train_en_model.py

import pandas as pd
from collections import namedtuple

import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'

import argparse
import logging
import math
import os
import random
from pathlib import Path
from typing import Iterable, Optional

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 set_seed
from datasets import load_dataset
from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from huggingface_hub import HfFolder, Repository, whoami
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer


logger = get_logger(__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(
        "--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(
        "--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",
        action="store_true",
        help="Whether to center crop images before resizing to resolution (if not set, random crop will be used)",
    )
    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(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
    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(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")

    #args = parser.parse_args()
    return parser
    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 parse_parser_add_arg(parser, as_named_tuple = False):
    args_df = pd.DataFrame(
    pd.Series(parser.__dict__["_actions"]).map(
    lambda x:x.__dict__
    ).values.tolist())
    args_df = args_df.explode("option_strings")
    args_df["option_strings"] = args_df["option_strings"].map(
    lambda x: x[2:] if x.startswith("--") else x
    ).map(
        lambda x: x[1:] if x.startswith("-") else x
        )
    args_df = args_df[["option_strings", "default"]]
    args = dict(args_df.values.tolist())
    if as_named_tuple:
        args_parser_namedtuple = namedtuple("args_config", args)
        return args_parser_namedtuple(**args)
    return args_df

def transform_named_tuple_to_dict(N_tuple):
    return dict(map(
    lambda x: (x, getattr(N_tuple, x))
    ,filter(lambda x: not x.startswith("_") ,dir(N_tuple))
))

def transform_dict_to_named_tuple(dict_, name = "NamedTuple"):
    args_parser_namedtuple = namedtuple(name, dict_)
    return args_parser_namedtuple(**dict_)

def setattr_gen_option_cls(src_obj):
    assert isinstance(src_obj, tuple) or isinstance(src_obj, dict)
    if isinstance(src_obj, tuple):
        src_obj_ = transform_named_tuple_to_dict(src_obj)
    else:
        src_obj_ = src_obj
    assert isinstance(src_obj_, dict)
    class Option(object):
        pass
    option = Option()
    for k, v in src_obj_.items():
        setattr(option, k, v)
    return option

args = parse_args()
args = parse_parser_add_arg(args,  as_named_tuple = True)
'''
export MODEL_NAME="stable-diffusion-v1-4/"
export dataset_name="lambdalabs/pokemon-blip-captions"

accelerate launch train_text_to_image_ori.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$dataset_name \
  --use_ema \
  --resolution=32 --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"
'''
args_dict = transform_named_tuple_to_dict(args)
args_dict["pretrained_model_name_or_path"] = "stable-diffusion-v1-4/"
#args_dict["pretrained_model_name_or_path"] = "japanese-stable-diffusion/"
args_dict["dataset_name"] = "svjack/pokemon-blip-captions-en-ja"
args_dict["use_ema"] = True
###args_dict["use_ema"] = False
args_dict["resolution"] = 256
args_dict["center_crop"] = True
args_dict["random_flip"] = True
args_dict["train_batch_size"] = 1
args_dict["gradient_accumulation_steps"] = 4
args_dict["train_batch_size"] = 4
args_dict["gradient_checkpointing"] = True
#### to 15000
args_dict["max_train_steps"] = 50000
args_dict["learning_rate"] = 1e-05
args_dict["max_grad_norm"] = 1
args_dict["lr_scheduler"] = "constant"
args_dict["lr_warmup_steps"] = 0
args_dict["output_dir"] = "sd-pokemon-model"
args_dict["caption_column"] = "en_text"
args_dict["mixed_precision"] = "no"
args =  transform_dict_to_named_tuple(args_dict)

def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
    if token is None:
        token = HfFolder.get_token()
    if organization is None:
        username = whoami(token)["name"]
        return f"{username}/{model_id}"
    else:
        return f"{organization}/{model_id}"

dataset_name_mapping = {
    "svjack/pokemon-blip-captions-en-ja": ("image", "en_text"),
}


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
        ]

#### model prepare
'''
from japanese_stable_diffusion import JapaneseStableDiffusionPipeline
import torch
import pandas as pd

from torch import autocast
from diffusers import LMSDiscreteScheduler
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012,
     beta_schedule="scaled_linear", num_train_timesteps=1000)

