation_prompt is None and args.validation_image is not None:
raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
if (
args.validation_image is not None
and args.validation_prompt is not None
and len(args.validation_image) != 1
and len(args.validation_prompt) != 1
and len(args.validation_image) != len(args.validation_prompt)
):
raise ValueError(
"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
" or the same number of `--validation_prompt`s and `--validation_image`s"
)
if args.resolution % 8 != 0:
raise ValueError(
"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
)
return args
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):
prompt_embeds_list = []
captions = []
for caption in prompt_batch:
if random.random() < proportion_empty_prompts:
captions.append("")
elif isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
with torch.no_grad():
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
captions,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
)
# 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]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return prompt_embeds, pooled_prompt_embeds
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name,
exist_ok=True,
token=args.hub_token,
private=True,
).repo_id
# Load the tokenizers
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision, use_fast=False
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
# Load scheduler and models
# noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision
)
vae_path = (
args.pretrained_model_name_or_path
if args.pretrained_vae_model_name_or_path is None
else args.pretrained_vae_model_name_or_path
)
vae = AutoencoderKL.from_pretrained(
vae_path,
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
revision=args.revision,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, use_auth_token=True
)
if args.controlnet_model_name_or_path:
logger.info("Loading existing controlnet weights")
pre_controlnet = ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
else:
logger.info("Initializing controlnet weights from unet")
pre_controlnet = ControlNetModel.from_unet(unet)
if args.transformer_layers_per_block is not None:
transformer_layers_per_block = [int(x) for x in args.transformer_layers_per_block.split(",")]
down_block_types = ["DownBlock2D" if l == 0 else "CrossAttnDownBlock2D" for l in transformer_layers_per_block]
controlnet = ControlNetModel.from_config(
pre_controlnet.config,
down_block_types=down_block_types,
transformer_layers_per_block=transformer_layers_per_block,
)
controlnet.load_state_dict(pre_controlnet.state_dict(), strict=False)
del pre_controlnet
else:
controlnet = pre_controlnet
if args.control_type == "depth":
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas")
depth_model.requires_grad_(False)
else:
feature_extractor = None
depth_model = None
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
i = len(weights) - 1
while len(weights) > 0:
weights.pop()
model = models[i]
sub_dir = "controlnet"
model.save_pretrained(os.path.join(output_dir, sub_dir))
i -= 1
def load_model_hook(models, input_dir):
while len(models) > 0:
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = ControlNetModel.from_pretrained(input_dir, subfolder="controlnet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
vae.requires_grad_(False)
unet.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
controlnet.train()
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
controlnet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
controlnet.enable_gradient_checkpointing()
# Check that all trainable models are in full precision
low_precision_error_string = (
" Please make sure to always have all model weights in full float32 precision when starting training - even if"
" doing mixed precision training, copy of the weights should still be float32."
)
if accelerator.unwrap_model(controlnet).dtype != torch.float32:
raise ValueError(
f"Controlnet loaded as datatype {accelerator.unwrap_model(controlnet).dtype}. {low_precision_error_string}"
)
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = controlnet.parameters()
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae, unet and text_encoder to device and cast to weight_dtype
# The VAE is in float32 to avoid NaN losses.
if args.pretrained_vae_model_name_or_path is not None:
vae.to(accelerator.device, dtype=weight_dtype)
else:
vae.to(accelerator.device, dtype=torch.float32)
unet.to(accelerator.device, dtype=weight_dtype)
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
if args.control_type == "depth":
depth_model.to(accelerator.device, dtype=weight_dtype)
# Here, we compute not just the text embeddings but also the additional embeddings
# needed for the SD XL UNet to operate.
def compute_embeddings(
prompt_batch, original_sizes, crop_coords, proportion_empty_prompts, text_encoders, tokenizers, is_train=True
):
target_size = (args.resolution, args.resolution)
original_sizes = list(map(list, zip(*original_sizes)))
crops_coords_top_left = list(map(list, zip(*crop_coords)))
original_sizes = torch.tensor(original_sizes, dtype=torch.long)
crops_coords_top_left = torch.tensor(crops_coords_top_left, dtype=torch.long)
# crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)
prompt_embeds, pooled_prompt_embeds = encode_prompt(
prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train
)
add_text_embeds = pooled_prompt_embeds
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
# add_time_ids = list(crops_coords_top_left + target_size)
add_time_ids = list(target_size)
add_time_ids = torch.tensor([add_time_ids])
add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)
# add_time_ids = torch.cat([torch.tensor(original_sizes, dtype=torch.long), add_time_ids], dim=-1)
add_time_ids = torch.cat([original_sizes, crops_coords_top_left, add_time_ids], dim=-1)
add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)
prompt_embeds = prompt_embeds.to(accelerator.device)
add_text_embeds = add_text_embeds.to(accelerator.device)
unet_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
return {"prompt_embeds": prompt_embeds, **unet_added_cond_kwargs}
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
timesteps = timesteps.to(accelerator.device)
step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < n_dim:
sigma = sigma.unsqueeze(-1)
return sigma
dataset = Text2ImageDataset(
train_shards_path_or_url=args.train_shards_path_or_url,
eval_shards_path_or_url=args.eval_shards_path_or_url,
num_train_examples=args.max_train_samples,
per_gpu_batch_size=args.train_batch_size,
global_batch_size=args.train_batch_size * accelerator.num_processes,
num_workers=args.dataloader_num_workers,
resolution=args.resolution,
center_crop=False,
random_flip=False,
shuffle_buffer_size=1000,
pin_memory=True,
persistent_workers=True,
control_type=args.control_type,
feature_extractor=feature_extractor,
)
train_dataloader = dataset.train_dataloader
# Let's first compute all the embeddings so that we can free up the text encoders
# from memory.
text_encoders = [text_encoder_one, text_encoder_two]
tokenizers = [tokenizer_one, tokenizer_two]
compute_embeddings_fn = functools.partial(
compute_embeddings,
proportion_empty_prompts=args.proportion_empty_prompts,
text_encoders=text_encoders,
tokenizers=tokenizers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
controlnet, optimizer, lr_scheduler = accelerator.prepare(controlnet, optimizer, lr_scheduler)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
# tensorboard cannot handle list types for config
tracker_config.pop("validation_prompt")
tracker_config.pop("validation_image")
accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num batches each epoch = {train_dataloader.num_batches}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
image_logs = None
for epoch in range(first_epoch, args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(controlnet):
image, control_image, text, orig_size, crop_coords = batch
encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)
image = image.to(accelerator.device, non_blocking=True)
control_image = control_image.to(accelerator.device, non_blocking=True)
if args.pretrained_vae_model_name_or_path is not None:
pixel_values = image.to(dtype=weight_dtype)
if vae.dtype != weight_dtype:
vae.to(dtype=weight_dtype)
else:
pixel_values = image
# latents = vae.encode(pixel_values).latent_dist.sample()
# encode pixel values with batch size of at most 8
latents = []
for i in range(0, pixel_values.shape[0], 8):
latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample())
latents = torch.cat(latents, dim=0)
latents = latents * vae.config.scaling_factor
if args.pretrained_vae_model_name_or_path is None:
latents = latents.to(weight_dtype)
if args.control_type == "depth":
control_image = control_image.to(weight_dtype)
with torch.autocast("cuda"):
depth_map = depth_model(control_image).predicted_depth
depth_map = torch.nn.functional.interpolate(
depth_map.unsqueeze(1),
size=image.shape[2:],
mode="bicubic",
align_corners=False,
)
depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
depth_map = (depth_map - depth_min) / (depth_max - depth_min)
control_image = (depth_map * 255.0).to(torch.uint8).float() / 255.0 # hack to match inference
control_image = torch.cat([control_image] * 3, dim=1)
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
sigmas = get_sigmas(timesteps, len(noisy_latents.shape), noisy_latents.dtype)
inp_noisy_latents = noisy_latents / ((sigmas**2 + 1) ** 0.5)
# ControlNet conditioning.
controlnet_image = control_image.to(dtype=weight_dtype)
prompt_embeds = encoded_text.pop("prompt_embeds")
down_block_res_samples, mid_block_res_sample = controlnet(
inp_noisy_latents,
timesteps,
encoder_hidden_states=prompt_embeds,
added_cond_kwargs=encoded_text,
controlnet_cond=controlnet_image,
return_dict=False,
)
# Predict the noise residual
model_pred = unet(
inp_noisy_latents,
timesteps,
encoder_hidden_states=prompt_embeds,
added_cond_kwargs=encoded_text,
down_block_additional_residuals=[
sample.to(dtype=weight_dtype) for sample in down_block_res_samples
],
mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
).sample
model_pred = model_pred * (-sigmas) + noisy_latents
weighing = sigmas**-2.0
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = latents # compute loss against the denoised latents
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = torch.mean(
(weighing.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1), 1
)
loss = loss.mean()
accelerator.backward(loss)
if accelerator.sync_gradients:
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=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
image_logs = log_validation(
vae, unet, controlnet, args, accelerator, weight_dtype, global_step
)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
controlnet = accelerator.unwrap_model(controlnet)
controlnet.save_pretrained(args.output_dir)
if args.push_to_hub:
save_model_card(
repo_id,
image_logs=image_logs,
base_model=args.pretrained_model_name_or_path,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| 0
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hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/colossalai/README.md
|
# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)
[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
The `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion.
By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel.
## Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
```bash
pip install -r requirements.txt
```
## Install [ColossalAI](https://github.com/hpcaitech/ColossalAI.git)
**From PyPI**
```bash
pip install colossalai
```
**From source**
```bash
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# install colossalai
pip install .
```
## Dataset for Teyvat BLIP captions
Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion).
BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided.
The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
## Training
The argument `placement` can be `cpu`, `auto`, `cuda`, with `cpu` the GPU RAM required can be minimized to 4GB but will deceleration, with `cuda` you can also reduce GPU memory by half but accelerated training， with `auto` a more balanced solution for speed and memory can be obtained。
**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export OUTPUT_DIR="path-to-save-model"
torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400 \
--placement="cuda"
```
### Training with prior-preservation loss
Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=800 \
--placement="cuda"
```
## Inference
Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "path-to-save-model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A photo of sks dog in a bucket"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
```
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/colossalai/train_dreambooth_colossalai.py
|
import argparse
import math
import os
from pathlib import Path
import colossalai
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import disable_existing_loggers, get_dist_logger
from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
from colossalai.nn.parallel.utils import get_static_torch_model
from colossalai.utils import get_current_device
from colossalai.utils.model.colo_init_context import ColoInitContext
from huggingface_hub import create_repo, upload_folder
from huggingface_hub.utils import insecure_hashlib
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
disable_existing_loggers()
logger = get_dist_logger()
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default="a photo of sks dog",
required=False,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--placement",
type=str,
default="cpu",
help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
if args.class_data_dir is not None:
logger.warning("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
logger.warning("You need not use --class_prompt without --with_prior_preservation.")
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
return example
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
# Gemini + ZeRO DDP
def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"):
from colossalai.nn.parallel import GeminiDDP
model = GeminiDDP(
model, device=get_current_device(), placement_policy=placememt_policy, pin_memory=True, search_range_mb=64
)
return model
def main(args):
if args.seed is None:
colossalai.launch_from_torch(config={})
else:
colossalai.launch_from_torch(config={}, seed=args.seed)
local_rank = gpc.get_local_rank(ParallelMode.DATA)
world_size = gpc.get_world_size(ParallelMode.DATA)
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
safety_checker=None,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
pipeline.to(get_current_device())
for example in tqdm(
sample_dataloader,
desc="Generating class images",
disable=not local_rank == 0,
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
# Handle the repository creation
if local_rank == 0:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer
if args.tokenizer_name:
logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0])
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name,
revision=args.revision,
use_fast=False,
)
elif args.pretrained_model_name_or_path:
logger.info("Loading tokenizer from pretrained model", ranks=[0])
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)
# Load models and create wrapper for stable diffusion
logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0])
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0])
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
)
logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
with ColoInitContext(device=get_current_device()):
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, low_cpu_mem_usage=False
)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.scale_lr:
args.learning_rate = args.learning_rate * args.train_batch_size * world_size
unet = gemini_zero_dpp(unet, args.placement)
# config optimizer for colossalai zero
optimizer = GeminiAdamOptimizer(unet, lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm)
# load noise_scheduler
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
# prepare dataset
logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0])
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
def collate_fn(examples):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if args.with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = tokenizer.pad(
{"input_ids": input_ids},
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
).input_ids
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
return batch
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps,
num_training_steps=args.max_train_steps,
)
weight_dtype = torch.float32
if args.mixed_precision == "fp16":
weight_dtype = torch.float16
elif args.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu.
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
vae.to(get_current_device(), dtype=weight_dtype)
text_encoder.to(get_current_device(), dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# Train!
total_batch_size = args.train_batch_size * world_size
logger.info("***** Running training *****", ranks=[0])
logger.info(f" Num examples = {len(train_dataset)}", ranks=[0])
logger.info(f" Num batches each epoch = {len(train_dataloader)}", ranks=[0])
logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0])
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}", ranks=[0])
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0])
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0)
progress_bar.set_description("Steps")
global_step = 0
torch.cuda.synchronize()
for epoch in range(args.num_train_epochs):
unet.train()
for step, batch in enumerate(train_dataloader):
torch.cuda.reset_peak_memory_stats()
# Move batch to gpu
for key, value in batch.items():
batch[key] = value.to(get_current_device(), non_blocking=True)
# Convert images to latent space
optimizer.zero_grad()
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
optimizer.backward(loss)
optimizer.step()
lr_scheduler.step()
logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0])
# Checks if the accelerator has performed an optimization step behind the scenes
progress_bar.update(1)
global_step += 1
logs = {
"loss": loss.detach().item(),
"lr": optimizer.param_groups[0]["lr"],
} # lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step % args.save_steps == 0:
torch.cuda.synchronize()
torch_unet = get_static_torch_model(unet)
if local_rank == 0:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=torch_unet,
revision=args.revision,
)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
pipeline.save_pretrained(save_path)
logger.info(f"Saving model checkpoint to {save_path}", ranks=[0])
if global_step >= args.max_train_steps:
break
torch.cuda.synchronize()
unet = get_static_torch_model(unet)
if local_rank == 0:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=unet,
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0])
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
if __name__ == "__main__":
args = parse_args()
main(args)
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hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/colossalai/inference.py
|
import torch
from diffusers import StableDiffusionPipeline
model_id = "path-to-your-trained-model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A photo of sks dog in a bucket"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
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hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/colossalai/requirement.txt
|
diffusers
torch
torchvision
ftfy
tensorboard
Jinja2
transformers
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hf_public_repos/diffusers/examples/research_projects
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hf_public_repos/diffusers/examples/research_projects/rdm/README.md
|
## Diffusers examples with ONNXRuntime optimizations
**This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Isamu Isozaki(isamu-isozaki) on github with any questions.**
The aim of this project is to provide retrieval augmented diffusion models to diffusers!
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hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/rdm/retriever.py
|
import os
from typing import List
import faiss
import numpy as np
import torch
from datasets import Dataset, load_dataset
from PIL import Image
from transformers import CLIPFeatureExtractor, CLIPModel, PretrainedConfig
from diffusers import logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def normalize_images(images: List[Image.Image]):
images = [np.array(image) for image in images]
images = [image / 127.5 - 1 for image in images]
return images
def preprocess_images(images: List[np.array], feature_extractor: CLIPFeatureExtractor) -> torch.FloatTensor:
"""
Preprocesses a list of images into a batch of tensors.
Args:
images (:obj:`List[Image.Image]`):
A list of images to preprocess.
Returns:
:obj:`torch.FloatTensor`: A batch of tensors.
"""
images = [np.array(image) for image in images]
images = [(image + 1.0) / 2.0 for image in images]
images = feature_extractor(images, return_tensors="pt").pixel_values
return images
class IndexConfig(PretrainedConfig):
def __init__(
self,
clip_name_or_path="openai/clip-vit-large-patch14",
dataset_name="Isamu136/oxford_pets_with_l14_emb",
image_column="image",
index_name="embeddings",
index_path=None,
dataset_set="train",
metric_type=faiss.METRIC_L2,
faiss_device=-1,
**kwargs,
):
super().__init__(**kwargs)
self.clip_name_or_path = clip_name_or_path
self.dataset_name = dataset_name
self.image_column = image_column
self.index_name = index_name
self.index_path = index_path
self.dataset_set = dataset_set
self.metric_type = metric_type
self.faiss_device = faiss_device
class Index:
"""
Each index for a retrieval model is specific to the clip model used and the dataset used.
"""
def __init__(self, config: IndexConfig, dataset: Dataset):
self.config = config
self.dataset = dataset
self.index_initialized = False
self.index_name = config.index_name
self.index_path = config.index_path
self.init_index()
def set_index_name(self, index_name: str):
self.index_name = index_name
def init_index(self):
if not self.index_initialized:
if self.index_path and self.index_name:
try:
self.dataset.add_faiss_index(
column=self.index_name, metric_type=self.config.metric_type, device=self.config.faiss_device
)
self.index_initialized = True
except Exception as e:
print(e)
logger.info("Index not initialized")
if self.index_name in self.dataset.features:
self.dataset.add_faiss_index(column=self.index_name)
self.index_initialized = True
def build_index(
self,
model=None,
feature_extractor: CLIPFeatureExtractor = None,
torch_dtype=torch.float32,
):
if not self.index_initialized:
model = model or CLIPModel.from_pretrained(self.config.clip_name_or_path).to(dtype=torch_dtype)
feature_extractor = feature_extractor or CLIPFeatureExtractor.from_pretrained(
self.config.clip_name_or_path
)
self.dataset = get_dataset_with_emb_from_clip_model(
self.dataset,
model,
feature_extractor,
image_column=self.config.image_column,
index_name=self.config.index_name,
)
self.init_index()
def retrieve_imgs(self, vec, k: int = 20):
vec = np.array(vec).astype(np.float32)
return self.dataset.get_nearest_examples(self.index_name, vec, k=k)
def retrieve_imgs_batch(self, vec, k: int = 20):
vec = np.array(vec).astype(np.float32)
return self.dataset.get_nearest_examples_batch(self.index_name, vec, k=k)
def retrieve_indices(self, vec, k: int = 20):
vec = np.array(vec).astype(np.float32)
return self.dataset.search(self.index_name, vec, k=k)
def retrieve_indices_batch(self, vec, k: int = 20):
vec = np.array(vec).astype(np.float32)
return self.dataset.search_batch(self.index_name, vec, k=k)
class Retriever:
def __init__(
self,
config: IndexConfig,
index: Index = None,
dataset: Dataset = None,
model=None,
feature_extractor: CLIPFeatureExtractor = None,
):
self.config = config
self.index = index or self._build_index(config, dataset, model=model, feature_extractor=feature_extractor)
@classmethod
def from_pretrained(
cls,
retriever_name_or_path: str,
index: Index = None,
dataset: Dataset = None,
model=None,
feature_extractor: CLIPFeatureExtractor = None,
**kwargs,
):
config = kwargs.pop("config", None) or IndexConfig.from_pretrained(retriever_name_or_path, **kwargs)
return cls(config, index=index, dataset=dataset, model=model, feature_extractor=feature_extractor)
@staticmethod
def _build_index(
config: IndexConfig, dataset: Dataset = None, model=None, feature_extractor: CLIPFeatureExtractor = None
):
dataset = dataset or load_dataset(config.dataset_name)
dataset = dataset[config.dataset_set]
index = Index(config, dataset)
index.build_index(model=model, feature_extractor=feature_extractor)
return index
def save_pretrained(self, save_directory):
os.makedirs(save_directory, exist_ok=True)
if self.config.index_path is None:
index_path = os.path.join(save_directory, "hf_dataset_index.faiss")
self.index.dataset.get_index(self.config.index_name).save(index_path)
self.config.index_path = index_path
self.config.save_pretrained(save_directory)
def init_retrieval(self):
logger.info("initializing retrieval")
self.index.init_index()
def retrieve_imgs(self, embeddings: np.ndarray, k: int):
return self.index.retrieve_imgs(embeddings, k)
def retrieve_imgs_batch(self, embeddings: np.ndarray, k: int):
return self.index.retrieve_imgs_batch(embeddings, k)
def retrieve_indices(self, embeddings: np.ndarray, k: int):
return self.index.retrieve_indices(embeddings, k)
def retrieve_indices_batch(self, embeddings: np.ndarray, k: int):
return self.index.retrieve_indices_batch(embeddings, k)
def __call__(
self,
embeddings,
k: int = 20,
):
return self.index.retrieve_imgs(embeddings, k)
def map_txt_to_clip_feature(clip_model, tokenizer, prompt):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > tokenizer.model_max_length:
removed_text = tokenizer.batch_decode(text_input_ids[:, tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : tokenizer.model_max_length]
text_embeddings = clip_model.get_text_features(text_input_ids.to(clip_model.device))
text_embeddings = text_embeddings / torch.linalg.norm(text_embeddings, dim=-1, keepdim=True)
text_embeddings = text_embeddings[:, None, :]
return text_embeddings[0][0].cpu().detach().numpy()
def map_img_to_model_feature(model, feature_extractor, imgs, device):
for i, image in enumerate(imgs):
if not image.mode == "RGB":
imgs[i] = image.convert("RGB")
imgs = normalize_images(imgs)
retrieved_images = preprocess_images(imgs, feature_extractor).to(device)
image_embeddings = model(retrieved_images)
image_embeddings = image_embeddings / torch.linalg.norm(image_embeddings, dim=-1, keepdim=True)
image_embeddings = image_embeddings[None, ...]
return image_embeddings.cpu().detach().numpy()[0][0]
def get_dataset_with_emb_from_model(dataset, model, feature_extractor, image_column="image", index_name="embeddings"):
return dataset.map(
lambda example: {
index_name: map_img_to_model_feature(model, feature_extractor, [example[image_column]], model.device)
}
)
def get_dataset_with_emb_from_clip_model(
dataset, clip_model, feature_extractor, image_column="image", index_name="embeddings"
):
return dataset.map(
lambda example: {
index_name: map_img_to_model_feature(
clip_model.get_image_features, feature_extractor, [example[image_column]], clip_model.device
)
}
)
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hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/rdm/pipeline_rdm.py
|
import inspect
from typing import Callable, List, Optional, Union
import torch
from PIL import Image
from retriever import Retriever, normalize_images, preprocess_images
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
ImagePipelineOutput,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
logging,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import is_accelerate_available, randn_tensor
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class RDMPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Retrieval Augmented Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
clip ([`CLIPModel`]):
Frozen CLIP model. Retrieval Augmented Diffusion uses the CLIP model, specifically the
[clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
feature_extractor ([`CLIPFeatureExtractor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""
def __init__(
self,
vae: AutoencoderKL,
clip: CLIPModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
],
feature_extractor: CLIPFeatureExtractor,
retriever: Optional[Retriever] = None,
):
super().__init__()
self.register_modules(
vae=vae,
clip=clip,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
feature_extractor=feature_extractor,
)
# Copy from statement here and all the methods we take from stable_diffusion_pipeline
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.retriever = retriever
def enable_xformers_memory_efficient_attention(self):
r"""
Enable memory efficient attention as implemented in xformers.
When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
time. Speed up at training time is not guaranteed.
Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
is used.
"""
self.unet.set_use_memory_efficient_attention_xformers(True)
def disable_xformers_memory_efficient_attention(self):
r"""
Disable memory efficient attention as implemented in xformers.
"""
self.unet.set_use_memory_efficient_attention_xformers(False)
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding.
When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding.
When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
"""
self.vae.enable_tiling()
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
`attention_head_dim` must be a multiple of `slice_size`.
"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
if isinstance(self.unet.config.attention_head_dim, int):
slice_size = self.unet.config.attention_head_dim // 2
else:
slice_size = self.unet.config.attention_head_dim[0] // 2
self.unet.set_attention_slice(slice_size)
def disable_attention_slicing(self):
r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
back to computing attention in one step.
"""
# set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None)
def enable_sequential_cpu_offload(self):
r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
device = torch.device("cuda")
for cpu_offloaded_model in [self.unet, self.clip, self.vae]:
if cpu_offloaded_model is not None:
cpu_offload(cpu_offloaded_model, device)
@property
def _execution_device(self):
r"""
Returns the device on which the pipeline's models will be executed. After calling
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
hooks.
"""
if not hasattr(self.unet, "_hf_hook"):
return self.device
for module in self.unet.modules():
if (
hasattr(module, "_hf_hook")
and hasattr(module._hf_hook, "execution_device")
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device)
return self.device
def _encode_prompt(self, prompt):
# get prompt text embeddings
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
)
text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
prompt_embeds = self.clip.get_text_features(text_input_ids.to(self.device))
prompt_embeds = prompt_embeds / torch.linalg.norm(prompt_embeds, dim=-1, keepdim=True)
prompt_embeds = prompt_embeds[:, None, :]
return prompt_embeds
def _encode_image(self, retrieved_images, batch_size):
if len(retrieved_images[0]) == 0:
return None
for i in range(len(retrieved_images)):
retrieved_images[i] = normalize_images(retrieved_images[i])
retrieved_images[i] = preprocess_images(retrieved_images[i], self.feature_extractor).to(
self.clip.device, dtype=self.clip.dtype
)
_, c, h, w = retrieved_images[0].shape
retrieved_images = torch.reshape(torch.cat(retrieved_images, dim=0), (-1, c, h, w))
image_embeddings = self.clip.get_image_features(retrieved_images)
image_embeddings = image_embeddings / torch.linalg.norm(image_embeddings, dim=-1, keepdim=True)
_, d = image_embeddings.shape
image_embeddings = torch.reshape(image_embeddings, (batch_size, -1, d))
return image_embeddings
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def retrieve_images(self, retrieved_images, prompt_embeds, knn=10):
if self.retriever is not None:
additional_images = self.retriever.retrieve_imgs_batch(prompt_embeds[:, 0].cpu(), knn).total_examples
for i in range(len(retrieved_images)):
retrieved_images[i] += additional_images[i][self.retriever.config.image_column]
return retrieved_images
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
retrieved_images: Optional[List[Image.Image]] = None,
height: int = 768,
width: int = 768,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[torch.Generator] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: Optional[int] = 1,
knn: Optional[int] = 10,
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 512):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
if isinstance(prompt, str):
batch_size = 1
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if retrieved_images is not None:
retrieved_images = [retrieved_images for _ in range(batch_size)]
else:
retrieved_images = [[] for _ in range(batch_size)]
device = self._execution_device
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt_embeds is None:
prompt_embeds = self._encode_prompt(prompt)
retrieved_images = self.retrieve_images(retrieved_images, prompt_embeds, knn=knn)
image_embeddings = self._encode_image(retrieved_images, batch_size)
if image_embeddings is not None:
prompt_embeds = torch.cat([prompt_embeds, image_embeddings], dim=1)
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_embeddings = torch.zeros_like(prompt_embeds).to(prompt_embeds.device)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([uncond_embeddings, prompt_embeds])
# get the initial random noise unless the user supplied it
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# set timesteps
self.scheduler.set_timesteps(num_inference_steps)
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
timesteps_tensor = self.scheduler.timesteps.to(self.device)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
for i, t in enumerate(self.progress_bar(timesteps_tensor)):
# expand the latents if we are doing classifier free guidance
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)
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
image = self.image_processor.postprocess(
image, output_type=output_type, do_denormalize=[True] * image.shape[0]
)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)
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hf_public_repos/diffusers/examples/research_projects
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hf_public_repos/diffusers/examples/research_projects/mulit_token_textual_inversion/README.md
|
## [Deprecated] Multi Token Textual Inversion
**IMPORTART: This research project is deprecated. Multi Token Textual Inversion is now supported natively in [the officail textual inversion example](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion#running-locally-with-pytorch).**
The author of this project is [Isamu Isozaki](https://github.com/isamu-isozaki) - please make sure to tag the author for issue and PRs as well as @patrickvonplaten.
We add multi token support to textual inversion. I added
1. num_vec_per_token for the number of used to reference that token
2. progressive_tokens for progressively training the token from 1 token to 2 token etc
3. progressive_tokens_max_steps for the max number of steps until we start full training
4. vector_shuffle to shuffle vectors
Feel free to add these options to your training! In practice num_vec_per_token around 10+vector shuffle works great!
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
## Running on Colab
Colab for training
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
Colab for inference
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd in the example folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
### Cat toy example
You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree.
You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
Run the following command to authenticate your token
```bash
huggingface-cli login
```
If you have already cloned the repo, then you won't need to go through these steps.
<br>
Now let's get our dataset.Download 3-4 images from [here](https://drive.google.com/drive/folders/1fmJMs25nxS_rSNqS5hTcRdLem_YQXbq5) and save them in a directory. This will be our training data.
And launch the training using
**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
```bash
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATA_DIR="path-to-dir-containing-images"
accelerate launch textual_inversion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<cat-toy>" --initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 --scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="textual_inversion_cat"
```
A full training run takes ~1 hour on one V100 GPU.
### Inference
Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt.
```python
from diffusers import StableDiffusionPipeline
model_id = "path-to-your-trained-model"
pipe = StableDiffusionPipeline.from_pretrained(model_id,torch_dtype=torch.float16).to("cuda")
prompt = "A <cat-toy> backpack"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("cat-backpack.png")
```
## Training with Flax/JAX
For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.
Before running the scripts, make sure to install the library's training dependencies:
```bash
pip install -U -r requirements_flax.txt
```
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export DATA_DIR="path-to-dir-containing-images"
python textual_inversion_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<cat-toy>" --initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 --scale_lr \
--output_dir="textual_inversion_cat"
```
It should be at least 70% faster than the PyTorch script with the same configuration.
### Training with xformers:
You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.
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hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/mulit_token_textual_inversion/requirements_flax.txt
|
transformers>=4.25.1
flax
optax
torch
torchvision
ftfy
tensorboard
Jinja2
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/mulit_token_textual_inversion/multi_token_clip.py
|
"""
The main idea for this code is to provide a way for users to not need to bother with the hassle of multiple tokens for a concept by typing
a photo of <concept>_0 <concept>_1 ... and so on
and instead just do
a photo of <concept>
which gets translated to the above. This needs to work for both inference and training.
For inference,
the tokenizer encodes the text. So, we would want logic for our tokenizer to replace the placeholder token with
it's underlying vectors
For training,
we would want to abstract away some logic like
1. Adding tokens
2. Updating gradient mask
3. Saving embeddings
to our Util class here.
so
TODO:
1. have tokenizer keep track of concept, multiconcept pairs and replace during encode call x
2. have mechanism for adding tokens x
3. have mech for saving emebeddings x
4. get mask to update x
5. Loading tokens from embedding x
6. Integrate to training x
7. Test
"""
import copy
import random
from transformers import CLIPTokenizer
class MultiTokenCLIPTokenizer(CLIPTokenizer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.token_map = {}
def try_adding_tokens(self, placeholder_token, *args, **kwargs):
num_added_tokens = super().add_tokens(placeholder_token, *args, **kwargs)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
def add_placeholder_tokens(self, placeholder_token, *args, num_vec_per_token=1, **kwargs):
output = []
if num_vec_per_token == 1:
self.try_adding_tokens(placeholder_token, *args, **kwargs)
output.append(placeholder_token)
else:
output = []
for i in range(num_vec_per_token):
ith_token = placeholder_token + f"_{i}"
self.try_adding_tokens(ith_token, *args, **kwargs)
output.append(ith_token)
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
f"The tokenizer already has placeholder token {token} that can get confused with"
f" {placeholder_token}keep placeholder tokens independent"
)
self.token_map[placeholder_token] = output
def replace_placeholder_tokens_in_text(self, text, vector_shuffle=False, prop_tokens_to_load=1.0):
"""
Here, we replace the placeholder tokens in text recorded in token_map so that the text_encoder
can encode them
vector_shuffle was inspired by https://github.com/rinongal/textual_inversion/pull/119
where shuffling tokens were found to force the model to learn the concepts more descriptively.
"""
if isinstance(text, list):
output = []
for i in range(len(text)):
output.append(self.replace_placeholder_tokens_in_text(text[i], vector_shuffle=vector_shuffle))
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
tokens = self.token_map[placeholder_token]
tokens = tokens[: 1 + int(len(tokens) * prop_tokens_to_load)]
if vector_shuffle:
tokens = copy.copy(tokens)
random.shuffle(tokens)
text = text.replace(placeholder_token, " ".join(tokens))
return text
def __call__(self, text, *args, vector_shuffle=False, prop_tokens_to_load=1.0, **kwargs):
return super().__call__(
self.replace_placeholder_tokens_in_text(
text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load
),
*args,
**kwargs,
)
def encode(self, text, *args, vector_shuffle=False, prop_tokens_to_load=1.0, **kwargs):
return super().encode(
self.replace_placeholder_tokens_in_text(
text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load
),
*args,
**kwargs,
)
| 0
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hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/mulit_token_textual_inversion/textual_inversion.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import random
from pathlib import Path
import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from multi_token_clip import MultiTokenCLIPTokenizer
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.14.0.dev0")
logger = get_logger(__name__)
def add_tokens(tokenizer, text_encoder, placeholder_token, num_vec_per_token=1, initializer_token=None):
"""
Add tokens to the tokenizer and set the initial value of token embeddings
"""
tokenizer.add_placeholder_tokens(placeholder_token, num_vec_per_token=num_vec_per_token)
text_encoder.resize_token_embeddings(len(tokenizer))
token_embeds = text_encoder.get_input_embeddings().weight.data
placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
if initializer_token:
token_ids = tokenizer.encode(initializer_token, add_special_tokens=False)
for i, placeholder_token_id in enumerate(placeholder_token_ids):
token_embeds[placeholder_token_id] = token_embeds[token_ids[i * len(token_ids) // num_vec_per_token]]
else:
for i, placeholder_token_id in enumerate(placeholder_token_ids):
token_embeds[placeholder_token_id] = torch.randn_like(token_embeds[placeholder_token_id])
return placeholder_token
def save_progress(tokenizer, text_encoder, accelerator, save_path):
for placeholder_token in tokenizer.token_map:
placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_ids]
if len(placeholder_token_ids) == 1:
learned_embeds = learned_embeds[None]
learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}
torch.save(learned_embeds_dict, save_path)
def load_multitoken_tokenizer(tokenizer, text_encoder, learned_embeds_dict):
for placeholder_token in learned_embeds_dict:
placeholder_embeds = learned_embeds_dict[placeholder_token]
num_vec_per_token = placeholder_embeds.shape[0]
placeholder_embeds = placeholder_embeds.to(dtype=text_encoder.dtype)
add_tokens(tokenizer, text_encoder, placeholder_token, num_vec_per_token=num_vec_per_token)
placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
token_embeds = text_encoder.get_input_embeddings().weight.data
for i, placeholder_token_id in enumerate(placeholder_token_ids):
token_embeds[placeholder_token_id] = placeholder_embeds[i]
def load_multitoken_tokenizer_from_automatic(tokenizer, text_encoder, automatic_dict, placeholder_token):
"""
Automatic1111's tokens have format
{'string_to_token': {'*': 265}, 'string_to_param': {'*': tensor([[ 0.0833, 0.0030, 0.0057, ..., -0.0264, -0.0616, -0.0529],
[ 0.0058, -0.0190, -0.0584, ..., -0.0025, -0.0945, -0.0490],
[ 0.0916, 0.0025, 0.0365, ..., -0.0685, -0.0124, 0.0728],
[ 0.0812, -0.0199, -0.0100, ..., -0.0581, -0.0780, 0.0254]],
requires_grad=True)}, 'name': 'FloralMarble-400', 'step': 399, 'sd_checkpoint': '4bdfc29c', 'sd_checkpoint_name': 'SD2.1-768'}
"""
learned_embeds_dict = {}
learned_embeds_dict[placeholder_token] = automatic_dict["string_to_param"]["*"]
load_multitoken_tokenizer(tokenizer, text_encoder, learned_embeds_dict)
def get_mask(tokenizer, accelerator):
# Get the mask of the weights that won't change
mask = torch.ones(len(tokenizer)).to(accelerator.device, dtype=torch.bool)
for placeholder_token in tokenizer.token_map:
placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
for i in range(len(placeholder_token_ids)):
mask = mask & (torch.arange(len(tokenizer)) != placeholder_token_ids[i]).to(accelerator.device)
return mask
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--progressive_tokens_max_steps",
type=int,
default=2000,
help="The number of steps until all tokens will be used.",
)
parser.add_argument(
"--progressive_tokens",
action="store_true",
help="Progressively train the tokens. For example, first train for 1 token, then 2 tokens and so on.",
)
parser.add_argument("--vector_shuffle", action="store_true", help="Shuffling tokens durint training")
parser.add_argument(
"--num_vec_per_token",
type=int,
default=1,
help=(
"The number of vectors used to represent the placeholder token. The higher the number, the better the"
" result at the cost of editability. This can be fixed by prompt editing."
