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sygil-webui/scripts/textual_inversion.py

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# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
# Copyright 2022 sd-webui team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import *
# streamlit imports
from streamlit import StopException
from streamlit_tensorboard import st_tensorboard
#other imports
from transformers import CLIPTextModel, CLIPTokenizer
# Temp imports
import argparse
import itertools
import math
import os
import random
#import datetime
#from pathlib import Path
#from typing import Optional
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset
import PIL
from accelerate import Accelerator, tracking
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, LMSDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel#, PNDMScheduler
from diffusers.optimization import get_scheduler
#from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from pipelines.stable_diffusion.no_check import NoCheck
from huggingface_hub import HfFolder, whoami#, Repository
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from slugify import slugify
import json
import os#, subprocess
#from io import StringIO
#import sys
from torch.utils.tensorboard import SummaryWriter
# end of imports
#---------------------------------------------------------------------------------------------------------------
logger = get_logger(__name__)
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",
set="train",
placeholder_token="*",
center_crop=False,
templates=None
):
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.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root) if file_path.lower().endswith(('.png', '.jpg', '.jpeg'))]
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.Image.LINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
}[interpolation]
self.templates = templates
self.cache = {}
self.tokenized_templates = [self.tokenizer(
text.format(self.placeholder_token),
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0] for text in self.templates]
def __len__(self):
return self._length
def get_example(self, image_path, flipped):
if image_path in self.cache:
return self.cache[image_path]
example = {}
image = Image.open(image_path)
if not image.mode == "RGB":
image = image.convert("RGB")
# 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 = transforms.RandomHorizontalFlip(p=1 if flipped else 0)(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["key"] = "-".join([image_path, "-", str(flipped)])
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
self.cache[image_path] = example
return example
def __getitem__(self, i):
flipped = random.choice([False, True])
example = self.get_example(self.image_paths[i % self.num_images], flipped)
example["input_ids"] = random.choice(self.tokenized_templates)
return example
def freeze_params(params):
for param in params:
param.requires_grad = False
def save_resume_file(basepath, extra = {}, config=''):
info = {"args": config["args"]}
info["args"].update(extra)
with open(f"{os.path.join(basepath, 'resume.json')}", "w") as f:
#print (info)
json.dump(info, f, indent=4)
with open(f"{basepath}/token_identifier.txt", "w") as f:
f.write(f"{config['args']['placeholder_token']}")
with open(f"{basepath}/type_of_concept.txt", "w") as f:
f.write(f"{config['args']['learnable_property']}")
config['args'] = info["args"]
return config['args']
class Checkpointer:
def __init__(
self,
accelerator,
vae,
unet,
tokenizer,
placeholder_token,
placeholder_token_id,
templates,
output_dir,
random_sample_batches,
sample_batch_size,
stable_sample_batches,
seed
):
self.accelerator = accelerator
self.vae = vae
self.unet = unet
self.tokenizer = tokenizer
self.placeholder_token = placeholder_token
self.placeholder_token_id = placeholder_token_id
self.templates = templates
self.output_dir = output_dir
self.seed = seed
self.random_sample_batches = random_sample_batches
self.sample_batch_size = sample_batch_size
self.stable_sample_batches = stable_sample_batches
@torch.no_grad()
def checkpoint(self, step, text_encoder, save_samples=True, path=None):
print("Saving checkpoint for step %d..." % step)
with torch.autocast("cuda"):
if path is None:
checkpoints_path = f"{self.output_dir}/checkpoints"
os.makedirs(checkpoints_path, exist_ok=True)
unwrapped = self.accelerator.unwrap_model(text_encoder)