# pretrained_model_name_or_path = "jap_to_zh_35000"
pretrained_model_name_or_path = "japanese-stable-diffusion/"
pipe = JapaneseStableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path,
                                                           scheduler=scheduler, use_auth_token=True)
tokenizer, text_encoder, vae, unet = pipe.tokenizer, pipe.text_encoder, pipe.vae, pipe.unet
'''
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")

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.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,
    )
noise_scheduler = DDPMScheduler(
        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000,
        #tensor_format="pt"
    )


dataset = load_dataset(
            args.dataset_name,
            args.dataset_config_name,
            cache_dir=args.cache_dir,
        )
column_names = dataset["train"].column_names
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="do_not_pad", truncation=True)
    input_ids = inputs.input_ids
    return input_ids


train_transforms = transforms.Compose(
        [
            transforms.Resize((args.resolution, 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

if args.max_train_samples is not None:
    dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
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 = [example["input_ids"] for example in examples]
    padded_tokens = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt")
    return {
            "pixel_values": pixel_values,
            "input_ids": padded_tokens.input_ids,
            "attention_mask": padded_tokens.attention_mask,
        }

train_dataloader = torch.utils.data.DataLoader(
        train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size
    )


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,
    )

def train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
    # Initialize accelerator and tensorboard logging
    args = config
    unet = model

    logging_dir = os.path.join(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,
            logging_dir=logging_dir,
        )
    logging.basicConfig(
            format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
            datefmt="%m/%d/%Y %H:%M:%S",
            level=logging.INFO,
        )
    if args.seed is not None:
        set_seed(args.seed)
    if accelerator.is_main_process:
        os.makedirs(args.output_dir, exist_ok=True)

    weight_dtype = torch.float32
    if args.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif args.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    text_encoder.to(accelerator.device, dtype=weight_dtype)
    vae.to(accelerator.device, dtype=weight_dtype)

    if args.use_ema:
        ema_unet = EMAModel(unet.parameters())
        ema_unet.to(accelerator.device, dtype=weight_dtype)

    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args_dict = transform_named_tuple_to_dict(args)
        args_dict["max_train_steps"] = args.num_train_epochs * num_update_steps_per_epoch
        args = transform_dict_to_named_tuple(args_dict)


    args_dict = transform_named_tuple_to_dict(args)
    args_dict["num_train_epochs"] = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
    args = transform_dict_to_named_tuple(args_dict)

    if accelerator.is_main_process:
        if config.push_to_hub:
            #repo = init_git_repo(config, at_init=True)
            pass
        accelerator.init_trackers("train_example")

        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}")

    # Prepare everything
    # There is no specific order to remember, you just need to unpack the
    # objects in the same order you gave them to the prepare method.
    '''
    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, lr_scheduler
    )
    '''
    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        unet, optimizer, train_dataloader, lr_scheduler
    )
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    progress_bar.set_description("Steps")
    global_step = 0

    for epoch in range(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
                latents = vae.encode(batch["pixel_values"].to(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.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 and compute loss
                noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample

                loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")
                #loss = F.mse_loss(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_(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

            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

from accelerate import notebook_launcher

#### train it.
args_ = (args, unet, noise_scheduler, optimizer, train_dataloader, lr_scheduler)
notebook_launcher(train_loop, args_, num_processes=1)

from diffusers import LMSDiscreteScheduler
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012,
     beta_schedule="scaled_linear", num_train_timesteps=1000)

#### save to local
pipeline = StableDiffusionPipeline(
            text_encoder=text_encoder,
            vae=vae,
            unet=unet,
            tokenizer=tokenizer,
            scheduler=scheduler,
            safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"),
            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
        )
pipeline.save_pretrained("en_model_26000")
pipeline.safety_checker = lambda images, clip_input: (images, False)
pipeline = pipeline.to("cuda")


imgs = pipeline("A cartoon character with a potted plant on his head",
                    num_inference_steps = 100
)
imgs.images[0]

imgs = pipeline("cartoon bird",
                    num_inference_steps = 100
)
imgs.images[0]

imgs = pipeline("yellow ball",
                    num_inference_steps = 100
)
imgs.images[0]

imgs = pipeline("blue dragon illustration",
                    num_inference_steps = 100
)
imgs.images[0]