),
)
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save learned_embeds.bin every X updates steps.",
)
parser.add_argument(
"--only_save_embeds",
action="store_true",
default=False,
help="Save only the embeddings for the new concept.",
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
)
parser.add_argument(
"--placeholder_token",
type=str,
default=None,
required=True,
help="A token to use as a placeholder for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
)
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution."
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help=(
"Run validation every X epochs. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.train_data_dir is None:
raise ValueError("You must specify a train data directory.")
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
vector_shuffle=False,
progressive_tokens=False,
):
self.data_root = data_root
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.vector_shuffle = vector_shuffle
self.progressive_tokens = progressive_tokens
self.prop_tokens_to_load = 0
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer.encode(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
vector_shuffle=self.vector_shuffle,
prop_tokens_to_load=self.prop_tokens_to_load if self.progressive_tokens else 1.0,
)[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
(
h,
w,
) = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
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(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load tokenizer
if args.tokenizer_name:
tokenizer = MultiTokenCLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = MultiTokenCLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
if is_xformers_available():
try:
unet.enable_xformers_memory_efficient_attention()
except Exception as e:
logger.warning(
"Could not enable memory efficient attention. Make sure xformers is installed"
f" correctly and a GPU is available: {e}"
)
add_tokens(tokenizer, text_encoder, args.placeholder_token, args.num_vec_per_token, args.initializer_token)
# Freeze vae and unet
vae.requires_grad_(False)
unet.requires_grad_(False)
# Freeze all parameters except for the token embeddings in text encoder
text_encoder.text_model.encoder.requires_grad_(False)
text_encoder.text_model.final_layer_norm.requires_grad_(False)
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
if args.gradient_checkpointing:
# Keep unet in train mode if we are using gradient checkpointing to save memory.
# The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
unet.train()
text_encoder.gradient_checkpointing_enable()
unet.enable_gradient_checkpointing()
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=args.placeholder_token,
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, lr_scheduler
)
# For mixed precision training we cast the unet and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and unet to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
# keep original embeddings as reference
orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
for epoch in range(first_epoch, args.num_train_epochs):
text_encoder.train()
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
if args.progressive_tokens:
train_dataset.prop_tokens_to_load = float(global_step) / args.progressive_tokens_max_steps
with accelerator.accumulate(text_encoder):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Let's make sure we don't update any embedding weights besides the newly added token
index_no_updates = get_mask(tokenizer, accelerator)
with torch.no_grad():
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
index_no_updates
] = orig_embeds_params[index_no_updates]
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % args.save_steps == 0:
save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
save_progress(tokenizer, text_encoder, accelerator, save_path)
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process and 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 (note: unet and vae are loaded again in float32)
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
unet=unet,
vae=vae,
revision=args.revision,
torch_dtype=weight_dtype,
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = (
None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
)
images = []
for _ in range(args.num_validation_images):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
images.append(image)
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()
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
if args.push_to_hub and args.only_save_embeds:
logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
save_full_model = True
else:
save_full_model = not args.only_save_embeds
if save_full_model:
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=accelerator.unwrap_model(text_encoder),
vae=vae,
unet=unet,
tokenizer=tokenizer,
)
pipeline.save_pretrained(args.output_dir)
# Save the newly trained embeddings
save_path = os.path.join(args.output_dir, "learned_embeds.bin")
save_progress(tokenizer, text_encoder, accelerator, save_path)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/mulit_token_textual_inversion/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.25.1
ftfy
tensorboard
Jinja2
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/mulit_token_textual_inversion/textual_inversion_flax.py
|
import argparse
import logging
import math
import os
import random
from pathlib import Path
import jax
import jax.numpy as jnp
import numpy as np
import optax
import PIL
import torch
import torch.utils.checkpoint
import transformers
from flax import jax_utils
from flax.training import train_state
from flax.training.common_utils import shard
from huggingface_hub import create_repo, upload_folder
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
from diffusers import (
FlaxAutoencoderKL,
FlaxDDPMScheduler,
FlaxPNDMScheduler,
FlaxStableDiffusionPipeline,
FlaxUNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
from diffusers.utils import check_min_version
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.14.0.dev0")
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
)
parser.add_argument(
"--placeholder_token",
type=str,
default=None,
required=True,
help="A token to use as a placeholder for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
)
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution."
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=True,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument(
"--use_auth_token",
action="store_true",
help=(
"Will use the token generated when running `huggingface-cli login` (necessary to use this script with"
" private models)."
),
)
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.train_data_dir is None:
raise ValueError("You must specify a train data directory.")
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
):
self.data_root = data_root
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
(
h,
w,
) = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng):
if model.config.vocab_size == new_num_tokens or new_num_tokens is None:
return
model.config.vocab_size = new_num_tokens
params = model.params
old_embeddings = params["text_model"]["embeddings"]["token_embedding"]["embedding"]
old_num_tokens, emb_dim = old_embeddings.shape
initializer = jax.nn.initializers.normal()
new_embeddings = initializer(rng, (new_num_tokens, emb_dim))
new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings)
new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id])
params["text_model"]["embeddings"]["token_embedding"]["embedding"] = new_embeddings
model.params = params
return model
def get_params_to_save(params):
return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
def main():
args = parse_args()
if args.seed is not None:
set_seed(args.seed)
if jax.process_index() == 0:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
# Load the tokenizer and add the placeholder token as a additional special token
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id = token_ids[0]
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
# Load models and create wrapper for stable diffusion
text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
# Create sampling rng
rng = jax.random.PRNGKey(args.seed)
rng, _ = jax.random.split(rng)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder = resize_token_embeddings(
text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng
)
original_token_embeds = text_encoder.params["text_model"]["embeddings"]["token_embedding"]["embedding"]
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=args.placeholder_token,
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
input_ids = torch.stack([example["input_ids"] for example in examples])
batch = {"pixel_values": pixel_values, "input_ids": input_ids}
batch = {k: v.numpy() for k, v in batch.items()}
return batch
total_train_batch_size = args.train_batch_size * jax.local_device_count()
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn
)
# Optimization
if args.scale_lr:
args.learning_rate = args.learning_rate * total_train_batch_size
constant_scheduler = optax.constant_schedule(args.learning_rate)
optimizer = optax.adamw(
learning_rate=constant_scheduler,
b1=args.adam_beta1,
b2=args.adam_beta2,
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
def create_mask(params, label_fn):
def _map(params, mask, label_fn):
for k in params:
if label_fn(k):
mask[k] = "token_embedding"
else:
if isinstance(params[k], dict):
mask[k] = {}
_map(params[k], mask[k], label_fn)
else:
mask[k] = "zero"
mask = {}
_map(params, mask, label_fn)
return mask
def zero_grads():
# from https://github.com/deepmind/optax/issues/159#issuecomment-896459491
def init_fn(_):
return ()
def update_fn(updates, state, params=None):
return jax.tree_util.tree_map(jnp.zeros_like, updates), ()
return optax.GradientTransformation(init_fn, update_fn)
# Zero out gradients of layers other than the token embedding layer
tx = optax.multi_transform(
{"token_embedding": optimizer, "zero": zero_grads()},
create_mask(text_encoder.params, lambda s: s == "token_embedding"),
)
state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx)
noise_scheduler = FlaxDDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
)
noise_scheduler_state = noise_scheduler.create_state()
# Initialize our training
train_rngs = jax.random.split(rng, jax.local_device_count())
# Define gradient train step fn
def train_step(state, vae_params, unet_params, batch, train_rng):
dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
def compute_loss(params):
vae_outputs = vae.apply(
{"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
)
latents = vae_outputs.latent_dist.sample(sample_rng)
# (NHWC) -> (NCHW)
latents = jnp.transpose(latents, (0, 3, 1, 2))
latents = latents * vae.config.scaling_factor
noise_rng, timestep_rng = jax.random.split(sample_rng)
noise = jax.random.normal(noise_rng, latents.shape)
bsz = latents.shape[0]
timesteps = jax.random.randint(
timestep_rng,
(bsz,),
0,
noise_scheduler.config.num_train_timesteps,
)
noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
encoder_hidden_states = state.apply_fn(
batch["input_ids"], params=params, dropout_rng=dropout_rng, train=True
)[0]
# Predict the noise residual and compute loss
model_pred = unet.apply(
{"params": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False
).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = (target - model_pred) ** 2
loss = loss.mean()
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad)
# Keep the token embeddings fixed except the newly added embeddings for the concept,
# as we only want to optimize the concept embeddings
token_embeds = original_token_embeds.at[placeholder_token_id].set(
new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"][placeholder_token_id]
)
new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"] = token_embeds
metrics = {"loss": loss}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics, new_train_rng
# Create parallel version of the train and eval step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
# Replicate the train state on each device
state = jax_utils.replicate(state)
vae_params = jax_utils.replicate(vae_params)
unet_params = jax_utils.replicate(unet_params)
# Train!
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
# Scheduler and math around the number of training steps.
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
epochs = tqdm(range(args.num_train_epochs), desc=f"Epoch ... (1/{args.num_train_epochs})", position=0)
for epoch in epochs:
# ======================== Training ================================
train_metrics = []
steps_per_epoch = len(train_dataset) // total_train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
# train
for batch in train_dataloader:
batch = shard(batch)
state, train_metric, train_rngs = p_train_step(state, vae_params, unet_params, batch, train_rngs)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
global_step += 1
if global_step >= args.max_train_steps:
break
train_metric = jax_utils.unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
# Create the pipeline using using the trained modules and save it.
if jax.process_index() == 0:
scheduler = FlaxPNDMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
)
safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker", from_pt=True
)
pipeline = FlaxStableDiffusionPipeline(
text_encoder=text_encoder,
vae=vae,
unet=unet,
tokenizer=tokenizer,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
)
pipeline.save_pretrained(
args.output_dir,
params={
"text_encoder": get_params_to_save(state.params),
"vae": get_params_to_save(vae_params),
"unet": get_params_to_save(unet_params),
"safety_checker": safety_checker.params,
},
)
# Also save the newly trained embeddings
learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][
placeholder_token_id
]
learned_embeds_dict = {args.placeholder_token: learned_embeds}
jnp.save(os.path.join(args.output_dir, "learned_embeds.npy"), learned_embeds_dict)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/multi_subject_dreambooth/README.md
|
# Multi Subject DreamBooth training
[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
This `train_multi_subject_dreambooth.py` script shows how to implement the training procedure for one or more subjects and adapt it for stable diffusion. Note that this code is based off of the `examples/dreambooth/train_dreambooth.py` script as of 01/06/2022.
This script was added by @kopsahlong, and is not actively maintained. However, if you come across anything that could use fixing, feel free to open an issue and tag @kopsahlong.
## Running locally with PyTorch
### Installing the dependencies
Before running the script, make sure to install the library's training dependencies:
To start, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
```
Then cd into the folder `diffusers/examples/research_projects/multi_subject_dreambooth` and run the following:
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
Or for a default accelerate configuration without answering questions about your environment
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell e.g. a notebook
```python
from accelerate.utils import write_basic_config
write_basic_config()
```
### Multi Subject Training Example
In order to have your model learn multiple concepts at once, we simply add in the additional data directories and prompts to our `instance_data_dir` and `instance_prompt` (as well as `class_data_dir` and `class_prompt` if `--with_prior_preservation` is specified) as one comma separated string.
See an example with 2 subjects below, which learns a model for one dog subject and one human subject:
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export OUTPUT_DIR="path-to-save-model"
# Subject 1
export INSTANCE_DIR_1="path-to-instance-images-concept-1"
export INSTANCE_PROMPT_1="a photo of a sks dog"
export CLASS_DIR_1="path-to-class-images-dog"
export CLASS_PROMPT_1="a photo of a dog"
# Subject 2
export INSTANCE_DIR_2="path-to-instance-images-concept-2"
export INSTANCE_PROMPT_2="a photo of a t@y person"
export CLASS_DIR_2="path-to-class-images-person"
export CLASS_PROMPT_2="a photo of a person"
accelerate launch train_multi_subject_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir="$INSTANCE_DIR_1,$INSTANCE_DIR_2" \
--output_dir=$OUTPUT_DIR \
--train_text_encoder \
--instance_prompt="$INSTANCE_PROMPT_1,$INSTANCE_PROMPT_2" \
--with_prior_preservation \
--prior_loss_weight=1.0 \
--class_data_dir="$CLASS_DIR_1,$CLASS_DIR_2" \
--class_prompt="$CLASS_PROMPT_1,$CLASS_PROMPT_2"\
--num_class_images=50 \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=1500
```
This example shows training for 2 subjects, but please note that the model can be trained on any number of new concepts. This can be done by continuing to add in the corresponding directories and prompts to the corresponding comma separated string.
Note also that in this script, `sks` and `t@y` were used as tokens to learn the new subjects ([this thread](https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/71) inspired the use of `t@y` as our second identifier). However, there may be better rare tokens to experiment with, and results also seemed to be good when more intuitive words are used.
**Important**: New parameters are added to the script, making possible to validate the progress of the training by
generating images at specified steps. Taking also into account that a comma separated list in a text field for a prompt
it's never a good idea (simply because it is very common in prompts to have them as part of a regular text) we
introduce the `concept_list` parameter: allowing to specify a json-like file where you can define the different
configuration for each subject that you want to train.
An example of how to generate the file:
```python
import json
# here we are using parameters for prior-preservation and validation as well.
concepts_list = [
{
"instance_prompt": "drawing of a t@y meme",
"class_prompt": "drawing of a meme",
"instance_data_dir": "/some_folder/meme_toy",
"class_data_dir": "/data/meme",
"validation_prompt": "drawing of a t@y meme about football in Uruguay",
"validation_negative_prompt": "black and white"
},
{
"instance_prompt": "drawing of a sks sir",
"class_prompt": "drawing of a sir",
"instance_data_dir": "/some_other_folder/sir_sks",
"class_data_dir": "/data/sir",
"validation_prompt": "drawing of a sks sir with the Uruguayan sun in his chest",
"validation_negative_prompt": "an old man",
"validation_guidance_scale": 20,
"validation_number_images": 3,
"validation_inference_steps": 10
}
]
with open("concepts_list.json", "w") as f:
json.dump(concepts_list, f, indent=4)
```
And then just point to the file when executing the script:
```bash
# exports...
accelerate launch train_multi_subject_dreambooth.py \
# more parameters...
--concepts_list="concepts_list.json"
```
You can use the helper from the script to get a better sense of each parameter.
### Inference
Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "path-to-your-trained-model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A photo of a t@y person petting an sks dog"
image = pipe(prompt, num_inference_steps=200, guidance_scale=7.5).images[0]
image.save("person-petting-dog.png")
```
### Inference from a training checkpoint
You can also perform inference from one of the checkpoints saved during the training process, if you used the `--checkpointing_steps` argument. Please, refer to [the documentation](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint) to see how to do it.
## Additional Dreambooth documentation
Because the `train_multi_subject_dreambooth.py` script here was forked from an original version of `train_dreambooth.py` in the `examples/dreambooth` folder, I've included the original applicable training documentation for single subject examples below.
This should explain how to play with training variables such as prior preservation, fine tuning the text encoder, etc. which is still applicable to our multi subject training code. Note also that the examples below, which are single subject examples, also work with `train_multi_subject_dreambooth.py`, as this script supports 1 (or more) subjects.
### Single subject dog toy example
Let's get our dataset. Download images from [here](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) and save them in a directory. This will be our training data.
And launch the training using
**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400
```
### Training with prior-preservation loss
Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### Training on a 16GB GPU:
With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU.
To install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=2 --gradient_checkpointing \
--use_8bit_adam \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### Training on a 8 GB GPU:
By using [DeepSpeed](https://www.deepspeed.ai/) it's possible to offload some
tensors from VRAM to either CPU or NVME allowing to train with less VRAM.
DeepSpeed needs to be enabled with `accelerate config`. During configuration
answer yes to "Do you want to use DeepSpeed?". With DeepSpeed stage 2, fp16
mixed precision and offloading both parameters and optimizer state to cpu it's
possible to train on under 8 GB VRAM with a drawback of requiring significantly
more RAM (about 25 GB). See [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options.
Changing the default Adam optimizer to DeepSpeed's special version of Adam
`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but enabling
it requires CUDA toolchain with the same version as pytorch. 8-bit optimizer
does not seem to be compatible with DeepSpeed at the moment.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch --mixed_precision="fp16" train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--sample_batch_size=1 \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### Fine-tune text encoder with the UNet.
The script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces.
Pass the `--train_text_encoder` argument to the script to enable training `text_encoder`.
___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_text_encoder \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--use_8bit_adam \
--gradient_checkpointing \
--learning_rate=2e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### Using DreamBooth for other pipelines than Stable Diffusion
Altdiffusion also support dreambooth now, the runing comman is basically the same as above, all you need to do is replace the `MODEL_NAME` like this:
One can now simply change the `pretrained_model_name_or_path` to another architecture such as [`AltDiffusion`](https://huggingface.co/docs/diffusers/api/pipelines/alt_diffusion).
```
export MODEL_NAME="CompVis/stable-diffusion-v1-4" --> export MODEL_NAME="BAAI/AltDiffusion-m9"
or
export MODEL_NAME="CompVis/stable-diffusion-v1-4" --> export MODEL_NAME="BAAI/AltDiffusion"
```
### Training with xformers:
You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script. This is not available with the Flax/JAX implementation.
You can also use Dreambooth to train the specialized in-painting model. See [the script in the research folder for details](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/dreambooth_inpaint).
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hf_public_repos/diffusers/examples/research_projects
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hf_public_repos/diffusers/examples/research_projects/multi_subject_dreambooth/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.25.1
ftfy
tensorboard
Jinja2
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hf_public_repos/diffusers/examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py
|
import argparse
import itertools
import json
import logging
import math
import uuid
import warnings
from os import environ, listdir, makedirs
from os.path import basename, join
from pathlib import Path
from typing import List
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from huggingface_hub.utils import insecure_hashlib
from PIL import Image
from torch import dtype
from torch.nn import Module
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
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("0.13.0.dev0")
logger = get_logger(__name__)
def log_validation_images_to_tracker(
images: List[np.array], label: str, validation_prompt: str, accelerator: Accelerator, epoch: int
):
logger.info(f"Logging images to tracker for validation prompt: {validation_prompt}.")
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"{label}_{epoch}_{i}: {validation_prompt}")
for i, image in enumerate(images)
]
}
)
# TODO: Add `prompt_embeds` and `negative_prompt_embeds` parameters to the function when `pre_compute_text_embeddings`
# argument is implemented.
def generate_validation_images(
text_encoder: Module,
tokenizer: Module,
unet: Module,
vae: Module,
arguments: argparse.Namespace,
accelerator: Accelerator,
weight_dtype: dtype,
):
logger.info("Running validation images.")
pipeline_args = {}
if text_encoder is not None:
pipeline_args["text_encoder"] = accelerator.unwrap_model(text_encoder)
if vae is not None:
pipeline_args["vae"] = vae
# create pipeline (note: unet and vae are loaded again in float32)
pipeline = DiffusionPipeline.from_pretrained(
arguments.pretrained_model_name_or_path,
tokenizer=tokenizer,
unet=accelerator.unwrap_model(unet),
revision=arguments.revision,
torch_dtype=weight_dtype,
**pipeline_args,
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the
# scheduler to ignore it
scheduler_args = {}
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)
generator = (
None if arguments.seed is None else torch.Generator(device=accelerator.device).manual_seed(arguments.seed)
)
images_sets = []
for vp, nvi, vnp, vis, vgs in zip(
arguments.validation_prompt,
arguments.validation_number_images,
arguments.validation_negative_prompt,
arguments.validation_inference_steps,
arguments.validation_guidance_scale,
):
images = []
if vp is not None:
logger.info(
f"Generating {nvi} images with prompt: '{vp}', negative prompt: '{vnp}', inference steps: {vis}, "
f"guidance scale: {vgs}."
)
pipeline_args = {"prompt": vp, "negative_prompt": vnp, "num_inference_steps": vis, "guidance_scale": vgs}
# run inference
# TODO: it would be good to measure whether it's faster to run inference on all images at once, one at a
# time or in small batches
for _ in range(nvi):
with torch.autocast("cuda"):
image = pipeline(**pipeline_args, num_images_per_prompt=1, generator=generator).images[0]
images.append(image)
images_sets.append(images)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
return images_sets
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=False,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--validation_steps",
type=int,
default=None,
help=(
"Run validation every X steps. Validation consists of running the prompt(s) `validation_prompt` "
"multiple times (`validation_number_images`) and logging the images."
),
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning. You can use commas to "
"define multiple negative prompts. This parameter can be defined also within the file given by "
"`concepts_list` parameter in the respective subject.",
)
parser.add_argument(
"--validation_number_images",
type=int,
default=4,
help="Number of images that should be generated during validation with the validation parameters given. This "
"can be defined within the file given by `concepts_list` parameter in the respective subject.",
)
parser.add_argument(
"--validation_negative_prompt",
type=str,
default=None,
help="A negative prompt that is used during validation to verify that the model is learning. You can use commas"
" to define multiple negative prompts, each one corresponding to a validation prompt. This parameter can "
"be defined also within the file given by `concepts_list` parameter in the respective subject.",
)
parser.add_argument(
"--validation_inference_steps",
type=int,
default=25,
help="Number of inference steps (denoising steps) to run during validation. This can be defined within the "
"file given by `concepts_list` parameter in the respective subject.",
)
parser.add_argument(
"--validation_guidance_scale",
type=float,
default=7.5,
help="To control how much the image generation process follows the text prompt. This can be defined within the "
"file given by `concepts_list` parameter in the respective subject.",
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--concepts_list",
type=str,
default=None,
help="Path to json file containing a list of multiple concepts, will overwrite parameters like instance_prompt,"
" class_prompt, etc.",
)
if input_args:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if not args.concepts_list and (not args.instance_data_dir or not args.instance_prompt):
raise ValueError(
"You must specify either instance parameters (data directory, prompt, etc.) or use "
"the `concept_list` parameter and specify them within the file."
)
if args.concepts_list:
if args.instance_prompt:
raise ValueError("If you are using `concepts_list` parameter, define the instance prompt within the file.")
if args.instance_data_dir:
raise ValueError(
"If you are using `concepts_list` parameter, define the instance data directory within the file."
)
if args.validation_steps and (args.validation_prompt or args.validation_negative_prompt):
raise ValueError(
"If you are using `concepts_list` parameter, define validation parameters for "
"each subject within the file:\n - `validation_prompt`."
"\n - `validation_negative_prompt`.\n - `validation_guidance_scale`."
"\n - `validation_number_images`.\n - `validation_prompt`."
"\n - `validation_inference_steps`.\nThe `validation_steps` parameter is the only one "
"that needs to be defined outside the file."
)
env_local_rank = int(environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if not args.concepts_list:
if not args.class_data_dir:
raise ValueError("You must specify a data directory for class images.")
if not args.class_prompt:
raise ValueError("You must specify prompt for class images.")
else:
if args.class_data_dir:
raise ValueError(
"If you are using `concepts_list` parameter, define the class data directory within the file."
)
if args.class_prompt:
raise ValueError(
"If you are using `concepts_list` parameter, define the class prompt within the file."
)
else:
# logger is not available yet
if not args.class_data_dir:
warnings.warn(
"Ignoring `class_data_dir` parameter, you need to use it together with `with_prior_preservation`."
)
if not args.class_prompt:
warnings.warn(
"Ignoring `class_prompt` parameter, you need to use it together with `with_prior_preservation`."
)
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and then tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = []
self.instance_images_path = []
self.num_instance_images = []
self.instance_prompt = []
self.class_data_root = [] if class_data_root is not None else None
self.class_images_path = []
self.num_class_images = []
self.class_prompt = []
self._length = 0
for i in range(len(instance_data_root)):
self.instance_data_root.append(Path(instance_data_root[i]))
if not self.instance_data_root[i].exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path.append(list(Path(instance_data_root[i]).iterdir()))
self.num_instance_images.append(len(self.instance_images_path[i]))
self.instance_prompt.append(instance_prompt[i])
self._length += self.num_instance_images[i]
if class_data_root is not None:
self.class_data_root.append(Path(class_data_root[i]))
self.class_data_root[i].mkdir(parents=True, exist_ok=True)
self.class_images_path.append(list(self.class_data_root[i].iterdir()))
self.num_class_images.append(len(self.class_images_path))
if self.num_class_images[i] > self.num_instance_images[i]:
self._length -= self.num_instance_images[i]
self._length += self.num_class_images[i]
self.class_prompt.append(class_prompt[i])
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
for i in range(len(self.instance_images_path)):
instance_image = Image.open(self.instance_images_path[i][index % self.num_instance_images[i]])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example[f"instance_images_{i}"] = self.image_transforms(instance_image)
example[f"instance_prompt_ids_{i}"] = self.tokenizer(
self.instance_prompt[i],
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
if self.class_data_root:
for i in range(len(self.class_data_root)):
class_image = Image.open(self.class_images_path[i][index % self.num_class_images[i]])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example[f"class_images_{i}"] = self.image_transforms(class_image)
example[f"class_prompt_ids_{i}"] = self.tokenizer(
self.class_prompt[i],
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def collate_fn(num_instances, examples, with_prior_preservation=False):
input_ids = []
pixel_values = []
for i in range(num_instances):
input_ids += [example[f"instance_prompt_ids_{i}"] for example in examples]
pixel_values += [example[f"instance_images_{i}"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
for i in range(num_instances):
input_ids += [example[f"class_prompt_ids_{i}"] for example in examples]
pixel_values += [example[f"class_images_{i}"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.cat(input_ids, dim=0)
batch = {
"input_ids": input_ids,
"pixel_values": pixel_values,
}
return batch
class PromptDataset(Dataset):
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
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.")
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
instance_data_dir = []
instance_prompt = []
class_data_dir = [] if args.with_prior_preservation else None
class_prompt = [] if args.with_prior_preservation else None
if args.concepts_list:
with open(args.concepts_list, "r") as f:
concepts_list = json.load(f)
if args.validation_steps:
args.validation_prompt = []
args.validation_number_images = []
args.validation_negative_prompt = []
args.validation_inference_steps = []
args.validation_guidance_scale = []
for concept in concepts_list:
instance_data_dir.append(concept["instance_data_dir"])
instance_prompt.append(concept["instance_prompt"])
if args.with_prior_preservation:
try:
class_data_dir.append(concept["class_data_dir"])
class_prompt.append(concept["class_prompt"])
except KeyError:
raise KeyError(
"`class_data_dir` or `class_prompt` not found in concepts_list while using "
"`with_prior_preservation`."
)
else:
if "class_data_dir" in concept:
warnings.warn(
"Ignoring `class_data_dir` key, to use it you need to enable `with_prior_preservation`."
)
if "class_prompt" in concept:
warnings.warn(
"Ignoring `class_prompt` key, to use it you need to enable `with_prior_preservation`."
)
if args.validation_steps:
args.validation_prompt.append(concept.get("validation_prompt", None))
args.validation_number_images.append(concept.get("validation_number_images", 4))
args.validation_negative_prompt.append(concept.get("validation_negative_prompt", None))
args.validation_inference_steps.append(concept.get("validation_inference_steps", 25))
args.validation_guidance_scale.append(concept.get("validation_guidance_scale", 7.5))
else:
# Parse instance and class inputs, and double check that lengths match
instance_data_dir = args.instance_data_dir.split(",")
instance_prompt = args.instance_prompt.split(",")
assert all(
x == len(instance_data_dir) for x in [len(instance_data_dir), len(instance_prompt)]
), "Instance data dir and prompt inputs are not of the same length."
if args.with_prior_preservation:
class_data_dir = args.class_data_dir.split(",")
class_prompt = args.class_prompt.split(",")
assert all(
x == len(instance_data_dir)
for x in [len(instance_data_dir), len(instance_prompt), len(class_data_dir), len(class_prompt)]
), "Instance & class data dir or prompt inputs are not of the same length."
if args.validation_steps:
validation_prompts = args.validation_prompt.split(",")
num_of_validation_prompts = len(validation_prompts)
args.validation_prompt = validation_prompts
args.validation_number_images = [args.validation_number_images] * num_of_validation_prompts
negative_validation_prompts = [None] * num_of_validation_prompts
if args.validation_negative_prompt:
negative_validation_prompts = args.validation_negative_prompt.split(",")
while len(negative_validation_prompts) < num_of_validation_prompts:
negative_validation_prompts.append(None)
args.validation_negative_prompt = negative_validation_prompts
assert num_of_validation_prompts == len(
negative_validation_prompts
), "The length of negative prompts for validation is greater than the number of validation prompts."
args.validation_inference_steps = [args.validation_inference_steps] * num_of_validation_prompts
args.validation_guidance_scale = [args.validation_guidance_scale] * num_of_validation_prompts
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
for i in range(len(class_data_dir)):
class_images_dir = Path(class_data_dir[i])
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
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
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
safety_checker=None,
revision=args.revision,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(class_prompt[i], num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for ii, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = (
class_images_dir / f"{example['index'][ii] + cur_class_images}-{hash_image}.jpg"
)
image.save(image_filename)
# Clean up the memory deleting one-time-use variables.
del pipeline
del sample_dataloader
del sample_dataset
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer
tokenizer = None
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
vae.requires_grad_(False)
if not args.train_text_encoder:
text_encoder.requires_grad_(False)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder.gradient_checkpointing_enable()
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = (
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
)
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=instance_data_dir,
instance_prompt=instance_prompt,
class_data_root=class_data_dir,
class_prompt=class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(len(instance_data_dir), examples, args.with_prior_preservation),
num_workers=1,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and text_encoder to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initialize automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder.train()
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
time_steps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
)
time_steps = time_steps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, time_steps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, time_steps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, time_steps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
save_path = join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if (
args.validation_steps
and any(args.validation_prompt)
and global_step % args.validation_steps == 0
):
images_set = generate_validation_images(
text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype
)
for images, validation_prompt in zip(images_set, args.validation_prompt):
if len(images) > 0:
label = str(uuid.uuid1())[:8] # generate an id for different set of images
log_validation_images_to_tracker(
images, label, validation_prompt, accelerator, global_step
)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/README.md
|
## Diffusers examples with ONNXRuntime optimizations
**This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.**
This aims to provide diffusers examples with ONNXRuntime optimizations for training/fine-tuning unconditional image generation, text to image, and textual inversion. Please see individual directories for more details on how to run each task using ONNXRuntime.
| 0
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/README.md
|
## Training examples
Creating a training image set is [described in a different document](https://huggingface.co/docs/datasets/image_process#image-datasets).
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd in the example folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
#### Use ONNXRuntime to accelerate training
In order to leverage onnxruntime to accelerate training, please use train_unconditional_ort.py
The command to train a DDPM UNet model on the Oxford Flowers dataset with onnxruntime:
```bash
accelerate launch train_unconditional.py \
--dataset_name="huggan/flowers-102-categories" \
--resolution=64 --center_crop --random_flip \
--output_dir="ddpm-ema-flowers-64" \
--use_ema \
--train_batch_size=16 \
--num_epochs=1 \
--gradient_accumulation_steps=1 \
--learning_rate=1e-4 \
--lr_warmup_steps=500 \
--mixed_precision=fp16
```
Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
| 0
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py
|
import argparse
import inspect
import logging
import math
import os
from pathlib import Path
import accelerate
import datasets
import torch
import torch.nn.functional as F
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer as ORT_FP16_Optimizer
from onnxruntime.training.ortmodule import ORTModule
from packaging import version
from torchvision import transforms
from tqdm.auto import tqdm
import diffusers
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version, is_accelerate_version, is_tensorboard_available, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.17.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def _extract_into_tensor(arr, timesteps, broadcast_shape):
"""
Extract values from a 1-D numpy array for a batch of indices.
:param arr: the 1-D numpy array.
:param timesteps: a tensor of indices into the array to extract.
:param broadcast_shape: a larger shape of K dimensions with the batch
dimension equal to the length of timesteps.
:return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
"""
if not isinstance(arr, torch.Tensor):
arr = torch.from_numpy(arr)
res = arr[timesteps].float().to(timesteps.device)
while len(res.shape) < len(broadcast_shape):
res = res[..., None]
return res.expand(broadcast_shape)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that HF Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--model_config_name_or_path",
type=str,
default=None,
help="The config of the UNet model to train, leave as None to use standard DDPM configuration.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="ddpm-model-64",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--overwrite_output_dir", action="store_true")
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--resolution",
type=int,
default=64,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
default=False,
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--eval_batch_size", type=int, default=16, help="The number of images to generate for evaluation."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
" process."
),
)
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--save_images_epochs", type=int, default=10, help="How often to save images during training.")
parser.add_argument(
"--save_model_epochs", type=int, default=10, help="How often to save the model during training."
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="cosine",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--adam_beta1", type=float, default=0.95, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay", type=float, default=1e-6, help="Weight decay magnitude for the Adam optimizer."
