Merge branch 'main' of https://github.com/oahzxl/ColossalAI into chunk

examples/images/diffusion/ldm/modules/midas/utils.py

+"""Utils for monoDepth."""
+import sys
+import re
+import numpy as np
+import cv2
+import torch
+
+
+def read_pfm(path):
+    """Read pfm file.
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        tuple: (data, scale)
+    """
+    with open(path, "rb") as file:
+
+        color = None
+        width = None
+        height = None
+        scale = None
+        endian = None
+
+        header = file.readline().rstrip()
+        if header.decode("ascii") == "PF":
+            color = True
+        elif header.decode("ascii") == "Pf":
+            color = False
+        else:
+            raise Exception("Not a PFM file: " + path)
+
+        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+        if dim_match:
+            width, height = list(map(int, dim_match.groups()))
+        else:
+            raise Exception("Malformed PFM header.")
+
+        scale = float(file.readline().decode("ascii").rstrip())
+        if scale < 0:
+            # little-endian
+            endian = "<"
+            scale = -scale
+        else:
+            # big-endian
+            endian = ">"
+
+        data = np.fromfile(file, endian + "f")
+        shape = (height, width, 3) if color else (height, width)
+
+        data = np.reshape(data, shape)
+        data = np.flipud(data)
+
+        return data, scale
+
+
+def write_pfm(path, image, scale=1):
+    """Write pfm file.
+
+    Args:
+        path (str): pathto file
+        image (array): data
+        scale (int, optional): Scale. Defaults to 1.
+    """
+
+    with open(path, "wb") as file:
+        color = None
+
+        if image.dtype.name != "float32":
+            raise Exception("Image dtype must be float32.")
+
+        image = np.flipud(image)
+
+        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
+            color = True
+        elif (
+            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
+        ):  # greyscale
+            color = False
+        else:
+            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+        file.write("PF\n" if color else "Pf\n".encode())
+        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+        endian = image.dtype.byteorder
+
+        if endian == "<" or endian == "=" and sys.byteorder == "little":
+            scale = -scale
+
+        file.write("%f\n".encode() % scale)
+
+        image.tofile(file)
+
+
+def read_image(path):
+    """Read image and output RGB image (0-1).
+
+    Args:
+        path (str): path to file
+
+    Returns:
+        array: RGB image (0-1)
+    """
+    img = cv2.imread(path)
+
+    if img.ndim == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+
+    return img
+
+
+def resize_image(img):
+    """Resize image and make it fit for network.
+
+    Args:
+        img (array): image
+
+    Returns:
+        tensor: data ready for network
+    """
+    height_orig = img.shape[0]
+    width_orig = img.shape[1]
+
+    if width_orig > height_orig:
+        scale = width_orig / 384
+    else:
+        scale = height_orig / 384
+
+    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
+    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
+
+    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
+
+    img_resized = (
+        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
+    )
+    img_resized = img_resized.unsqueeze(0)
+
+    return img_resized
+
+
+def resize_depth(depth, width, height):
+    """Resize depth map and bring to CPU (numpy).
+
+    Args:
+        depth (tensor): depth
+        width (int): image width
+        height (int): image height
+
+    Returns:
+        array: processed depth
+    """
+    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
+
+    depth_resized = cv2.resize(
+        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
+    )
+
+    return depth_resized
+
+def write_depth(path, depth, bits=1):
+    """Write depth map to pfm and png file.