# Save a checkpoint
learned_embeds = unwrapped.get_input_embeddings().weight[self.placeholder_token_id]
learned_embeds_dict = {self.placeholder_token: learned_embeds.detach().cpu()}
filename = f"%s_%d.bin" % (slugify(self.placeholder_token), step)
if path is not None:
torch.save(learned_embeds_dict, path)
else:
torch.save(learned_embeds_dict, f"{checkpoints_path}/{filename}")
torch.save(learned_embeds_dict, f"{checkpoints_path}/last.bin")
del unwrapped
del learned_embeds
@torch.no_grad()
def save_samples(self, step, text_encoder, height, width, guidance_scale, eta, num_inference_steps):
samples_path = f"{self.output_dir}/concept_images"
os.makedirs(samples_path, exist_ok=True)
#if "checker" not in server_state['textual_inversion']:
#with server_state_lock['textual_inversion']["checker"]:
server_state['textual_inversion']["checker"] = NoCheck()
#if "unwrapped" not in server_state['textual_inversion']:
# with server_state_lock['textual_inversion']["unwrapped"]:
server_state['textual_inversion']["unwrapped"] = self.accelerator.unwrap_model(text_encoder)
#if "pipeline" not in server_state['textual_inversion']:
# with server_state_lock['textual_inversion']["pipeline"]:
# Save a sample image
server_state['textual_inversion']["pipeline"] = StableDiffusionPipeline(
text_encoder=server_state['textual_inversion']["unwrapped"],
vae=self.vae,
unet=self.unet,
tokenizer=self.tokenizer,
scheduler=LMSDiscreteScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
),
safety_checker=NoCheck(),
feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
).to("cuda")
server_state['textual_inversion']["pipeline"].enable_attention_slicing()
if self.stable_sample_batches > 0:
stable_latents = torch.randn(
(self.sample_batch_size, server_state['textual_inversion']["pipeline"].unet.in_channels, height // 8, width // 8),
device=server_state['textual_inversion']["pipeline"].device,
generator=torch.Generator(device=server_state['textual_inversion']["pipeline"].device).manual_seed(self.seed),
)
stable_prompts = [choice.format(self.placeholder_token) for choice in (self.templates * self.sample_batch_size)[:self.sample_batch_size]]
# Generate and save stable samples
for i in range(0, self.stable_sample_batches):
samples = server_state['textual_inversion']["pipeline"](
prompt=stable_prompts,
height=384,
latents=stable_latents,
width=384,
guidance_scale=guidance_scale,
eta=eta,
num_inference_steps=num_inference_steps,
output_type='pil'
)["sample"]
for idx, im in enumerate(samples):
filename = f"stable_sample_%d_%d_step_%d.png" % (i+1, idx+1, step)
im.save(f"{samples_path}/{filename}")
del samples
del stable_latents
prompts = [choice.format(self.placeholder_token) for choice in random.choices(self.templates, k=self.sample_batch_size)]
# Generate and save random samples
for i in range(0, self.random_sample_batches):
samples = server_state['textual_inversion']["pipeline"](
prompt=prompts,
height=384,
width=384,
guidance_scale=guidance_scale,
eta=eta,
num_inference_steps=num_inference_steps,
output_type='pil'
)["sample"]
for idx, im in enumerate(samples):
filename = f"step_%d_sample_%d_%d.png" % (step, i+1, idx+1)
im.save(f"{samples_path}/{filename}")
del samples
del server_state['textual_inversion']["checker"]
del server_state['textual_inversion']["unwrapped"]
del server_state['textual_inversion']["pipeline"]
torch.cuda.empty_cache()
#@retry(RuntimeError, tries=5)
def textual_inversion(config):
print ("Running textual inversion.")
#if "pipeline" in server_state["textual_inversion"]:
#del server_state['textual_inversion']["checker"]
#del server_state['textual_inversion']["unwrapped"]
#del server_state['textual_inversion']["pipeline"]
#torch.cuda.empty_cache()
global_step_offset = 0
#print(config['args']['resume_from'])
if config['args']['resume_from']:
try:
basepath = f"{config['args']['resume_from']}"
with open(f"{basepath}/resume.json", 'r') as f:
state = json.load(f)
global_step_offset = state["args"].get("global_step", 0)
print("Resuming state from %s" % config['args']['resume_from'])
print("We've trained %d steps so far" % global_step_offset)
except json.decoder.JSONDecodeError:
pass
else:
basepath = f"{config['args']['output_dir']}/{slugify(config['args']['placeholder_token'])}"
os.makedirs(basepath, exist_ok=True)
accelerator = Accelerator(
gradient_accumulation_steps=config['args']['gradient_accumulation_steps'],
mixed_precision=config['args']['mixed_precision']
)