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer.")
parser.add_argument(
"--use_ema",
action="store_true",
help="Whether to use Exponential Moving Average for the final model weights.",
)
parser.add_argument("--ema_inv_gamma", type=float, default=1.0, help="The inverse gamma value for the EMA decay.")
parser.add_argument("--ema_power", type=float, default=3 / 4, help="The power value for the EMA decay.")
parser.add_argument("--ema_max_decay", type=float, default=0.9999, help="The maximum decay magnitude for EMA.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
)
parser.add_argument(
"--logger",
type=str,
default="tensorboard",
choices=["tensorboard", "wandb"],
help=(
"Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)"
" for experiment tracking and logging of model metrics and model checkpoints"
),
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument(
"--prediction_type",
type=str,
default="epsilon",
choices=["epsilon", "sample"],
help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
)
parser.add_argument("--ddpm_num_steps", type=int, default=1000)
parser.add_argument("--ddpm_num_inference_steps", type=int, default=1000)
parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
return args
def main(args):
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
if args.logger == "tensorboard":
if not is_tensorboard_available():
raise ImportError("Make sure to install tensorboard if you want to use it for logging during training.")
elif args.logger == "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
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
if args.use_ema:
ema_model.save_pretrained(os.path.join(output_dir, "unet_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(output_dir, "unet"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if args.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DModel)
ema_model.load_state_dict(load_model.state_dict())
ema_model.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = UNet2DModel.from_pretrained(input_dir, subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Initialize the model
if args.model_config_name_or_path is None:
model = UNet2DModel(
sample_size=args.resolution,
in_channels=3,
out_channels=3,
layers_per_block=2,
block_out_channels=(128, 128, 256, 256, 512, 512),
down_block_types=(
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D",
"DownBlock2D",
),
up_block_types=(
"UpBlock2D",
"AttnUpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
"UpBlock2D",
),
)
else:
config = UNet2DModel.load_config(args.model_config_name_or_path)
model = UNet2DModel.from_config(config)
# Create EMA for the model.
if args.use_ema:
ema_model = EMAModel(
model.parameters(),
decay=args.ema_max_decay,
use_ema_warmup=True,
inv_gamma=args.ema_inv_gamma,
power=args.ema_power,
model_cls=UNet2DModel,
model_config=model.config,
)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
model.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# Initialize the scheduler
accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
if accepts_prediction_type:
noise_scheduler = DDPMScheduler(
num_train_timesteps=args.ddpm_num_steps,
beta_schedule=args.ddpm_beta_schedule,
prediction_type=args.prediction_type,
)
else:
noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
model.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
optimizer = ORT_FP16_Optimizer(optimizer)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
split="train",
)
else:
dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets and DataLoaders creation.
augmentations = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def transform_images(examples):
images = [augmentations(image.convert("RGB")) for image in examples["image"]]
return {"input": images}
logger.info(f"Dataset size: {len(dataset)}")
dataset.set_transform(transform_images)
train_dataloader = torch.utils.data.DataLoader(
dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
)
# Initialize the learning rate scheduler
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=(len(train_dataloader) * args.num_epochs),
)
# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
model, optimizer, train_dataloader, lr_scheduler
)
if args.use_ema:
ema_model.to(accelerator.device)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
run = os.path.split(__file__)[-1].split(".")[0]
accelerator.init_trackers(run)
model = ORTModule(model)
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
max_train_steps = args.num_epochs * num_update_steps_per_epoch
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(dataset)}")
logger.info(f" Num Epochs = {args.num_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Train!
for epoch in range(first_epoch, args.num_epochs):
model.train()
progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
progress_bar.set_description(f"Epoch {epoch}")
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
clean_images = batch["input"]
# Sample noise that we'll add to the images
noise = torch.randn(
clean_images.shape, dtype=(torch.float32 if args.mixed_precision == "no" else torch.float16)
).to(clean_images.device)
bsz = clean_images.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device
).long()
# Add noise to the clean images according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
with accelerator.accumulate(model):
# Predict the noise residual
model_output = model(noisy_images, timesteps, return_dict=False)[0]
if args.prediction_type == "epsilon":
loss = F.mse_loss(model_output, noise) # this could have different weights!
elif args.prediction_type == "sample":
alpha_t = _extract_into_tensor(
noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)
)
snr_weights = alpha_t / (1 - alpha_t)
loss = snr_weights * F.mse_loss(
model_output, clean_images, reduction="none"
) # use SNR weighting from distillation paper
loss = loss.mean()
else:
raise ValueError(f"Unsupported prediction type: {args.prediction_type}")
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.use_ema:
ema_model.step(model.parameters())
progress_bar.update(1)
global_step += 1
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
if args.use_ema:
logs["ema_decay"] = ema_model.cur_decay_value
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
progress_bar.close()
accelerator.wait_for_everyone()
# Generate sample images for visual inspection
if accelerator.is_main_process:
if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
unet = accelerator.unwrap_model(model)
if args.use_ema:
ema_model.store(unet.parameters())
ema_model.copy_to(unet.parameters())
pipeline = DDPMPipeline(
unet=unet,
scheduler=noise_scheduler,
)
generator = torch.Generator(device=pipeline.device).manual_seed(0)
# run pipeline in inference (sample random noise and denoise)
images = pipeline(
generator=generator,
batch_size=args.eval_batch_size,
num_inference_steps=args.ddpm_num_inference_steps,
output_type="numpy",
).images
if args.use_ema:
ema_model.restore(unet.parameters())
# denormalize the images and save to tensorboard
images_processed = (images * 255).round().astype("uint8")
if args.logger == "tensorboard":
if is_accelerate_version(">=", "0.17.0.dev0"):
tracker = accelerator.get_tracker("tensorboard", unwrap=True)
else:
tracker = accelerator.get_tracker("tensorboard")
tracker.add_images("test_samples", images_processed.transpose(0, 3, 1, 2), epoch)
elif args.logger == "wandb":
# Upcoming `log_images` helper coming in https://github.com/huggingface/accelerate/pull/962/files
accelerator.get_tracker("wandb").log(
{"test_samples": [wandb.Image(img) for img in images_processed], "epoch": epoch},
step=global_step,
)
if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
# save the model
unet = accelerator.unwrap_model(model)
if args.use_ema:
ema_model.store(unet.parameters())
ema_model.copy_to(unet.parameters())
pipeline = DDPMPipeline(
unet=unet,
scheduler=noise_scheduler,
)
pipeline.save_pretrained(args.output_dir)
if args.use_ema:
ema_model.restore(unet.parameters())
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message=f"Epoch {epoch}",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
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hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/unconditional_image_generation/requirements.txt
|
accelerate>=0.16.0
torchvision
datasets
tensorboard
| 0
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hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/textual_inversion/README.md
|
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
## Running on Colab
Colab for training
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
Colab for inference
[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd in the example folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
### Cat toy example
You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree.
You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
Run the following command to authenticate your token
```bash
huggingface-cli login
```
If you have already cloned the repo, then you won't need to go through these steps.
<br>
Now let's get our dataset. For this example we will use some cat images: https://huggingface.co/datasets/diffusers/cat_toy_example .
Let's first download it locally:
```py
from huggingface_hub import snapshot_download
local_dir = "./cat"
snapshot_download("diffusers/cat_toy_example", local_dir=local_dir, repo_type="dataset", ignore_patterns=".gitattributes")
```
This will be our training data.
Now we can launch the training using
## Use ONNXRuntime to accelerate training
In order to leverage onnxruntime to accelerate training, please use textual_inversion.py
The command to train on custom data with onnxruntime:
```bash
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATA_DIR="path-to-dir-containing-images"
accelerate launch textual_inversion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<cat-toy>" --initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 --scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="textual_inversion_cat"
```
Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
| 0
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/textual_inversion/textual_inversion.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import random
import warnings
from pathlib import Path
import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer as ORT_FP16_Optimizer
from onnxruntime.training.ortmodule import ORTModule
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
if is_wandb_available():
import wandb
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.17.0.dev0")
logger = get_logger(__name__)
def save_model_card(repo_id: str, images=None, base_model=str, repo_folder=None):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
"""
model_card = f"""
# Textual inversion text2image fine-tuning - {repo_id}
These are textual inversion adaption weights for {base_model}. You can find some example images in the following. \n
{img_str}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline (note: unet and vae are loaded again in float32)
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
unet=unet,
vae=vae,
safety_checker=None,
revision=args.revision,
torch_dtype=weight_dtype,
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
for _ in range(args.num_validation_images):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
images.append(image)
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()
return images
def save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path):
logger.info("Saving embeddings")
learned_embeds = (
accelerator.unwrap_model(text_encoder)
.get_input_embeddings()
.weight[min(placeholder_token_ids) : max(placeholder_token_ids) + 1]
)
learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
torch.save(learned_embeds_dict, save_path)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save learned_embeds.bin every X updates steps.",
)
parser.add_argument(
"--save_as_full_pipeline",
action="store_true",
help="Save the complete stable diffusion pipeline.",
)
parser.add_argument(
"--num_vectors",
type=int,
default=1,
help="How many textual inversion vectors shall be used to learn the concept.",
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
)
parser.add_argument(
"--placeholder_token",
type=str,
default=None,
required=True,
help="A token to use as a placeholder for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
)
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution."
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run validation every X steps. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
parser.add_argument(
"--validation_epochs",
type=int,
default=None,
help=(
"Deprecated in favor of validation_steps. Run validation every X epochs. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.train_data_dir is None:
raise ValueError("You must specify a train data directory.")
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
):
self.data_root = data_root
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
(
h,
w,
) = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
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.")
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load tokenizer
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
# Add the placeholder token in tokenizer
placeholder_tokens = [args.placeholder_token]
if args.num_vectors < 1:
raise ValueError(f"--num_vectors has to be larger or equal to 1, but is {args.num_vectors}")
# add dummy tokens for multi-vector
additional_tokens = []
for i in range(1, args.num_vectors):
additional_tokens.append(f"{args.placeholder_token}_{i}")
placeholder_tokens += additional_tokens
num_added_tokens = tokenizer.add_tokens(placeholder_tokens)
if num_added_tokens != args.num_vectors:
raise ValueError(
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id = token_ids[0]
placeholder_token_ids = tokenizer.convert_tokens_to_ids(placeholder_tokens)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
with torch.no_grad():
for token_id in placeholder_token_ids:
token_embeds[token_id] = token_embeds[initializer_token_id].clone()
# Freeze vae and unet
vae.requires_grad_(False)
unet.requires_grad_(False)
# Freeze all parameters except for the token embeddings in text encoder
text_encoder.text_model.encoder.requires_grad_(False)
text_encoder.text_model.final_layer_norm.requires_grad_(False)
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
if args.gradient_checkpointing:
# Keep unet in train mode if we are using gradient checkpointing to save memory.
# The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
unet.train()
text_encoder.gradient_checkpointing_enable()
unet.enable_gradient_checkpointing()
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
optimizer = ORT_FP16_Optimizer(optimizer)
# Dataset and DataLoaders creation:
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=args.placeholder_token,
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
)
if args.validation_epochs is not None:
warnings.warn(
f"FutureWarning: You are doing logging with validation_epochs={args.validation_epochs}."
" Deprecated validation_epochs in favor of `validation_steps`"
f"Setting `args.validation_steps` to {args.validation_epochs * len(train_dataset)}",
FutureWarning,
stacklevel=2,
)
args.validation_steps = args.validation_epochs * len(train_dataset)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, lr_scheduler
)
text_encoder = ORTModule(text_encoder)
unet = ORTModule(unet)
vae = ORTModule(vae)
# For mixed precision training we cast the unet and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and unet to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
# keep original embeddings as reference
orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
for epoch in range(first_epoch, args.num_train_epochs):
text_encoder.train()
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(text_encoder):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Let's make sure we don't update any embedding weights besides the newly added token
index_no_updates = torch.ones((len(tokenizer),), dtype=torch.bool)
index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False
with torch.no_grad():
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
index_no_updates
] = orig_embeds_params[index_no_updates]
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
images = []
progress_bar.update(1)
global_step += 1
if global_step % args.save_steps == 0:
save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
images = log_validation(
text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch
)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
if args.push_to_hub and not args.save_as_full_pipeline:
logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
save_full_model = True
else:
save_full_model = args.save_as_full_pipeline
if save_full_model:
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=accelerator.unwrap_model(text_encoder),
vae=vae,
unet=unet,
tokenizer=tokenizer,
)
pipeline.save_pretrained(args.output_dir)
# Save the newly trained embeddings
save_path = os.path.join(args.output_dir, "learned_embeds.bin")
save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
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|
hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/textual_inversion/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.25.1
ftfy
tensorboard
modelcards
| 0
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/text_to_image/README.md
|
# Stable Diffusion text-to-image fine-tuning
The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.
___Note___:
___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd in the example folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
### Pokemon example
You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree.
You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
Run the following command to authenticate your token
```bash
huggingface-cli login
```
If you have already cloned the repo, then you won't need to go through these steps.
<br>
## Use ONNXRuntime to accelerate training
In order to leverage onnxruntime to accelerate training, please use train_text_to_image.py
The command to train a DDPM UNetCondition model on the Pokemon dataset with onnxruntime:
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export dataset_name="lambdalabs/pokemon-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$dataset_name \
--use_ema \
--resolution=512 --center_crop --random_flip \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--output_dir="sd-pokemon-model"
```
Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
| 0
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import random
from pathlib import Path
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer as ORT_FP16_Optimizer
from onnxruntime.training.ortmodule import ORTModule
from packaging import version
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from transformers.utils import ContextManagers
import diffusers
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel, compute_snr
from diffusers.utils import check_min_version, deprecate, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
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("0.17.0.dev0")
logger = get_logger(__name__, log_level="INFO")
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):
logger.info("Running validation... ")
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=accelerator.unwrap_model(vae),
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
unet=accelerator.unwrap_model(unet),
safety_checker=None,
revision=args.revision,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
if args.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
images.append(image)
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")
elif tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
for i, image in enumerate(images)
]
}
)
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
torch.cuda.empty_cache()
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--input_pertubation", type=float, default=0, help="The scale of input pretubation. Recommended 0.1."
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--validation_prompts",
type=str,
default=None,
nargs="+",
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument(
"--non_ema_revision",
type=str,
default=None,
required=False,
help=(
"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
" remote repository specified with --pretrained_model_name_or_path."
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
parser.add_argument(
"--validation_epochs",
type=int,
default=5,
help="Run validation every X epochs.",
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
# default to using the same revision for the non-ema model if not specified
if args.non_ema_revision is None:
args.non_ema_revision = args.revision
return args
def main():
args = parse_args()
if args.non_ema_revision is not None:
deprecate(
"non_ema_revision!=None",
"0.15.0",
message=(
"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
" use `--variant=non_ema` instead."
),
)
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load scheduler, tokenizer and models.
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
def deepspeed_zero_init_disabled_context_manager():
"""
returns either a context list that includes one that will disable zero.Init or an empty context list
"""
deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
if deepspeed_plugin is None:
return []
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
# Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
# For this to work properly all models must be run through `accelerate.prepare`. But accelerate
# will try to assign the same optimizer with the same weights to all models during
# `deepspeed.initialize`, which of course doesn't work.
#
# For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
# frozen models from being partitioned during `zero.Init` which gets called during
# `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
# across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
)
# Freeze vae and text_encoder
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
# Create EMA for the unet.
if args.use_ema:
ema_unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
if args.use_ema:
ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(output_dir, "unet"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if args.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
ema_unet.load_state_dict(load_model.state_dict())
ema_unet.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
unet.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
optimizer = ORT_FP16_Optimizer(optimizer)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
return inputs.input_ids
# Preprocessing the datasets.
train_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["input_ids"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.stack([example["input_ids"] for example in examples])
return {"pixel_values": pixel_values, "input_ids": input_ids}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
if args.use_ema:
ema_unet.to(accelerator.device)
unet = ORTModule(unet)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu and cast to weight_dtype
text_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
tracker_config.pop("validation_prompts")
accelerator.init_trackers(args.tracker_project_name, tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
if args.noise_offset:
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
noise += args.noise_offset * torch.randn(
(latents.shape[0], latents.shape[1], 1, 1), device=latents.device
)
if args.input_pertubation:
new_noise = noise + args.input_pertubation * torch.randn_like(noise)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
if args.input_pertubation:
noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)
else:
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
# Predict the noise residual and compute loss
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective requires that we add one to SNR values before we divide by them.
snr = snr + 1
mse_loss_weights = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.use_ema:
ema_unet.step(unet.parameters())
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
if args.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
ema_unet.store(unet.parameters())
ema_unet.copy_to(unet.parameters())
log_validation(
vae,
text_encoder,
tokenizer,
unet,
args,
accelerator,
weight_dtype,
global_step,
)
if args.use_ema:
# Switch back to the original UNet parameters.
ema_unet.restore(unet.parameters())
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
if args.use_ema:
ema_unet.copy_to(unet.parameters())
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=text_encoder,
vae=vae,
unet=unet,
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime
|
hf_public_repos/diffusers/examples/research_projects/onnxruntime/text_to_image/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.25.1
datasets
ftfy
tensorboard
modelcards
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/lora/README.md
|
# Stable Diffusion text-to-image fine-tuning
This extended LoRA training script was authored by [haofanwang](https://github.com/haofanwang).
This is an experimental LoRA extension of [this example](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py). We further support add LoRA layers for text encoder.
## Training with LoRA
Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.
In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).
- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.
[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.
With LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset
on consumer GPUs like Tesla T4, Tesla V100.
### Training
First, you need to set up your development environment as is explained in the [installation section](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Stable Diffusion v1-4](https://hf.co/CompVis/stable-diffusion-v1-4) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [Weights and Biases](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training to automatically log images.___**
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
```
For this example we want to directly store the trained LoRA embeddings on the Hub, so
we need to be logged in and add the `--push_to_hub` flag.
```bash
huggingface-cli login
```
Now we can start training!
```bash
accelerate launch --mixed_precision="fp16" train_text_to_image_lora.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--dataset_name=$DATASET_NAME --caption_column="text" \
--resolution=512 --random_flip \
--train_batch_size=1 \
--num_train_epochs=100 --checkpointing_steps=5000 \
--learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
--seed=42 \
--output_dir="sd-pokemon-model-lora" \
--validation_prompt="cute dragon creature" --report_to="wandb"
--use_peft \
--lora_r=4 --lora_alpha=32 \
--lora_text_encoder_r=4 --lora_text_encoder_alpha=32
```
The above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.
**___Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use *1e-4* instead of the usual *1e-5*. Also, by using LoRA, it's possible to run `train_text_to_image_lora.py` in consumer GPUs like T4 or V100.___**
The final LoRA embedding weights have been uploaded to [sayakpaul/sd-model-finetuned-lora-t4](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4). **___Note: [The final weights](https://huggingface.co/sayakpaul/sd-model-finetuned-lora-t4/blob/main/pytorch_lora_weights.bin) are only 3 MB in size, which is orders of magnitudes smaller than the original model.___**
You can check some inference samples that were logged during the course of the fine-tuning process [here](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/q4lc0xsw).
### Inference
Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline` after loading the trained LoRA weights. You
need to pass the `output_dir` for loading the LoRA weights which, in this case, is `sd-pokemon-model-lora`.
```python
from diffusers import StableDiffusionPipeline
import torch
model_path = "sayakpaul/sd-model-finetuned-lora-t4"
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
pipe.unet.load_attn_procs(model_path)
pipe.to("cuda")
prompt = "A pokemon with green eyes and red legs."
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("pokemon.png")
```
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/lora/train_text_to_image_lora.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fine-tuning script for Stable Diffusion for text2image with support for LoRA."""
import argparse
import itertools
import json
import logging
import math
import os
import random
from pathlib import Path
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.14.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
"""
model_card = f"""
# LoRA text2image fine-tuning - {repo_id}
These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n
{img_str}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference."
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=1,
help=(
"Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
# lora args
parser.add_argument("--use_peft", action="store_true", help="Whether to use peft to support lora")
parser.add_argument("--lora_r", type=int, default=4, help="Lora rank, only used if use_lora is True")
parser.add_argument("--lora_alpha", type=int, default=32, help="Lora alpha, only used if lora is True")
parser.add_argument("--lora_dropout", type=float, default=0.0, help="Lora dropout, only used if use_lora is True")
parser.add_argument(
"--lora_bias",
type=str,
default="none",
help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora is True",
)
parser.add_argument(
"--lora_text_encoder_r",
type=int,
default=4,
help="Lora rank for text encoder, only used if `use_lora` and `train_text_encoder` are True",
)
parser.add_argument(
"--lora_text_encoder_alpha",
type=int,
default=32,
help="Lora alpha for text encoder, only used if `use_lora` and `train_text_encoder` are True",
)
parser.add_argument(
"--lora_text_encoder_dropout",
type=float,
default=0.0,
help="Lora dropout for text encoder, only used if `use_lora` and `train_text_encoder` are True",
)
parser.add_argument(
"--lora_text_encoder_bias",
type=str,
default="none",
help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora and `train_text_encoder` are True",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
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(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load scheduler, tokenizer and models.
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if args.use_peft:
from peft import LoraConfig, LoraModel, get_peft_model_state_dict, set_peft_model_state_dict
UNET_TARGET_MODULES = ["to_q", "to_v", "query", "value"]
TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
config = LoraConfig(
r=args.lora_r,
lora_alpha=args.lora_alpha,
target_modules=UNET_TARGET_MODULES,
lora_dropout=args.lora_dropout,
bias=args.lora_bias,
)
unet = LoraModel(config, unet)
vae.requires_grad_(False)
if args.train_text_encoder:
config = LoraConfig(
r=args.lora_text_encoder_r,
lora_alpha=args.lora_text_encoder_alpha,
target_modules=TEXT_ENCODER_TARGET_MODULES,
lora_dropout=args.lora_text_encoder_dropout,
bias=args.lora_text_encoder_bias,
)
text_encoder = LoraModel(config, text_encoder)
else:
# freeze parameters of models to save more memory
unet.requires_grad_(False)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
# now we will add new LoRA weights to the attention layers
# It's important to realize here how many attention weights will be added and of which sizes
# The sizes of the attention layers consist only of two different variables:
# 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
# 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
# Let's first see how many attention processors we will have to set.
# For Stable Diffusion, it should be equal to:
# - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
# - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
# - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
# => 32 layers
# Set correct lora layers
lora_attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
unet.set_attn_processor(lora_attn_procs)
lora_layers = AttnProcsLayers(unet.attn_processors)
# Move unet, vae and text_encoder to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
if args.use_peft:
# Optimizer creation
params_to_optimize = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
optimizer = optimizer_cls(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
else:
optimizer = optimizer_cls(
lora_layers.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
return inputs.input_ids
# Preprocessing the datasets.
train_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["input_ids"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = torch.stack([example["input_ids"] for example in examples])
return {"pixel_values": pixel_values, "input_ids": input_ids}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
if args.use_peft:
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
else:
lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
lora_layers, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("text2image-fine-tune", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
# Predict the noise residual and compute loss
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
if args.use_peft:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
else:
params_to_clip = lora_layers.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
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
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
revision=args.revision,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
for _ in range(args.num_validation_images):
images.append(
pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
)
if accelerator.is_main_process:
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()
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
if args.use_peft:
lora_config = {}
unwarpped_unet = accelerator.unwrap_model(unet)
state_dict = get_peft_model_state_dict(unwarpped_unet, state_dict=accelerator.get_state_dict(unet))
lora_config["peft_config"] = unwarpped_unet.get_peft_config_as_dict(inference=True)
if args.train_text_encoder:
unwarpped_text_encoder = accelerator.unwrap_model(text_encoder)
text_encoder_state_dict = get_peft_model_state_dict(
unwarpped_text_encoder, state_dict=accelerator.get_state_dict(text_encoder)
)
text_encoder_state_dict = {f"text_encoder_{k}": v for k, v in text_encoder_state_dict.items()}
state_dict.update(text_encoder_state_dict)
lora_config["text_encoder_peft_config"] = unwarpped_text_encoder.get_peft_config_as_dict(
inference=True
)
accelerator.save(state_dict, os.path.join(args.output_dir, f"{global_step}_lora.pt"))
with open(os.path.join(args.output_dir, f"{global_step}_lora_config.json"), "w") as f:
json.dump(lora_config, f)
else:
unet = unet.to(torch.float32)
unet.save_attn_procs(args.output_dir)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
dataset_name=args.dataset_name,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
# Final inference
# Load previous pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
)
if args.use_peft:
def load_and_set_lora_ckpt(pipe, ckpt_dir, global_step, device, dtype):
with open(os.path.join(args.output_dir, f"{global_step}_lora_config.json"), "r") as f:
lora_config = json.load(f)
print(lora_config)
checkpoint = os.path.join(args.output_dir, f"{global_step}_lora.pt")
lora_checkpoint_sd = torch.load(checkpoint)
unet_lora_ds = {k: v for k, v in lora_checkpoint_sd.items() if "text_encoder_" not in k}
text_encoder_lora_ds = {
k.replace("text_encoder_", ""): v for k, v in lora_checkpoint_sd.items() if "text_encoder_" in k
}
unet_config = LoraConfig(**lora_config["peft_config"])
pipe.unet = LoraModel(unet_config, pipe.unet)
set_peft_model_state_dict(pipe.unet, unet_lora_ds)
if "text_encoder_peft_config" in lora_config:
text_encoder_config = LoraConfig(**lora_config["text_encoder_peft_config"])
pipe.text_encoder = LoraModel(text_encoder_config, pipe.text_encoder)
set_peft_model_state_dict(pipe.text_encoder, text_encoder_lora_ds)
if dtype in (torch.float16, torch.bfloat16):
pipe.unet.half()
pipe.text_encoder.half()
pipe.to(device)
return pipe
pipeline = load_and_set_lora_ckpt(pipeline, args.output_dir, global_step, accelerator.device, weight_dtype)
else:
pipeline = pipeline.to(accelerator.device)
# load attention processors
pipeline.unet.load_attn_procs(args.output_dir)
# run inference
if args.seed is not None:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
else:
generator = None
images = []
for _ in range(args.num_validation_images):
images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])
if accelerator.is_main_process:
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)
]
}
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/lora/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.25.1
datasets
ftfy
tensorboard
Jinja2
git+https://github.com/huggingface/peft.git
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/sdxl_flax/README.md
|
# Stable Diffusion XL for JAX + TPUv5e
[TPU v5e](https://cloud.google.com/blog/products/compute/how-cloud-tpu-v5e-accelerates-large-scale-ai-inference) is a new generation of TPUs from Google Cloud. It is the most cost-effective, versatile, and scalable Cloud TPU to date. This makes them ideal for serving and scaling large diffusion models.
[JAX](https://github.com/google/jax) is a high-performance numerical computation library that is well-suited to develop and deploy diffusion models:
- **High performance**. All JAX operations are implemented in terms of operations in [XLA](https://www.tensorflow.org/xla/) - the Accelerated Linear Algebra compiler
- **Compilation**. JAX uses just-in-time (jit) compilation of JAX Python functions so it can be executed efficiently in XLA. In order to get the best performance, we must use static shapes for jitted functions, this is because JAX transforms work by tracing a function and to determine its effect on inputs of a specific shape and type. When a new shape is introduced to an already compiled function, it retriggers compilation on the new shape, which can greatly reduce performance. **Note**: JIT compilation is particularly well-suited for text-to-image generation because all inputs and outputs (image input / output sizes) are static.
- **Parallelization**. Workloads can be scaled across multiple devices using JAX's [pmap](https://jax.readthedocs.io/en/latest/_autosummary/jax.pmap.html), which expresses single-program multiple-data (SPMD) programs. Applying pmap to a function will compile a function with XLA, then execute in parallel on XLA devices. For text-to-image generation workloads this means that increasing the number of images rendered simultaneously is straightforward to implement and doesn't compromise performance.
👉 Try it out for yourself:
[](https://huggingface.co/spaces/google/sdxl)
## Stable Diffusion XL pipeline in JAX
Upon having access to a TPU VM (TPUs higher than version 3), you should first install
a TPU-compatible version of JAX:
```
pip install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
```
Next, we can install [flax](https://github.com/google/flax) and the diffusers library:
```
pip install flax diffusers transformers
```
In [sdxl_single.py](./sdxl_single.py) we give a simple example of how to write a text-to-image generation pipeline in JAX using [StabilityAI's Stable Diffusion XL](stabilityai/stable-diffusion-xl-base-1.0).
Let's explain it step-by-step:
**Imports and Setup**
```python
import jax
import jax.numpy as jnp
import numpy as np
from flax.jax_utils import replicate
from diffusers import FlaxStableDiffusionXLPipeline
from jax.experimental.compilation_cache import compilation_cache as cc
cc.initialize_cache("/tmp/sdxl_cache")
import time
NUM_DEVICES = jax.device_count()
```
First, we import the necessary libraries:
- `jax` is provides the primitives for TPU operations
- `flax.jax_utils` contains some useful utility functions for `Flax`, a neural network library built on top of JAX
- `diffusers` has all the code that is relevant for SDXL.
- We also initialize a cache to speed up the JAX model compilation.
- We automatically determine the number of available TPU devices.
**1. Downloading Model and Loading Pipeline**
```python
pipeline, params = FlaxStableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", revision="refs/pr/95", split_head_dim=True
)
```
Here, a pre-trained model `stable-diffusion-xl-base-1.0` from the namespace `stabilityai` is loaded. It returns a pipeline for inference and its parameters.
**2. Casting Parameter Types**
```python
scheduler_state = params.pop("scheduler")
params = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params)
params["scheduler"] = scheduler_state
```
This section adjusts the data types of the model parameters.
We convert all parameters to `bfloat16` to speed-up the computation with model weights.
**Note** that the scheduler parameters are **not** converted to `blfoat16` as the loss
in precision is degrading the pipeline's performance too significantly.
**3. Define Inputs to Pipeline**
```python
default_prompt = ...
default_neg_prompt = ...
default_seed = 33
default_guidance_scale = 5.0
default_num_steps = 25
```
Here, various default inputs for the pipeline are set, including the prompt, negative prompt, random seed, guidance scale, and the number of inference steps.
**4. Tokenizing Inputs**
```python
def tokenize_prompt(prompt, neg_prompt):
prompt_ids = pipeline.prepare_inputs(prompt)
neg_prompt_ids = pipeline.prepare_inputs(neg_prompt)
return prompt_ids, neg_prompt_ids
```
This function tokenizes the given prompts. It's essential because the text encoders of SDXL don't understand raw text; they work with numbers. Tokenization converts text to numbers.
**5. Parallelization and Replication**
```python
p_params = replicate(params)
def replicate_all(prompt_ids, neg_prompt_ids, seed):
...
```
To utilize JAX's parallel capabilities, the parameters and input tensors are duplicated across devices. The `replicate_all` function also ensures that every device produces a different image by creating a unique random seed for each device.
**6. Putting Everything Together**
```python
def generate(...):
...
```
This function integrates all the steps to produce the desired outputs from the model. It takes in prompts, tokenizes them, replicates them across devices, runs them through the pipeline, and converts the images to a format that's more interpretable (PIL format).
**7. Compilation Step**
```python
start = time.time()
print(f"Compiling ...")
generate(default_prompt, default_neg_prompt)
print(f"Compiled in {time.time() - start}")
```
The initial run of the `generate` function will be slow because JAX compiles the function during this call. By running it once here, subsequent calls will be much faster. This section measures and prints the compilation time.
**8. Fast Inference**
```python
start = time.time()
prompt = ...
neg_prompt = ...
images = generate(prompt, neg_prompt)
print(f"Inference in {time.time() - start}")
```
Now that the function is compiled, this section shows how to use it for fast inference. It measures and prints the inference time.
In summary, the code demonstrates how to load a pre-trained model using Flax and JAX, prepare it for inference, and run it efficiently using JAX's capabilities.
## Ahead of Time (AOT) Compilation
FlaxStableDiffusionXLPipeline takes care of parallelization across multiple devices using jit. Now let's build parallelization ourselves.
For this we will be using a JAX feature called [Ahead of Time](https://jax.readthedocs.io/en/latest/aot.html) (AOT) lowering and compilation. AOT allows to fully compile prior to execution time and have control over different parts of the compilation process.
In [sdxl_single_aot.py](./sdxl_single_aot.py) we give a simple example of how to write our own parallelization logic for text-to-image generation pipeline in JAX using [StabilityAI's Stable Diffusion XL](stabilityai/stable-diffusion-xl-base-1.0)
We add a `aot_compile` function that compiles the `pipeline._generate` function
telling JAX which input arguments are static, that is, arguments that
are known at compile time and won't change. In our case, it is num_inference_steps,
height, width and return_latents.
Once the function is compiled, these parameters are omitted from future calls and
cannot be changed without modifying the code and recompiling.
```python
def aot_compile(
prompt=default_prompt,
negative_prompt=default_neg_prompt,
seed=default_seed,
guidance_scale=default_guidance_scale,
num_inference_steps=default_num_steps
):
prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)
prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)
g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)
g = g[:, None]
return pmap(
pipeline._generate,static_broadcasted_argnums=[3, 4, 5, 9]
).lower(
prompt_ids,
p_params,
rng,
num_inference_steps, # num_inference_steps
height, # height
width, # width
g,
None,
neg_prompt_ids,
False # return_latents
).compile()
````
Next we can compile the generate function by executing `aot_compile`.
```python
start = time.time()
print("Compiling ...")
p_generate = aot_compile()
print(f"Compiled in {time.time() - start}")
```
And again we put everything together in a `generate` function.
```python
def generate(
prompt,
negative_prompt,
seed=default_seed,
guidance_scale=default_guidance_scale
):
prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)
prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)
g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)
g = g[:, None]
images = p_generate(
prompt_ids,
p_params,
rng,
g,
None,
neg_prompt_ids)
# convert the images to PIL
images = images.reshape((images.shape[0] * images.shape[1], ) + images.shape[-3:])
return pipeline.numpy_to_pil(np.array(images))
```
The first forward pass after AOT compilation still takes a while longer than
subsequent passes, this is because on the first pass, JAX uses Python dispatch, which
Fills the C++ dispatch cache.
When using jit, this extra step is done automatically, but when using AOT compilation,
it doesn't happen until the function call is made.
```python
start = time.time()
prompt = "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
neg_prompt = "cartoon, illustration, animation. face. male, female"
images = generate(prompt, neg_prompt)
print(f"First inference in {time.time() - start}")
```
From this point forward, any calls to generate should result in a faster inference
time and it won't change.