+
+    Args:
+        path (str): filepath without extension
+        depth (array): depth
+    """
+    write_pfm(path + ".pfm", depth.astype(np.float32))
+
+    depth_min = depth.min()
+    depth_max = depth.max()
+
+    max_val = (2**(8*bits))-1
+
+    if depth_max - depth_min > np.finfo("float").eps:
+        out = max_val * (depth - depth_min) / (depth_max - depth_min)
+    else:
+        out = np.zeros(depth.shape, dtype=depth.type)
+
+    if bits == 1:
+        cv2.imwrite(path + ".png", out.astype("uint8"))
+    elif bits == 2:
+        cv2.imwrite(path + ".png", out.astype("uint16"))
+
+    return

examples/images/diffusion/ldm/util.py

+import importlib
+
+import torch
+from torch import optim
+import numpy as np
+
+from inspect import isfunction
+from PIL import Image, ImageDraw, ImageFont
+
+
+def log_txt_as_img(wh, xc, size=10):
+    # wh a tuple of (width, height)
+    # xc a list of captions to plot
+    b = len(xc)
+    txts = list()
+    for bi in range(b):
+        txt = Image.new("RGB", wh, color="white")
+        draw = ImageDraw.Draw(txt)
+        font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
+        nc = int(40 * (wh[0] / 256))
+        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+
+        try:
+            draw.text((0, 0), lines, fill="black", font=font)
+        except UnicodeEncodeError:
+            print("Cant encode string for logging. Skipping.")
+
+        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+        txts.append(txt)
+    txts = np.stack(txts)
+    txts = torch.tensor(txts)
+    return txts
+
+
+def ismap(x):
+    if not isinstance(x, torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+    if not isinstance(x,torch.Tensor):
+        return False
+    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def exists(x):
+    return x is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+    """
+    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+    total_params = sum(p.numel() for p in model.parameters())
+    if verbose:
+        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+    return total_params
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        if config == '__is_first_stage__':
+            return None
+        elif config == "__is_unconditional__":
+            return None
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+class AdamWwithEMAandWings(optim.Optimizer):
+    # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
+    def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  # TODO: check hyperparameters before using
+                 weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999,   # ema decay to match previous code
+                 ema_power=1., param_names=()):
+        """AdamW that saves EMA versions of the parameters."""
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+        if not 0.0 <= weight_decay:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+        if not 0.0 <= ema_decay <= 1.0:
+            raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
+        defaults = dict(lr=lr, betas=betas, eps=eps,
+                        weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
+                        ema_power=ema_power, param_names=param_names)
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('amsgrad', False)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+        Args:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad = []
+            grads = []
+            exp_avgs = []
+            exp_avg_sqs = []
+            ema_params_with_grad = []
+            state_sums = []
+            max_exp_avg_sqs = []
+            state_steps = []
+            amsgrad = group['amsgrad']
+            beta1, beta2 = group['betas']
+            ema_decay = group['ema_decay']
+            ema_power = group['ema_power']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError('AdamW does not support sparse gradients')
+                grads.append(p.grad)
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                    if amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                    # Exponential moving average of parameter values
+                    state['param_exp_avg'] = p.detach().float().clone()
+
+                exp_avgs.append(state['exp_avg'])
+                exp_avg_sqs.append(state['exp_avg_sq'])
+                ema_params_with_grad.append(state['param_exp_avg'])
+
+                if amsgrad:
+                    max_exp_avg_sqs.append(state['max_exp_avg_sq'])
+
+                # update the steps for each param group update
+                state['step'] += 1
+                # record the step after step update
+                state_steps.append(state['step'])
+
+            optim._functional.adamw(params_with_grad,
+                    grads,
+                    exp_avgs,
+                    exp_avg_sqs,
+                    max_exp_avg_sqs,
+                    state_steps,
+                    amsgrad=amsgrad,
+                    beta1=beta1,
+                    beta2=beta2,
+                    lr=group['lr'],
+                    weight_decay=group['weight_decay'],
+                    eps=group['eps'],
+                    maximize=False)
+
+            cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
+            for param, ema_param in zip(params_with_grad, ema_params_with_grad):
+                ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
+
+        return loss
\ No newline at end of file

examples/images/diffusion/main.py

+import argparse
+import csv
+import datetime
+import glob
+import importlib
+import os
+import sys
+import time
+
+import numpy as np
+import torch
+import torchvision
+
+try:
+    import lightning.pytorch as pl
+except:
+    import pytorch_lightning as pl
+
+from functools import partial
+
+from omegaconf import OmegaConf
+from packaging import version
+from PIL import Image
+from prefetch_generator import BackgroundGenerator
+from torch.utils.data import DataLoader, Dataset, Subset, random_split
+
+try:
+    from lightning.pytorch import seed_everything
+    from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
+    from lightning.pytorch.trainer import Trainer
+    from lightning.pytorch.utilities import rank_zero_info, rank_zero_only
+    LIGHTNING_PACK_NAME = "lightning.pytorch."