# If passed along, set the training seed.
if config['args']['seed']:
set_seed(config['args']['seed'])
#if "tokenizer" not in server_state["textual_inversion"]:
# Load the tokenizer and add the placeholder token as a additional special token
#with server_state_lock['textual_inversion']["tokenizer"]:
if config['args']['tokenizer_name']:
server_state['textual_inversion']["tokenizer"] = CLIPTokenizer.from_pretrained(config['args']['tokenizer_name'])
elif config['args']['pretrained_model_name_or_path']:
server_state['textual_inversion']["tokenizer"] = CLIPTokenizer.from_pretrained(
config['args']['pretrained_model_name_or_path'] + '/tokenizer'
)
# Add the placeholder token in tokenizer
num_added_tokens = server_state['textual_inversion']["tokenizer"].add_tokens(config['args']['placeholder_token'])
if num_added_tokens == 0:
st.error(
f"The tokenizer already contains the token {config['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 = server_state['textual_inversion']["tokenizer"].encode(config['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:
st.error("The initializer token must be a single token.")
initializer_token_id = token_ids[0]
placeholder_token_id = server_state['textual_inversion']["tokenizer"].convert_tokens_to_ids(config['args']['placeholder_token'])
#if "text_encoder" not in server_state['textual_inversion']:
# Load models and create wrapper for stable diffusion
#with server_state_lock['textual_inversion']["text_encoder"]:
server_state['textual_inversion']["text_encoder"] = CLIPTextModel.from_pretrained(
config['args']['pretrained_model_name_or_path'] + '/text_encoder',
)
#if "vae" not in server_state['textual_inversion']:
#with server_state_lock['textual_inversion']["vae"]:
server_state['textual_inversion']["vae"] = AutoencoderKL.from_pretrained(
config['args']['pretrained_model_name_or_path'] + '/vae',
)
#if "unet" not in server_state['textual_inversion']:
#with server_state_lock['textual_inversion']["unet"]:
server_state['textual_inversion']["unet"] = UNet2DConditionModel.from_pretrained(
config['args']['pretrained_model_name_or_path'] + '/unet',
)
base_templates = imagenet_style_templates_small if config['args']['learnable_property'] == "style" else imagenet_templates_small
if config['args']['custom_templates']:
templates = config['args']['custom_templates'].split(";")
else:
templates = base_templates
slice_size = server_state['textual_inversion']["unet"].config.attention_head_dim // 2
server_state['textual_inversion']["unet"].set_attention_slice(slice_size)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
server_state['textual_inversion']["text_encoder"].resize_token_embeddings(len(server_state['textual_inversion']["tokenizer"]))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = server_state['textual_inversion']["text_encoder"].get_input_embeddings().weight.data
if "resume_checkpoint" in config['args']:
if config['args']['resume_checkpoint'] is not None:
token_embeds[placeholder_token_id] = torch.load(config['args']['resume_checkpoint'])[config['args']['placeholder_token']]
else:
token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
# Freeze vae and unet
freeze_params(server_state['textual_inversion']["vae"].parameters())
freeze_params(server_state['textual_inversion']["unet"].parameters())
# Freeze all parameters except for the token embeddings in text encoder
params_to_freeze = itertools.chain(
server_state['textual_inversion']["text_encoder"].text_model.encoder.parameters(),
server_state['textual_inversion']["text_encoder"].text_model.final_layer_norm.parameters(),
server_state['textual_inversion']["text_encoder"].text_model.embeddings.position_embedding.parameters(),
)
freeze_params(params_to_freeze)
checkpointer = Checkpointer(
accelerator=accelerator,
vae=server_state['textual_inversion']["vae"],
unet=server_state['textual_inversion']["unet"],
tokenizer=server_state['textual_inversion']["tokenizer"],
placeholder_token=config['args']['placeholder_token'],
placeholder_token_id=placeholder_token_id,
templates=templates,
output_dir=basepath,
sample_batch_size=config['args']['sample_batch_size'],
random_sample_batches=config['args']['random_sample_batches'],
stable_sample_batches=config['args']['stable_sample_batches'],
seed=config['args']['seed']
)
if config['args']['scale_lr']:
config['args']['learning_rate'] = (
config['args']['learning_rate'] * config[
'args']['gradient_accumulation_steps'] * config['args']['train_batch_size'] * accelerator.num_processes
)
# Initialize the optimizer
optimizer = torch.optim.AdamW(
server_state['textual_inversion']["text_encoder"].get_input_embeddings().parameters(), # only optimize the embeddings
lr=config['args']['learning_rate'],
betas=(config['args']['adam_beta1'], config['args']['adam_beta2']),
weight_decay=config['args']['adam_weight_decay'],
eps=config['args']['adam_epsilon'],
)
# TODO (patil-suraj): load scheduler using args
noise_scheduler = DDPMScheduler(
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, tensor_format="pt"
)