```python
start = time.time()
prompt = "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
neg_prompt = "cartoon, illustration, animation. face. male, female"
images = generate(prompt, neg_prompt)
print(f"Inference in {time.time() - start}")
```
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hf_public_repos/diffusers/examples/research_projects/sdxl_flax/sdxl_single_aot.py
|
import time
import jax
import jax.numpy as jnp
import numpy as np
from flax.jax_utils import replicate
from jax import pmap
# Let's cache the model compilation, so that it doesn't take as long the next time around.
from jax.experimental.compilation_cache import compilation_cache as cc
from diffusers import FlaxStableDiffusionXLPipeline
cc.initialize_cache("/tmp/sdxl_cache")
NUM_DEVICES = jax.device_count()
# 1. Let's start by downloading the model and loading it into our pipeline class
# Adhering to JAX's functional approach, the model's parameters are returned seperatetely and
# will have to be passed to the pipeline during inference
pipeline, params = FlaxStableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", revision="refs/pr/95", split_head_dim=True
)
# 2. We cast all parameters to bfloat16 EXCEPT the scheduler which we leave in
# float32 to keep maximal precision
scheduler_state = params.pop("scheduler")
params = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params)
params["scheduler"] = scheduler_state
# 3. Next, we define the different inputs to the pipeline
default_prompt = "a colorful photo of a castle in the middle of a forest with trees and bushes, by Ismail Inceoglu, shadows, high contrast, dynamic shading, hdr, detailed vegetation, digital painting, digital drawing, detailed painting, a detailed digital painting, gothic art, featured on deviantart"
default_neg_prompt = "fog, grainy, purple"
default_seed = 33
default_guidance_scale = 5.0
default_num_steps = 25
width = 1024
height = 1024
# 4. In order to be able to compile the pipeline
# all inputs have to be tensors or strings
# Let's tokenize the prompt and negative prompt
def tokenize_prompt(prompt, neg_prompt):
prompt_ids = pipeline.prepare_inputs(prompt)
neg_prompt_ids = pipeline.prepare_inputs(neg_prompt)
return prompt_ids, neg_prompt_ids
# 5. To make full use of JAX's parallelization capabilities
# the parameters and input tensors are duplicated across devices
# To make sure every device generates a different image, we create
# different seeds for each image. The model parameters won't change
# during inference so we do not wrap them into a function
p_params = replicate(params)
def replicate_all(prompt_ids, neg_prompt_ids, seed):
p_prompt_ids = replicate(prompt_ids)
p_neg_prompt_ids = replicate(neg_prompt_ids)
rng = jax.random.PRNGKey(seed)
rng = jax.random.split(rng, NUM_DEVICES)
return p_prompt_ids, p_neg_prompt_ids, rng
# 6. To compile the pipeline._generate function, we must pass all parameters
# to the function and tell JAX which are static arguments, that is, arguments that
# are known at compile time and won't change. In our case, it is num_inference_steps,
# height, width and return_latents.
# Once the function is compiled, these parameters are ommited from future calls and
# cannot be changed without modifying the code and recompiling.
def aot_compile(
prompt=default_prompt,
negative_prompt=default_neg_prompt,
seed=default_seed,
guidance_scale=default_guidance_scale,
num_inference_steps=default_num_steps,
):
prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)
prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)
g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)
g = g[:, None]
return (
pmap(pipeline._generate, static_broadcasted_argnums=[3, 4, 5, 9])
.lower(
prompt_ids,
p_params,
rng,
num_inference_steps, # num_inference_steps
height, # height
width, # width
g,
None,
neg_prompt_ids,
False, # return_latents
)
.compile()
)
start = time.time()
print("Compiling ...")
p_generate = aot_compile()
print(f"Compiled in {time.time() - start}")
# 7. Let's now put it all together in a generate function.
def generate(prompt, negative_prompt, seed=default_seed, guidance_scale=default_guidance_scale):
prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)
prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)
g = jnp.array([guidance_scale] * prompt_ids.shape[0], dtype=jnp.float32)
g = g[:, None]
images = p_generate(prompt_ids, p_params, rng, g, None, neg_prompt_ids)
# convert the images to PIL
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
return pipeline.numpy_to_pil(np.array(images))
# 8. The first forward pass after AOT compilation still takes a while longer than
# subsequent passes, this is because on the first pass, JAX uses Python dispatch, which
# Fills the C++ dispatch cache.
# When using jit, this extra step is done automatically, but when using AOT compilation,
# it doesn't happen until the function call is made.
start = time.time()
prompt = "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
neg_prompt = "cartoon, illustration, animation. face. male, female"
images = generate(prompt, neg_prompt)
print(f"First inference in {time.time() - start}")
# 9. From this point forward, any calls to generate should result in a faster inference
# time and it won't change.
start = time.time()
prompt = "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
neg_prompt = "cartoon, illustration, animation. face. male, female"
images = generate(prompt, neg_prompt)
print(f"Inference in {time.time() - start}")
for i, image in enumerate(images):
image.save(f"castle_{i}.png")
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hf_public_repos/diffusers/examples/research_projects/sdxl_flax/sdxl_single.py
|
# Show best practices for SDXL JAX
import time
import jax
import jax.numpy as jnp
import numpy as np
from flax.jax_utils import replicate
# Let's cache the model compilation, so that it doesn't take as long the next time around.
from jax.experimental.compilation_cache import compilation_cache as cc
from diffusers import FlaxStableDiffusionXLPipeline
cc.initialize_cache("/tmp/sdxl_cache")
NUM_DEVICES = jax.device_count()
# 1. Let's start by downloading the model and loading it into our pipeline class
# Adhering to JAX's functional approach, the model's parameters are returned seperatetely and
# will have to be passed to the pipeline during inference
pipeline, params = FlaxStableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", revision="refs/pr/95", split_head_dim=True
)
# 2. We cast all parameters to bfloat16 EXCEPT the scheduler which we leave in
# float32 to keep maximal precision
scheduler_state = params.pop("scheduler")
params = jax.tree_util.tree_map(lambda x: x.astype(jnp.bfloat16), params)
params["scheduler"] = scheduler_state
# 3. Next, we define the different inputs to the pipeline
default_prompt = "a colorful photo of a castle in the middle of a forest with trees and bushes, by Ismail Inceoglu, shadows, high contrast, dynamic shading, hdr, detailed vegetation, digital painting, digital drawing, detailed painting, a detailed digital painting, gothic art, featured on deviantart"
default_neg_prompt = "fog, grainy, purple"
default_seed = 33
default_guidance_scale = 5.0
default_num_steps = 25
# 4. In order to be able to compile the pipeline
# all inputs have to be tensors or strings
# Let's tokenize the prompt and negative prompt
def tokenize_prompt(prompt, neg_prompt):
prompt_ids = pipeline.prepare_inputs(prompt)
neg_prompt_ids = pipeline.prepare_inputs(neg_prompt)
return prompt_ids, neg_prompt_ids
# 5. To make full use of JAX's parallelization capabilities
# the parameters and input tensors are duplicated across devices
# To make sure every device generates a different image, we create
# different seeds for each image. The model parameters won't change
# during inference so we do not wrap them into a function
p_params = replicate(params)
def replicate_all(prompt_ids, neg_prompt_ids, seed):
p_prompt_ids = replicate(prompt_ids)
p_neg_prompt_ids = replicate(neg_prompt_ids)
rng = jax.random.PRNGKey(seed)
rng = jax.random.split(rng, NUM_DEVICES)
return p_prompt_ids, p_neg_prompt_ids, rng
# 6. Let's now put it all together in a generate function
def generate(
prompt,
negative_prompt,
seed=default_seed,
guidance_scale=default_guidance_scale,
num_inference_steps=default_num_steps,
):
prompt_ids, neg_prompt_ids = tokenize_prompt(prompt, negative_prompt)
prompt_ids, neg_prompt_ids, rng = replicate_all(prompt_ids, neg_prompt_ids, seed)
images = pipeline(
prompt_ids,
p_params,
rng,
num_inference_steps=num_inference_steps,
neg_prompt_ids=neg_prompt_ids,
guidance_scale=guidance_scale,
jit=True,
).images
# convert the images to PIL
images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
return pipeline.numpy_to_pil(np.array(images))
# 7. Remember that the first call will compile the function and hence be very slow. Let's run generate once
# so that the pipeline call is compiled
start = time.time()
print("Compiling ...")
generate(default_prompt, default_neg_prompt)
print(f"Compiled in {time.time() - start}")
# 8. Now the model forward pass will run very quickly, let's try it again
start = time.time()
prompt = "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
neg_prompt = "cartoon, illustration, animation. face. male, female"
images = generate(prompt, neg_prompt)
print(f"Inference in {time.time() - start}")
for i, image in enumerate(images):
image.save(f"castle_{i}.png")
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hf_public_repos/diffusers/examples/research_projects/intel_opts/inference_bf16.py
|
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
parser = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
args = parser.parse_args()
device = "cpu"
prompt = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"
model_id = "path-to-your-trained-model"
pipe = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(device)
# to channels last
pipe.unet = pipe.unet.to(memory_format=torch.channels_last)
pipe.vae = pipe.vae.to(memory_format=torch.channels_last)
pipe.text_encoder = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
pipe.safety_checker = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
sample = torch.randn(2, 4, 64, 64)
timestep = torch.rand(1) * 999
encoder_hidden_status = torch.randn(2, 77, 768)
input_example = (sample, timestep, encoder_hidden_status)
try:
pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=input_example)
except Exception:
pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True)
pipe.vae = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloat16, inplace=True)
pipe.text_encoder = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)
if pipe.requires_safety_checker:
pipe.safety_checker = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)
# compute
seed = 666
generator = torch.Generator(device).manual_seed(seed)
generate_kwargs = {"generator": generator}
if args.steps is not None:
generate_kwargs["num_inference_steps"] = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
image = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save("generated.png")
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hf_public_repos/diffusers/examples/research_projects/intel_opts/README.md
|
## Diffusers examples with Intel optimizations
**This research project is not actively maintained by the diffusers team. For any questions or comments, please make sure to tag @hshen14 .**
This aims to provide diffusers examples with Intel optimizations such as Bfloat16 for training/fine-tuning acceleration and 8-bit integer (INT8) for inference acceleration on Intel platforms.
## Accelerating the fine-tuning for textual inversion
We accelereate the fine-tuning for textual inversion with Intel Extension for PyTorch. The [examples](textual_inversion) enable both single node and multi-node distributed training with Bfloat16 support on Intel Xeon Scalable Processor.
## Accelerating the inference for Stable Diffusion using Bfloat16
We start the inference acceleration with Bfloat16 using Intel Extension for PyTorch. The [script](inference_bf16.py) is generally designed to support standard Stable Diffusion models with Bfloat16 support.
```bash
pip install diffusers transformers accelerate scipy safetensors
export KMP_BLOCKTIME=1
export KMP_SETTINGS=1
export KMP_AFFINITY=granularity=fine,compact,1,0
# Intel OpenMP
export OMP_NUM_THREADS=< Cores to use >
export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libiomp5.so
# Jemalloc is a recommended malloc implementation that emphasizes fragmentation avoidance and scalable concurrency support.
export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libjemalloc.so
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:9000000000"
# Launch with default DDIM
numactl --membind <node N> -C <cpu list> python python inference_bf16.py
# Launch with DPMSolverMultistepScheduler
numactl --membind <node N> -C <cpu list> python python inference_bf16.py --dpm
```
## Accelerating the inference for Stable Diffusion using INT8
Coming soon ...
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hf_public_repos/diffusers/examples/research_projects/intel_opts
|
hf_public_repos/diffusers/examples/research_projects/intel_opts/textual_inversion/README.md
|
## Textual Inversion fine-tuning example
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
## Training with Intel Extension for PyTorch
Intel Extension for PyTorch provides the optimizations for faster training and inference on CPUs. You can leverage the training example "textual_inversion.py". Follow the [instructions](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) to get the model and [dataset](https://huggingface.co/sd-concepts-library/dicoo2) before running the script.
The example supports both single node and multi-node distributed training:
### Single node training
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR="path-to-dir-containing-dicoo-images"
python textual_inversion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<dicoo>" --initializer_token="toy" \
--seed=7 \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--max_train_steps=3000 \
--learning_rate=2.5e-03 --scale_lr \
--output_dir="textual_inversion_dicoo"
```
Note: Bfloat16 is available on Intel Xeon Scalable Processors Cooper Lake or Sapphire Rapids. You may not get performance speedup without Bfloat16 support.
### Multi-node distributed training
Before running the scripts, make sure to install the library's training dependencies successfully:
```bash
python -m pip install oneccl_bind_pt==1.13 -f https://developer.intel.com/ipex-whl-stable-cpu
```
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR="path-to-dir-containing-dicoo-images"
oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
source $oneccl_bindings_for_pytorch_path/env/setvars.sh
python -m intel_extension_for_pytorch.cpu.launch --distributed \
--hostfile hostfile --nnodes 2 --nproc_per_node 2 textual_inversion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<dicoo>" --initializer_token="toy" \
--seed=7 \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--max_train_steps=750 \
--learning_rate=2.5e-03 --scale_lr \
--output_dir="textual_inversion_dicoo"
```
The above is a simple distributed training usage on 2 nodes with 2 processes on each node. Add the right hostname or ip address in the "hostfile" and make sure these 2 nodes are reachable from each other. For more details, please refer to the [user guide](https://github.com/intel/torch-ccl).
### Reference
We publish a [Medium blog](https://medium.com/intel-analytics-software/personalized-stable-diffusion-with-few-shot-fine-tuning-on-a-single-cpu-f01a3316b13) on how to create your own Stable Diffusion model on CPUs using textual inversion. Try it out now, if you have interests.
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hf_public_repos/diffusers/examples/research_projects/intel_opts
|
hf_public_repos/diffusers/examples/research_projects/intel_opts/textual_inversion/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.21.0
ftfy
tensorboard
Jinja2
intel_extension_for_pytorch>=1.13
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hf_public_repos/diffusers/examples/research_projects/intel_opts
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hf_public_repos/diffusers/examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py
|
import argparse
import itertools
import math
import os
import random
from pathlib import Path
import intel_extension_for_pytorch as ipex
import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
# TODO: remove and import from diffusers.utils when the new version of diffusers is released
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.utils import check_min_version
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.13.0.dev0")
logger = get_logger(__name__)
def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
logger.info("Saving embeddings")
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
torch.save(learned_embeds_dict, save_path)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save learned_embeds.bin every X updates steps.",
)
parser.add_argument(
"--only_save_embeds",
action="store_true",
default=False,
help="Save only the embeddings for the new concept.",
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
)
parser.add_argument(
"--placeholder_token",
type=str,
default=None,
required=True,
help="A token to use as a placeholder for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
)
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution."
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=True,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.train_data_dir is None:
raise ValueError("You must specify a train data directory.")
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
):
self.data_root = data_root
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
(
h,
w,
) = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def freeze_params(params):
for param in params:
param.requires_grad = False
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer and add the placeholder token as a additional special token
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id = token_ids[0]
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
# Freeze vae and unet
freeze_params(vae.parameters())
freeze_params(unet.parameters())
# Freeze all parameters except for the token embeddings in text encoder
params_to_freeze = itertools.chain(
text_encoder.text_model.encoder.parameters(),
text_encoder.text_model.final_layer_norm.parameters(),
text_encoder.text_model.embeddings.position_embedding.parameters(),
)
freeze_params(params_to_freeze)
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=args.placeholder_token,
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, lr_scheduler
)
# Move vae and unet to device
vae.to(accelerator.device)
unet.to(accelerator.device)
# Keep vae and unet in eval model as we don't train these
vae.eval()
unet.eval()
unet = ipex.optimize(unet, dtype=torch.bfloat16, inplace=True)
vae = ipex.optimize(vae, dtype=torch.bfloat16, inplace=True)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
text_encoder.train()
text_encoder, optimizer = ipex.optimize(text_encoder, optimizer=optimizer, dtype=torch.bfloat16)
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
with accelerator.accumulate(text_encoder):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
latents = latents * vae.config.scaling_factor
# Sample noise that we'll add to the latents
noise = torch.randn(latents.shape).to(latents.device)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
).long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = F.mse_loss(model_pred, target, reduction="none").mean([1, 2, 3]).mean()
accelerator.backward(loss)
# Zero out the gradients for all token embeddings except the newly added
# embeddings for the concept, as we only want to optimize the concept embeddings
if accelerator.num_processes > 1:
grads = text_encoder.module.get_input_embeddings().weight.grad
else:
grads = text_encoder.get_input_embeddings().weight.grad
# Get the index for tokens that we want to zero the grads for
index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % args.save_steps == 0:
save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
accelerator.wait_for_everyone()
# Create the pipeline using using the trained modules and save it.
if accelerator.is_main_process:
if args.push_to_hub and args.only_save_embeds:
logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
save_full_model = True
else:
save_full_model = not args.only_save_embeds
if save_full_model:
pipeline = StableDiffusionPipeline(
text_encoder=accelerator.unwrap_model(text_encoder),
vae=vae,
unet=unet,
tokenizer=tokenizer,
scheduler=PNDMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
)
pipeline.save_pretrained(args.output_dir)
# Save the newly trained embeddings
save_path = os.path.join(args.output_dir, "learned_embeds.bin")
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects/intel_opts
|
hf_public_repos/diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/README.md
|
# Distillation for quantization on Textual Inversion models to personalize text2image
[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images._By using just 3-5 images new concepts can be taught to Stable Diffusion and the model personalized on your own images_
The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
We have enabled distillation for quantization in `textual_inversion.py` to do quantization aware training as well as distillation on the model generated by Textual Inversion method.
## Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
```bash
pip install -r requirements.txt
```
## Prepare Datasets
One picture which is from the huggingface datasets [sd-concepts-library/dicoo2](https://huggingface.co/sd-concepts-library/dicoo2) is needed, and save it to the `./dicoo` directory. The picture is shown below:
<a href="https://huggingface.co/sd-concepts-library/dicoo2/blob/main/concept_images/1.jpeg">
<img src="https://huggingface.co/sd-concepts-library/dicoo2/resolve/main/concept_images/1.jpeg" width = "300" height="300">
</a>
## Get a FP32 Textual Inversion model
Use the following command to fine-tune the Stable Diffusion model on the above dataset to obtain the FP32 Textual Inversion model.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR="./dicoo"
accelerate launch textual_inversion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<dicoo>" --initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 --scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="dicoo_model"
```
## Do distillation for quantization
Distillation for quantization is a method that combines [intermediate layer knowledge distillation](https://github.com/intel/neural-compressor/blob/master/docs/source/distillation.md#intermediate-layer-knowledge-distillation) and [quantization aware training](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#quantization-aware-training) in the same training process to improve the performance of the quantized model. Provided a FP32 model, the distillation for quantization approach will take this model itself as the teacher model and transfer the knowledges of the specified layers to the student model, i.e. quantized version of the FP32 model, during the quantization aware training process.
Once you have the FP32 Textual Inversion model, the following command will take the FP32 Textual Inversion model as input to do distillation for quantization and generate the INT8 Textual Inversion model.
```bash
export FP32_MODEL_NAME="./dicoo_model"
export DATA_DIR="./dicoo"
accelerate launch textual_inversion.py \
--pretrained_model_name_or_path=$FP32_MODEL_NAME \
--train_data_dir=$DATA_DIR \
--use_ema --learnable_property="object" \
--placeholder_token="<dicoo>" --initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=300 \
--learning_rate=5.0e-04 --max_grad_norm=3 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="int8_model" \
--do_quantization --do_distillation --verify_loading
```
After the distillation for quantization process, the quantized UNet would be 4 times smaller (3279MB -> 827MB).
## Inference
Once you have trained a INT8 model with the above command, the inference can be done simply using the `text2images.py` script. Make sure to include the `placeholder_token` in your prompt.
```bash
export INT8_MODEL_NAME="./int8_model"
python text2images.py \
--pretrained_model_name_or_path=$INT8_MODEL_NAME \
--caption "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings." \
--images_num 4
```
Here is the comparison of images generated by the FP32 model (left) and INT8 model (right) respectively:
<p float="left">
<img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/FP32.png" width = "300" height = "300" alt="FP32" align=center />
<img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/INT8.png" width = "300" height = "300" alt="INT8" align=center />
</p>
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|
hf_public_repos/diffusers/examples/research_projects/intel_opts
|
hf_public_repos/diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/text2images.py
|
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNet2DConditionModel
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"-m",
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"-c",
"--caption",
type=str,
default="robotic cat with wings",
help="Text used to generate images.",
)
parser.add_argument(
"-n",
"--images_num",
type=int,
default=4,
help="How much images to generate.",
)
parser.add_argument(
"-s",
"--seed",
type=int,
default=42,
help="Seed for random process.",
)
parser.add_argument(
"-ci",
"--cuda_id",
type=int,
default=0,
help="cuda_id.",
)
args = parser.parse_args()
return args
def image_grid(imgs, rows, cols):
if not len(imgs) == rows * cols:
raise ValueError("The specified number of rows and columns are not correct.")
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
grid_w, grid_h = grid.size
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def generate_images(
pipeline,
prompt="robotic cat with wings",
guidance_scale=7.5,
num_inference_steps=50,
num_images_per_prompt=1,
seed=42,
):
generator = torch.Generator(pipeline.device).manual_seed(seed)
images = pipeline(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
num_images_per_prompt=num_images_per_prompt,
).images
_rows = int(math.sqrt(num_images_per_prompt))
grid = image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)
return grid, images
args = parse_args()
# Load models and create wrapper for stable diffusion
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
pipeline.safety_checker = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
unet = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, "unet", unet)
else:
unet = unet.to(torch.device("cuda", args.cuda_id))
pipeline = pipeline.to(unet.device)
grid, images = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
dirname = os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, "{}.png".format(idx + 1)))
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hf_public_repos/diffusers/examples/research_projects/intel_opts
|
hf_public_repos/diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py
|
import argparse
import itertools
import math
import os
import random
from pathlib import Path
from typing import Iterable
import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from neural_compressor.utils import logger
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.utils import make_image_grid
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
logger.info("Saving embeddings")
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
torch.save(learned_embeds_dict, save_path)
def parse_args():
parser = argparse.ArgumentParser(description="Example of distillation for quantization on Textual Inversion.")
parser.add_argument(
"--save_steps",
type=int,
default=500,
help="Save learned_embeds.bin every X updates steps.",
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
)
parser.add_argument(
"--placeholder_token",
type=str,
default=None,
required=True,
help="A token to use as a placeholder for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
)
parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--do_quantization", action="store_true", help="Whether or not to do quantization.")
parser.add_argument("--do_distillation", action="store_true", help="Whether or not to do distillation.")
parser.add_argument(
"--verify_loading", action="store_true", help="Whether or not to verify the loading of the quantized model."
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.train_data_dir is None:
raise ValueError("You must specify a train data directory.")
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14
class EMAModel:
"""
Exponential Moving Average of models weights
"""
def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):
parameters = list(parameters)
self.shadow_params = [p.clone().detach() for p in parameters]
self.decay = decay
self.optimization_step = 0
def get_decay(self, optimization_step):
"""
Compute the decay factor for the exponential moving average.
"""
value = (1 + optimization_step) / (10 + optimization_step)
return 1 - min(self.decay, value)
@torch.no_grad()
def step(self, parameters):
parameters = list(parameters)
self.optimization_step += 1
self.decay = self.get_decay(self.optimization_step)
for s_param, param in zip(self.shadow_params, parameters):
if param.requires_grad:
tmp = self.decay * (s_param - param)
s_param.sub_(tmp)
else:
s_param.copy_(param)
torch.cuda.empty_cache()
def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
"""
Copy current averaged parameters into given collection of parameters.
Args:
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
updated with the stored moving averages. If `None`, the
parameters with which this `ExponentialMovingAverage` was
initialized will be used.
"""
parameters = list(parameters)
for s_param, param in zip(self.shadow_params, parameters):
param.data.copy_(s_param.data)
def to(self, device=None, dtype=None) -> None:
r"""Move internal buffers of the ExponentialMovingAverage to `device`.
Args:
device: like `device` argument to `torch.Tensor.to`
"""
# .to() on the tensors handles None correctly
self.shadow_params = [
p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
for p in self.shadow_params
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
):
self.data_root = data_root
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
(
h,
w,
) = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def freeze_params(params):
for param in params:
param.requires_grad = False
def generate_images(pipeline, prompt="", guidance_scale=7.5, num_inference_steps=50, num_images_per_prompt=1, seed=42):
generator = torch.Generator(pipeline.device).manual_seed(seed)
images = pipeline(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
num_images_per_prompt=num_images_per_prompt,
).images
_rows = int(math.sqrt(num_images_per_prompt))
grid = make_image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)
return grid
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
project_config=accelerator_project_config,
)
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer and add the placeholder token as a additional special token
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Load models and create wrapper for stable diffusion
noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
)
train_unet = False
# Freeze vae and unet
freeze_params(vae.parameters())
if not args.do_quantization and not args.do_distillation:
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id = token_ids[0]
placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
freeze_params(unet.parameters())
# Freeze all parameters except for the token embeddings in text encoder
params_to_freeze = itertools.chain(
text_encoder.text_model.encoder.parameters(),
text_encoder.text_model.final_layer_norm.parameters(),
text_encoder.text_model.embeddings.position_embedding.parameters(),
)
freeze_params(params_to_freeze)
else:
train_unet = True
freeze_params(text_encoder.parameters())
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
# only optimize the unet or embeddings of text_encoder
unet.parameters() if train_unet else text_encoder.get_input_embeddings().parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
train_dataset = TextualInversionDataset(
data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
placeholder_token=args.placeholder_token,
repeats=args.repeats,
learnable_property=args.learnable_property,
center_crop=args.center_crop,
set="train",
)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
if not train_unet:
text_encoder = accelerator.prepare(text_encoder)
unet.to(accelerator.device)
unet.eval()
else:
unet = accelerator.prepare(unet)
text_encoder.to(accelerator.device)
text_encoder.eval()
optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
# Move vae to device
vae.to(accelerator.device)
# Keep vae in eval model as we don't train these
vae.eval()
compression_manager = None
def train_func(model):
if train_unet:
unet_ = model
text_encoder_ = text_encoder
else:
unet_ = unet
text_encoder_ = model
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
if train_unet and args.use_ema:
ema_unet = EMAModel(unet_.parameters())
for epoch in range(args.num_train_epochs):
model.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(model):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn(latents.shape).to(latents.device)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
).long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder_(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet_(noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(model_pred, noise, reduction="none").mean([1, 2, 3]).mean()
if train_unet and compression_manager:
unet_inputs = {
"sample": noisy_latents,
"timestep": timesteps,
"encoder_hidden_states": encoder_hidden_states,
}
loss = compression_manager.callbacks.on_after_compute_loss(unet_inputs, model_pred, loss)
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if train_unet:
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(unet_.parameters(), args.max_grad_norm)
else:
# Zero out the gradients for all token embeddings except the newly added
# embeddings for the concept, as we only want to optimize the concept embeddings
if accelerator.num_processes > 1:
grads = text_encoder_.module.get_input_embeddings().weight.grad
else:
grads = text_encoder_.get_input_embeddings().weight.grad
# Get the index for tokens that we want to zero the grads for
index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if train_unet and args.use_ema:
ema_unet.step(unet_.parameters())
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if not train_unet and global_step % args.save_steps == 0:
save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
save_progress(text_encoder_, placeholder_token_id, accelerator, args, save_path)
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
accelerator.wait_for_everyone()
if train_unet and args.use_ema:
ema_unet.copy_to(unet_.parameters())
if not train_unet:
return text_encoder_
if not train_unet:
text_encoder = train_func(text_encoder)
else:
import copy
model = copy.deepcopy(unet)
confs = []
if args.do_quantization:
from neural_compressor import QuantizationAwareTrainingConfig
q_conf = QuantizationAwareTrainingConfig()
confs.append(q_conf)
if args.do_distillation:
teacher_model = copy.deepcopy(model)
def attention_fetcher(x):
return x.sample
layer_mappings = [
[
[
"conv_in",
]
],
[
[
"time_embedding",
]
],
[["down_blocks.0.attentions.0", attention_fetcher]],
[["down_blocks.0.attentions.1", attention_fetcher]],
[
[
"down_blocks.0.resnets.0",
]
],
[
[
"down_blocks.0.resnets.1",
]
],
[
[
"down_blocks.0.downsamplers.0",
]
],
[["down_blocks.1.attentions.0", attention_fetcher]],
[["down_blocks.1.attentions.1", attention_fetcher]],
[
[
"down_blocks.1.resnets.0",
]
],
[
[
"down_blocks.1.resnets.1",
]
],
[
[
"down_blocks.1.downsamplers.0",
]
],
[["down_blocks.2.attentions.0", attention_fetcher]],
[["down_blocks.2.attentions.1", attention_fetcher]],
[
[
"down_blocks.2.resnets.0",
]
],
[
[
"down_blocks.2.resnets.1",
]
],
[
[
"down_blocks.2.downsamplers.0",
]
],
[
[
"down_blocks.3.resnets.0",
]
],
[
[
"down_blocks.3.resnets.1",
]
],
[
[
"up_blocks.0.resnets.0",
]
],
[
[
"up_blocks.0.resnets.1",
]
],
[
[
"up_blocks.0.resnets.2",
]
],
[
[
"up_blocks.0.upsamplers.0",
]
],
[["up_blocks.1.attentions.0", attention_fetcher]],
[["up_blocks.1.attentions.1", attention_fetcher]],
[["up_blocks.1.attentions.2", attention_fetcher]],
[
[
"up_blocks.1.resnets.0",
]
],
[
[
"up_blocks.1.resnets.1",
]
],
[
[
"up_blocks.1.resnets.2",
]
],
[
[
"up_blocks.1.upsamplers.0",
]
],
[["up_blocks.2.attentions.0", attention_fetcher]],
[["up_blocks.2.attentions.1", attention_fetcher]],
[["up_blocks.2.attentions.2", attention_fetcher]],
[
[
"up_blocks.2.resnets.0",
]
],
[
[
"up_blocks.2.resnets.1",
]
],
[
[
"up_blocks.2.resnets.2",
]
],
[
[
"up_blocks.2.upsamplers.0",
]
],
[["up_blocks.3.attentions.0", attention_fetcher]],
[["up_blocks.3.attentions.1", attention_fetcher]],
[["up_blocks.3.attentions.2", attention_fetcher]],
[
[
"up_blocks.3.resnets.0",
]
],
[
[
"up_blocks.3.resnets.1",
]
],
[
[
"up_blocks.3.resnets.2",
]
],
[["mid_block.attentions.0", attention_fetcher]],
[
[
"mid_block.resnets.0",
]
],
[
[
"mid_block.resnets.1",
]
],
[
[
"conv_out",
]
],
]
layer_names = [layer_mapping[0][0] for layer_mapping in layer_mappings]
if not set(layer_names).issubset([n[0] for n in model.named_modules()]):
raise ValueError(
"Provided model is not compatible with the default layer_mappings, "
'please use the model fine-tuned from "CompVis/stable-diffusion-v1-4", '
"or modify the layer_mappings variable to fit your model."
f"\nDefault layer_mappings are as such:\n{layer_mappings}"
)
from neural_compressor.config import DistillationConfig, IntermediateLayersKnowledgeDistillationLossConfig
distillation_criterion = IntermediateLayersKnowledgeDistillationLossConfig(
layer_mappings=layer_mappings,
loss_types=["MSE"] * len(layer_mappings),
loss_weights=[1.0 / len(layer_mappings)] * len(layer_mappings),
add_origin_loss=True,
)
d_conf = DistillationConfig(teacher_model=teacher_model, criterion=distillation_criterion)
confs.append(d_conf)
from neural_compressor.training import prepare_compression
compression_manager = prepare_compression(model, confs)
compression_manager.callbacks.on_train_begin()
model = compression_manager.model
train_func(model)
compression_manager.callbacks.on_train_end()
# Save the resulting model and its corresponding configuration in the given directory
model.save(args.output_dir)
logger.info(f"Optimized model saved to: {args.output_dir}.")
# change to framework model for further use
model = model.model
# Create the pipeline using using the trained modules and save it.
templates = imagenet_style_templates_small if args.learnable_property == "style" else imagenet_templates_small
prompt = templates[0].format(args.placeholder_token)
if accelerator.is_main_process:
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
text_encoder=accelerator.unwrap_model(text_encoder),
vae=vae,
unet=accelerator.unwrap_model(unet),
tokenizer=tokenizer,
)
pipeline.save_pretrained(args.output_dir)
pipeline = pipeline.to(unet.device)
baseline_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
baseline_model_images.save(
os.path.join(args.output_dir, "{}_baseline_model.png".format("_".join(prompt.split())))
)
if not train_unet:
# Also save the newly trained embeddings
save_path = os.path.join(args.output_dir, "learned_embeds.bin")
save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
else:
setattr(pipeline, "unet", accelerator.unwrap_model(model))
if args.do_quantization:
pipeline = pipeline.to(torch.device("cpu"))
optimized_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
optimized_model_images.save(
os.path.join(args.output_dir, "{}_optimized_model.png".format("_".join(prompt.split())))
)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if args.do_quantization and args.verify_loading:
# Load the model obtained after Intel Neural Compressor quantization
from neural_compressor.utils.pytorch import load
loaded_model = load(args.output_dir, model=unet)
loaded_model.eval()
setattr(pipeline, "unet", loaded_model)
if args.do_quantization:
pipeline = pipeline.to(torch.device("cpu"))
loaded_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
if loaded_model_images != optimized_model_images:
logger.info("The quantized model was not successfully loaded.")
else:
logger.info("The quantized model was successfully loaded.")
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects/intel_opts
|
hf_public_repos/diffusers/examples/research_projects/intel_opts/textual_inversion_dfq/requirements.txt
|
accelerate
torchvision
transformers>=4.25.0
ftfy
tensorboard
modelcards
neural-compressor
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/realfill/README.md
|
# RealFill
[RealFill](https://arxiv.org/abs/2309.16668) is a method to personalize text2image inpainting models like stable diffusion inpainting given just a few(1~5) images of a scene.
The `train_realfill.py` script shows how to implement the training procedure for stable diffusion inpainting.
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
cd to the realfill folder and run
```bash
cd realfill
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
Or for a default accelerate configuration without answering questions about your environment
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell e.g. a notebook
```python
from accelerate.utils import write_basic_config
write_basic_config()
```
When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
### Toy example
Now let's fill the real. For this example, we will use some images of the flower girl example from the paper.
We already provide some images for testing in [this link](https://github.com/thuanz123/realfill/tree/main/data/flowerwoman)
You only have to launch the training using:
```bash
export MODEL_NAME="stabilityai/stable-diffusion-2-inpainting"
export TRAIN_DIR="data/flowerwoman"
export OUTPUT_DIR="flowerwoman-model"
accelerate launch train_realfill.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$TRAIN_DIR \
--output_dir=$OUTPUT_DIR \
--resolution=512 \
--train_batch_size=16 \
--gradient_accumulation_steps=1 \
--unet_learning_rate=2e-4 \
--text_encoder_learning_rate=4e-5 \
--lr_scheduler="constant" \
--lr_warmup_steps=100 \
--max_train_steps=2000 \
--lora_rank=8 \
--lora_dropout=0.1 \
--lora_alpha=16 \
```
### Training on a low-memory GPU:
It is possible to run realfill on a low-memory GPU by using the following optimizations:
- [gradient checkpointing and the 8-bit optimizer](#training-with-gradient-checkpointing-and-8-bit-optimizers)
- [xformers](#training-with-xformers)
- [setting grads to none](#set-grads-to-none)
```bash
export MODEL_NAME="stabilityai/stable-diffusion-2-inpainting"
export TRAIN_DIR="data/flowerwoman"
export OUTPUT_DIR="flowerwoman-model"
accelerate launch train_realfill.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$TRAIN_DIR \
--output_dir=$OUTPUT_DIR \
--resolution=512 \
--train_batch_size=16 \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--use_8bit_adam \
--enable_xformers_memory_efficient_attention \
--set_grads_to_none \
--unet_learning_rate=2e-4 \
--text_encoder_learning_rate=4e-5 \
--lr_scheduler="constant" \
--lr_warmup_steps=100 \
--max_train_steps=2000 \
--lora_rank=8 \
--lora_dropout=0.1 \
--lora_alpha=16 \
```
### Training with gradient checkpointing and 8-bit optimizers:
With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train realfill on a 16GB GPU.
To install `bitsandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
### Training with xformers:
You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script.
### Set grads to none
To save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.
More info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html
## Acknowledge
This repo is built upon the code of DreamBooth from diffusers and we thank the developers for their great works and efforts to release source code. Furthermore, a special "thank you" to RealFill's authors for publishing such an amazing work.
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/realfill/train_realfill.py
|
import argparse
import copy
import itertools
import logging
import math
import os
import random
import shutil
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms.v2 as transforms_v2
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import create_repo, upload_folder
from packaging import version
from peft import LoraConfig, PeftModel, get_peft_model
from PIL import Image
from PIL.ImageOps import exif_transpose
from torch.utils.data import Dataset
from tqdm.auto import tqdm
from transformers import AutoTokenizer, CLIPTextModel
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DPMSolverMultistepScheduler,
StableDiffusionInpaintPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
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("0.20.1")
logger = get_logger(__name__)
def make_mask(images, resolution, times=30):
mask, times = torch.ones_like(images[0:1, :, :]), np.random.randint(1, times)
min_size, max_size, margin = np.array([0.03, 0.25, 0.01]) * resolution
max_size = min(max_size, resolution - margin * 2)
for _ in range(times):
width = np.random.randint(int(min_size), int(max_size))
height = np.random.randint(int(min_size), int(max_size))
x_start = np.random.randint(int(margin), resolution - int(margin) - width + 1)
y_start = np.random.randint(int(margin), resolution - int(margin) - height + 1)
mask[:, y_start : y_start + height, x_start : x_start + width] = 0
mask = 1 - mask if random.random() < 0.5 else mask
return mask
def save_model_card(
repo_id: str,
images=None,
base_model=str,
repo_folder=None,
):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
prompt: "a photo of sks"
tags:
- stable-diffusion-inpainting
- stable-diffusion-inpainting-diffusers
- text-to-image
- diffusers
- realfill
inference: true
---
"""
model_card = f"""
# RealFill - {repo_id}
This is a realfill model derived from {base_model}. The weights were trained using [RealFill](https://realfill.github.io/).