+except:
+    from pytorch_lightning import seed_everything
+    from pytorch_lightning.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
+    from pytorch_lightning.trainer import Trainer
+    from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
+    LIGHTNING_PACK_NAME = "pytorch_lightning."
+
+from ldm.data.base import Txt2ImgIterableBaseDataset
+from ldm.util import instantiate_from_config
+
+# from ldm.modules.attention import enable_flash_attentions
+
+
+class DataLoaderX(DataLoader):
+
+    def __iter__(self):
+        return BackgroundGenerator(super().__iter__())
+
+
+def get_parser(**parser_kwargs):
+
+    def str2bool(v):
+        if isinstance(v, bool):
+            return v
+        if v.lower() in ("yes", "true", "t", "y", "1"):
+            return True
+        elif v.lower() in ("no", "false", "f", "n", "0"):
+            return False
+        else:
+            raise argparse.ArgumentTypeError("Boolean value expected.")
+
+    parser = argparse.ArgumentParser(**parser_kwargs)
+    parser.add_argument(
+        "-n",
+        "--name",
+        type=str,
+        const=True,
+        default="",
+        nargs="?",
+        help="postfix for logdir",
+    )
+    parser.add_argument(
+        "-r",
+        "--resume",
+        type=str,
+        const=True,
+        default="",
+        nargs="?",
+        help="resume from logdir or checkpoint in logdir",
+    )
+    parser.add_argument(
+        "-b",
+        "--base",
+        nargs="*",
+        metavar="base_config.yaml",
+        help="paths to base configs. Loaded from left-to-right. "
+        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
+        default=list(),
+    )
+    parser.add_argument(
+        "-t",
+        "--train",
+        type=str2bool,
+        const=True,
+        default=False,
+        nargs="?",
+        help="train",
+    )
+    parser.add_argument(
+        "--no-test",
+        type=str2bool,
+        const=True,
+        default=False,
+        nargs="?",
+        help="disable test",
+    )
+    parser.add_argument("-p", "--project", help="name of new or path to existing project")
+    parser.add_argument(
+        "-d",
+        "--debug",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=False,
+        help="enable post-mortem debugging",
+    )
+    parser.add_argument(
+        "-s",
+        "--seed",
+        type=int,
+        default=23,
+        help="seed for seed_everything",
+    )
+    parser.add_argument(
+        "-f",
+        "--postfix",
+        type=str,
+        default="",
+        help="post-postfix for default name",
+    )
+    parser.add_argument(
+        "-l",
+        "--logdir",
+        type=str,
+        default="logs",
+        help="directory for logging dat shit",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=True,
+        help="scale base-lr by ngpu * batch_size * n_accumulate",
+    )
+    parser.add_argument(
+        "--use_fp16",
+        type=str2bool,
+        nargs="?",
+        const=True,
+        default=True,
+        help="whether to use fp16",
+    )
+    parser.add_argument(
+        "--flash",
+        type=str2bool,
+        const=True,
+        default=False,
+        nargs="?",
+        help="whether to use flash attention",
+    )
+    return parser
+
+
+def nondefault_trainer_args(opt):
+    parser = argparse.ArgumentParser()
+    parser = Trainer.add_argparse_args(parser)
+    args = parser.parse_args([])
+    return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
+
+
+class WrappedDataset(Dataset):
+    """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
+
+    def __init__(self, dataset):
+        self.data = dataset
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+
+def worker_init_fn(_):
+    worker_info = torch.utils.data.get_worker_info()
+
+    dataset = worker_info.dataset
+    worker_id = worker_info.id
+
+    if isinstance(dataset, Txt2ImgIterableBaseDataset):
+        split_size = dataset.num_records // worker_info.num_workers
+        # reset num_records to the true number to retain reliable length information
+        dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
+        current_id = np.random.choice(len(np.random.get_state()[1]), 1)
+        return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
+    else:
+        return np.random.seed(np.random.get_state()[1][0] + worker_id)
+
+
+class DataModuleFromConfig(pl.LightningDataModule):
+
+    def __init__(self,
+                 batch_size,
+                 train=None,
+                 validation=None,
+                 test=None,
+                 predict=None,
+                 wrap=False,
+                 num_workers=None,
+                 shuffle_test_loader=False,
+                 use_worker_init_fn=False,
+                 shuffle_val_dataloader=False):
+        super().__init__()
+        self.batch_size = batch_size
+        self.dataset_configs = dict()
+        self.num_workers = num_workers if num_workers is not None else batch_size * 2
+        self.use_worker_init_fn = use_worker_init_fn
+        if train is not None:
+            self.dataset_configs["train"] = train
+            self.train_dataloader = self._train_dataloader
+        if validation is not None:
+            self.dataset_configs["validation"] = validation
+            self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
+        if test is not None:
+            self.dataset_configs["test"] = test
+            self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
+        if predict is not None:
+            self.dataset_configs["predict"] = predict
+            self.predict_dataloader = self._predict_dataloader
+        self.wrap = wrap
+
+    def prepare_data(self):
+        for data_cfg in self.dataset_configs.values():
+            instantiate_from_config(data_cfg)
+
+    def setup(self, stage=None):
+        self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
+        if self.wrap:
+            for k in self.datasets:
+                self.datasets[k] = WrappedDataset(self.datasets[k])
+
+    def _train_dataloader(self):
+        is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
+        if is_iterable_dataset or self.use_worker_init_fn:
+            init_fn = worker_init_fn
+        else:
+            init_fn = None
+        return DataLoaderX(self.datasets["train"],
+                           batch_size=self.batch_size,
+                           num_workers=self.num_workers,
+                           shuffle=False if is_iterable_dataset else True,
+                           worker_init_fn=init_fn)
+
+    def _val_dataloader(self, shuffle=False):
+        if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
+            init_fn = worker_init_fn
+        else:
+            init_fn = None
+        return DataLoaderX(self.datasets["validation"],
+                           batch_size=self.batch_size,
+                           num_workers=self.num_workers,
+                           worker_init_fn=init_fn,
+                           shuffle=shuffle)
+
+    def _test_dataloader(self, shuffle=False):
+        is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
+        if is_iterable_dataset or self.use_worker_init_fn:
+            init_fn = worker_init_fn
+        else:
+            init_fn = None
+
+        # do not shuffle dataloader for iterable dataset
+        shuffle = shuffle and (not is_iterable_dataset)
+
+        return DataLoaderX(self.datasets["test"],
+                           batch_size=self.batch_size,
+                           num_workers=self.num_workers,
+                           worker_init_fn=init_fn,
+                           shuffle=shuffle)
+
+    def _predict_dataloader(self, shuffle=False):
+        if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
+            init_fn = worker_init_fn
+        else:
+            init_fn = None
+        return DataLoaderX(self.datasets["predict"],
+                           batch_size=self.batch_size,
+                           num_workers=self.num_workers,
+                           worker_init_fn=init_fn)
+
+
+class SetupCallback(Callback):
+
+    def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
+        super().__init__()
+        self.resume = resume
+        self.now = now
+        self.logdir = logdir
+        self.ckptdir = ckptdir
+        self.cfgdir = cfgdir
+        self.config = config
+        self.lightning_config = lightning_config
+
+    def on_keyboard_interrupt(self, trainer, pl_module):
+        if trainer.global_rank == 0:
+            print("Summoning checkpoint.")