train_dataset = TextualInversionDataset(
data_root=config['args']['train_data_dir'],
tokenizer=server_state['textual_inversion']["tokenizer"],
size=config['args']['resolution'],
placeholder_token=config['args']['placeholder_token'],
repeats=config['args']['repeats'],
learnable_property=config['args']['learnable_property'],
center_crop=config['args']['center_crop'],
set="train",
templates=templates
)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=config['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) / config['args']['gradient_accumulation_steps'])
if config['args']['max_train_steps'] is None:
config['args']['max_train_steps'] = config['args']['num_train_epochs'] * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
config['args']['lr_scheduler'],
optimizer=optimizer,
num_warmup_steps=config['args']['lr_warmup_steps'] * config['args']['gradient_accumulation_steps'],
num_training_steps=config['args']['max_train_steps'] * config['args']['gradient_accumulation_steps'],
)
server_state['textual_inversion']["text_encoder"], optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
server_state['textual_inversion']["text_encoder"], optimizer, train_dataloader, lr_scheduler
)
# Move vae and unet to device
server_state['textual_inversion']["vae"].to(accelerator.device)
server_state['textual_inversion']["unet"].to(accelerator.device)
# Keep vae and unet in eval mode as we don't train these
server_state['textual_inversion']["vae"].eval()
server_state['textual_inversion']["unet"].eval()
# 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) / config['args']['gradient_accumulation_steps'])
if overrode_max_train_steps:
config['args']['max_train_steps'] = config['args']['num_train_epochs'] * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
config['args']['num_train_epochs'] = math.ceil(config['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=config['args'])
# Train!
total_batch_size = config['args']['train_batch_size'] * accelerator.num_processes * st.session_state[
'textual_inversion']['args']['gradient_accumulation_steps']
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {config['args']['num_train_epochs']}")
logger.info(f" Instantaneous batch size per device = {config['args']['train_batch_size']}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {config['args']['gradient_accumulation_steps']}")
logger.info(f" Total optimization steps = {config['args']['max_train_steps']}")
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(config['args']['max_train_steps']), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
global_step = 0
encoded_pixel_values_cache = {}
try:
for epoch in range(config['args']['num_train_epochs']):
server_state['textual_inversion']["text_encoder"].train()
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(server_state['textual_inversion']["text_encoder"]):
# Convert images to latent space
key = "|".join(batch["key"])
if encoded_pixel_values_cache.get(key, None) is None:
encoded_pixel_values_cache[key] = server_state['textual_inversion']["vae"].encode(batch["pixel_values"]).latent_dist
latents = encoded_pixel_values_cache[key].sample().detach().half() * 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.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 = server_state['textual_inversion']["text_encoder"](batch["input_ids"])[0]
# Predict the noise residual
noise_pred = server_state['textual_inversion']["unet"](noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(noise_pred, noise, 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 = server_state['textual_inversion']["text_encoder"].module.get_input_embeddings().weight.grad
else:
grads = server_state['textual_inversion']["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(server_state['textual_inversion']["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()
#try:
# 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 % config['args']['checkpoint_frequency'] == 0 and global_step > 0 and accelerator.is_main_process:
checkpointer.checkpoint(global_step + global_step_offset, server_state['textual_inversion']["text_encoder"])
save_resume_file(basepath, {
"global_step": global_step + global_step_offset,
"resume_checkpoint": f"{basepath}/checkpoints/last.bin"
}, config)
checkpointer.save_samples(
global_step + global_step_offset,
server_state['textual_inversion']["text_encoder"],
config['args']['resolution'], config['args'][
'resolution'], 7.5, 0.0, config['args']['sample_steps'])
checkpointer.checkpoint(
global_step + global_step_offset,
server_state['textual_inversion']["text_encoder"],
path=f"{basepath}/learned_embeds.bin"
)
#except KeyError:
#raise StopException
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
#accelerator.log(logs, step=global_step)
#try:
if global_step >= config['args']['max_train_steps']:
break
#except:
#pass
accelerator.wait_for_everyone()
# Create the pipeline using the trained modules and save it.
if accelerator.is_main_process:
print("Finished! Saving final checkpoint and resume state.")