You can find some example images in the following. \n
{img_str}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def log_validation(
text_encoder,
tokenizer,
unet,
args,
accelerator,
weight_dtype,
epoch,
):
logger.info(f"Running validation... \nGenerating {args.num_validation_images} images")
# create pipeline (note: unet and vae are loaded again in float32)
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
args.pretrained_model_name_or_path,
tokenizer=tokenizer,
revision=args.revision,
torch_dtype=weight_dtype,
)
# set `keep_fp32_wrapper` to True because we do not want to remove
# mixed precision hooks while we are still training
pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
target_dir = Path(args.train_data_dir) / "target"
target_image, target_mask = target_dir / "target.png", target_dir / "mask.png"
image, mask_image = Image.open(target_image), Image.open(target_mask)
if image.mode != "RGB":
image = image.convert("RGB")
images = []
for _ in range(args.num_validation_images):
image = pipeline(
prompt="a photo of sks",
image=image,
mask_image=mask_image,
num_inference_steps=25,
guidance_scale=5,
generator=generator,
).images[0]
images.append(image)
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=str(i)) for i, image in enumerate(images)]})
del pipeline
torch.cuda.empty_cache()
return images
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of images.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_conditioning`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run realfill validation every X steps. RealFill validation consists of running the conditioning"
" `args.validation_conditioning` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="realfill-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--unet_learning_rate",
type=float,
default=2e-4,
help="Learning rate to use for unet.",
)
parser.add_argument(
"--text_encoder_learning_rate",
type=float,
default=4e-5,
help="Learning rate to use for text encoder.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--wandb_key",
type=str,
default=None,
help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
)
parser.add_argument(
"--wandb_project_name",
type=str,
default=None,
help=("If report to option is set to wandb, project name in wandb for log tracking "),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--lora_rank",
type=int,
default=16,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--lora_alpha",
type=int,
default=27,
help=("The alpha constant of the LoRA update matrices."),
)
parser.add_argument(
"--lora_dropout",
type=float,
default=0.0,
help="The dropout rate of the LoRA update matrices.",
)
parser.add_argument(
"--lora_bias",
type=str,
default="none",
help="The bias type of the Lora update matrices. Must be 'none', 'all' or 'lora_only'.",
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
return args
class RealFillDataset(Dataset):
"""
A dataset to prepare the training and conditioning images and
the masks with the dummy prompt for fine-tuning the model.
It pre-processes the images, masks and tokenizes the prompts.
"""
def __init__(
self,
train_data_root,
tokenizer,
size=512,
):
self.size = size
self.tokenizer = tokenizer
self.ref_data_root = Path(train_data_root) / "ref"
self.target_image = Path(train_data_root) / "target" / "target.png"
self.target_mask = Path(train_data_root) / "target" / "mask.png"
if not (self.ref_data_root.exists() and self.target_image.exists() and self.target_mask.exists()):
raise ValueError("Train images root doesn't exists.")
self.train_images_path = list(self.ref_data_root.iterdir()) + [self.target_image]
self.num_train_images = len(self.train_images_path)
self.train_prompt = "a photo of sks"
self.transform = transforms_v2.Compose(
[
transforms_v2.ToImage(),
transforms_v2.RandomResize(size, int(1.125 * size)),
transforms_v2.RandomCrop(size),
transforms_v2.ToDtype(torch.float32, scale=True),
transforms_v2.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self.num_train_images
def __getitem__(self, index):
example = {}
image = Image.open(self.train_images_path[index])
image = exif_transpose(image)
if not image.mode == "RGB":
image = image.convert("RGB")
if index < len(self) - 1:
weighting = Image.new("L", image.size)
else:
weighting = Image.open(self.target_mask)
weighting = exif_transpose(weighting)
image, weighting = self.transform(image, weighting)
example["images"], example["weightings"] = image, weighting < 0
if random.random() < 0.1:
example["masks"] = torch.ones_like(example["images"][0:1, :, :])
else:
example["masks"] = make_mask(example["images"], self.size)
example["conditioning_images"] = example["images"] * (example["masks"] < 0.5)
train_prompt = "" if random.random() < 0.1 else self.train_prompt
example["prompt_ids"] = self.tokenizer(
train_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def collate_fn(examples):
input_ids = [example["prompt_ids"] for example in examples]
images = [example["images"] for example in examples]
masks = [example["masks"] for example in examples]
weightings = [example["weightings"] for example in examples]
conditioning_images = [example["conditioning_images"] for example in examples]
images = torch.stack(images)
images = images.to(memory_format=torch.contiguous_format).float()
masks = torch.stack(masks)
masks = masks.to(memory_format=torch.contiguous_format).float()
weightings = torch.stack(weightings)
weightings = weightings.to(memory_format=torch.contiguous_format).float()
conditioning_images = torch.stack(conditioning_images)
conditioning_images = conditioning_images.to(memory_format=torch.contiguous_format).float()
input_ids = torch.cat(input_ids, dim=0)
batch = {
"input_ids": input_ids,
"images": images,
"masks": masks,
"weightings": weightings,
"conditioning_images": conditioning_images,
}
return batch
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_dir=logging_dir,
)
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.")
wandb.login(key=args.wandb_key)
wandb.init(project=args.wandb_project_name)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
)
config = LoraConfig(
r=args.lora_rank,
lora_alpha=args.lora_alpha,
target_modules=["to_k", "to_q", "to_v", "key", "query", "value"],
lora_dropout=args.lora_dropout,
bias=args.lora_bias,
)
unet = get_peft_model(unet, config)
config = LoraConfig(
r=args.lora_rank,
lora_alpha=args.lora_alpha,
target_modules=["k_proj", "q_proj", "v_proj"],
lora_dropout=args.lora_dropout,
bias=args.lora_bias,
)
text_encoder = get_peft_model(text_encoder, config)
vae.requires_grad_(False)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
text_encoder.gradient_checkpointing_enable()
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
for model in models:
sub_dir = (
"unet"
if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))
else "text_encoder"
)
model.save_pretrained(os.path.join(output_dir, sub_dir))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
while len(models) > 0:
# pop models so that they are not loaded again
model = models.pop()
sub_dir = (
"unet"
if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))
else "text_encoder"
)
model_cls = (
UNet2DConditionModel
if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))
else CLIPTextModel
)
load_model = model_cls.from_pretrained(args.pretrained_model_name_or_path, subfolder=sub_dir)
load_model = PeftModel.from_pretrained(load_model, input_dir, subfolder=sub_dir)
model.load_state_dict(load_model.state_dict())
del load_model
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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.unet_learning_rate = (
args.unet_learning_rate
* args.gradient_accumulation_steps
* args.train_batch_size
* accelerator.num_processes
)
args.text_encoder_learning_rate = (
args.text_encoder_learning_rate
* args.gradient_accumulation_steps
* args.train_batch_size
* accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
optimizer = optimizer_class(
[
{"params": unet.parameters(), "lr": args.unet_learning_rate},
{"params": text_encoder.parameters(), "lr": args.text_encoder_learning_rate},
],
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = RealFillDataset(
train_data_root=args.train_data_dir,
tokenizer=tokenizer,
size=args.resolution,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=1,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
unet, text_encoder, optimizer, train_dataloader = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader
)
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae to device and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = vars(copy.deepcopy(args))
accelerator.init_trackers("realfill", config=tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
text_encoder.train()
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet, text_encoder):
# Convert images to latent space
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * 0.18215
# Convert masked images to latent space
conditionings = vae.encode(batch["conditioning_images"].to(dtype=weight_dtype)).latent_dist.sample()
conditionings = conditionings * 0.18215
# Downsample mask and weighting so that they match with the latents
masks, size = batch["masks"].to(dtype=weight_dtype), latents.shape[2:]
masks = F.interpolate(masks, size=size)
weightings = batch["weightings"].to(dtype=weight_dtype)
weightings = F.interpolate(weightings, size=size)
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Concatenate noisy latents, masks and conditionings to get inputs to unet
inputs = torch.cat([noisy_latents, masks, conditionings], dim=1)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
model_pred = unet(inputs, timesteps, encoder_hidden_states).sample
# Compute the diffusion loss
assert noise_scheduler.config.prediction_type == "epsilon"
loss = (weightings * F.mse_loss(model_pred.float(), noise.float(), reduction="none")).mean()
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
if args.report_to == "wandb":
accelerator.print(progress_bar)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if global_step % args.validation_steps == 0:
log_validation(
text_encoder,
tokenizer,
unet,
args,
accelerator,
weight_dtype,
global_step,
)
logs = {"loss": loss.detach().item()}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet, keep_fp32_wrapper=True).merge_and_unload(),
text_encoder=accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True).merge_and_unload(),
revision=args.revision,
)
pipeline.save_pretrained(args.output_dir)
# Final inference
images = log_validation(
text_encoder,
tokenizer,
unet,
args,
accelerator,
weight_dtype,
global_step,
)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/realfill/requirements.txt
|
diffusers==0.20.1
accelerate==0.23.0
transformers==4.34.0
peft==0.5.0
torch==2.0.1
torchvision>=0.16
ftfy==6.1.1
tensorboard==2.14.0
Jinja2==3.1.2
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/realfill/infer.py
|
import argparse
import os
import torch
from PIL import Image, ImageFilter
from transformers import CLIPTextModel
from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
parser = argparse.ArgumentParser(description="Inference")
parser.add_argument(
"--model_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--validation_image",
type=str,
default=None,
required=True,
help="The directory of the validation image",
)
parser.add_argument(
"--validation_mask",
type=str,
default=None,
required=True,
help="The directory of the validation mask",
)
parser.add_argument(
"--output_dir",
type=str,
default="./test-infer/",
help="The output directory where predictions are saved",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible inference.")
args = parser.parse_args()
if __name__ == "__main__":
os.makedirs(args.output_dir, exist_ok=True)
generator = None
# create & load model
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-inpainting", torch_dtype=torch.float32, revision=None
)
pipe.unet = UNet2DConditionModel.from_pretrained(
args.model_path,
subfolder="unet",
revision=None,
)
pipe.text_encoder = CLIPTextModel.from_pretrained(
args.model_path,
subfolder="text_encoder",
revision=None,
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
if args.seed is not None:
generator = torch.Generator(device="cuda").manual_seed(args.seed)
image = Image.open(args.validation_image)
mask_image = Image.open(args.validation_mask)
results = pipe(
["a photo of sks"] * 16,
image=image,
mask_image=mask_image,
num_inference_steps=25,
guidance_scale=5,
generator=generator,
).images
erode_kernel = ImageFilter.MaxFilter(3)
mask_image = mask_image.filter(erode_kernel)
blur_kernel = ImageFilter.BoxBlur(1)
mask_image = mask_image.filter(blur_kernel)
for idx, result in enumerate(results):
result = Image.composite(result, image, mask_image)
result.save(f"{args.output_dir}/{idx}.png")
del pipe
torch.cuda.empty_cache()
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/dreambooth_inpaint/README.md
|
# Dreambooth for the inpainting model
This script was added by @thedarkzeno .
Please note that this script is not actively maintained, you can open an issue and tag @thedarkzeno or @patil-suraj though.
```bash
export MODEL_NAME="runwayml/stable-diffusion-inpainting"
export INSTANCE_DIR="path-to-instance-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth_inpaint.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400
```
### Training with prior-preservation loss
Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases.
```bash
export MODEL_NAME="runwayml/stable-diffusion-inpainting"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth_inpaint.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### Training with gradient checkpointing and 8-bit optimizer:
With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU.
To install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
```bash
export MODEL_NAME="runwayml/stable-diffusion-inpainting"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth_inpaint.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=2 --gradient_checkpointing \
--use_8bit_adam \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### Fine-tune text encoder with the UNet.
The script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces.
Pass the `--train_text_encoder` argument to the script to enable training `text_encoder`.
___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___
```bash
export MODEL_NAME="runwayml/stable-diffusion-inpainting"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth_inpaint.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_text_encoder \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--use_8bit_adam \
--gradient_checkpointing \
--learning_rate=2e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py
|
import argparse
import math
import os
import random
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from huggingface_hub.utils import insecure_hashlib
from PIL import Image, ImageDraw
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.13.0.dev0")
logger = get_logger(__name__)
def prepare_mask_and_masked_image(image, mask):
image = np.array(image.convert("RGB"))
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
mask = np.array(mask.convert("L"))
mask = mask.astype(np.float32) / 255.0
mask = mask[None, None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = image * (mask < 0.5)
return mask, masked_image
# generate random masks
def random_mask(im_shape, ratio=1, mask_full_image=False):
mask = Image.new("L", im_shape, 0)
draw = ImageDraw.Draw(mask)
size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio)))
# use this to always mask the whole image
if mask_full_image:
size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio))
limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2)
center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1]))
draw_type = random.randint(0, 1)
if draw_type == 0 or mask_full_image:
draw.rectangle(
(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
fill=255,
)
else:
draw.ellipse(
(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
fill=255,
)
return mask
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If not have enough images, additional images will be"
" sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="dreambooth-inpaint-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint and are suitable for resuming training"
" using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.instance_data_dir is None:
raise ValueError("You must specify a train data directory.")
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms_resize_and_crop = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
]
)
self.image_transforms = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
instance_image = self.image_transforms_resize_and_crop(instance_image)
example["PIL_images"] = instance_image
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
class_image = self.image_transforms_resize_and_crop(class_image)
example["class_images"] = self.image_transforms(class_image)
example["class_PIL_images"] = class_image
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
return example
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def main():
args = parse_args()
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
project_config=accelerator_project_config,
)
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
if args.seed is not None:
set_seed(args.seed)
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
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
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(
sample_dataset, batch_size=args.sample_batch_size, num_workers=1
)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
transform_to_pil = transforms.ToPILImage()
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
bsz = len(example["prompt"])
fake_images = torch.rand((3, args.resolution, args.resolution))
transform_to_pil = transforms.ToPILImage()
fake_pil_images = transform_to_pil(fake_images)
fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True)
images = pipeline(prompt=example["prompt"], mask_image=fake_mask, image=fake_pil_images).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
# We only train the additional adapter LoRA layers
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
unet.requires_grad_(False)
weight_dtype = torch.float32
if args.mixed_precision == "fp16":
weight_dtype = torch.float16
elif args.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu.
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# now we will add new LoRA weights to the attention layers
# It's important to realize here how many attention weights will be added and of which sizes
# The sizes of the attention layers consist only of two different variables:
# 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
# 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
# Let's first see how many attention processors we will have to set.
# For Stable Diffusion, it should be equal to:
# - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
# - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
# - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
# => 32 layers
# Set correct lora layers
lora_attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
unet.set_attn_processor(lora_attn_procs)
lora_layers = AttnProcsLayers(unet.attn_processors)
accelerator.register_for_checkpointing(lora_layers)
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
optimizer = optimizer_class(
lora_layers.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
def collate_fn(examples):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if args.with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pior_pil = [example["class_PIL_images"] for example in examples]
masks = []
masked_images = []
for example in examples:
pil_image = example["PIL_images"]
# generate a random mask
mask = random_mask(pil_image.size, 1, False)
# prepare mask and masked image
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
masks.append(mask)
masked_images.append(masked_image)
if args.with_prior_preservation:
for pil_image in pior_pil:
# generate a random mask
mask = random_mask(pil_image.size, 1, False)
# prepare mask and masked image
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
masks.append(mask)
masked_images.append(masked_image)
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
masks = torch.stack(masks)
masked_images = torch.stack(masked_images)
batch = {"input_ids": input_ids, "pixel_values": pixel_values, "masks": masks, "masked_images": masked_images}
return batch
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
lora_layers, optimizer, train_dataloader, lr_scheduler
)
# accelerator.register_for_checkpointing(lr_scheduler)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth-inpaint-lora", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Convert masked images to latent space
masked_latents = vae.encode(
batch["masked_images"].reshape(batch["pixel_values"].shape).to(dtype=weight_dtype)
).latent_dist.sample()
masked_latents = masked_latents * vae.config.scaling_factor
masks = batch["masks"]
# resize the mask to latents shape as we concatenate the mask to the latents
mask = torch.stack(
[
torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))
for mask in masks
]
).to(dtype=weight_dtype)
mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# concatenate the noised latents with the mask and the masked latents
latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
noise_pred = unet(latent_model_input, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(noise_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
# Compute prior loss
prior_loss = F.mse_loss(noise_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = lora_layers.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
accelerator.wait_for_everyone()
# Save the lora layers
if accelerator.is_main_process:
unet = unet.to(torch.float32)
unet.save_attn_procs(args.output_dir)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py
|
import argparse
import itertools
import math
import os
import random
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from huggingface_hub.utils import insecure_hashlib
from PIL import Image, ImageDraw
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDPMScheduler,
StableDiffusionInpaintPipeline,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.13.0.dev0")
logger = get_logger(__name__)
def prepare_mask_and_masked_image(image, mask):
image = np.array(image.convert("RGB"))
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
mask = np.array(mask.convert("L"))
mask = mask.astype(np.float32) / 255.0
mask = mask[None, None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = image * (mask < 0.5)
return mask, masked_image
# generate random masks
def random_mask(im_shape, ratio=1, mask_full_image=False):
mask = Image.new("L", im_shape, 0)
draw = ImageDraw.Draw(mask)
size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio)))
# use this to always mask the whole image
if mask_full_image:
size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio))
limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2)
center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1]))
draw_type = random.randint(0, 1)
if draw_type == 0 or mask_full_image:
draw.rectangle(
(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
fill=255,
)
else:
draw.ellipse(
(center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2),
fill=255,
)
return mask
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
required=True,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If not have enough images, additional images will be"
" sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder")
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint and are suitable for resuming training"
" using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=(
"Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
" See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
" for more docs"
),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.instance_data_dir is None:
raise ValueError("You must specify a train data directory.")
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
return args
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
tokenizer,
class_data_root=None,
class_prompt=None,
size=512,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
self.instance_images_path = list(Path(instance_data_root).iterdir())
self.num_instance_images = len(self.instance_images_path)
self.instance_prompt = instance_prompt
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.class_prompt = class_prompt
else:
self.class_data_root = None
self.image_transforms_resize_and_crop = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
]
)
self.image_transforms = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
instance_image = self.image_transforms_resize_and_crop(instance_image)
example["PIL_images"] = instance_image
example["instance_images"] = self.image_transforms(instance_image)
example["instance_prompt_ids"] = self.tokenizer(
self.instance_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
class_image = self.image_transforms_resize_and_crop(class_image)
example["class_images"] = self.image_transforms(class_image)
example["class_PIL_images"] = class_image
example["class_prompt_ids"] = self.tokenizer(
self.class_prompt,
padding="do_not_pad",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
return example
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def main():
args = parse_args()
logging_dir = Path(args.output_dir, args.logging_dir)
project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with="tensorboard",
project_config=project_config,
)
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
if args.seed is not None:
set_seed(args.seed)
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
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
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(
sample_dataset, batch_size=args.sample_batch_size, num_workers=1
)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
transform_to_pil = transforms.ToPILImage()
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
bsz = len(example["prompt"])
fake_images = torch.rand((3, args.resolution, args.resolution))
transform_to_pil = transforms.ToPILImage()
fake_pil_images = transform_to_pil(fake_images)
fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True)
images = pipeline(prompt=example["prompt"], mask_image=fake_mask, image=fake_pil_images).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer
if args.tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
elif args.pretrained_model_name_or_path:
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
# Load models and create wrapper for stable diffusion
text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
vae.requires_grad_(False)
if not args.train_text_encoder:
text_encoder.requires_grad_(False)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder.gradient_checkpointing_enable()
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
params_to_optimize = (
itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
)
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
)
def collate_fn(examples):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if args.with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
pior_pil = [example["class_PIL_images"] for example in examples]
masks = []
masked_images = []
for example in examples:
pil_image = example["PIL_images"]
# generate a random mask
mask = random_mask(pil_image.size, 1, False)
# prepare mask and masked image
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
masks.append(mask)
masked_images.append(masked_image)
if args.with_prior_preservation:
for pil_image in pior_pil:
# generate a random mask
mask = random_mask(pil_image.size, 1, False)
# prepare mask and masked image
mask, masked_image = prepare_mask_and_masked_image(pil_image, mask)
masks.append(mask)
masked_images.append(masked_image)
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids
masks = torch.stack(masks)
masked_images = torch.stack(masked_images)
batch = {"input_ids": input_ids, "pixel_values": pixel_values, "masks": masks, "masked_images": masked_images}
return batch
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
if args.train_text_encoder:
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
accelerator.register_for_checkpointing(lr_scheduler)
weight_dtype = torch.float32
if args.mixed_precision == "fp16":
weight_dtype = torch.float16
elif args.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu.
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
vae.to(accelerator.device, dtype=weight_dtype)
if not args.train_text_encoder:
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
latents = latents * vae.config.scaling_factor
# Convert masked images to latent space
masked_latents = vae.encode(
batch["masked_images"].reshape(batch["pixel_values"].shape).to(dtype=weight_dtype)
).latent_dist.sample()
masked_latents = masked_latents * vae.config.scaling_factor
masks = batch["masks"]
# resize the mask to latents shape as we concatenate the mask to the latents
mask = torch.stack(
[
torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8))
for mask in masks
]
)
mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8)
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# concatenate the noised latents with the mask and the masked latents
latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
# Predict the noise residual
noise_pred = unet(latent_model_input, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and noise_pred into two parts and compute the loss on each part separately.
noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute instance loss
loss = F.mse_loss(noise_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
# Compute prior loss
prior_loss = F.mse_loss(noise_pred_prior.float(), target_prior.float(), reduction="mean")
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
else:
loss = F.mse_loss(noise_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet.parameters(), text_encoder.parameters())
if args.train_text_encoder
else unet.parameters()
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
accelerator.wait_for_everyone()
# Create the pipeline using using the trained modules and save it.
if accelerator.is_main_process:
pipeline = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
)
pipeline.save_pretrained(args.output_dir)
if args.push_to_hub:
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/research_projects
|
hf_public_repos/diffusers/examples/research_projects/dreambooth_inpaint/requirements.txt
|
diffusers==0.9.0
accelerate>=0.16.0
torchvision
transformers>=4.21.0
ftfy
tensorboard
Jinja2
| 0
|
hf_public_repos/diffusers/examples/kandinsky2_2
|
hf_public_repos/diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_decoder.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fine-tuning script for Kandinsky with support for LoRA."""
import argparse
import logging
import math
import os
import shutil
from pathlib import Path
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from PIL import Image
from tqdm import tqdm
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
import diffusers
from diffusers import AutoPipelineForText2Image, DDPMScheduler, UNet2DConditionModel, VQModel
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnAddedKVProcessor
from diffusers.optimization import get_scheduler
from diffusers.training_utils import compute_snr
from diffusers.utils import check_min_version, is_wandb_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.24.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- kandinsky
- text-to-image
- diffusers
- lora
inference: true
---
"""
model_card = f"""
# LoRA text2image fine-tuning - {repo_id}
These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n
{img_str}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of finetuning Kandinsky 2.2 with LoRA.")
parser.add_argument(
"--pretrained_decoder_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-decoder",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_prior_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-prior",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference."
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=1,
help=(
"Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="kandi_2_2-model-finetuned-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
def main():
args = parse_args()
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
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(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load scheduler, tokenizer and models.
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="scheduler")
image_processor = CLIPImageProcessor.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_processor"
)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_encoder"
)
vae = VQModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="movq")
unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="unet")
# freeze parameters of models to save more memory
unet.requires_grad_(False)
vae.requires_grad_(False)
image_encoder.requires_grad_(False)
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
image_encoder.to(accelerator.device, dtype=weight_dtype)
lora_attn_procs = {}
for name in unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
lora_attn_procs[name] = LoRAAttnAddedKVProcessor(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
rank=args.rank,
)
unet.set_attn_processor(lora_attn_procs)
lora_layers = AttnProcsLayers(unet.attn_processors)
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
lora_layers.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
image_column = args.image_column
if image_column not in column_names:
raise ValueError(f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}")
def center_crop(image):
width, height = image.size
new_size = min(width, height)
left = (width - new_size) / 2
top = (height - new_size) / 2
right = (width + new_size) / 2
bottom = (height + new_size) / 2
return image.crop((left, top, right, bottom))
def train_transforms(img):
img = center_crop(img)
img = img.resize((args.resolution, args.resolution), resample=Image.BICUBIC, reducing_gap=1)
img = np.array(img).astype(np.float32) / 127.5 - 1
img = torch.from_numpy(np.transpose(img, [2, 0, 1]))
return img
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["clip_pixel_values"] = image_processor(images, return_tensors="pt").pixel_values
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
clip_pixel_values = torch.stack([example["clip_pixel_values"] for example in examples])
clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()
return {"pixel_values": pixel_values, "clip_pixel_values": clip_pixel_values}
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
# Prepare everything with our `accelerator`.
lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
lora_layers, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("text2image-fine-tune", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# Convert images to latent space
images = batch["pixel_values"].to(weight_dtype)
clip_images = batch["clip_pixel_values"].to(weight_dtype)
latents = vae.encode(images).latents
image_embeds = image_encoder(clip_images).image_embeds
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
target = noise
# Predict the noise residual and compute loss
added_cond_kwargs = {"image_embeds": image_embeds}
model_pred = unet(noisy_latents, timesteps, None, added_cond_kwargs=added_cond_kwargs).sample[:, :4]
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective requires that we add one to SNR values before we divide by them.
snr = snr + 1
mse_loss_weights = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = lora_layers.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
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
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
unet=accelerator.unwrap_model(unet),
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device)
if args.seed is not None:
generator = generator.manual_seed(args.seed)
images = []
for _ in range(args.num_validation_images):
images.append(
pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
)
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()
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = unet.to(torch.float32)
unet.save_attn_procs(args.output_dir)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_decoder_model_name_or_path,
dataset_name=args.dataset_name,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
# Final inference
# Load previous pipeline
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path, torch_dtype=weight_dtype
)
pipeline = pipeline.to(accelerator.device)
# load attention processors
pipeline.unet.load_attn_procs(args.output_dir)
# run inference
generator = torch.Generator(device=accelerator.device)
if args.seed is not None:
generator = generator.manual_seed(args.seed)
images = []
for _ in range(args.num_validation_images):
images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])
if accelerator.is_main_process:
for tracker in accelerator.trackers:
if len(images) != 0:
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)
]
}
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/kandinsky2_2
|
hf_public_repos/diffusers/examples/kandinsky2_2/text_to_image/README.md
|
# Kandinsky2.2 text-to-image fine-tuning
Kandinsky 2.2 includes a prior pipeline that generates image embeddings from text prompts, and a decoder pipeline that generates the output image based on the image embeddings. We provide `train_text_to_image_prior.py` and `train_text_to_image_decoder.py` scripts to show you how to fine-tune the Kandinsky prior and decoder models separately based on your own dataset. To achieve the best results, you should fine-tune **_both_** your prior and decoder models.
___Note___:
___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparameters to get the best result on your dataset.___
## Running locally with PyTorch
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd in the example folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
For this example we want to directly store the trained LoRA embeddings on the Hub, so we need to be logged in and add the --push_to_hub flag.
___
### Pokemon example
For all our examples, we will directly store the trained weights on the Hub, so we need to be logged in and add the `--push_to_hub` flag. In order to do that, you have to be a registered user on the 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to the [User Access Tokens](https://huggingface.co/docs/hub/security-tokens) guide.
Run the following command to authenticate your token
```bash
huggingface-cli login
```
We also use [Weights and Biases](https://docs.wandb.ai/quickstart) logging by default, because it is really useful to monitor the training progress by regularly generating sample images during training. To install wandb, run
```bash
pip install wandb
```
To disable wandb logging, remove the `--report_to=="wandb"` and `--validation_prompts="A robot pokemon, 4k photo"` flags from below examples
#### Fine-tune decoder
<br>
<!-- accelerate_snippet_start -->
```bash
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image_decoder.py \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--validation_prompts="A robot pokemon, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="kandi2-decoder-pokemon-model"
```
<!-- accelerate_snippet_end -->
To train on your own training files, prepare the dataset according to the format required by `datasets`. You can find the instructions for how to do that in the [ImageFolder with metadata](https://huggingface.co/docs/datasets/en/image_load#imagefolder-with-metadata) guide.
If you wish to use custom loading logic, you should modify the script and we have left pointers for that in the training script.
```bash
export TRAIN_DIR="path_to_your_dataset"
accelerate launch --mixed_precision="fp16" train_text_to_image_decoder.py \
--train_data_dir=$TRAIN_DIR \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--validation_prompts="A robot pokemon, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="kandi22-decoder-pokemon-model"
```
Once the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `kandi22-decoder-pokemon-model`. To load the fine-tuned model for inference just pass that path to `AutoPipelineForText2Image`
```python
from diffusers import AutoPipelineForText2Image
import torch
pipe = AutoPipelineForText2Image.from_pretrained(output_dir, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt='A robot pokemon, 4k photo'
images = pipe(prompt=prompt).images
images[0].save("robot-pokemon.png")
```
Checkpoints only save the unet, so to run inference from a checkpoint, just load the unet
```python
from diffusers import AutoPipelineForText2Image, UNet2DConditionModel
model_path = "path_to_saved_model"
unet = UNet2DConditionModel.from_pretrained(model_path + "/checkpoint-<N>/unet")
pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", unet=unet, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
image = pipe(prompt="A robot pokemon, 4k photo").images[0]
image.save("robot-pokemon.png")
```
#### Fine-tune prior
You can fine-tune the Kandinsky prior model with `train_text_to_image_prior.py` script. Note that we currently do not support `--gradient_checkpointing` for prior model fine-tuning.
<br>
<!-- accelerate_snippet_start -->
```bash
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image_prior.py \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--validation_prompts="A robot pokemon, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="kandi2-prior-pokemon-model"
```
<!-- accelerate_snippet_end -->
To perform inference with the fine-tuned prior model, you will need to first create a prior pipeline by passing the `output_dir` to `DiffusionPipeline`. Then create a `KandinskyV22CombinedPipeline` from a pretrained or fine-tuned decoder checkpoint along with all the modules of the prior pipeline you just created.
```python
from diffusers import AutoPipelineForText2Image, DiffusionPipeline
import torch
pipe_prior = DiffusionPipeline.from_pretrained(output_dir, torch_dtype=torch.float16)
prior_components = {"prior_" + k: v for k,v in pipe_prior.components.items()}
pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", **prior_components, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
prompt='A robot pokemon, 4k photo'
images = pipe(prompt=prompt, negative_prompt=negative_prompt).images
images[0]
```
If you want to use a fine-tuned decoder checkpoint along with your fine-tuned prior checkpoint, you can simply replace the "kandinsky-community/kandinsky-2-2-decoder" in above code with your custom model repo name. Note that in order to be able to create a `KandinskyV22CombinedPipeline`, your model repository need to have a prior tag. If you have created your model repo using our training script, the prior tag is automatically included.
#### Training with multiple GPUs
`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
for running distributed training with `accelerate`. Here is an example command:
```bash
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
accelerate launch --mixed_precision="fp16" --multi_gpu train_text_to_image_decoder.py \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--validation_prompts="A robot pokemon, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="kandi2-decoder-pokemon-model"
```
#### Training with Min-SNR weighting
We support training with the Min-SNR weighting strategy proposed in [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://arxiv.org/abs/2303.09556) which helps achieve faster convergence
by rebalancing the loss. Enable the `--snr_gamma` argument and set it to the recommended
value of 5.0.
## Training with LoRA
Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.
In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
- Previous pretrained weights are kept frozen so that model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).
- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.
[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.
With LoRA, it's possible to fine-tune Kandinsky 2.2 on a custom image-caption pair dataset
on consumer GPUs like Tesla T4, Tesla V100.
### Training
First, you need to set up your development environment as explained in the [installation](#installing-the-dependencies). Make sure to set the `MODEL_NAME` and `DATASET_NAME` environment variables. Here, we will use [Kandinsky 2.2](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder) and the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
#### Train decoder
```bash
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image_decoder_lora.py \
--dataset_name=$DATASET_NAME --caption_column="text" \
--resolution=768 \
--train_batch_size=1 \
--num_train_epochs=100 --checkpointing_steps=5000 \
--learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
--seed=42 \
--rank=4 \
--gradient_checkpointing \
--output_dir="kandi22-decoder-pokemon-lora" \
--validation_prompt="cute dragon creature" --report_to="wandb" \
--push_to_hub \
```
#### Train prior
```bash
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
accelerate launch --mixed_precision="fp16" train_text_to_image_prior_lora.py \
--dataset_name=$DATASET_NAME --caption_column="text" \
--resolution=768 \
--train_batch_size=1 \
--num_train_epochs=100 --checkpointing_steps=5000 \
--learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
--seed=42 \
--rank=4 \
--output_dir="kandi22-prior-pokemon-lora" \
--validation_prompt="cute dragon creature" --report_to="wandb" \
--push_to_hub \
```
**___Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Here we use *1e-4* instead of the usual *1e-5*. Also, by using LoRA, it's possible to run above scripts in consumer GPUs like T4 or V100.___**
### Inference
#### Inference using fine-tuned LoRA checkpoint for decoder
Once you have trained a Kandinsky decoder model using the above command, inference can be done with the `AutoPipelineForText2Image` after loading the trained LoRA weights. You need to pass the `output_dir` for loading the LoRA weights, which in this case is `kandi22-decoder-pokemon-lora`.
```python
from diffusers import AutoPipelineForText2Image
import torch
pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
pipe.unet.load_attn_procs(output_dir)
pipe.enable_model_cpu_offload()
prompt='A robot pokemon, 4k photo'
image = pipe(prompt=prompt).images[0]
image.save("robot_pokemon.png")
```
#### Inference using fine-tuned LoRA checkpoint for prior
```python
from diffusers import AutoPipelineForText2Image
import torch
pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
pipe.prior_prior.load_attn_procs(output_dir)
pipe.enable_model_cpu_offload()
prompt='A robot pokemon, 4k photo'
image = pipe(prompt=prompt).images[0]
image.save("robot_pokemon.png")
image
```
### Training with xFormers:
You can enable memory efficient attention by [installing xFormers](https://huggingface.co/docs/diffusers/main/en/optimization/xformers) and passing the `--enable_xformers_memory_efficient_attention` argument to the script.
xFormers training is not available for fine-tuning the prior model.
**Note**:
According to [this issue](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212), xFormers `v0.0.16` cannot be used for training in some GPUs. If you observe that problem, please install a development version as indicated in that comment.
| 0
|
hf_public_repos/diffusers/examples/kandinsky2_2
|
hf_public_repos/diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from tqdm import tqdm
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from transformers.utils import ContextManagers
import diffusers
from diffusers import AutoPipelineForText2Image, DDPMScheduler, PriorTransformer
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel, compute_snr
from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
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("0.24.0.dev0")
logger = get_logger(__name__, log_level="INFO")
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def save_model_card(
args,
repo_id: str,
images=None,
repo_folder=None,
):
img_str = ""
if len(images) > 0:
image_grid = make_image_grid(images, 1, len(args.validation_prompts))
image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
img_str += "\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {args.pretrained_prior_model_name_or_path}
datasets:
- {args.dataset_name}
tags:
- kandinsky
- text-to-image
- diffusers
inference: true
---
"""
model_card = f"""
# Finetuning - {repo_id}
This pipeline was finetuned from **{args.pretrained_prior_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n
{img_str}
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipe_prior = DiffusionPipeline.from_pretrained("{repo_id}", torch_dtype=torch.float16)
pipe_t2i = DiffusionPipeline.from_pretrained("{args.pretrained_decoder_model_name_or_path}", torch_dtype=torch.float16)
prompt = "{args.validation_prompts[0]}"
image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
image = pipe_t2i(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: {args.num_train_epochs}
* Learning rate: {args.learning_rate}
* Batch size: {args.train_batch_size}
* Gradient accumulation steps: {args.gradient_accumulation_steps}
* Image resolution: {args.resolution}
* Mixed-precision: {args.mixed_precision}
"""
wandb_info = ""
if is_wandb_available():
wandb_run_url = None
if wandb.run is not None:
wandb_run_url = wandb.run.url
if wandb_run_url is not None:
wandb_info = f"""
More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).