+            ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
+            trainer.save_checkpoint(ckpt_path)
+
+    # def on_pretrain_routine_start(self, trainer, pl_module):
+    def on_fit_start(self, trainer, pl_module):
+        if trainer.global_rank == 0:
+            # Create logdirs and save configs
+            os.makedirs(self.logdir, exist_ok=True)
+            os.makedirs(self.ckptdir, exist_ok=True)
+            os.makedirs(self.cfgdir, exist_ok=True)
+
+            if "callbacks" in self.lightning_config:
+                if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
+                    os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
+            print("Project config")
+            print(OmegaConf.to_yaml(self.config))
+            OmegaConf.save(self.config, os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
+
+            print("Lightning config")
+            print(OmegaConf.to_yaml(self.lightning_config))
+            OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
+                           os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
+
+        else:
+            # ModelCheckpoint callback created log directory --- remove it
+            if not self.resume and os.path.exists(self.logdir):
+                dst, name = os.path.split(self.logdir)
+                dst = os.path.join(dst, "child_runs", name)
+                os.makedirs(os.path.split(dst)[0], exist_ok=True)
+                try:
+                    os.rename(self.logdir, dst)
+                except FileNotFoundError:
+                    pass
+
+
+class ImageLogger(Callback):
+
+    def __init__(self,
+                 batch_frequency,
+                 max_images,
+                 clamp=True,
+                 increase_log_steps=True,
+                 rescale=True,
+                 disabled=False,
+                 log_on_batch_idx=False,
+                 log_first_step=False,
+                 log_images_kwargs=None):
+        super().__init__()
+        self.rescale = rescale
+        self.batch_freq = batch_frequency
+        self.max_images = max_images
+        self.logger_log_images = {
+            pl.loggers.CSVLogger: self._testtube,
+        }
+        self.log_steps = [2**n for n in range(int(np.log2(self.batch_freq)) + 1)]
+        if not increase_log_steps:
+            self.log_steps = [self.batch_freq]
+        self.clamp = clamp
+        self.disabled = disabled
+        self.log_on_batch_idx = log_on_batch_idx
+        self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
+        self.log_first_step = log_first_step
+
+    @rank_zero_only
+    def _testtube(self, pl_module, images, batch_idx, split):
+        for k in images:
+            grid = torchvision.utils.make_grid(images[k])
+            grid = (grid + 1.0) / 2.0    # -1,1 -> 0,1; c,h,w
+
+            tag = f"{split}/{k}"
+            pl_module.logger.experiment.add_image(tag, grid, global_step=pl_module.global_step)
+
+    @rank_zero_only
+    def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
+        root = os.path.join(save_dir, "images", split)
+        for k in images:
+            grid = torchvision.utils.make_grid(images[k], nrow=4)
+            if self.rescale:
+                grid = (grid + 1.0) / 2.0    # -1,1 -> 0,1; c,h,w
+            grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
+            grid = grid.numpy()
+            grid = (grid * 255).astype(np.uint8)
+            filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx)
+            path = os.path.join(root, filename)
+            os.makedirs(os.path.split(path)[0], exist_ok=True)
+            Image.fromarray(grid).save(path)
+
+    def log_img(self, pl_module, batch, batch_idx, split="train"):
+        check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
+        if (self.check_frequency(check_idx) and    # batch_idx % self.batch_freq == 0
+                hasattr(pl_module, "log_images") and callable(pl_module.log_images) and self.max_images > 0):
+            logger = type(pl_module.logger)
+
+            is_train = pl_module.training
+            if is_train:
+                pl_module.eval()
+
+            with torch.no_grad():
+                images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
+
+            for k in images:
+                N = min(images[k].shape[0], self.max_images)
+                images[k] = images[k][:N]
+                if isinstance(images[k], torch.Tensor):
+                    images[k] = images[k].detach().cpu()
+                    if self.clamp:
+                        images[k] = torch.clamp(images[k], -1., 1.)