checkpointer.checkpoint(
global_step + global_step_offset,
server_state['textual_inversion']["text_encoder"],
path=f"{basepath}/learned_embeds.bin"
)
save_resume_file(basepath, {
"global_step": global_step + global_step_offset,
"resume_checkpoint": f"{basepath}/checkpoints/last.bin"
}, config)
accelerator.end_training()
except (KeyboardInterrupt, StopException) as e:
print(f"Received Streamlit StopException or KeyboardInterrupt")
if accelerator.is_main_process:
print("Interrupted, saving checkpoint and resume state...")
checkpointer.checkpoint(global_step + global_step_offset, server_state['textual_inversion']["text_encoder"])
config['args'] = save_resume_file(basepath, {
"global_step": global_step + global_step_offset,
"resume_checkpoint": f"{basepath}/checkpoints/last.bin"
}, config)
checkpointer.checkpoint(
global_step + global_step_offset,
server_state['textual_inversion']["text_encoder"],
path=f"{basepath}/learned_embeds.bin"
)
quit()
def layout():
with st.form("textual-inversion"):
#st.info("Under Construction. :construction_worker:")
#parser = argparse.ArgumentParser(description="Simple example of a training script.")
set_page_title("Textual Inversion - Stable Diffusion Playground")
config_tab, output_tab, tensorboard_tab = st.tabs(["Textual Inversion Config", "Ouput", "TensorBoard"])
with config_tab:
col1, col2, col3, col4, col5 = st.columns(5, gap='large')
if "textual_inversion" not in st.session_state:
st.session_state["textual_inversion"] = {}
if "textual_inversion" not in server_state:
server_state["textual_inversion"] = {}
if "args" not in st.session_state["textual_inversion"]:
st.session_state["textual_inversion"]["args"] = {}
with col1:
st.session_state["textual_inversion"]["args"]["pretrained_model_name_or_path"] = st.text_input("Pretrained Model Path",
value=st.session_state["defaults"].textual_inversion.pretrained_model_name_or_path,
help="Path to pretrained model or model identifier from huggingface.co/models.")
st.session_state["textual_inversion"]["args"]["tokenizer_name"] = st.text_input("Tokenizer Name",
value=st.session_state["defaults"].textual_inversion.tokenizer_name,
help="Pretrained tokenizer name or path if not the same as model_name")
st.session_state["textual_inversion"]["args"]["train_data_dir"] = st.text_input("train_data_dir", value="", help="A folder containing the training data.")
st.session_state["textual_inversion"]["args"]["placeholder_token"] = st.text_input("Placeholder Token", value="", help="A token to use as a placeholder for the concept.")
st.session_state["textual_inversion"]["args"]["initializer_token"] = st.text_input("Initializer Token", value="", help="A token to use as initializer word.")
st.session_state["textual_inversion"]["args"]["learnable_property"] = st.selectbox("Learnable Property", ["object", "style"], index=0, help="Choose between 'object' and 'style'")
st.session_state["textual_inversion"]["args"]["repeats"] = int(st.text_input("Number of times to Repeat", value=100, help="How many times to repeat the training data."))
with col2:
st.session_state["textual_inversion"]["args"]["output_dir"] = st.text_input("Output Directory",
value=str(os.path.join("outputs", "textual_inversion")),
help="The output directory where the model predictions and checkpoints will be written.")
st.session_state["textual_inversion"]["args"]["seed"] = seed_to_int(st.text_input("Seed", value=0,
help="A seed for reproducible training, if left empty a random one will be generated. Default: 0"))
st.session_state["textual_inversion"]["args"]["resolution"] = int(st.text_input("Resolution", value=512,
help="The resolution for input images, all the images in the train/validation dataset will be resized to this resolution"))
st.session_state["textual_inversion"]["args"]["center_crop"] = st.checkbox("Center Image", value=True, help="Whether to center crop images before resizing to resolution")
st.session_state["textual_inversion"]["args"]["train_batch_size"] = int(st.text_input("Train Batch Size", value=1, help="Batch size (per device) for the training dataloader."))
st.session_state["textual_inversion"]["args"]["num_train_epochs"] = int(st.text_input("Number of Steps to Train", value=100, help="Number of steps to train."))
st.session_state["textual_inversion"]["args"]["max_train_steps"] = int(st.text_input("Max Number of Steps to Train", value=5000,
help="Total number of training steps to perform. If provided, overrides 'Number of Steps to Train'."))