"""
model_card += wandb_info
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def log_validation(
image_encoder, image_processor, text_encoder, tokenizer, prior, args, accelerator, weight_dtype, epoch
):
logger.info("Running validation... ")
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
prior_image_encoder=accelerator.unwrap_model(image_encoder),
prior_image_processor=image_processor,
prior_text_encoder=accelerator.unwrap_model(text_encoder),
prior_tokenizer=tokenizer,
prior_prior=accelerator.unwrap_model(prior),
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
images.append(image)
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")
elif tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
for i, image in enumerate(images)
]
}
)
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
torch.cuda.empty_cache()
return images
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of finetuning Kandinsky 2.2.")
parser.add_argument(
"--pretrained_decoder_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-decoder",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_prior_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-prior",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--validation_prompts",
type=str,
default=None,
nargs="+",
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
)
parser.add_argument(
"--output_dir",
type=str,
default="kandi_2_2-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="learning rate",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay",
type=float,
default=0.0,
required=False,
help="weight decay_to_use",
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--validation_epochs",
type=int,
default=5,
help="Run validation every X epochs.",
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load scheduler, image_processor, tokenizer and models.
noise_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", prediction_type="sample")
image_processor = CLIPImageProcessor.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_processor"
)
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="tokenizer")
def deepspeed_zero_init_disabled_context_manager():
"""
returns either a context list that includes one that will disable zero.Init or an empty context list
"""
deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
if deepspeed_plugin is None:
return []
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_encoder", torch_dtype=weight_dtype
).eval()
text_encoder = CLIPTextModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="text_encoder", torch_dtype=weight_dtype
).eval()
prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
# Freeze text_encoder and image_encoder
text_encoder.requires_grad_(False)
image_encoder.requires_grad_(False)
# Set prior to trainable.
prior.train()
# Create EMA for the prior.
if args.use_ema:
ema_prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
ema_prior = EMAModel(ema_prior.parameters(), model_cls=PriorTransformer, model_config=ema_prior.config)
ema_prior.to(accelerator.device)
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if args.use_ema:
ema_prior.save_pretrained(os.path.join(output_dir, "prior_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(output_dir, "prior"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if args.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "prior_ema"), PriorTransformer)
ema_prior.load_state_dict(load_model.state_dict())
ema_prior.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = PriorTransformer.from_pretrained(input_dir, subfolder="prior")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
prior.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
text_input_ids = inputs.input_ids
text_mask = inputs.attention_mask.bool()
return text_input_ids, text_mask
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["clip_pixel_values"] = image_processor(images, return_tensors="pt").pixel_values
examples["text_input_ids"], examples["text_mask"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
clip_pixel_values = torch.stack([example["clip_pixel_values"] for example in examples])
clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()
text_input_ids = torch.stack([example["text_input_ids"] for example in examples])
text_mask = torch.stack([example["text_mask"] for example in examples])
return {"clip_pixel_values": clip_pixel_values, "text_input_ids": text_input_ids, "text_mask": text_mask}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
clip_mean = prior.clip_mean.clone()
clip_std = prior.clip_std.clone()
prior, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
prior, optimizer, train_dataloader, lr_scheduler
)
image_encoder.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
tracker_config.pop("validation_prompts")
accelerator.init_trackers(args.tracker_project_name, tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
clip_mean = clip_mean.to(weight_dtype).to(accelerator.device)
clip_std = clip_std.to(weight_dtype).to(accelerator.device)
for epoch in range(first_epoch, args.num_train_epochs):
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(prior):
# Convert images to latent space
text_input_ids, text_mask, clip_images = (
batch["text_input_ids"],
batch["text_mask"],
batch["clip_pixel_values"].to(weight_dtype),
)
with torch.no_grad():
text_encoder_output = text_encoder(text_input_ids)
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state
image_embeds = image_encoder(clip_images).image_embeds
# Sample noise that we'll add to the image_embeds
noise = torch.randn_like(image_embeds)
bsz = image_embeds.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=image_embeds.device
)
timesteps = timesteps.long()
image_embeds = (image_embeds - clip_mean) / clip_std
noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)
target = image_embeds
# Predict the noise residual and compute loss
model_pred = prior(
noisy_latents,
timestep=timesteps,
proj_embedding=prompt_embeds,
encoder_hidden_states=text_encoder_hidden_states,
attention_mask=text_mask,
).predicted_image_embedding
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective requires that we add one to SNR values before we divide by them.
snr = snr + 1
mse_loss_weights = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(prior.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.use_ema:
ema_prior.step(prior.parameters())
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
if args.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
ema_prior.store(prior.parameters())
ema_prior.copy_to(prior.parameters())
log_validation(
image_encoder,
image_processor,
text_encoder,
tokenizer,
prior,
args,
accelerator,
weight_dtype,
global_step,
)
if args.use_ema:
# Switch back to the original UNet parameters.
ema_prior.restore(prior.parameters())
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
prior = accelerator.unwrap_model(prior)
if args.use_ema:
ema_prior.copy_to(prior.parameters())
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
prior_image_encoder=image_encoder,
prior_text_encoder=text_encoder,
prior_prior=prior,
)
pipeline.prior_pipe.save_pretrained(args.output_dir)
# Run a final round of inference.
images = []
if args.validation_prompts is not None:
logger.info("Running inference for collecting generated images...")
pipeline = pipeline.to(accelerator.device)
pipeline.torch_dtype = weight_dtype
pipeline.set_progress_bar_config(disable=True)
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
images.append(image)
if args.push_to_hub:
save_model_card(args, repo_id, images, repo_folder=args.output_dir)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/kandinsky2_2
|
hf_public_repos/diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_lora_prior.py
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Fine-tuning script for Stable Diffusion for text2image with support for LoRA."""
import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from tqdm import tqdm
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
import diffusers
from diffusers import AutoPipelineForText2Image, DDPMScheduler, PriorTransformer
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.optimization import get_scheduler
from diffusers.training_utils import compute_snr
from diffusers.utils import check_min_version, is_wandb_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.24.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- kandinsky
- text-to-image
- diffusers
- lora
inference: true
---
"""
model_card = f"""
# LoRA text2image fine-tuning - {repo_id}
These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n
{img_str}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of finetuning Kandinsky 2.2.")
parser.add_argument(
"--pretrained_decoder_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-decoder",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_prior_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-prior",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference."
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=1,
help=(
"Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="kandi_2_2-model-finetuned-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="learning rate",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay",
type=float,
default=0.0,
required=False,
help="weight decay_to_use",
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def main():
args = parse_args()
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
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(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load scheduler, image_processor, tokenizer and models.
noise_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", prediction_type="sample")
image_processor = CLIPImageProcessor.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_processor"
)
tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="tokenizer")
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_encoder"
)
text_encoder = CLIPTextModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="text_encoder"
)
prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
# freeze parameters of models to save more memory
image_encoder.requires_grad_(False)
prior.requires_grad_(False)
text_encoder.requires_grad_(False)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move image_encoder, text_encoder and prior to device and cast to weight_dtype
prior.to(accelerator.device, dtype=weight_dtype)
image_encoder.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
lora_attn_procs = {}
for name in prior.attn_processors.keys():
lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=2048, rank=args.rank)
prior.set_attn_processor(lora_attn_procs)
lora_layers = AttnProcsLayers(prior.attn_processors)
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
lora_layers.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
text_input_ids = inputs.input_ids
text_mask = inputs.attention_mask.bool()
return text_input_ids, text_mask
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["clip_pixel_values"] = image_processor(images, return_tensors="pt").pixel_values
examples["text_input_ids"], examples["text_mask"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
clip_pixel_values = torch.stack([example["clip_pixel_values"] for example in examples])
clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()
text_input_ids = torch.stack([example["text_input_ids"] for example in examples])
text_mask = torch.stack([example["text_mask"] for example in examples])
return {"clip_pixel_values": clip_pixel_values, "text_input_ids": text_input_ids, "text_mask": text_mask}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
clip_mean = prior.clip_mean.clone()
clip_std = prior.clip_std.clone()
lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
lora_layers, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("text2image-fine-tune", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
clip_mean = clip_mean.to(weight_dtype).to(accelerator.device)
clip_std = clip_std.to(weight_dtype).to(accelerator.device)
for epoch in range(first_epoch, args.num_train_epochs):
prior.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(prior):
# Convert images to latent space
text_input_ids, text_mask, clip_images = (
batch["text_input_ids"],
batch["text_mask"],
batch["clip_pixel_values"].to(weight_dtype),
)
with torch.no_grad():
text_encoder_output = text_encoder(text_input_ids)
prompt_embeds = text_encoder_output.text_embeds
text_encoder_hidden_states = text_encoder_output.last_hidden_state
image_embeds = image_encoder(clip_images).image_embeds
# Sample noise that we'll add to the image_embeds
noise = torch.randn_like(image_embeds)
bsz = image_embeds.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=image_embeds.device
)
timesteps = timesteps.long()
image_embeds = (image_embeds - clip_mean) / clip_std
noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)
target = image_embeds
# Predict the noise residual and compute loss
model_pred = prior(
noisy_latents,
timestep=timesteps,
proj_embedding=prompt_embeds,
encoder_hidden_states=text_encoder_hidden_states,
attention_mask=text_mask,
).predicted_image_embedding
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective requires that we add one to SNR values before we divide by them.
snr = snr + 1
mse_loss_weights = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(lora_layers.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
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
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
prior_prior=accelerator.unwrap_model(prior),
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device)
if args.seed is not None:
generator = generator.manual_seed(args.seed)
images = []
for _ in range(args.num_validation_images):
images.append(
pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
)
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()
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
prior = prior.to(torch.float32)
prior.save_attn_procs(args.output_dir)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_prior_model_name_or_path,
dataset_name=args.dataset_name,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
# Final inference
# Load previous pipeline
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path, torch_dtype=weight_dtype
)
pipeline = pipeline.to(accelerator.device)
# load attention processors
pipeline.prior_prior.load_attn_procs(args.output_dir)
# run inference
generator = torch.Generator(device=accelerator.device)
if args.seed is not None:
generator = generator.manual_seed(args.seed)
images = []
for _ in range(args.num_validation_images):
images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])
if accelerator.is_main_process:
for tracker in accelerator.trackers:
if len(images) != 0:
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)
]
}
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/kandinsky2_2
|
hf_public_repos/diffusers/examples/kandinsky2_2/text_to_image/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.25.1
datasets
ftfy
tensorboard
Jinja2
| 0
|
hf_public_repos/diffusers/examples/kandinsky2_2
|
hf_public_repos/diffusers/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import shutil
from pathlib import Path
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from tqdm import tqdm
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from transformers.utils import ContextManagers
import diffusers
from diffusers import AutoPipelineForText2Image, DDPMScheduler, UNet2DConditionModel, VQModel
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel, compute_snr
from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
from diffusers.utils.import_utils import is_xformers_available
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("0.24.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def save_model_card(
args,
repo_id: str,
images=None,
repo_folder=None,
):
img_str = ""
if len(images) > 0:
image_grid = make_image_grid(images, 1, len(args.validation_prompts))
image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
img_str += "\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {args.pretrained_decoder_model_name_or_path}
datasets:
- {args.dataset_name}
prior:
- {args.pretrained_prior_model_name_or_path}
tags:
- kandinsky
- text-to-image
- diffusers
inference: true
---
"""
model_card = f"""
# Finetuning - {repo_id}
This pipeline was finetuned from **{args.pretrained_decoder_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n
{img_str}
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("{repo_id}", torch_dtype=torch.float16)
prompt = "{args.validation_prompts[0]}"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: {args.num_train_epochs}
* Learning rate: {args.learning_rate}
* Batch size: {args.train_batch_size}
* Gradient accumulation steps: {args.gradient_accumulation_steps}
* Image resolution: {args.resolution}
* Mixed-precision: {args.mixed_precision}
"""
wandb_info = ""
if is_wandb_available():
wandb_run_url = None
if wandb.run is not None:
wandb_run_url = wandb.run.url
if wandb_run_url is not None:
wandb_info = f"""
More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).
"""
model_card += wandb_info
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def log_validation(vae, image_encoder, image_processor, unet, args, accelerator, weight_dtype, epoch):
logger.info("Running validation... ")
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
vae=accelerator.unwrap_model(vae),
prior_image_encoder=accelerator.unwrap_model(image_encoder),
prior_image_processor=image_processor,
unet=accelerator.unwrap_model(unet),
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
if args.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
images.append(image)
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")
elif tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
for i, image in enumerate(images)
]
}
)
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
torch.cuda.empty_cache()
return images
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of finetuning Kandinsky 2.2.")
parser.add_argument(
"--pretrained_decoder_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-decoder",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_prior_model_name_or_path",
type=str,
default="kandinsky-community/kandinsky-2-2-prior",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--validation_prompts",
type=str,
default=None,
nargs="+",
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
)
parser.add_argument(
"--output_dir",
type=str,
default="kandi_2_2-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="learning rate",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay",
type=float,
default=0.0,
required=False,
help="weight decay_to_use",
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--validation_epochs",
type=int,
default=5,
help="Run validation every X epochs.",
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="scheduler")
image_processor = CLIPImageProcessor.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_processor"
)
def deepspeed_zero_init_disabled_context_manager():
"""
returns either a context list that includes one that will disable zero.Init or an empty context list
"""
deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
if deepspeed_plugin is None:
return []
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
vae = VQModel.from_pretrained(
args.pretrained_decoder_model_name_or_path, subfolder="movq", torch_dtype=weight_dtype
).eval()
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_encoder", torch_dtype=weight_dtype
).eval()
unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="unet")
# Freeze vae and image_encoder
vae.requires_grad_(False)
image_encoder.requires_grad_(False)
# Set unet to trainable.
unet.train()
# Create EMA for the unet.
if args.use_ema:
ema_unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="unet")
ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)
ema_unet.to(accelerator.device)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if args.use_ema:
ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(output_dir, "unet"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if args.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
ema_unet.load_state_dict(load_model.state_dict())
ema_unet.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
unet.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
image_column = args.image_column
if image_column not in column_names:
raise ValueError(f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}")
def center_crop(image):
width, height = image.size
new_size = min(width, height)
left = (width - new_size) / 2
top = (height - new_size) / 2
right = (width + new_size) / 2
bottom = (height + new_size) / 2
return image.crop((left, top, right, bottom))
def train_transforms(img):
img = center_crop(img)
img = img.resize((args.resolution, args.resolution), resample=Image.BICUBIC, reducing_gap=1)
img = np.array(img).astype(np.float32) / 127.5 - 1
img = torch.from_numpy(np.transpose(img, [2, 0, 1]))
return img
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["pixel_values"] = [train_transforms(image) for image in images]
examples["clip_pixel_values"] = image_processor(images, return_tensors="pt").pixel_values
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
clip_pixel_values = torch.stack([example["clip_pixel_values"] for example in examples])
clip_pixel_values = clip_pixel_values.to(memory_format=torch.contiguous_format).float()
return {"pixel_values": pixel_values, "clip_pixel_values": clip_pixel_values}
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# Move image_encode and vae to gpu and cast to weight_dtype
image_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
tracker_config.pop("validation_prompts")
accelerator.init_trackers(args.tracker_project_name, tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
# Convert images to latent space
images = batch["pixel_values"].to(weight_dtype)
clip_images = batch["clip_pixel_values"].to(weight_dtype)
latents = vae.encode(images).latents
image_embeds = image_encoder(clip_images).image_embeds
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
target = noise
# Predict the noise residual and compute loss
added_cond_kwargs = {"image_embeds": image_embeds}
model_pred = unet(noisy_latents, timesteps, None, added_cond_kwargs=added_cond_kwargs).sample[:, :4]
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective requires that we add one to SNR values before we divide by them.
snr = snr + 1
mse_loss_weights = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.use_ema:
ema_unet.step(unet.parameters())
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
if args.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
ema_unet.store(unet.parameters())
ema_unet.copy_to(unet.parameters())
log_validation(
vae,
image_encoder,
image_processor,
unet,
args,
accelerator,
weight_dtype,
global_step,
)
if args.use_ema:
# Switch back to the original UNet parameters.
ema_unet.restore(unet.parameters())
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
if args.use_ema:
ema_unet.copy_to(unet.parameters())
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
vae=vae,
unet=unet,
)
pipeline.decoder_pipe.save_pretrained(args.output_dir)
# Run a final round of inference.
images = []
if args.validation_prompts is not None:
logger.info("Running inference for collecting generated images...")
pipeline = pipeline.to(accelerator.device)
pipeline.torch_dtype = weight_dtype
pipeline.set_progress_bar_config(disable=True)
pipeline.enable_model_cpu_offload()
if args.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
images.append(image)
if args.push_to_hub:
save_model_card(args, repo_id, images, repo_folder=args.output_dir)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/wuerstchen
|
hf_public_repos/diffusers/examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py
|
import torch.nn as nn
from torchvision.models import efficientnet_v2_l, efficientnet_v2_s
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
class EfficientNetEncoder(ModelMixin, ConfigMixin):
@register_to_config
def __init__(self, c_latent=16, c_cond=1280, effnet="efficientnet_v2_s"):
super().__init__()
if effnet == "efficientnet_v2_s":
self.backbone = efficientnet_v2_s(weights="DEFAULT").features
else:
self.backbone = efficientnet_v2_l(weights="DEFAULT").features
self.mapper = nn.Sequential(
nn.Conv2d(c_cond, c_latent, kernel_size=1, bias=False),
nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1
)
def forward(self, x):
return self.mapper(self.backbone(x))
| 0
|
hf_public_repos/diffusers/examples/wuerstchen
|
hf_public_repos/diffusers/examples/wuerstchen/text_to_image/README.md
|
# Würstchen text-to-image fine-tuning
## Running locally with PyTorch
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then cd into the example folder and run
```bash
cd examples/wuerstchen/text_to_image
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
For this example we want to directly store the trained LoRA embeddings on the Hub, so we need to be logged in and add the `--push_to_hub` flag to the training script. To log in, run:
```bash
huggingface-cli login
```
## Prior training
You can fine-tune the Würstchen prior model with the `train_text_to_image_prior.py` script. Note that we currently support `--gradient_checkpointing` for prior model fine-tuning so you can use it for more GPU memory constrained setups.
<br>
<!-- accelerate_snippet_start -->
```bash
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
accelerate launch train_text_to_image_prior.py \
--mixed_precision="fp16" \
--dataset_name=$DATASET_NAME \
--resolution=768 \
--train_batch_size=4 \
--gradient_accumulation_steps=4 \
--gradient_checkpointing \
--dataloader_num_workers=4 \
--max_train_steps=15000 \
--learning_rate=1e-05 \
--max_grad_norm=1 \
--checkpoints_total_limit=3 \
--lr_scheduler="constant" --lr_warmup_steps=0 \
--validation_prompts="A robot pokemon, 4k photo" \
--report_to="wandb" \
--push_to_hub \
--output_dir="wuerstchen-prior-pokemon-model"
```
<!-- accelerate_snippet_end -->
## Training with LoRA
Low-Rank Adaption of Large Language Models (or LoRA) was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*.
In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
- Previous pretrained weights are kept frozen so that the model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114).
- Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
- LoRA attention layers allow to control to which extent the model is adapted toward new training images via a `scale` parameter.
### Prior Training
First, you need to set up your development environment as explained in the [installation](#Running-locally-with-PyTorch) section. Make sure to set the `DATASET_NAME` environment variable. Here, we will use the [Pokemon captions dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions).
```bash
export DATASET_NAME="lambdalabs/pokemon-blip-captions"
accelerate launch train_text_to_image_prior_lora.py \
--mixed_precision="fp16" \
--dataset_name=$DATASET_NAME --caption_column="text" \
--resolution=768 \
--train_batch_size=8 \
--num_train_epochs=100 --checkpointing_steps=5000 \
--learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
--seed=42 \
--rank=4 \
--validation_prompt="cute dragon creature" \
--report_to="wandb" \
--push_to_hub \
--output_dir="wuerstchen-prior-pokemon-lora"
```
| 0
|
hf_public_repos/diffusers/examples/wuerstchen
|
hf_public_repos/diffusers/examples/wuerstchen/text_to_image/train_text_to_image_prior.py
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import accelerate
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState, is_initialized
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, hf_hub_download, upload_folder
from modeling_efficient_net_encoder import EfficientNetEncoder
from packaging import version
from torchvision import transforms
from tqdm import tqdm
from transformers import CLIPTextModel, PreTrainedTokenizerFast
from transformers.utils import ContextManagers
from diffusers import AutoPipelineForText2Image, DDPMWuerstchenScheduler
from diffusers.optimization import get_scheduler
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPrior
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
from diffusers.utils.logging import set_verbosity_error, set_verbosity_info
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("0.24.0.dev0")
logger = get_logger(__name__, log_level="INFO")
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def save_model_card(
args,
repo_id: str,
images=None,
repo_folder=None,
):
img_str = ""
if len(images) > 0:
image_grid = make_image_grid(images, 1, len(args.validation_prompts))
image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
img_str += "\n"
yaml = f"""
---
license: mit
base_model: {args.pretrained_prior_model_name_or_path}
datasets:
- {args.dataset_name}
tags:
- wuerstchen
- text-to-image
- diffusers
inference: true
---
"""
model_card = f"""
# Finetuning - {repo_id}
This pipeline was finetuned from **{args.pretrained_prior_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n
{img_str}
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipe_prior = DiffusionPipeline.from_pretrained("{repo_id}", torch_dtype={args.weight_dtype})
pipe_t2i = DiffusionPipeline.from_pretrained("{args.pretrained_decoder_model_name_or_path}", torch_dtype={args.weight_dtype})
prompt = "{args.validation_prompts[0]}"
(image_embeds,) = pipe_prior(prompt).to_tuple()
image = pipe_t2i(image_embeddings=image_embeds, prompt=prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: {args.num_train_epochs}
* Learning rate: {args.learning_rate}
* Batch size: {args.train_batch_size}
* Gradient accumulation steps: {args.gradient_accumulation_steps}
* Image resolution: {args.resolution}
* Mixed-precision: {args.mixed_precision}
"""
wandb_info = ""
if is_wandb_available():
wandb_run_url = None
if wandb.run is not None:
wandb_run_url = wandb.run.url
if wandb_run_url is not None:
wandb_info = f"""
More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).
"""
model_card += wandb_info
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, epoch):
logger.info("Running validation... ")
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
prior_prior=accelerator.unwrap_model(prior),
prior_text_encoder=accelerator.unwrap_model(text_encoder),
prior_tokenizer=tokenizer,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(
args.validation_prompts[i],
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
generator=generator,
height=args.resolution,
width=args.resolution,
).images[0]
images.append(image)
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")
elif tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
for i, image in enumerate(images)
]
}
)
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
torch.cuda.empty_cache()
return images
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of finetuning Würstchen Prior.")
parser.add_argument(
"--pretrained_decoder_model_name_or_path",
type=str,
default="warp-ai/wuerstchen",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_prior_model_name_or_path",
type=str,
default="warp-ai/wuerstchen-prior",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--validation_prompts",
type=str,
default=None,
nargs="+",
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
)
parser.add_argument(
"--output_dir",
type=str,
default="wuerstchen-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="learning rate",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay",
type=float,
default=0.0,
required=False,
help="weight decay_to_use",
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--validation_epochs",
type=int,
default=5,
help="Run validation every X epochs.",
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load scheduler, effnet, tokenizer, clip_model
noise_scheduler = DDPMWuerstchenScheduler()
tokenizer = PreTrainedTokenizerFast.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="tokenizer"
)
def deepspeed_zero_init_disabled_context_manager():
"""
returns either a context list that includes one that will disable zero.Init or an empty context list
"""
deepspeed_plugin = AcceleratorState().deepspeed_plugin if is_initialized() else None
if deepspeed_plugin is None:
return []
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
pretrained_checkpoint_file = hf_hub_download("dome272/wuerstchen", filename="model_v2_stage_b.pt")
state_dict = torch.load(pretrained_checkpoint_file, map_location="cpu")
image_encoder = EfficientNetEncoder()
image_encoder.load_state_dict(state_dict["effnet_state_dict"])
image_encoder.eval()
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="text_encoder", torch_dtype=weight_dtype
).eval()
# Freeze text_encoder and image_encoder
text_encoder.requires_grad_(False)
image_encoder.requires_grad_(False)
# load prior model
prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
# Create EMA for the prior
if args.use_ema:
ema_prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
ema_prior = EMAModel(ema_prior.parameters(), model_cls=WuerstchenPrior, model_config=ema_prior.config)
ema_prior.to(accelerator.device)
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if args.use_ema:
ema_prior.save_pretrained(os.path.join(output_dir, "prior_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(output_dir, "prior"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if args.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "prior_ema"), WuerstchenPrior)
ema_prior.load_state_dict(load_model.state_dict())
ema_prior.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = WuerstchenPrior.from_pretrained(input_dir, subfolder="prior")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if args.gradient_checkpointing:
prior.enable_gradient_checkpointing()
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
prior.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
text_input_ids = inputs.input_ids
text_mask = inputs.attention_mask.bool()
return text_input_ids, text_mask
effnet_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR, antialias=True),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["effnet_pixel_values"] = [effnet_transforms(image) for image in images]
examples["text_input_ids"], examples["text_mask"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
effnet_pixel_values = torch.stack([example["effnet_pixel_values"] for example in examples])
effnet_pixel_values = effnet_pixel_values.to(memory_format=torch.contiguous_format).float()
text_input_ids = torch.stack([example["text_input_ids"] for example in examples])
text_mask = torch.stack([example["text_mask"] for example in examples])
return {"effnet_pixel_values": effnet_pixel_values, "text_input_ids": text_input_ids, "text_mask": text_mask}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
prior, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
prior, optimizer, train_dataloader, lr_scheduler
)
image_encoder.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
tracker_config.pop("validation_prompts")
accelerator.init_trackers(args.tracker_project_name, tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
prior.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(prior):
# Convert images to latent space
text_input_ids, text_mask, effnet_images = (
batch["text_input_ids"],
batch["text_mask"],
batch["effnet_pixel_values"].to(weight_dtype),
)
with torch.no_grad():
text_encoder_output = text_encoder(text_input_ids, attention_mask=text_mask)
prompt_embeds = text_encoder_output.last_hidden_state
image_embeds = image_encoder(effnet_images)
# scale
image_embeds = image_embeds.add(1.0).div(42.0)
# Sample noise that we'll add to the image_embeds
noise = torch.randn_like(image_embeds)
bsz = image_embeds.shape[0]
# Sample a random timestep for each image
timesteps = torch.rand((bsz,), device=image_embeds.device, dtype=weight_dtype)
# add noise to latent
noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)
# Predict the noise residual and compute losscd
pred_noise = prior(noisy_latents, timesteps, prompt_embeds)
# vanilla loss
loss = F.mse_loss(pred_noise.float(), noise.float(), reduction="mean")
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(prior.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if args.use_ema:
ema_prior.step(prior.parameters())
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
if args.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
ema_prior.store(prior.parameters())
ema_prior.copy_to(prior.parameters())
log_validation(text_encoder, tokenizer, prior, args, accelerator, weight_dtype, global_step)
if args.use_ema:
# Switch back to the original UNet parameters.
ema_prior.restore(prior.parameters())
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
prior = accelerator.unwrap_model(prior)
if args.use_ema:
ema_prior.copy_to(prior.parameters())
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
prior_prior=prior,
prior_text_encoder=accelerator.unwrap_model(text_encoder),
prior_tokenizer=tokenizer,
)
pipeline.prior_pipe.save_pretrained(os.path.join(args.output_dir, "prior_pipeline"))
# Run a final round of inference.
images = []
if args.validation_prompts is not None:
logger.info("Running inference for collecting generated images...")
pipeline = pipeline.to(accelerator.device, torch_dtype=weight_dtype)
pipeline.set_progress_bar_config(disable=True)
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(
args.validation_prompts[i],
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
generator=generator,
width=args.resolution,
height=args.resolution,
).images[0]
images.append(image)
if args.push_to_hub:
save_model_card(args, repo_id, images, repo_folder=args.output_dir)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/wuerstchen
|
hf_public_repos/diffusers/examples/wuerstchen/text_to_image/train_text_to_image_lora_prior.py
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import logging
import math
import os
import random
import shutil
from pathlib import Path
import datasets
import numpy as np
import torch
import torch.nn.functional as F
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState, is_initialized
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, hf_hub_download, upload_folder
from modeling_efficient_net_encoder import EfficientNetEncoder
from torchvision import transforms
from tqdm import tqdm
from transformers import CLIPTextModel, PreTrainedTokenizerFast
from transformers.utils import ContextManagers
from diffusers import AutoPipelineForText2Image, DDPMWuerstchenScheduler, WuerstchenPriorPipeline
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.optimization import get_scheduler
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPrior
from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
from diffusers.utils.logging import set_verbosity_error, set_verbosity_info
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("0.24.0.dev0")
logger = get_logger(__name__, log_level="INFO")
DATASET_NAME_MAPPING = {
"lambdalabs/pokemon-blip-captions": ("image", "text"),
}
def save_model_card(
args,
repo_id: str,
images=None,
repo_folder=None,
):
img_str = ""
if len(images) > 0:
image_grid = make_image_grid(images, 1, len(args.validation_prompts))
image_grid.save(os.path.join(repo_folder, "val_imgs_grid.png"))
img_str += "\n"
yaml = f"""
---
license: mit
base_model: {args.pretrained_prior_model_name_or_path}
datasets:
- {args.dataset_name}
tags:
- wuerstchen
- text-to-image
- diffusers
- lora
inference: true
---
"""
model_card = f"""
# LoRA Finetuning - {repo_id}
This pipeline was finetuned from **{args.pretrained_prior_model_name_or_path}** on the **{args.dataset_name}** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: {args.validation_prompts}: \n
{img_str}
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"{args.pretrained_decoder_model_name_or_path}", torch_dtype={args.weight_dtype}
)
# load lora weights from folder:
pipeline.prior_pipe.load_lora_weights("{repo_id}", torch_dtype={args.weight_dtype})
image = pipeline(prompt=prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* LoRA rank: {args.rank}
* Epochs: {args.num_train_epochs}
* Learning rate: {args.learning_rate}
* Batch size: {args.train_batch_size}
* Gradient accumulation steps: {args.gradient_accumulation_steps}
* Image resolution: {args.resolution}
* Mixed-precision: {args.mixed_precision}
"""
wandb_info = ""
if is_wandb_available():
wandb_run_url = None
if wandb.run is not None:
wandb_run_url = wandb.run.url
if wandb_run_url is not None:
wandb_info = f"""
More information on all the CLI arguments and the environment are available on your [`wandb` run page]({wandb_run_url}).
"""
model_card += wandb_info
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def log_validation(text_encoder, tokenizer, attn_processors, args, accelerator, weight_dtype, epoch):
logger.info("Running validation... ")
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
prior_text_encoder=accelerator.unwrap_model(text_encoder),
prior_tokenizer=tokenizer,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.prior_prior.set_attn_processor(attn_processors)
pipeline.set_progress_bar_config(disable=True)
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(
args.validation_prompts[i],
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
generator=generator,
height=args.resolution,
width=args.resolution,
).images[0]
images.append(image)
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")
elif tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
for i, image in enumerate(images)
]
}
)
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
torch.cuda.empty_cache()
return images
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of finetuning Würstchen Prior.")
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--pretrained_decoder_model_name_or_path",
type=str,
default="warp-ai/wuerstchen",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_prior_model_name_or_path",
type=str,
default="warp-ai/wuerstchen-prior",
required=False,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing an image."
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--validation_prompts",
type=str,
default=None,
nargs="+",
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
)
parser.add_argument(
"--output_dir",
type=str,
default="wuerstchen-model-finetuned-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="learning rate",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay",
type=float,
default=0.0,
required=False,
help="weight decay_to_use",
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--validation_epochs",
type=int,
default=5,
help="Run validation every X epochs.",
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Need either a dataset name or a training folder.")
return args
def main():
args = parse_args()
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(
total_limit=args.checkpoints_total_limit, project_dir=args.output_dir, logging_dir=logging_dir
)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load scheduler, effnet, tokenizer, clip_model
noise_scheduler = DDPMWuerstchenScheduler()
tokenizer = PreTrainedTokenizerFast.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="tokenizer"
)
def deepspeed_zero_init_disabled_context_manager():
"""
returns either a context list that includes one that will disable zero.Init or an empty context list
"""
deepspeed_plugin = AcceleratorState().deepspeed_plugin if is_initialized() else None
if deepspeed_plugin is None:
return []
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
pretrained_checkpoint_file = hf_hub_download("dome272/wuerstchen", filename="model_v2_stage_b.pt")
state_dict = torch.load(pretrained_checkpoint_file, map_location="cpu")
image_encoder = EfficientNetEncoder()
image_encoder.load_state_dict(state_dict["effnet_state_dict"])
image_encoder.eval()
text_encoder = CLIPTextModel.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="text_encoder", torch_dtype=weight_dtype
).eval()
# Freeze text_encoder, cast to weight_dtype and image_encoder and move to device
text_encoder.requires_grad_(False)
image_encoder.requires_grad_(False)
image_encoder.to(accelerator.device, dtype=weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
# load prior model, cast to weight_dtype and move to device
prior = WuerstchenPrior.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
prior.to(accelerator.device, dtype=weight_dtype)
# lora attn processor
lora_attn_procs = {}
for name in prior.attn_processors.keys():
lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=prior.config["c"], rank=args.rank)
prior.set_attn_processor(lora_attn_procs)
lora_layers = AttnProcsLayers(prior.attn_processors)
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
lora_layers.parameters(),
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
data_files = {}
if args.train_data_dir is not None:
data_files["train"] = os.path.join(args.train_data_dir, "**")
dataset = load_dataset(
"imagefolder",
data_files=data_files,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# Get the column names for input/target.
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
if args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images
def tokenize_captions(examples, is_train=True):
captions = []
for caption in examples[caption_column]:
if isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
else:
raise ValueError(
f"Caption column `{caption_column}` should contain either strings or lists of strings."
)
inputs = tokenizer(
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
)
text_input_ids = inputs.input_ids
text_mask = inputs.attention_mask.bool()
return text_input_ids, text_mask
effnet_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR, antialias=True),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[image_column]]
examples["effnet_pixel_values"] = [effnet_transforms(image) for image in images]
examples["text_input_ids"], examples["text_mask"] = tokenize_captions(examples)
return examples
with accelerator.main_process_first():
if args.max_train_samples is not None:
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
# Set the training transforms
train_dataset = dataset["train"].with_transform(preprocess_train)
def collate_fn(examples):
effnet_pixel_values = torch.stack([example["effnet_pixel_values"] for example in examples])
effnet_pixel_values = effnet_pixel_values.to(memory_format=torch.contiguous_format).float()
text_input_ids = torch.stack([example["text_input_ids"] for example in examples])
text_mask = torch.stack([example["text_mask"] for example in examples])
return {"effnet_pixel_values": effnet_pixel_values, "text_input_ids": text_input_ids, "text_mask": text_mask}
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
)
lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
lora_layers, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
tracker_config.pop("validation_prompts")
accelerator.init_trackers(args.tracker_project_name, tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, args.num_train_epochs):
prior.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % args.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(prior):
# Convert images to latent space
text_input_ids, text_mask, effnet_images = (
batch["text_input_ids"],
batch["text_mask"],
batch["effnet_pixel_values"].to(weight_dtype),
)
with torch.no_grad():
text_encoder_output = text_encoder(text_input_ids, attention_mask=text_mask)
prompt_embeds = text_encoder_output.last_hidden_state
image_embeds = image_encoder(effnet_images)
# scale
image_embeds = image_embeds.add(1.0).div(42.0)
# Sample noise that we'll add to the image_embeds
noise = torch.randn_like(image_embeds)
bsz = image_embeds.shape[0]
# Sample a random timestep for each image
timesteps = torch.rand((bsz,), device=image_embeds.device, dtype=weight_dtype)
# add noise to latent
noisy_latents = noise_scheduler.add_noise(image_embeds, noise, timesteps)
# Predict the noise residual and compute losscd
pred_noise = prior(noisy_latents, timesteps, prompt_embeds)
# vanilla loss
loss = F.mse_loss(pred_noise.float(), noise.float(), reduction="mean")
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(lora_layers.parameters(), args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
log_validation(
text_encoder, tokenizer, prior.attn_processors, args, accelerator, weight_dtype, global_step
)
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
prior = prior.to(torch.float32)
WuerstchenPriorPipeline.save_lora_weights(
os.path.join(args.output_dir, "prior_lora"),
unet_lora_layers=lora_layers,
)
# Run a final round of inference.
images = []
if args.validation_prompts is not None:
logger.info("Running inference for collecting generated images...")
pipeline = AutoPipelineForText2Image.from_pretrained(
args.pretrained_decoder_model_name_or_path,
prior_text_encoder=accelerator.unwrap_model(text_encoder),
prior_tokenizer=tokenizer,
)
pipeline = pipeline.to(accelerator.device, torch_dtype=weight_dtype)
# load lora weights
pipeline.prior_pipe.load_lora_weights(os.path.join(args.output_dir, "prior_lora"))
pipeline.set_progress_bar_config(disable=True)
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(
args.validation_prompts[i],
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
generator=generator,
width=args.resolution,
height=args.resolution,
).images[0]
images.append(image)
if args.push_to_hub:
save_model_card(args, repo_id, images, repo_folder=args.output_dir)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
main()
| 0
|
hf_public_repos/diffusers/examples/wuerstchen
|
hf_public_repos/diffusers/examples/wuerstchen/text_to_image/requirements.txt
|
accelerate>=0.16.0
torchvision
transformers>=4.25.1
wandb
huggingface-cli
bitsandbytes
deepspeed
| 0
|
hf_public_repos/diffusers/examples
|
hf_public_repos/diffusers/examples/t2i_adapter/README_sdxl.md
|
# T2I-Adapter training example for Stable Diffusion XL (SDXL)
The `train_t2i_adapter_sdxl.py` script shows how to implement the [T2I-Adapter training procedure](https://hf.co/papers/2302.08453) for [Stable Diffusion XL](https://huggingface.co/papers/2307.01952).
