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Adding better way to define multiple concepts and also validation capabilities. (#3807)

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* - Added validation parameters
- Changed some parameter descriptions to better explain their use.
- Fixed a few typos.
- Added concept_list parameter for better management of multiple subjects
- changed logic for image validation

* - Fixed bad logic for class data root directories

* Defaulting validation_steps to None for an easier logic

* Fixed multiple validation prompts

* Fixed bug on validation negative prompt

* Changed validation logic for tracker.

* Added uuid for validation image labeling

* Fix error when comparing validation prompts and validation negative prompts

* Improved error message when negative prompts for validation are more than the number of prompts

* - Changed image tracking number from epoch to global_step
- Added Typing for functions

* Added some validations more when using concept_list parameter and the regular ones.

* Fixed error message

* Added more validations for validation parameters

* Improved messaging for errors

* Fixed validation error for parameters with default values

* - Added train step to image name for validation
- reformatted code

* - Added train step to image's name for validation
- reformatted code

* Updated README.md file.

* reverted back original script of train_dreambooth.py

* reverted back original script of train_dreambooth.py

* left one blank line at the eof

* reverted back setup.py

* reverted back setup.py

* added same logic for when parameters for prior preservation are used without enabling the flag while using concept_list parameter.

* Ran black formatter.

* fixed a few strings

* fixed import sort with isort and removed fstrings without placeholder

* fixed import order with ruff (since with isort wasn't ok)

---------

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Andrés Mauricio Repetto Ferrero
2023-07-03 10:55:45 -05:00
committed by GitHub
parent 2e8668f0af
commit 572d8e2002
2 changed files with 404 additions and 53 deletions
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47
examples/research_projects/multi_subject_dreambooth/README.md
View File

@@ -86,6 +86,53 @@ This example shows training for 2 subjects, but please note that the model can b
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.

410
examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py
View File

@@ -1,13 +1,18 @@
import argparse
import hashlib
import itertools
import json
import logging
import math
import os
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
@@ -17,24 +22,140 @@ from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
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, UNet2DConditionModel
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
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,
@@ -81,7 +202,7 @@ def parse_args(input_args=None):
"--instance_data_dir",
type=str,
default=None,
required=True,
required=False,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
@@ -95,7 +216,7 @@ def parse_args(input_args=None):
"--instance_prompt",
type=str,
default=None,
required=True,
required=False,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
@@ -272,6 +393,52 @@ def parse_args(input_args=None):
' (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,
@@ -297,27 +464,80 @@ def parse_args(input_args=None):
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 is not None:
if input_args:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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 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.")
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 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.")
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
@@ -325,7 +545,7 @@ def parse_args(input_args=None):
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.
It pre-processes the images and then tokenizes prompts.
"""
def __init__(
@@ -346,7 +566,7 @@ class DreamBoothDataset(Dataset):
self.instance_images_path = []
self.num_instance_images = []
self.instance_prompt = []
self.class_data_root = []
self.class_data_root = [] if class_data_root is not None else None
self.class_images_path = []
self.num_class_images = []
self.class_prompt = []
@@ -371,8 +591,6 @@ class DreamBoothDataset(Dataset):
self._length -= self.num_instance_images[i]
self._length += self.num_class_images[i]
self.class_prompt.append(class_prompt[i])
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
@@ -446,7 +664,7 @@ def collate_fn(num_instances, examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt
@@ -474,6 +692,10 @@ def main(args):
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.
@@ -483,23 +705,84 @@ def main(args):
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
# 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."
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.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:
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:
class_data_dir = args.class_data_dir
class_prompt = args.class_prompt
# 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(
@@ -559,21 +842,24 @@ def main(args):
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
for ii, 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"
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:
os.makedirs(args.output_dir, exist_ok=True)
makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
@@ -581,6 +867,7 @@ def main(args):
).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:
@@ -658,7 +945,7 @@ def main(args):
train_dataset = DreamBoothDataset(
instance_data_root=instance_data_dir,
instance_prompt=instance_prompt,
class_data_root=class_data_dir if args.with_prior_preservation else None,
class_data_root=class_data_dir,
class_prompt=class_prompt,
tokenizer=tokenizer,
size=args.resolution,
@@ -720,7 +1007,7 @@ def main(args):
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.
# The trackers initialize automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth", config=vars(args))
@@ -741,10 +1028,10 @@ def main(args):
# 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)
path = basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
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
@@ -756,7 +1043,7 @@ def main(args):
args.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
accelerator.load_state(join(args.output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * args.gradient_accumulation_steps
@@ -787,24 +1074,26 @@ def main(args):
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()
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, timesteps)
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, timesteps, encoder_hidden_states).sample
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, timesteps)
target = noise_scheduler.get_velocity(latents, noise, time_steps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
@@ -834,19 +1123,34 @@ def main(args):
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
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 global_step % args.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
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)
@@ -854,7 +1158,7 @@ def main(args):
if global_step >= args.max_train_steps:
break
# Create the pipeline using using the trained modules and save it.
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
pipeline = DiffusionPipeline.from_pretrained(