with col3:
st.session_state["textual_inversion"]["args"]["gradient_accumulation_steps"] = int(st.text_input("Gradient Accumulation Steps", value=1,
help="Number of updates steps to accumulate before performing a backward/update pass."))
st.session_state["textual_inversion"]["args"]["learning_rate"] = float(st.text_input("Learning Rate", value=5.0e-04,
help="Initial learning rate (after the potential warmup period) to use."))
st.session_state["textual_inversion"]["args"]["scale_lr"] = st.checkbox("Scale Learning Rate", value=True,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.")
st.session_state["textual_inversion"]["args"]["lr_scheduler"] = st.text_input("Learning Rate Scheduler", value="constant",
help=("The scheduler type to use. Choose between ['linear', 'cosine', 'cosine_with_restarts', 'polynomial',"
" 'constant', 'constant_with_warmup']" ))
st.session_state["textual_inversion"]["args"]["lr_warmup_steps"] = int(st.text_input("Learning Rate Warmup Steps", value=500, help="Number of steps for the warmup in the lr scheduler."))
st.session_state["textual_inversion"]["args"]["adam_beta1"] = float(st.text_input("Adam Beta 1", value=0.9, help="The beta1 parameter for the Adam optimizer."))
st.session_state["textual_inversion"]["args"]["adam_beta2"] = float(st.text_input("Adam Beta 2", value=0.999, help="The beta2 parameter for the Adam optimizer."))
st.session_state["textual_inversion"]["args"]["adam_weight_decay"] = float(st.text_input("Adam Weight Decay", value=1e-2, help="Weight decay to use."))
st.session_state["textual_inversion"]["args"]["adam_epsilon"] = float(st.text_input("Adam Epsilon", value=1e-08, help="Epsilon value for the Adam optimizer"))
with col4:
st.session_state["textual_inversion"]["args"]["mixed_precision"] = st.selectbox("Mixed Precision", ["no", "fp16", "bf16"], index=1,
help="Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU.")
st.session_state["textual_inversion"]["args"]["local_rank"] = int(st.text_input("Local Rank", value=1, help="For distributed training: local_rank"))
st.session_state["textual_inversion"]["args"]["checkpoint_frequency"] = int(st.text_input("Checkpoint Frequency", value=500, help="How often to save a checkpoint and sample image"))
# stable_sample_batches is crashing when saving the samples so for now I will disable it util its fixed.
#st.session_state["textual_inversion"]["args"]["stable_sample_batches"] = int(st.text_input("Stable Sample Batches", value=0,
#help="Number of fixed seed sample batches to generate per checkpoint"))
st.session_state["textual_inversion"]["args"]["stable_sample_batches"] = 0
st.session_state["textual_inversion"]["args"]["random_sample_batches"] = int(st.text_input("Random Sample Batches", value=2,
help="Number of random seed sample batches to generate per checkpoint"))
st.session_state["textual_inversion"]["args"]["sample_batch_size"] = int(st.text_input("Sample Batch Size", value=1, help="Number of samples to generate per batch"))
st.session_state["textual_inversion"]["args"]["sample_steps"] = int(st.text_input("Sample Steps", value=100,
help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes."))
st.session_state["textual_inversion"]["args"]["custom_templates"] = st.text_input("Custom Templates", value="",
help="A semicolon-delimited list of custom template to use for samples, using {} as a placeholder for the concept.")
with col5:
st.session_state["textual_inversion"]["args"]["resume"] = st.checkbox(label="Resume Previous Run?", value=False,
help="Resume previous run, if a valid resume.json file is on the output dir \
it will be used, otherwise if the 'Resume From' field bellow contains a valid resume.json file \
that one will be used.")
st.session_state["textual_inversion"]["args"]["resume_from"] = st.text_input(label="Resume From", help="Path to a directory to resume training from (ie, logs/token_name)")
#st.session_state["textual_inversion"]["args"]["resume_checkpoint"] = st.file_uploader("Resume Checkpoint", type=["bin"],
#help="Path to a specific checkpoint to resume training from (ie, logs/token_name/checkpoints/something.bin).")
#st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"] = st.file_uploader("st.session_state["textual_inversion"] File", type=["json"],
#help="Path to a JSON st.session_state["textual_inversion"]uration file containing arguments for invoking this script."