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
```
Then cd in the `examples/t2i_adapter` folder and run
```bash
pip install -r requirements.txt
```
And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
```bash
accelerate config
```
Or for a default accelerate configuration without answering questions about your environment
```bash
accelerate config default
```
Or if your environment doesn't support an interactive shell (e.g., a notebook)
```python
from accelerate.utils import write_basic_config
write_basic_config()
```
When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
## Circle filling dataset
The original dataset is hosted in the [ControlNet repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip). We re-uploaded it to be compatible with `datasets` [here](https://huggingface.co/datasets/fusing/fill50k). Note that `datasets` handles dataloading within the training script.
## Training
Our training examples use two test conditioning images. They can be downloaded by running
```sh
wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
```
Then run `huggingface-cli login` to log into your Hugging Face account. This is needed to be able to push the trained T2IAdapter parameters to Hugging Face Hub.
```bash
export MODEL_DIR="stabilityai/stable-diffusion-xl-base-1.0"
export OUTPUT_DIR="path to save model"
accelerate launch train_t2i_adapter_sdxl.py \
--pretrained_model_name_or_path=$MODEL_DIR \
--output_dir=$OUTPUT_DIR \
--dataset_name=fusing/fill50k \
--mixed_precision="fp16" \
--resolution=1024 \
--learning_rate=1e-5 \
--max_train_steps=15000 \
--validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
--validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
--validation_steps=100 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--report_to="wandb" \
--seed=42 \
--push_to_hub
```
To better track our training experiments, we're using the following flags in the command above:
* `report_to="wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.
* `validation_image`, `validation_prompt`, and `validation_steps` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
Our experiments were conducted on a single 40GB A100 GPU.
### Inference
Once training is done, we can perform inference like so:
```python
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteSchedulerTest
from diffusers.utils import load_image
import torch
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
adapter_path = "path to adapter"
adapter = T2IAdapter.from_pretrained(adapter_path, torch_dtype=torch.float16)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
base_model_path, adapter=adapter, torch_dtype=torch.float16
)
# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = EulerAncestralDiscreteSchedulerTest.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed or when using Torch 2.0.
pipe.enable_xformers_memory_efficient_attention()
# memory optimization.
pipe.enable_model_cpu_offload()
control_image = load_image("./conditioning_image_1.png")
prompt = "pale golden rod circle with old lace background"
# generate image
generator = torch.manual_seed(0)
image = pipe(
prompt, num_inference_steps=20, generator=generator, image=control_image
).images[0]
image.save("./output.png")
```
## Notes
### Specifying a better VAE
SDXL's VAE is known to suffer from numerical instability issues. This is why we also expose a CLI argument namely `--pretrained_vae_model_name_or_path` that lets you specify the location of a better VAE (such as [this one](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
| 0
|
hf_public_repos/diffusers/examples
|
hf_public_repos/diffusers/examples/t2i_adapter/README.md
|
We don't yet support training T2I-Adapters on Stable Diffusion yet. For training T2I-Adapters on Stable Diffusion XL, refer [here](./README_sdxl.md).
| 0
|
hf_public_repos/diffusers/examples
|
hf_public_repos/diffusers/examples/t2i_adapter/train_t2i_adapter_sdxl.py
|
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import functools
import gc
import logging
import math
import os
import random
import shutil
from pathlib import Path
import accelerate
import numpy as np
import torch
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionXLAdapterPipeline,
T2IAdapter,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
MAX_SEQ_LENGTH = 77
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("0.24.0.dev0")
logger = get_logger(__name__)
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
def log_validation(vae, unet, adapter, args, accelerator, weight_dtype, step):
logger.info("Running validation... ")
adapter = accelerator.unwrap_model(adapter)
pipeline = StableDiffusionXLAdapterPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
unet=unet,
adapter=adapter,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
if args.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
if len(args.validation_image) == len(args.validation_prompt):
validation_images = args.validation_image
validation_prompts = args.validation_prompt
elif len(args.validation_image) == 1:
validation_images = args.validation_image * len(args.validation_prompt)
validation_prompts = args.validation_prompt
elif len(args.validation_prompt) == 1:
validation_images = args.validation_image
validation_prompts = args.validation_prompt * len(args.validation_image)
else:
raise ValueError(
"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
)
image_logs = []
for validation_prompt, validation_image in zip(validation_prompts, validation_images):
validation_image = Image.open(validation_image).convert("RGB")
validation_image = validation_image.resize((args.resolution, args.resolution))
images = []
for _ in range(args.num_validation_images):
with torch.autocast("cuda"):
image = pipeline(
prompt=validation_prompt, image=validation_image, num_inference_steps=20, generator=generator
).images[0]
images.append(image)
image_logs.append(
{"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
)
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
for log in image_logs:
images = log["images"]
validation_prompt = log["validation_prompt"]
validation_image = log["validation_image"]
formatted_images = []
formatted_images.append(np.asarray(validation_image))
for image in images:
formatted_images.append(np.asarray(image))
formatted_images = np.stack(formatted_images)
tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
elif tracker.name == "wandb":
formatted_images = []
for log in image_logs:
images = log["images"]
validation_prompt = log["validation_prompt"]
validation_image = log["validation_image"]
formatted_images.append(wandb.Image(validation_image, caption="adapter conditioning"))
for image in images:
image = wandb.Image(image, caption=validation_prompt)
formatted_images.append(image)
tracker.log({"validation": formatted_images})
else:
logger.warn(f"image logging not implemented for {tracker.name}")
del pipeline
gc.collect()
torch.cuda.empty_cache()
return image_logs
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
img_str = ""
if image_logs is not None:
img_str = "You can find some example images below.\n"
for i, log in enumerate(image_logs):
images = log["images"]
validation_prompt = log["validation_prompt"]
validation_image = log["validation_image"]
validation_image.save(os.path.join(repo_folder, "image_control.png"))
img_str += f"prompt: {validation_prompt}\n"
images = [validation_image] + images
image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
img_str += f"\n"
yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- t2iadapter
inference: true
---
"""
model_card = f"""
# t2iadapter-{repo_id}
These are t2iadapter weights trained on {base_model} with new type of conditioning.
{img_str}
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--adapter_model_name_or_path",
type=str,
default=None,
help="Path to pretrained adapter model or model identifier from huggingface.co/models."
" If not specified adapter weights are initialized w.r.t the configurations of SDXL.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help=(
"Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
" float32 precision."
),
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--output_dir",
type=str,
default="t2iadapter-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--detection_resolution",
type=int,
default=None,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--crops_coords_top_left_h",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--crops_coords_top_left_w",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
"instructions."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=3,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-6,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=1,
help=("Number of subprocesses to use for data loading."),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--image_column", type=str, default="image", help="The column of the dataset containing the target image."
)
parser.add_argument(
"--conditioning_image_column",
type=str,
default="conditioning_image",
help="The column of the dataset containing the adapter conditioning image.",
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing a caption or a list of captions.",
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--proportion_empty_prompts",
type=float,
default=0,
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
nargs="+",
help=(
"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
" Provide either a matching number of `--validation_image`s, a single `--validation_image`"
" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
),
)
parser.add_argument(
"--validation_image",
type=str,
default=None,
nargs="+",
help=(
"A set of paths to the t2iadapter conditioning image be evaluated every `--validation_steps`"
" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
" a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
" `--validation_image` that will be used with all `--validation_prompt`s."
),
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
)
parser.add_argument(
"--validation_steps",
type=int,
default=100,
help=(
"Run validation every X steps. Validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`"
" and logging the images."
),
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="sd_xl_train_t2iadapter",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
if args.dataset_name is not None and args.train_data_dir is not None:
raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
if args.validation_prompt is not None and args.validation_image is None:
raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
if args.validation_prompt is None and args.validation_image is not None:
raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
if (
args.validation_image is not None
and args.validation_prompt is not None
and len(args.validation_image) != 1
and len(args.validation_prompt) != 1
and len(args.validation_image) != len(args.validation_prompt)
):
raise ValueError(
"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
" or the same number of `--validation_prompt`s and `--validation_image`s"
)
if args.resolution % 8 != 0:
raise ValueError(
"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the t2iadapter encoder."
)
return args
def get_train_dataset(args, accelerator):
# Get the datasets: you can either provide your own training and evaluation files (see below)
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
args.dataset_name,
args.dataset_config_name,
cache_dir=args.cache_dir,
)
else:
if args.train_data_dir is not None:
dataset = load_dataset(
args.train_data_dir,
cache_dir=args.cache_dir,
)
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
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 '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
if args.caption_column is None:
caption_column = column_names[1]
logger.info(f"caption column defaulting to {caption_column}")
else:
caption_column = args.caption_column
if caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
if args.conditioning_image_column is None:
conditioning_image_column = column_names[2]
logger.info(f"conditioning image column defaulting to {conditioning_image_column}")
else:
conditioning_image_column = args.conditioning_image_column
if conditioning_image_column not in column_names:
raise ValueError(
f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
with accelerator.main_process_first():
train_dataset = dataset["train"].shuffle(seed=args.seed)
if args.max_train_samples is not None:
train_dataset = train_dataset.select(range(args.max_train_samples))
return train_dataset
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):
prompt_embeds_list = []
captions = []
for caption in prompt_batch:
if random.random() < proportion_empty_prompts:
captions.append("")
elif isinstance(caption, str):
captions.append(caption)
elif isinstance(caption, (list, np.ndarray)):
# take a random caption if there are multiple
captions.append(random.choice(caption) if is_train else caption[0])
with torch.no_grad():
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
captions,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
)
# 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]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return prompt_embeds, pooled_prompt_embeds
def prepare_train_dataset(dataset, accelerator):
image_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
conditioning_image_transforms = transforms.Compose(
[
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(args.resolution),
transforms.ToTensor(),
]
)
def preprocess_train(examples):
images = [image.convert("RGB") for image in examples[args.image_column]]
images = [image_transforms(image) for image in images]
conditioning_images = [image.convert("RGB") for image in examples[args.conditioning_image_column]]
conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
examples["pixel_values"] = images
examples["conditioning_pixel_values"] = conditioning_images
return examples
with accelerator.main_process_first():
dataset = dataset.with_transform(preprocess_train)
return dataset
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
prompt_ids = torch.stack([torch.tensor(example["prompt_embeds"]) for example in examples])
add_text_embeds = torch.stack([torch.tensor(example["text_embeds"]) for example in examples])
add_time_ids = torch.stack([torch.tensor(example["time_ids"]) for example in examples])
return {
"pixel_values": pixel_values,
"conditioning_pixel_values": conditioning_pixel_values,
"prompt_ids": prompt_ids,
"unet_added_conditions": {"text_embeds": add_text_embeds, "time_ids": add_time_ids},
}
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name,
exist_ok=True,
token=args.hub_token,
private=True,
).repo_id
# Load the tokenizers
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=args.revision,
use_fast=False,
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
# Load scheduler and models
noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
)
vae_path = (
args.pretrained_model_name_or_path
if args.pretrained_vae_model_name_or_path is None
else args.pretrained_vae_model_name_or_path
)
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,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
if args.adapter_model_name_or_path:
logger.info("Loading existing adapter weights.")
t2iadapter = T2IAdapter.from_pretrained(args.adapter_model_name_or_path)
else:
logger.info("Initializing t2iadapter weights.")
t2iadapter = T2IAdapter(
in_channels=3,
channels=(320, 640, 1280, 1280),
num_res_blocks=2,
downscale_factor=16,
adapter_type="full_adapter_xl",
)
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
i = len(weights) - 1
while len(weights) > 0:
weights.pop()
model = models[i]
sub_dir = "t2iadapter"
model.save_pretrained(os.path.join(output_dir, sub_dir))
i -= 1
def load_model_hook(models, input_dir):
while len(models) > 0:
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = T2IAdapter.from_pretrained(os.path.join(input_dir, "t2iadapter"))
if args.control_type != "style":
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
t2iadapter.train()
unet.train()
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# Check that all trainable models are in full precision
low_precision_error_string = (
" Please make sure to always have all model weights in full float32 precision when starting training - even if"
" doing mixed precision training, copy of the weights should still be float32."
)
if accelerator.unwrap_model(t2iadapter).dtype != torch.float32:
raise ValueError(
f"Controlnet loaded as datatype {accelerator.unwrap_model(t2iadapter).dtype}. {low_precision_error_string}"
)
# 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:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
# Optimizer creation
params_to_optimize = t2iadapter.parameters()
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae, unet and text_encoder to device and cast to weight_dtype
# The VAE is in float32 to avoid NaN losses.
if args.pretrained_vae_model_name_or_path is not None:
vae.to(accelerator.device, dtype=weight_dtype)
else:
vae.to(accelerator.device, dtype=torch.float32)
unet.to(accelerator.device, dtype=weight_dtype)
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
# Here, we compute not just the text embeddings but also the additional embeddings
# needed for the SD XL UNet to operate.
def compute_embeddings(batch, proportion_empty_prompts, text_encoders, tokenizers, is_train=True):
original_size = (args.resolution, args.resolution)
target_size = (args.resolution, args.resolution)
crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)
prompt_batch = batch[args.caption_column]
prompt_embeds, pooled_prompt_embeds = encode_prompt(
prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train
)
add_text_embeds = pooled_prompt_embeds
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_time_ids = torch.tensor([add_time_ids])
prompt_embeds = prompt_embeds.to(accelerator.device)
add_text_embeds = add_text_embeds.to(accelerator.device)
add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)
add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)
unet_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
return {"prompt_embeds": prompt_embeds, **unet_added_cond_kwargs}
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
timesteps = timesteps.to(accelerator.device)
step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
sigma = sigmas[step_indices].flatten()
while len(sigma.shape) < n_dim:
sigma = sigma.unsqueeze(-1)
return sigma
# Let's first compute all the embeddings so that we can free up the text encoders
# from memory.
text_encoders = [text_encoder_one, text_encoder_two]
tokenizers = [tokenizer_one, tokenizer_two]
train_dataset = get_train_dataset(args, accelerator)
compute_embeddings_fn = functools.partial(
compute_embeddings,
proportion_empty_prompts=args.proportion_empty_prompts,
text_encoders=text_encoders,
tokenizers=tokenizers,
)
with accelerator.main_process_first():
from datasets.fingerprint import Hasher
# fingerprint used by the cache for the other processes to load the result
# details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
new_fingerprint = Hasher.hash(args)
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
# Then get the training dataset ready to be passed to the dataloader.
train_dataset = prepare_train_dataset(train_dataset, accelerator)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
shuffle=True,
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps,
num_training_steps=args.max_train_steps,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
t2iadapter, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
t2iadapter, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
# tensorboard cannot handle list types for config
tracker_config.pop("validation_prompt")
tracker_config.pop("validation_image")
accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
image_logs = None
for epoch in range(first_epoch, args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(t2iadapter):
if args.pretrained_vae_model_name_or_path is not None:
pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
else:
pixel_values = batch["pixel_values"]
# encode pixel values with batch size of at most 8 to avoid OOM
latents = []
for i in range(0, pixel_values.shape[0], 8):
latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample())
latents = torch.cat(latents, dim=0)
latents = latents * vae.config.scaling_factor
if args.pretrained_vae_model_name_or_path is None:
latents = latents.to(weight_dtype)
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Cubic sampling to sample a random timestep for each image.
# For more details about why cubic sampling is used, refer to section 3.4 of https://arxiv.org/abs/2302.08453
timesteps = torch.rand((bsz,), device=latents.device)
timesteps = (1 - timesteps**3) * noise_scheduler.config.num_train_timesteps
timesteps = timesteps.long().to(noise_scheduler.timesteps.dtype)
timesteps = timesteps.clamp(0, noise_scheduler.config.num_train_timesteps - 1)
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Scale the noisy latents for the UNet
sigmas = get_sigmas(timesteps, len(noisy_latents.shape), noisy_latents.dtype)
inp_noisy_latents = noisy_latents / ((sigmas**2 + 1) ** 0.5)
# Adapter conditioning.
t2iadapter_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
down_block_additional_residuals = t2iadapter(t2iadapter_image)
down_block_additional_residuals = [
sample.to(dtype=weight_dtype) for sample in down_block_additional_residuals
]
# Predict the noise residual
model_pred = unet(
inp_noisy_latents,
timesteps,
encoder_hidden_states=batch["prompt_ids"],
added_cond_kwargs=batch["unet_added_conditions"],
down_block_additional_residuals=down_block_additional_residuals,
).sample
# Denoise the latents
denoised_latents = model_pred * (-sigmas) + noisy_latents
weighing = sigmas**-2.0
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = latents # we are computing loss against denoise latents
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
# MSE loss
loss = torch.mean(
(weighing.float() * (denoised_latents.float() - target.float()) ** 2).reshape(target.shape[0], -1),
dim=1,
)
loss = loss.mean()
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = t2iadapter.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
image_logs = log_validation(
vae,
unet,
t2iadapter,
args,
accelerator,
weight_dtype,
global_step,
)
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
t2iadapter = accelerator.unwrap_model(t2iadapter)
t2iadapter.save_pretrained(args.output_dir)
if args.push_to_hub:
save_model_card(
repo_id,
image_logs=image_logs,
base_model=args.pretrained_model_name_or_path,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
| 0
|
hf_public_repos/diffusers/examples
|
hf_public_repos/diffusers/examples/t2i_adapter/requirements.txt
|
transformers>=4.25.1
accelerate>=0.16.0
safetensors
datasets
torchvision
ftfy
tensorboard
wandb
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/Makefile
|
install-server:
cd server && make install
install-custom-kernels:
if [ "$$BUILD_EXTENSIONS" = "True" ]; then cd server/custom_kernels && python setup.py install; else echo "Custom kernels are disabled, you need to set the BUILD_EXTENSIONS environment variable to 'True' in order to build them. (Please read the docs, kernels might not work on all hardware)"; fi
install-integration-tests:
cd integration-tests && pip install -r requirements.txt
cd clients/python && pip install .
install-router:
cd router && cargo install --path .
install-launcher:
cd launcher && cargo install --path .
install-benchmark:
cd benchmark && cargo install --path .
install: install-server install-router install-launcher install-custom-kernels
server-dev:
cd server && make run-dev
router-dev:
cd router && cargo run -- --port 8080
rust-tests: install-router install-launcher
cargo test
integration-tests: install-integration-tests
pytest -s -vv -m "not private" integration-tests
update-integration-tests: install-integration-tests
pytest -s -vv --snapshot-update integration-tests
python-server-tests:
HF_HUB_ENABLE_HF_TRANSFER=1 pytest -s -vv -m "not private" server/tests
python-client-tests:
pytest clients/python/tests
python-tests: python-server-tests python-client-tests
run-falcon-7b-instruct:
text-generation-launcher --model-id tiiuae/falcon-7b-instruct --port 8080
run-falcon-7b-instruct-quantize:
text-generation-launcher --model-id tiiuae/falcon-7b-instruct --quantize bitsandbytes --port 8080
clean:
rm -rf target aml
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/.dockerignore
|
aml
target
server/transformers
server/flash-attention
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/LICENSE
|
Hugging Face Optimized Inference License 1.0 (HFOILv1.0)
This License Agreement governs the use of the Software and its Modifications. It is a
binding agreement between the Licensor and You.
This License Agreement shall be referred to as Hugging Face Optimized Inference License
1.0 or HFOILv1.0. We may publish revised versions of this License Agreement from time to
time. Each version will be given a distinguished number.
By downloading, accessing, modifying, distributing or otherwise using the Software, You
consent to all of the terms and conditions below. So, if You do not agree with those,
please do not download, access, modify, distribute, or use the Software.
1. PERMISSIONS
You may use, modify and distribute the Software pursuant to the following terms and
conditions:
Copyright License. Subject to the terms and conditions of this License Agreement and where
and as applicable, each Contributor hereby grants You a perpetual, worldwide,
non-exclusive, royalty-free, copyright license to reproduce, prepare, publicly display,
publicly perform, sublicense under the terms herein, and distribute the Software and
Modifications of the Software.
Patent License. Subject to the terms and conditions of this License Agreement and where
and as applicable, each Contributor hereby grants You a perpetual, worldwide,
non-exclusive, royalty-free patent license to make, have made, Use, offer to sell, sell,
import, and otherwise transfer the Software, where such license applies only to those
patent claims licensable by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s) with the Software to
which such Contribution(s) was submitted. If You institute patent litigation against any
entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Software
or a Contribution incorporated within the Software constitutes direct or contributory
patent infringement, then any rights granted to You under this License Agreement for the
Software shall terminate as of the date such litigation is filed.
No other rights. All rights not expressly granted herein are retained.
2. RESTRICTIONS
You may not distribute the Software as a hosted or managed, and paid service, where the
service grants users access to any substantial set of the features or functionality of the
Software. If you wish to do so, You will need to be granted additional rights from the
Licensor which will be subject to a separate mutually agreed agreement.
You may not sublicense the Software under any other terms than those listed in this
License.
3. OBLIGATIONS
When You modify the Software, You agree to: - attach a notice stating the Modifications of
the Software You made; and - attach a notice stating that the Modifications of the
Software are released under this License Agreement.
When You distribute the Software or Modifications of the Software, You agree to: - give
any recipients of the Software a copy of this License Agreement; - retain all Explanatory
Documentation; and if sharing the Modifications of the Software, add Explanatory
Documentation documenting the changes made to create the Modifications of the Software; -
retain all copyright, patent, trademark and attribution notices.
4. MISCELLANEOUS
Termination. Licensor reserves the right to restrict Use of the Software in violation of
this License Agreement, upon which Your licenses will automatically terminate.
Contributions. Unless You explicitly state otherwise, any Contribution intentionally
submitted for inclusion in the Software by You to the Licensor shall be under the terms
and conditions of this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify the terms of any
separate license agreement you may have executed with Licensor regarding such
Contributions.
Trademarks and related. Nothing in this License Agreement permits You (i) to make Use of
Licensors’ trademarks, trade names, or logos, (ii) otherwise suggest endorsement by
Licensor, or (iii) misrepresent the relationship between the parties; and any rights not
expressly granted herein are reserved by the Licensors.
Output You generate. Licensor claims no rights in the Output. You agree not to contravene
any provision as stated in the License Agreement with your Use of the Output.
Disclaimer of Warranty. Except as expressly provided otherwise herein, and to the fullest
extent permitted by law, Licensor provides the Software (and each Contributor provides its
Contributions) AS IS, and Licensor disclaims all warranties or guarantees of any kind,
express or implied, whether arising under any law or from any usage in trade, or otherwise
including but not limited to the implied warranties of merchantability, non-infringement,
quiet enjoyment, fitness for a particular purpose, or otherwise. You are solely
responsible for determining the appropriateness of the Software and Modifications of the
Software for your purposes (including your use or distribution of the Software and
Modifications of the Software), and assume any risks associated with Your exercise of
permissions under this License Agreement.
Limitation of Liability. In no event and under no legal theory, whether in tort (including
negligence), contract, or otherwise, unless required by applicable law (such as deliberate
and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to
You for damages, including any direct, indirect, special, incidental, or consequential
damages of any character arising as a result of this License Agreement or out of the Use
or inability to Use the Software (including but not limited to damages for loss of
goodwill, work stoppage, computer failure or malfunction, model failure or malfunction, or
any and all other commercial damages or losses), even if such Contributor has been advised
of the possibility of such damages.
Accepting Warranty or Additional Liability. While sharing the Software or Modifications of
the Software thereof, You may choose to offer and charge a fee for, acceptance of support,
warranty, indemnity, or other liability obligations and/or rights consistent with this
License Agreement. However, in accepting such obligations, You may act only on Your own
behalf and on Your sole responsibility, not on behalf of Licensor or any other
Contributor, and you hereby agree to indemnify, defend, and hold Licensor and each other
Contributor (and their successors or assigns) harmless for any liability incurred by, or
claims asserted against, such Licensor or Contributor (and their successors or assigns) by
reason of your accepting any such warranty or additional liability.
Severability. This License Agreement is a license of copyright and patent rights and an
agreement in contract between You and the Licensor. If any provision of this License
Agreement is held to be invalid, illegal or unenforceable, the remaining provisions shall
be unaffected thereby and remain valid as if such provision had not been set forth herein.
5. DEFINITIONS
“Contribution” refers to any work of authorship, including the original version of the
Software and any Modifications of the Software that is intentionally submitted to Licensor
for inclusion in the Software by the copyright owner or by an individual or entity
authorized to submit on behalf of the copyright owner. For the purposes of this
definition, “submitted” means any form of electronic, verbal, or written communication
sent to the Licensor or its representatives, including but not limited to communication on
electronic mailing lists, source code control systems, and issue tracking systems that are
managed by, or on behalf of, the Licensor for the purpose of discussing and improving the
Software, but excluding communication that is conspicuously marked or otherwise designated
in writing by the copyright owner as “Not a Contribution.”
“Contributor” refers to Licensor and any individual or entity on behalf of whom a
Contribution has been received by Licensor and subsequently incorporated within the
Software.
“Data” refers to a collection of information extracted from the dataset used with the
Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not
licensed under this License Agreement.
“Explanatory Documentation” refers to any documentation or related information including
but not limited to model cards or data cards dedicated to inform the public about the
characteristics of the Software. Explanatory documentation is not licensed under this
License.
"License Agreement" refers to these terms and conditions.
“Licensor” refers to the rights owners or entity authorized by the rights owners that are
granting the terms and conditions of this License Agreement.
“Model” refers to machine-learning based assemblies (including checkpoints), consisting of
learnt weights and parameters (including optimizer states), corresponding to a model
architecture as embodied in Software source code. Source code is not licensed under this
License Agreement.
“Modifications of the Software” refers to all changes to the Software, including without
limitation derivative works of the Software.
“Output” refers to the results of operating the Software.
“Share” refers to any transmission, reproduction, publication or other sharing of the
Software or Modifications of the Software to a third party, including providing the
Softwaire as a hosted service made available by electronic or other remote means,
including - but not limited to - API-based or web access.
“Software” refers to the software and Model (or parts of either) that Licensor makes
available under this License Agreement.
“Third Parties” refers to individuals or legal entities that are not under common control
with Licensor or You.
“Use” refers to anything You or your representatives do with the Software, including but
not limited to generating any Output, fine tuning, updating, running, training, evaluating
and/or reparametrizing the Model.
"You" (or "Your") refers to an individual or Legal Entity exercising permissions granted
by this License Agreement and/or making Use of the Software for whichever purpose and in
any field of Use.
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/README.md
|
<div align="center">
<a href="https://www.youtube.com/watch?v=jlMAX2Oaht0">
<img width=560 width=315 alt="Making TGI deployment optimal" src="https://huggingface.co/datasets/Narsil/tgi_assets/resolve/main/thumbnail.png">
</a>
# Text Generation Inference
<a href="https://github.com/huggingface/text-generation-inference">
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/huggingface/text-generation-inference?style=social">
</a>
<a href="https://huggingface.github.io/text-generation-inference">
<img alt="Swagger API documentation" src="https://img.shields.io/badge/API-Swagger-informational">
</a>
A Rust, Python and gRPC server for text generation inference. Used in production at [HuggingFace](https://huggingface.co)
to power Hugging Chat, the Inference API and Inference Endpoint.
</div>
## Table of contents
- [Get Started](#get-started)
- [API Documentation](#api-documentation)
- [Using a private or gated model](#using-a-private-or-gated-model)
- [A note on Shared Memory](#a-note-on-shared-memory-shm)
- [Distributed Tracing](#distributed-tracing)
- [Local Install](#local-install)
- [CUDA Kernels](#cuda-kernels)
- [Optimized architectures](#optimized-architectures)
- [Run Falcon](#run-falcon)
- [Run](#run)
- [Quantization](#quantization)
- [Develop](#develop)
- [Testing](#testing)
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and [more](https://huggingface.co/docs/text-generation-inference/supported_models). TGI implements many features, such as:
- Simple launcher to serve most popular LLMs
- Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
- Tensor Parallelism for faster inference on multiple GPUs
- Token streaming using Server-Sent Events (SSE)
- Continuous batching of incoming requests for increased total throughput
- Optimized transformers code for inference using [Flash Attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323)
- [Safetensors](https://github.com/huggingface/safetensors) weight loading
- Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
- Logits warper (temperature scaling, top-p, top-k, repetition penalty, more details see [transformers.LogitsProcessor](https://huggingface.co/docs/transformers/internal/generation_utils#transformers.LogitsProcessor))
- Stop sequences
- Log probabilities
- Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output
- Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance
## Get Started
### Docker
For a detailed starting guide, please see the [Quick Tour](https://huggingface.co/docs/text-generation-inference/quicktour). The easiest way of getting started is using the official Docker container:
```shell
model=HuggingFaceH4/zephyr-7b-beta
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 --model-id $model
```
And then you can make requests like
```bash
curl 127.0.0.1:8080/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json'
```
**Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 11.8 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
**Note:** TGI supports AMD Instinct MI210 and MI250 [to some extent](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.0+rocm --model-id $model` instead of the command above.
To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
```
text-generation-launcher --help
```
### API documentation
You can consult the OpenAPI documentation of the `text-generation-inference` REST API using the `/docs` route.
The Swagger UI is also available at: [https://huggingface.github.io/text-generation-inference](https://huggingface.github.io/text-generation-inference).
### Using a private or gated model
You have the option to utilize the `HUGGING_FACE_HUB_TOKEN` environment variable for configuring the token employed by
`text-generation-inference`. This allows you to gain access to protected resources.
For example, if you want to serve the gated Llama V2 model variants:
1. Go to https://huggingface.co/settings/tokens
2. Copy your cli READ token
3. Export `HUGGING_FACE_HUB_TOKEN=<your cli READ token>`
or with Docker:
```shell
model=meta-llama/Llama-2-7b-chat-hf
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>
docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 --model-id $model
```
### A note on Shared Memory (shm)
[`NCCL`](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html) is a communication framework used by
`PyTorch` to do distributed training/inference. `text-generation-inference` make
use of `NCCL` to enable Tensor Parallelism to dramatically speed up inference for large language models.
In order to share data between the different devices of a `NCCL` group, `NCCL` might fall back to using the host memory if
peer-to-peer using NVLink or PCI is not possible.
To allow the container to use 1G of Shared Memory and support SHM sharing, we add `--shm-size 1g` on the above command.
If you are running `text-generation-inference` inside `Kubernetes`. You can also add Shared Memory to the container by
creating a volume with:
```yaml
- name: shm
emptyDir:
medium: Memory
sizeLimit: 1Gi
```
and mounting it to `/dev/shm`.
Finally, you can also disable SHM sharing by using the `NCCL_SHM_DISABLE=1` environment variable. However, note that
this will impact performance.
### Distributed Tracing
`text-generation-inference` is instrumented with distributed tracing using OpenTelemetry. You can use this feature
by setting the address to an OTLP collector with the `--otlp-endpoint` argument.
### Architecture
### Local install
You can also opt to install `text-generation-inference` locally.
First [install Rust](https://rustup.rs/) and create a Python virtual environment with at least
Python 3.9, e.g. using `conda`:
```shell
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
conda create -n text-generation-inference python=3.9
conda activate text-generation-inference
```
You may also need to install Protoc.
On Linux:
```shell
PROTOC_ZIP=protoc-21.12-linux-x86_64.zip
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
rm -f $PROTOC_ZIP
```
On MacOS, using Homebrew:
```shell
brew install protobuf
```
Then run:
```shell
BUILD_EXTENSIONS=True make install # Install repository and HF/transformer fork with CUDA kernels
make run-falcon-7b-instruct
```
**Note:** on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
```shell
sudo apt-get install libssl-dev gcc -y
```
### CUDA Kernels
The custom CUDA kernels are only tested on NVIDIA A100, AMD MI210 and AMD MI250. If you have any installation or runtime issues, you can remove
the kernels by using the `DISABLE_CUSTOM_KERNELS=True` environment variable.
Be aware that the official Docker image has them enabled by default.
## Optimized architectures
TGI works out of the box to serve optimized models in [this list](https://huggingface.co/docs/text-generation-inference/supported_models).
Other architectures are supported on a best-effort basis using:
`AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")`
or
`AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")`
## Run Falcon
### Run
```shell
make run-falcon-7b-instruct
```
### Quantization
You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:
```shell
make run-falcon-7b-instruct-quantize
```
4bit quantization is available using the [NF4 and FP4 data types from bitsandbytes](https://arxiv.org/pdf/2305.14314.pdf). It can be enabled by providing `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` as a command line argument to `text-generation-launcher`.
## Develop
```shell
make server-dev
make router-dev
```
## Testing
```shell
# python
make python-server-tests
make python-client-tests
# or both server and client tests
make python-tests
# rust cargo tests
make rust-tests
# integration tests
make integration-tests
```
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/Dockerfile
|
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.71 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef as planner
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN cargo chef prepare --recipe-path recipe.json
FROM chef AS builder
ARG GIT_SHA
ARG DOCKER_LABEL
RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \
unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \
unzip -o $PROTOC_ZIP -d /usr/local 'include/*' && \
rm -f $PROTOC_ZIP
COPY --from=planner /usr/src/recipe.json recipe.json
RUN cargo chef cook --release --recipe-path recipe.json
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN cargo build --release
# Python builder
# Adapted from: https://github.com/pytorch/pytorch/blob/master/Dockerfile
FROM nvidia/cuda:12.1.0-devel-ubuntu20.04 as pytorch-install
ARG PYTORCH_VERSION=2.1.1
ARG PYTHON_VERSION=3.10
# Keep in sync with `server/pyproject.toml
ARG CUDA_VERSION=12.1
ARG MAMBA_VERSION=23.3.1-1
ARG CUDA_CHANNEL=nvidia
ARG INSTALL_CHANNEL=pytorch
# Automatically set by buildx
ARG TARGETPLATFORM
ENV PATH /opt/conda/bin:$PATH
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
build-essential \
ca-certificates \
ccache \
curl \
git && \
rm -rf /var/lib/apt/lists/*
# Install conda
# translating Docker's TARGETPLATFORM into mamba arches
RUN case ${TARGETPLATFORM} in \
"linux/arm64") MAMBA_ARCH=aarch64 ;; \
*) MAMBA_ARCH=x86_64 ;; \
esac && \
curl -fsSL -v -o ~/mambaforge.sh -O "https://github.com/conda-forge/miniforge/releases/download/${MAMBA_VERSION}/Mambaforge-${MAMBA_VERSION}-Linux-${MAMBA_ARCH}.sh"
RUN chmod +x ~/mambaforge.sh && \
bash ~/mambaforge.sh -b -p /opt/conda && \
rm ~/mambaforge.sh
# Install pytorch
# On arm64 we exit with an error code
RUN case ${TARGETPLATFORM} in \
"linux/arm64") exit 1 ;; \
*) /opt/conda/bin/conda update -y conda && \
/opt/conda/bin/conda install -c "${INSTALL_CHANNEL}" -c "${CUDA_CHANNEL}" -y "python=${PYTHON_VERSION}" "pytorch=$PYTORCH_VERSION" "pytorch-cuda=$(echo $CUDA_VERSION | cut -d'.' -f 1-2)" ;; \
esac && \
/opt/conda/bin/conda clean -ya
# CUDA kernels builder image
FROM pytorch-install as kernel-builder
ARG MAX_JOBS=8
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
ninja-build \
&& rm -rf /var/lib/apt/lists/*
# Build Flash Attention CUDA kernels
FROM kernel-builder as flash-att-builder
WORKDIR /usr/src
COPY server/Makefile-flash-att Makefile
# Build specific version of flash attention
RUN make build-flash-attention
# Build Flash Attention v2 CUDA kernels
FROM kernel-builder as flash-att-v2-builder
WORKDIR /usr/src
COPY server/Makefile-flash-att-v2 Makefile
# Build specific version of flash attention v2
RUN make build-flash-attention-v2-cuda
# Build Transformers exllama kernels
FROM kernel-builder as exllama-kernels-builder
WORKDIR /usr/src
COPY server/exllama_kernels/ .