+
+            self.log_local(pl_module.logger.save_dir, split, images, pl_module.global_step, pl_module.current_epoch,
+                           batch_idx)
+
+            logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
+            logger_log_images(pl_module, images, pl_module.global_step, split)
+
+            if is_train:
+                pl_module.train()
+
+    def check_frequency(self, check_idx):
+        if ((check_idx % self.batch_freq) == 0 or
+            (check_idx in self.log_steps)) and (check_idx > 0 or self.log_first_step):
+            try:
+                self.log_steps.pop(0)
+            except IndexError as e:
+                print(e)
+                pass
+            return True
+        return False
+
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
+        # if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
+        #     self.log_img(pl_module, batch, batch_idx, split="train")
+        pass
+
+    def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
+        if not self.disabled and pl_module.global_step > 0:
+            self.log_img(pl_module, batch, batch_idx, split="val")
+        if hasattr(pl_module, 'calibrate_grad_norm'):
+            if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
+                self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
+
+
+class CUDACallback(Callback):
+    # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
+
+    def on_train_start(self, trainer, pl_module):
+        rank_zero_info("Training is starting")
+
+    def on_train_end(self, trainer, pl_module):
+        rank_zero_info("Training is ending")
+
+    def on_train_epoch_start(self, trainer, pl_module):
+        # Reset the memory use counter
+        torch.cuda.reset_peak_memory_stats(trainer.strategy.root_device.index)
+        torch.cuda.synchronize(trainer.strategy.root_device.index)
+        self.start_time = time.time()
+
+    def on_train_epoch_end(self, trainer, pl_module):
+        torch.cuda.synchronize(trainer.strategy.root_device.index)
+        max_memory = torch.cuda.max_memory_allocated(trainer.strategy.root_device.index) / 2**20
+        epoch_time = time.time() - self.start_time
+
+        try:
+            max_memory = trainer.strategy.reduce(max_memory)
+            epoch_time = trainer.strategy.reduce(epoch_time)
+
+            rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
+            rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
+        except AttributeError:
+            pass
+
+
+if __name__ == "__main__":
+    # custom parser to specify config files, train, test and debug mode,
+    # postfix, resume.
+    # `--key value` arguments are interpreted as arguments to the trainer.
+    # `nested.key=value` arguments are interpreted as config parameters.
+    # configs are merged from left-to-right followed by command line parameters.
+
+    # model:
+    #   base_learning_rate: float
+    #   target: path to lightning module
+    #   params:
+    #       key: value
+    # data:
+    #   target: main.DataModuleFromConfig
+    #   params:
+    #      batch_size: int
+    #      wrap: bool
+    #      train:
+    #          target: path to train dataset
+    #          params:
+    #              key: value
+    #      validation:
+    #          target: path to validation dataset
+    #          params:
+    #              key: value
+    #      test:
+    #          target: path to test dataset
+    #          params:
+    #              key: value
+    # lightning: (optional, has sane defaults and can be specified on cmdline)
+    #   trainer:
+    #       additional arguments to trainer
+    #   logger:
+    #       logger to instantiate
+    #   modelcheckpoint:
+    #       modelcheckpoint to instantiate
+    #   callbacks:
+    #       callback1:
+    #           target: importpath
+    #           params:
+    #               key: value
+
+    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+
+    # add cwd for convenience and to make classes in this file available when
+    # running as `python main.py`
+    # (in particular `main.DataModuleFromConfig`)
+    sys.path.append(os.getcwd())
+
+    parser = get_parser()
+    parser = Trainer.add_argparse_args(parser)
+
+    opt, unknown = parser.parse_known_args()
+    if opt.name and opt.resume:
+        raise ValueError("-n/--name and -r/--resume cannot be specified both."