#"If resume_from is given, its resume.json takes priority over this.")
#
#print (os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"),"resume.json"))
#print (os.path.exists(os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"),"resume.json")))
if os.path.exists(os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"),"resume.json")):
st.session_state["textual_inversion"]["args"]["resume_from"] = os.path.join(
st.session_state["textual_inversion"]["args"]["output_dir"], st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"))
#print (st.session_state["textual_inversion"]["args"]["resume_from"])
if os.path.exists(os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"), "checkpoints","last.bin")):
st.session_state["textual_inversion"]["args"]["resume_checkpoint"] = os.path.join(
st.session_state["textual_inversion"]["args"]["output_dir"], st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"), "checkpoints","last.bin")
#if "resume_from" in st.session_state["textual_inversion"]["args"]:
#if st.session_state["textual_inversion"]["args"]["resume_from"]:
#if os.path.exists(os.path.join(st.session_state["textual_inversion"]['args']['resume_from'], "resume.json")):
#with open(os.path.join(st.session_state["textual_inversion"]['args']['resume_from'], "resume.json"), 'rt') as f:
#try:
#resume_json = json.load(f)["args"]
#st.session_state["textual_inversion"]["args"] = OmegaConf.merge(st.session_state["textual_inversion"]["args"], resume_json)
#st.session_state["textual_inversion"]["args"]["resume_from"] = os.path.join(
#st.session_state["textual_inversion"]["args"]["output_dir"], st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"))
#except json.decoder.JSONDecodeError:
#pass
#print(st.session_state["textual_inversion"]["args"])
#print(st.session_state["textual_inversion"]["args"]['resume_from'])
#elif st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"] is not None:
#with open(st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"], 'rt') as f:
#args = parser.parse_args(namespace=argparse.Namespace(**json.load(f)["args"]))
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != st.session_state["textual_inversion"]["args"]["local_rank"]:
st.session_state["textual_inversion"]["args"]["local_rank"] = env_local_rank
if st.session_state["textual_inversion"]["args"]["train_data_dir"] is None:
st.error("You must specify --train_data_dir")
if st.session_state["textual_inversion"]["args"]["pretrained_model_name_or_path"] is None:
st.error("You must specify --pretrained_model_name_or_path")
if st.session_state["textual_inversion"]["args"]["placeholder_token"] is None:
st.error("You must specify --placeholder_token")
if st.session_state["textual_inversion"]["args"]["initializer_token"] is None:
st.error("You must specify --initializer_token")
if st.session_state["textual_inversion"]["args"]["output_dir"] is None:
st.error("You must specify --output_dir")
# add a spacer and the submit button for the form.
st.session_state["textual_inversion"]["message"] = st.empty()
st.session_state["textual_inversion"]["progress_bar"] = st.empty()
st.write("---")
submit = st.form_submit_button("Run",help="")
if submit:
if "pipe" in st.session_state:
del st.session_state["pipe"]
if "model" in st.session_state:
del st.session_state["model"]
set_page_title("Running Textual Inversion - Stable Diffusion WebUI")
#st.session_state["textual_inversion"]["message"].info("Textual Inversion Running. For more info check the progress on your console or the Ouput Tab.")
try:
#try:
# run textual inversion.
config = st.session_state['textual_inversion']
textual_inversion(config)
#except RuntimeError:
#if "pipeline" in server_state["textual_inversion"]:
#del server_state['textual_inversion']["checker"]
#del server_state['textual_inversion']["unwrapped"]
#del server_state['textual_inversion']["pipeline"]
# run textual inversion.
#config = st.session_state['textual_inversion']
#textual_inversion(config)
set_page_title("Textual Inversion - Stable Diffusion WebUI")
except StopException:
set_page_title("Textual Inversion - Stable Diffusion WebUI")
print(f"Received Streamlit StopException")
st.session_state["textual_inversion"]["message"].empty()
#
with output_tab:
st.info("Under Construction. :construction_worker:")
#st.info("Nothing to show yet. Maybe try running some training first.")
#st.session_state["textual_inversion"]["preview_image"] = st.empty()
#st.session_state["textual_inversion"]["progress_bar"] = st.empty()
with tensorboard_tab:
#st.info("Under Construction. :construction_worker:")
# Start TensorBoard
st_tensorboard(logdir=os.path.join("outputs", "textual_inversion"), port=8888)
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