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" python setup.py build
# Build Transformers exllama kernels
FROM kernel-builder as exllamav2-kernels-builder
WORKDIR /usr/src
COPY server/exllamav2_kernels/ .
# Build specific version of transformers
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" python setup.py build
# Build Transformers awq kernels
FROM kernel-builder as awq-kernels-builder
WORKDIR /usr/src
COPY server/Makefile-awq Makefile
# Build specific version of transformers
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" make build-awq
# Build eetq kernels
FROM kernel-builder as eetq-kernels-builder
WORKDIR /usr/src
COPY server/Makefile-eetq Makefile
# Build specific version of transformers
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" make build-eetq
# Build Transformers CUDA kernels
FROM kernel-builder as custom-kernels-builder
WORKDIR /usr/src
COPY server/custom_kernels/ .
# Build specific version of transformers
RUN python setup.py build
# Build vllm CUDA kernels
FROM kernel-builder as vllm-builder
WORKDIR /usr/src
COPY server/Makefile-vllm Makefile
# Build specific version of vllm
RUN make build-vllm-cuda
# Text Generation Inference base image
FROM nvidia/cuda:12.1.0-base-ubuntu20.04 as base
# Conda env
ENV PATH=/opt/conda/bin:$PATH \
CONDA_PREFIX=/opt/conda
# Text Generation Inference base env
ENV HUGGINGFACE_HUB_CACHE=/data \
HF_HUB_ENABLE_HF_TRANSFER=1 \
PORT=80
WORKDIR /usr/src
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
libssl-dev \
ca-certificates \
make \
curl \
&& rm -rf /var/lib/apt/lists/*
# Copy conda with PyTorch installed
COPY --from=pytorch-install /opt/conda /opt/conda
# Copy build artifacts from flash attention builder
COPY --from=flash-att-builder /usr/src/flash-attention/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
COPY --from=flash-att-builder /usr/src/flash-attention/csrc/layer_norm/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
COPY --from=flash-att-builder /usr/src/flash-attention/csrc/rotary/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from flash attention v2 builder
COPY --from=flash-att-v2-builder /usr/src/flash-attention-v2/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from custom kernels builder
COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from exllama kernels builder
COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from exllamav2 kernels builder
COPY --from=exllamav2-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from awq kernels builder
COPY --from=awq-kernels-builder /usr/src/llm-awq/awq/kernels/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from eetq kernels builder
COPY --from=eetq-kernels-builder /usr/src/eetq/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy builds artifacts from vllm builder
COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Install flash-attention dependencies
RUN pip install einops --no-cache-dir
# Install server
COPY proto proto
COPY server server
COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements_cuda.txt && \
pip install ".[bnb, accelerate, quantize, peft]" --no-cache-dir
# Install benchmarker
COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark
# Install router
COPY --from=builder /usr/src/target/release/text-generation-router /usr/local/bin/text-generation-router
# Install launcher
COPY --from=builder /usr/src/target/release/text-generation-launcher /usr/local/bin/text-generation-launcher
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
build-essential \
g++ \
&& rm -rf /var/lib/apt/lists/*
# AWS Sagemaker compatible image
FROM base as sagemaker
COPY sagemaker-entrypoint.sh entrypoint.sh
RUN chmod +x entrypoint.sh
ENTRYPOINT ["./entrypoint.sh"]
# Final image
FROM base
ENTRYPOINT ["text-generation-launcher"]
CMD ["--json-output"]
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/Dockerfile_amd
|
# Rust builder
FROM lukemathwalker/cargo-chef:latest-rust-1.71 AS chef
WORKDIR /usr/src
ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
FROM chef as planner
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN cargo chef prepare --recipe-path recipe.json
FROM chef AS builder
ARG GIT_SHA
ARG DOCKER_LABEL
RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \
unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \
unzip -o $PROTOC_ZIP -d /usr/local 'include/*' && \
rm -f $PROTOC_ZIP
COPY --from=planner /usr/src/recipe.json recipe.json
RUN cargo chef cook --release --recipe-path recipe.json
COPY Cargo.toml Cargo.toml
COPY rust-toolchain.toml rust-toolchain.toml
COPY proto proto
COPY benchmark benchmark
COPY router router
COPY launcher launcher
RUN cargo build --release
# Text Generation Inference base image for RoCm
FROM rocm/dev-ubuntu-20.04:5.7 as base
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
build-essential \
ca-certificates \
ccache \
curl \
git \
make \
libssl-dev \
g++ \
# Needed to build VLLM & flash.
rocthrust-dev \
hipsparse-dev \
hipblas-dev && \
rm -rf /var/lib/apt/lists/*
# Keep in sync with `server/pyproject.toml
ARG MAMBA_VERSION=23.1.0-1
ARG PYTORCH_VERSION='2.2.0.dev0'
ARG ROCM_VERSION='5.7'
ARG PYTHON_VERSION='3.10.10'
# Automatically set by buildx
ARG TARGETPLATFORM
ENV PATH /opt/conda/bin:$PATH
# TGI seem to require libssl.so.1.1 instead of libssl.so.3 so we can't use ubuntu 22.04. Ubuntu 20.04 has python==3.8, and TGI requires python>=3.9, hence the need for miniconda.
# Install mamba
# translating Docker's TARGETPLATFORM into mamba arches
RUN case ${TARGETPLATFORM} in \
"linux/arm64") MAMBA_ARCH=aarch64 ;; \
*) MAMBA_ARCH=x86_64 ;; \
esac && \
curl -fsSL -v -o ~/mambaforge.sh -O "https://github.com/conda-forge/miniforge/releases/download/${MAMBA_VERSION}/Mambaforge-${MAMBA_VERSION}-Linux-${MAMBA_ARCH}.sh"
RUN chmod +x ~/mambaforge.sh && \
bash ~/mambaforge.sh -b -p /opt/conda && \
mamba init && \
rm ~/mambaforge.sh
# Install PyTorch nightly (2.2.0.dev2023) compiled against RoCm 5.7, as VLLM can not be compiled with RoCm 5.6.
RUN pip install --pre torch==2.2.0.dev20231106 --index-url https://download.pytorch.org/whl/nightly/rocm5.7
FROM base AS kernel-builder
# Build vllm kernels
FROM kernel-builder AS vllm-builder
WORKDIR /usr/src
COPY server/Makefile-vllm Makefile
# Build specific version of vllm
RUN make build-vllm-rocm
# Build Flash Attention v2 kernels
FROM kernel-builder AS flash-att-v2-builder
WORKDIR /usr/src
COPY server/Makefile-flash-att-v2 Makefile
# Build specific version of flash attention v2
RUN make build-flash-attention-v2-rocm
# Build Transformers CUDA kernels (gpt-neox and bloom)
FROM kernel-builder as custom-kernels-builder
WORKDIR /usr/src
COPY server/custom_kernels/ .
RUN PYTORCH_ROCM_ARCH=gfx90a python setup.py build
FROM base as base-copy
# Text Generation Inference base env
ENV HUGGINGFACE_HUB_CACHE=/data \
HF_HUB_ENABLE_HF_TRANSFER=1 \
PORT=80
# Copy builds artifacts from vllm builder
COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from flash attention v2 builder
COPY --from=flash-att-v2-builder /usr/src/flash-attention-v2/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from custom kernels builder
COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Install flash-attention dependencies
RUN pip install einops --no-cache-dir
# Install server
COPY proto proto
COPY server server
COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements_rocm.txt && \
pip install ".[accelerate, peft]" --no-cache-dir
# Install benchmarker
COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark
# Install router
COPY --from=builder /usr/src/target/release/text-generation-router /usr/local/bin/text-generation-router
# Install launcher
COPY --from=builder /usr/src/target/release/text-generation-launcher /usr/local/bin/text-generation-launcher
# AWS Sagemaker compatible image
FROM base-copy as sagemaker
COPY sagemaker-entrypoint.sh entrypoint.sh
RUN chmod +x entrypoint.sh
ENTRYPOINT ["./entrypoint.sh"]
# Final image
FROM base-copy
ENTRYPOINT ["text-generation-launcher"]
CMD ["--json-output"]
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/update_doc.py
|
import subprocess
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--check", action="store_true")
args = parser.parse_args()
output = subprocess.check_output(["text-generation-launcher", "--help"]).decode(
"utf-8"
)
wrap_code_blocks_flag = "<!-- WRAP CODE BLOCKS -->"
final_doc = f"# Text-generation-launcher arguments\n\n{wrap_code_blocks_flag}\n\n"
lines = output.split("\n")
header = ""
block = []
for line in lines:
if line.startswith(" -") or line.startswith(" -"):
rendered_block = '\n'.join(block)
if header:
final_doc += f"## {header}\n```shell\n{rendered_block}\n```\n"
else:
final_doc += f"```shell\n{rendered_block}\n```\n"
block = []
tokens = line.split("<")
if len(tokens)>1:
header = tokens[-1][:-1]
else:
header = line.split("--")[-1]
header = header.upper().replace("-", "_")
block.append(line)
rendered_block = '\n'.join(block)
final_doc += f"## {header}\n```shell\n{rendered_block}\n```\n"
block = []
filename = "docs/source/basic_tutorials/launcher.md"
if args.check:
with open(filename, "r") as f:
doc = f.read()
if doc != final_doc:
tmp = "launcher.md"
with open(tmp, "w") as g:
g.write(final_doc)
diff = subprocess.run(
["diff", tmp, filename], capture_output=True
).stdout.decode("utf-8")
print(diff)
raise Exception(
"Doc is not up-to-date, run `python update_doc.py` in order to update it"
)
else:
with open(filename, "w") as f:
f.write(final_doc)
if __name__ == "__main__":
main()
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/sagemaker-entrypoint.sh
|
#!/bin/bash
if [[ -z "${HF_MODEL_ID}" ]]; then
echo "HF_MODEL_ID must be set"
exit 1
fi
export MODEL_ID="${HF_MODEL_ID}"
if [[ -n "${HF_MODEL_REVISION}" ]]; then
export REVISION="${HF_MODEL_REVISION}"
fi
if [[ -n "${SM_NUM_GPUS}" ]]; then
export NUM_SHARD="${SM_NUM_GPUS}"
fi
if [[ -n "${HF_MODEL_QUANTIZE}" ]]; then
export QUANTIZE="${HF_MODEL_QUANTIZE}"
fi
if [[ -n "${HF_MODEL_TRUST_REMOTE_CODE}" ]]; then
export TRUST_REMOTE_CODE="${HF_MODEL_TRUST_REMOTE_CODE}"
fi
text-generation-launcher --port 8080
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/Cargo.lock
|
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source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "9aec5da331524158c6d1a4ac0ab1541149c0b9505fde06423b02f5ef0106b9f0"
[[package]]
name = "windows_x86_64_msvc"
version = "0.48.5"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "ed94fce61571a4006852b7389a063ab983c02eb1bb37b47f8272ce92d06d9538"
[[package]]
name = "winreg"
version = "0.50.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "524e57b2c537c0f9b1e69f1965311ec12182b4122e45035b1508cd24d2adadb1"
dependencies = [
"cfg-if",
"windows-sys 0.48.0",
]
[[package]]
name = "zip"
version = "0.6.6"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "760394e246e4c28189f19d488c058bf16f564016aefac5d32bb1f3b51d5e9261"
dependencies = [
"byteorder",
"crc32fast",
"crossbeam-utils",
"flate2",
]
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/rust-toolchain.toml
|
[toolchain]
channel = "1.70.0"
components = ["rustfmt", "clippy"]
| 0
|
hf_public_repos
|
hf_public_repos/text-generation-inference/Cargo.toml
|
[workspace]
members = [
"benchmark",
"router",
"router/client",
"router/grpc-metadata",
"launcher"
]
[workspace.package]
version = "1.1.1"
edition = "2021"
authors = ["Olivier Dehaene"]
homepage = "https://github.com/huggingface/text-generation-inference"
[profile.release]
debug = 1
incremental = true
lto = "off"
panic = "abort"
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/integration-tests/conftest.py
|
import sys
import subprocess
import contextlib
import pytest
import asyncio
import os
import docker
import json
import math
import time
import random
from docker.errors import NotFound
from typing import Optional, List, Dict
from syrupy.extensions.json import JSONSnapshotExtension
from aiohttp import ClientConnectorError, ClientOSError, ServerDisconnectedError
from text_generation import AsyncClient
from text_generation.types import Response, Details, InputToken, Token, BestOfSequence
DOCKER_IMAGE = os.getenv("DOCKER_IMAGE", None)
HUGGING_FACE_HUB_TOKEN = os.getenv("HUGGING_FACE_HUB_TOKEN", None)
DOCKER_VOLUME = os.getenv("DOCKER_VOLUME", "/data")
class ResponseComparator(JSONSnapshotExtension):
rtol = 0.2
def serialize(
self,
data,
*,
exclude=None,
matcher=None,
):
if isinstance(data, List):
data = [d.dict() for d in data]
data = self._filter(
data=data, depth=0, path=(), exclude=exclude, matcher=matcher
)
return json.dumps(data, indent=2, ensure_ascii=False, sort_keys=False) + "\n"
def matches(
self,
*,
serialized_data,
snapshot_data,
) -> bool:
def convert_data(data):
data = json.loads(data)
if isinstance(data, Dict):
return Response(**data)
if isinstance(data, List):
return [Response(**d) for d in data]
raise NotImplementedError
def eq_token(token: Token, other: Token) -> bool:
return (
token.id == other.id
and token.text == other.text
and math.isclose(token.logprob, other.logprob, rel_tol=self.rtol)
and token.special == other.special
)
def eq_prefill_token(prefill_token: InputToken, other: InputToken) -> bool:
try:
return (
prefill_token.id == other.id
and prefill_token.text == other.text
and (
math.isclose(prefill_token.logprob, other.logprob, rel_tol=self.rtol)
if prefill_token.logprob is not None
else prefill_token.logprob == other.logprob
)
)
except TypeError:
return False
def eq_best_of(details: BestOfSequence, other: BestOfSequence) -> bool:
return (
details.finish_reason == other.finish_reason
and details.generated_tokens == other.generated_tokens
and details.seed == other.seed
and len(details.prefill) == len(other.prefill)
and all(
[
eq_prefill_token(d, o)
for d, o in zip(details.prefill, other.prefill)
]
)
and len(details.tokens) == len(other.tokens)
and all([eq_token(d, o) for d, o in zip(details.tokens, other.tokens)])
)
def eq_details(details: Details, other: Details) -> bool:
return (
details.finish_reason == other.finish_reason
and details.generated_tokens == other.generated_tokens
and details.seed == other.seed
and len(details.prefill) == len(other.prefill)
and all(
[
eq_prefill_token(d, o)
for d, o in zip(details.prefill, other.prefill)
]
)
and len(details.tokens) == len(other.tokens)
and all([eq_token(d, o) for d, o in zip(details.tokens, other.tokens)])
and (
len(details.best_of_sequences)
if details.best_of_sequences is not None
else 0
)
== (
len(other.best_of_sequences)
if other.best_of_sequences is not None
else 0
)
and (
all(
[
eq_best_of(d, o)
for d, o in zip(
details.best_of_sequences, other.best_of_sequences
)
]
)
if details.best_of_sequences is not None
else details.best_of_sequences == other.best_of_sequences
)
)
def eq_response(response: Response, other: Response) -> bool:
return response.generated_text == other.generated_text and eq_details(
response.details, other.details
)
serialized_data = convert_data(serialized_data)
snapshot_data = convert_data(snapshot_data)
if not isinstance(serialized_data, List):
serialized_data = [serialized_data]
if not isinstance(snapshot_data, List):
snapshot_data = [snapshot_data]
return len(snapshot_data) == len(serialized_data) and all(
[eq_response(r, o) for r, o in zip(serialized_data, snapshot_data)]
)
class GenerousResponseComparator(ResponseComparator):
# Needed for GPTQ with exllama which has serious numerical fluctuations.
rtol = 0.75
class LauncherHandle:
def __init__(self, port: int):
self.client = AsyncClient(f"http://localhost:{port}")
def _inner_health(self):
raise NotImplementedError
async def health(self, timeout: int = 60):
assert timeout > 0
for _ in range(timeout):
if not self._inner_health():
raise RuntimeError("Launcher crashed")
try:
await self.client.generate("test")
return
except (ClientConnectorError, ClientOSError, ServerDisconnectedError) as e:
time.sleep(1)
raise RuntimeError("Health check failed")
class ContainerLauncherHandle(LauncherHandle):
def __init__(self, docker_client, container_name, port: int):
super(ContainerLauncherHandle, self).__init__(port)
self.docker_client = docker_client
self.container_name = container_name
def _inner_health(self) -> bool:
container = self.docker_client.containers.get(self.container_name)
return container.status in ["running", "created"]
class ProcessLauncherHandle(LauncherHandle):
def __init__(self, process, port: int):
super(ProcessLauncherHandle, self).__init__(port)
self.process = process
def _inner_health(self) -> bool:
return self.process.poll() is None
@pytest.fixture
def response_snapshot(snapshot):
return snapshot.use_extension(ResponseComparator)
@pytest.fixture
def generous_response_snapshot(snapshot):
return snapshot.use_extension(GenerousResponseComparator)
@pytest.fixture(scope="module")
def event_loop():
loop = asyncio.get_event_loop()
yield loop
loop.close()
@pytest.fixture(scope="module")
def launcher(event_loop):
@contextlib.contextmanager
def local_launcher(
model_id: str,
num_shard: Optional[int] = None,
quantize: Optional[str] = None,
trust_remote_code: bool = False,
use_flash_attention: bool = True,
dtype: Optional[str] = None
):
port = random.randint(8000, 10_000)
master_port = random.randint(10_000, 20_000)
shard_uds_path = (
f"/tmp/tgi-tests-{model_id.split('/')[-1]}-{num_shard}-{quantize}-server"
)
args = [
"text-generation-launcher",
"--model-id",
model_id,
"--port",
str(port),
"--master-port",
str(master_port),
"--shard-uds-path",
shard_uds_path,
]
env = os.environ
if num_shard is not None:
args.extend(["--num-shard", str(num_shard)])
if quantize is not None:
args.append("--quantize")
args.append(quantize)
if dtype is not None:
args.append("--dtype")
args.append(dtype)
if trust_remote_code:
args.append("--trust-remote-code")
env["LOG_LEVEL"] = "info,text_generation_router=debug"
if not use_flash_attention:
env["USE_FLASH_ATTENTION"] = "false"
with subprocess.Popen(
args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env
) as process:
yield ProcessLauncherHandle(process, port)
process.terminate()
process.wait(60)
launcher_output = process.stdout.read().decode("utf-8")
print(launcher_output, file=sys.stderr)
process.stdout.close()
process.stderr.close()
if not use_flash_attention:
del env["USE_FLASH_ATTENTION"]
@contextlib.contextmanager
def docker_launcher(
model_id: str,
num_shard: Optional[int] = None,
quantize: Optional[str] = None,
trust_remote_code: bool = False,
use_flash_attention: bool = True,
dtype: Optional[str] = None
):
port = random.randint(8000, 10_000)
args = ["--model-id", model_id, "--env"]
if num_shard is not None:
args.extend(["--num-shard", str(num_shard)])
if quantize is not None:
args.append("--quantize")
args.append(quantize)
if dtype is not None:
args.append("--dtype")
args.append(dtype)
if trust_remote_code:
args.append("--trust-remote-code")
client = docker.from_env()
container_name = f"tgi-tests-{model_id.split('/')[-1]}-{num_shard}-{quantize}"
try:
container = client.containers.get(container_name)
container.stop()
container.wait()
except NotFound:
pass
gpu_count = num_shard if num_shard is not None else 1
env = {"LOG_LEVEL": "info,text_generation_router=debug"}
if not use_flash_attention:
env["USE_FLASH_ATTENTION"] = "false"
if HUGGING_FACE_HUB_TOKEN is not None:
env["HUGGING_FACE_HUB_TOKEN"] = HUGGING_FACE_HUB_TOKEN
volumes = []
if DOCKER_VOLUME:
volumes = [f"{DOCKER_VOLUME}:/data"]
container = client.containers.run(
DOCKER_IMAGE,
command=args,
name=container_name,
environment=env,
auto_remove=False,
detach=True,
device_requests=[
docker.types.DeviceRequest(count=gpu_count, capabilities=[["gpu"]])
],
volumes=volumes,
ports={"80/tcp": port},
shm_size="1G"
)
yield ContainerLauncherHandle(client, container.name, port)
if not use_flash_attention:
del env["USE_FLASH_ATTENTION"]
try:
container.stop()
container.wait()
except NotFound:
pass
container_output = container.logs().decode("utf-8")
print(container_output, file=sys.stderr)
container.remove()
if DOCKER_IMAGE is not None:
return docker_launcher
return local_launcher
@pytest.fixture(scope="module")
def generate_load():
async def generate_load_inner(
client: AsyncClient, prompt: str, max_new_tokens: int, n: int
) -> List[Response]:
futures = [
client.generate(
prompt, max_new_tokens=max_new_tokens, decoder_input_details=True
)
for _ in range(n)
]
return await asyncio.gather(*futures)
return generate_load_inner
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/integration-tests/pytest.ini
|
[pytest]
addopts = --snapshot-warn-unused
asyncio_mode = auto
markers =
private: marks tests as requiring an admin hf token (deselect with '-m "not private"')
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/integration-tests/poetry.lock
|
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]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
content-hash = "bdad1d22d29138010cd6b11e1b92dc0630b35634422413a8456dc85a15bee05e"
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/integration-tests/requirements.txt
|
aiohttp==3.8.5 ; python_version >= "3.9" and python_version < "3.13"
aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.13"
async-timeout==4.0.3 ; python_version >= "3.9" and python_version < "3.13"
attrs==23.1.0 ; python_version >= "3.9" and python_version < "3.13"
certifi==2023.7.22 ; python_version >= "3.9" and python_version < "3.13"
charset-normalizer==3.2.0 ; python_version >= "3.9" and python_version < "3.13"
colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
colored==1.4.4 ; python_version >= "3.9" and python_version < "3.13"
docker==6.1.3 ; python_version >= "3.9" and python_version < "3.13"
exceptiongroup==1.1.3 ; python_version >= "3.9" and python_version < "3.11"
filelock==3.12.3 ; python_version >= "3.9" and python_version < "3.13"
frozenlist==1.4.0 ; python_version >= "3.9" and python_version < "3.13"
fsspec==2023.6.0 ; python_version >= "3.9" and python_version < "3.13"
huggingface-hub==0.16.4 ; python_version >= "3.9" and python_version < "3.13"
idna==3.4 ; python_version >= "3.9" and python_version < "3.13"
iniconfig==2.0.0 ; python_version >= "3.9" and python_version < "3.13"
multidict==6.0.4 ; python_version >= "3.9" and python_version < "3.13"
packaging==23.1 ; python_version >= "3.9" and python_version < "3.13"
pluggy==1.3.0 ; python_version >= "3.9" and python_version < "3.13"
pydantic==1.10.12 ; python_version >= "3.9" and python_version < "3.13"
pytest-asyncio==0.21.1 ; python_version >= "3.9" and python_version < "3.13"
pytest==7.4.0 ; python_version >= "3.9" and python_version < "3.13"
pywin32==306 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
syrupy==4.0.1 ; python_version >= "3.9" and python_version < "3.13"
text-generation==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
tomli==2.0.1 ; python_version >= "3.9" and python_version < "3.11"
tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.7.1 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.0.4 ; python_version >= "3.9" and python_version < "3.13"
websocket-client==1.6.2 ; python_version >= "3.9" and python_version < "3.13"
yarl==1.9.2 ; python_version >= "3.9" and python_version < "3.13"
| 0
|
hf_public_repos/text-generation-inference
|
hf_public_repos/text-generation-inference/integration-tests/pyproject.toml
|
[tool.poetry]
name = "text-generation-integration-tests"
version = "1.1.1"
description = "Text Generation Inference integration tests"
authors = ["Nicolas Patry <nicolas@huggingface.co>"]
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
syrupy = "4.0.1"
text-generation = "^0.6.0"
pytest = "^7.4.0"
pytest-asyncio = "^0.21.1"
docker = "^6.1.3"
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_neox_sharded.py
|
import pytest
@pytest.fixture(scope="module")
def neox_sharded_handle(launcher):
with launcher(
"OpenAssistant/oasst-sft-1-pythia-12b", num_shard=2, use_flash_attention=False
) as handle:
yield handle
@pytest.fixture(scope="module")
async def neox_sharded(neox_sharded_handle):
await neox_sharded_handle.health(300)
return neox_sharded_handle.client
@pytest.mark.skip
@pytest.mark.asyncio
async def test_neox(neox_sharded, response_snapshot):
response = await neox_sharded.generate(
"<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.skip
@pytest.mark.asyncio
async def test_neox_load(neox_sharded, generate_load, response_snapshot):
responses = await generate_load(
neox_sharded,
"<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>",
max_new_tokens=10,
n=4,
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_llama_gptq.py
|
import pytest
@pytest.fixture(scope="module")
def flash_llama_gptq_handle(launcher):
with launcher("huggingface/llama-7b-gptq", num_shard=2, quantize="gptq") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_gptq(flash_llama_gptq_handle):
await flash_llama_gptq_handle.health(300)
return flash_llama_gptq_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_gptq(flash_llama_gptq, response_snapshot):
response = await flash_llama_gptq.generate(
"Test request", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_gptq_all_params(flash_llama_gptq, response_snapshot):
response = await flash_llama_gptq.generate(
"Test request",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_gptq_load(
flash_llama_gptq, generate_load, response_snapshot
):
responses = await generate_load(
flash_llama_gptq, "Test request", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_bloom_560m.py
|
import pytest
@pytest.fixture(scope="module")
def bloom_560_handle(launcher):
with launcher("bigscience/bloom-560m") as handle:
yield handle
@pytest.fixture(scope="module")
async def bloom_560(bloom_560_handle):
await bloom_560_handle.health(240)
return bloom_560_handle.client
@pytest.mark.asyncio
async def test_bloom_560m(bloom_560, response_snapshot):
response = await bloom_560.generate(
"Pour déguster un ortolan, il faut tout d'abord",
max_new_tokens=10,
top_p=0.9,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_bloom_560m_all_params(bloom_560, response_snapshot):
response = await bloom_560.generate(
"Pour déguster un ortolan, il faut tout d'abord",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_bloom_560m_load(bloom_560, generate_load, response_snapshot):
responses = await generate_load(
bloom_560,
"Pour déguster un ortolan, il faut tout d'abord",
max_new_tokens=10,
n=4,
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_mistral.py
|
import pytest
@pytest.fixture(scope="module")
def flash_mistral_handle(launcher):
with launcher("mistralai/Mistral-7B-Instruct-v0.1") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_mistral(flash_mistral_handle):
await flash_mistral_handle.health(300)
return flash_mistral_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_mistral(flash_mistral, response_snapshot):
response = await flash_mistral.generate(
"Test request", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_mistral_all_params(flash_mistral, response_snapshot):
response = await flash_mistral.generate(
"Test request",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_mistral_load(flash_mistral, generate_load, response_snapshot):
responses = await generate_load(
flash_mistral, "Test request", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_starcoder.py
|
import pytest
@pytest.fixture(scope="module")
def flash_starcoder_handle(launcher):
with launcher("bigcode/starcoder", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_starcoder(flash_starcoder_handle):
await flash_starcoder_handle.health(300)
return flash_starcoder_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_starcoder(flash_starcoder, response_snapshot):
response = await flash_starcoder.generate(
"def print_hello", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_starcoder_default_params(flash_starcoder, response_snapshot):
response = await flash_starcoder.generate(
"def print_hello",
max_new_tokens=60,
temperature=0.2,
top_p=0.95,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 60
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_starcoder_load(flash_starcoder, generate_load, response_snapshot):
responses = await generate_load(
flash_starcoder, "def print_hello", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_santacoder.py
|
import pytest
@pytest.fixture(scope="module")
def flash_santacoder_handle(launcher):
with launcher("bigcode/santacoder") as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_santacoder(flash_santacoder_handle):
await flash_santacoder_handle.health(300)
return flash_santacoder_handle.client
@pytest.mark.asyncio
async def test_flash_santacoder(flash_santacoder, response_snapshot):
response = await flash_santacoder.generate(
"def print_hello", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_flash_santacoder_load(
flash_santacoder, generate_load, response_snapshot
):
responses = await generate_load(
flash_santacoder, "def print_hello", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_mpt.py
|
import pytest
@pytest.fixture(scope="module")
def mpt_sharded_handle(launcher):
with launcher("mosaicml/mpt-7b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def mpt_sharded(mpt_sharded_handle):
await mpt_sharded_handle.health(300)
return mpt_sharded_handle.client
@pytest.mark.asyncio
async def test_mpt(mpt_sharded, response_snapshot):
response = await mpt_sharded.generate(
"What is Deep Learning?",
max_new_tokens=17,
decoder_input_details=True,
)
assert response.details.generated_tokens == 17
assert (
response.generated_text
== " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
)
assert response == response_snapshot
@pytest.mark.asyncio
async def test_mpt_load(mpt_sharded, generate_load, response_snapshot):
responses = await generate_load(
mpt_sharded,
"What is Deep Learning?",
max_new_tokens=17,
n=4,
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert (
responses[0].generated_text
== " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
)
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_idefics.py
|
import pytest
@pytest.fixture(scope="module")
def idefics_handle(launcher):
with launcher("HuggingFaceM4/idefics-9b-instruct", num_shard=2, dtype="float16") as handle:
yield handle
@pytest.fixture(scope="module")
async def idefics(idefics_handle):
await idefics_handle.health(300)
return idefics_handle.client
@pytest.mark.asyncio
async def test_idefics(idefics, response_snapshot):
response = await idefics.generate(
"User:Can you tell me a very short story based on the image?",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_idefics_load(idefics, generate_load, response_snapshot):
responses = await generate_load(
idefics,
"User:Can you tell me a very short story based on the image?",
max_new_tokens=10,
n=4,
)
generated_texts = [r.generated_text for r in responses]
assert len(generated_texts) == 4
assert generated_texts, all(
[text == generated_texts[0] for text in generated_texts]
)
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_neox.py
|
import pytest
@pytest.fixture(scope="module")
def flash_neox_handle(launcher):
with launcher("stabilityai/stablelm-tuned-alpha-3b", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_neox(flash_neox_handle):
await flash_neox_handle.health(300)
return flash_neox_handle.client
@pytest.mark.skip
@pytest.mark.asyncio
async def test_flash_neox(flash_neox, response_snapshot):
response = await flash_neox.generate(
"<|USER|>What's your mood today?<|ASSISTANT|>",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.skip
@pytest.mark.asyncio
async def test_flash_neox_load(flash_neox, generate_load, response_snapshot):
responses = await generate_load(
flash_neox,
"<|USER|>What's your mood today?<|ASSISTANT|>",
max_new_tokens=10,
n=4,
)
generated_texts = [r.generated_text for r in responses]
assert len(generated_texts) == 4
assert all(
[text == generated_texts[0] for text in generated_texts]
), generated_texts
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_neox_sharded.py
|
import pytest
@pytest.fixture(scope="module")
def flash_neox_sharded_handle(launcher):
with launcher("OpenAssistant/oasst-sft-1-pythia-12b", num_shard=2) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_neox_sharded(flash_neox_sharded_handle):
await flash_neox_sharded_handle.health(300)
return flash_neox_sharded_handle.client
@pytest.mark.asyncio
async def test_flash_neox(flash_neox_sharded, response_snapshot):
response = await flash_neox_sharded.generate(
"<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_flash_neox_load(flash_neox_sharded, generate_load, response_snapshot):
responses = await generate_load(
flash_neox_sharded,
"<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>",
max_new_tokens=10,
n=4,
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_falcon.py
|
import pytest
@pytest.fixture(scope="module")
def flash_falcon_handle(launcher):
with launcher("tiiuae/falcon-7b", trust_remote_code=True) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_falcon(flash_falcon_handle):
await flash_falcon_handle.health(300)
return flash_falcon_handle.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_falcon(flash_falcon, response_snapshot):
response = await flash_falcon.generate(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_new_tokens=10,
decoder_input_details=True,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_falcon_all_params(flash_falcon, response_snapshot):
response = await flash_falcon.generate(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_falcon_load(flash_falcon, generate_load, response_snapshot):
responses = await generate_load(
flash_falcon,
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_new_tokens=10,
n=4,
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_mt0_base.py
|
import pytest
@pytest.fixture(scope="module")
def mt0_base_handle(launcher):
with launcher("bigscience/mt0-base") as handle:
yield handle
@pytest.fixture(scope="module")
async def mt0_base(mt0_base_handle):
await mt0_base_handle.health(300)
return mt0_base_handle.client
@pytest.mark.asyncio
async def test_mt0_base(mt0_base, response_snapshot):
response = await mt0_base.generate(
"Why is the sky blue?",
max_new_tokens=10,
top_p=0.9,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 5
assert response == response_snapshot
@pytest.mark.asyncio
async def test_mt0_base_all_params(mt0_base, response_snapshot):
response = await mt0_base.generate(
"Why is the sky blue?",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 9
assert response == response_snapshot
@pytest.mark.asyncio
async def test_mt0_base_load(mt0_base, generate_load, response_snapshot):
responses = await generate_load(
mt0_base,
"Why is the sky blue?",
max_new_tokens=10,
n=4,
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot
| 0
|
hf_public_repos/text-generation-inference/integration-tests
|
hf_public_repos/text-generation-inference/integration-tests/models/test_flash_awq_sharded.py
|
import pytest
@pytest.fixture(scope="module")
def flash_llama_awq_handle_sharded(launcher):
with launcher(
"abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
num_shard=2,
quantize="awq",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_llama_awq_sharded(flash_llama_awq_handle_sharded):
await flash_llama_awq_handle_sharded.health(300)
return flash_llama_awq_handle_sharded.client
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_sharded(flash_llama_awq_sharded, response_snapshot):
response = await flash_llama_awq_sharded.generate(
"What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
)
assert response.details.generated_tokens == 10
assert (
response.generated_text
== "\nWhat is the difference between Deep Learning and Machine"
)
assert response == response_snapshot
@pytest.mark.asyncio
@pytest.mark.private
async def test_flash_llama_awq_load_sharded(
flash_llama_awq_sharded, generate_load, response_snapshot
):
responses = await generate_load(
flash_llama_awq_sharded, "What is Deep Learning?", max_new_tokens=10, n=4
)
assert len(responses) == 4
assert all(
[
r.generated_text
== "\nWhat is the difference between Deep Learning and Machine"
for r in responses
]
)
assert responses == response_snapshot
| 0
|
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