+                         "If you want to resume training in a new log folder, "
+                         "use -n/--name in combination with --resume_from_checkpoint")
+    if opt.resume:
+        if not os.path.exists(opt.resume):
+            raise ValueError("Cannot find {}".format(opt.resume))
+        if os.path.isfile(opt.resume):
+            paths = opt.resume.split("/")
+            # idx = len(paths)-paths[::-1].index("logs")+1
+            # logdir = "/".join(paths[:idx])
+            logdir = "/".join(paths[:-2])
+            ckpt = opt.resume
+        else:
+            assert os.path.isdir(opt.resume), opt.resume
+            logdir = opt.resume.rstrip("/")
+            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
+
+        opt.resume_from_checkpoint = ckpt
+        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
+        opt.base = base_configs + opt.base
+        _tmp = logdir.split("/")
+        nowname = _tmp[-1]
+    else:
+        if opt.name:
+            name = "_" + opt.name
+        elif opt.base:
+            cfg_fname = os.path.split(opt.base[0])[-1]
+            cfg_name = os.path.splitext(cfg_fname)[0]
+            name = "_" + cfg_name
+        else:
+            name = ""
+        nowname = now + name + opt.postfix
+        logdir = os.path.join(opt.logdir, nowname)
+
+    ckptdir = os.path.join(logdir, "checkpoints")
+    cfgdir = os.path.join(logdir, "configs")
+    seed_everything(opt.seed)
+
+    try:
+        # init and save configs
+        configs = [OmegaConf.load(cfg) for cfg in opt.base]
+        cli = OmegaConf.from_dotlist(unknown)
+        config = OmegaConf.merge(*configs, cli)
+        lightning_config = config.pop("lightning", OmegaConf.create())
+        # merge trainer cli with config
+        trainer_config = lightning_config.get("trainer", OmegaConf.create())
+
+        for k in nondefault_trainer_args(opt):
+            trainer_config[k] = getattr(opt, k)
+
+        print(trainer_config)
+        if not trainer_config["accelerator"] == "gpu":
+            del trainer_config["accelerator"]
+            cpu = True
+            print("Running on CPU")
+        else:
+            cpu = False
+            print("Running on GPU")
+        trainer_opt = argparse.Namespace(**trainer_config)
+        lightning_config.trainer = trainer_config
+
+        # model
+        use_fp16 = trainer_config.get("precision", 32) == 16
+        if use_fp16:
+            config.model["params"].update({"use_fp16": True})
+            print("Using FP16 = {}".format(config.model["params"]["use_fp16"]))
+        else:
+            config.model["params"].update({"use_fp16": False})
+            print("Using FP16 = {}".format(config.model["params"]["use_fp16"]))
+
+        model = instantiate_from_config(config.model)
+        # trainer and callbacks
+        trainer_kwargs = dict()
+
+        # config the logger
+        # default logger configs
+        default_logger_cfgs = {
+            "wandb": {
+                "target": LIGHTNING_PACK_NAME + "loggers.WandbLogger",
+                "params": {
+                    "name": nowname,
+                    "save_dir": logdir,
+                    "offline": opt.debug,
+                    "id": nowname,
+                }
+            },
+            "tensorboard": {
+                "target": LIGHTNING_PACK_NAME + "loggers.TensorBoardLogger",
+                "params": {
+                    "save_dir": logdir,
+                    "name": "diff_tb",
+                    "log_graph": True
+                }
+            }
+        }
+
+        default_logger_cfg = default_logger_cfgs["tensorboard"]
+        if "logger" in lightning_config:
+            logger_cfg = lightning_config.logger
+        else:
+            logger_cfg = default_logger_cfg
+        logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
+        trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
+
+        # config the strategy, defualt is ddp
+        if "strategy" in trainer_config:
+            strategy_cfg = trainer_config["strategy"]
+            print("Using strategy: {}".format(strategy_cfg["target"]))
+            strategy_cfg["target"] = LIGHTNING_PACK_NAME + strategy_cfg["target"]
+        else:
+            strategy_cfg = {
+                "target": LIGHTNING_PACK_NAME + "strategies.DDPStrategy",
+                "params": {
+                    "find_unused_parameters": False
+                }
+            }
+            print("Using strategy: DDPStrategy")
+
+        trainer_kwargs["strategy"] = instantiate_from_config(strategy_cfg)
+
+        # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
+        # specify which metric is used to determine best models
+        default_modelckpt_cfg = {
+            "target": LIGHTNING_PACK_NAME + "callbacks.ModelCheckpoint",
+            "params": {
+                "dirpath": ckptdir,
+                "filename": "{epoch:06}",
+                "verbose": True,
+                "save_last": True,
+            }
+        }
+        if hasattr(model, "monitor"):
+            print(f"Monitoring {model.monitor} as checkpoint metric.")
+            default_modelckpt_cfg["params"]["monitor"] = model.monitor
+            default_modelckpt_cfg["params"]["save_top_k"] = 3
+
+        if "modelcheckpoint" in lightning_config:
+            modelckpt_cfg = lightning_config.modelcheckpoint
+        else:
+            modelckpt_cfg = OmegaConf.create()
+        modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
+        print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
+        if version.parse(pl.__version__) < version.parse('1.4.0'):
+            trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
+
+        # add callback which sets up log directory
+        default_callbacks_cfg = {
+            "setup_callback": {
+                "target": "main.SetupCallback",
+                "params": {
+                    "resume": opt.resume,
+                    "now": now,
+                    "logdir": logdir,
+                    "ckptdir": ckptdir,
+                    "cfgdir": cfgdir,
+                    "config": config,
+                    "lightning_config": lightning_config,
+                }
+            },
+            "image_logger": {
+                "target": "main.ImageLogger",
+                "params": {
+                    "batch_frequency": 750,
+                    "max_images": 4,
+                    "clamp": True
+                }
+            },
+            "learning_rate_logger": {
+                "target": "main.LearningRateMonitor",
+                "params": {
+                    "logging_interval": "step",
+        # "log_momentum": True
+                }
+            },
+            "cuda_callback": {
+                "target": "main.CUDACallback"
+            },
+        }
+        if version.parse(pl.__version__) >= version.parse('1.4.0'):
+            default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})
+
+        if "callbacks" in lightning_config:
+            callbacks_cfg = lightning_config.callbacks
+        else:
+            callbacks_cfg = OmegaConf.create()
+
+        if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
+            print(
+                'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
+            default_metrics_over_trainsteps_ckpt_dict = {
+                'metrics_over_trainsteps_checkpoint': {
+                    "target": LIGHTNING_PACK_NAME + 'callbacks.ModelCheckpoint',
+                    'params': {
+                        "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
+                        "filename": "{epoch:06}-{step:09}",
+                        "verbose": True,
+                        'save_top_k': -1,
+                        'every_n_train_steps': 10000,
+                        'save_weights_only': True
+                    }
+                }
+            }
+            default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
+
+        callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
+        if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):
+            callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint
+        elif 'ignore_keys_callback' in callbacks_cfg:
+            del callbacks_cfg['ignore_keys_callback']
+
+        trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
+
+        trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
+        trainer.logdir = logdir    ###
+
+        # data
+        data = instantiate_from_config(config.data)
+        # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
+        # calling these ourselves should not be necessary but it is.
+        # lightning still takes care of proper multiprocessing though
+        data.prepare_data()
+        data.setup()
+        print("#### Data #####")
+        for k in data.datasets:
+            print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
+
+        # configure learning rate
+        bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
+        if not cpu:
+            ngpu = trainer_config["devices"]
+        else:
+            ngpu = 1
+        if 'accumulate_grad_batches' in lightning_config.trainer:
+            accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
+        else:
+            accumulate_grad_batches = 1
+        print(f"accumulate_grad_batches = {accumulate_grad_batches}")
+        lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
+        if opt.scale_lr:
+            model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
+            print(
+                "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)"
+                .format(model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
+        else:
+            model.learning_rate = base_lr
+            print("++++ NOT USING LR SCALING ++++")
+            print(f"Setting learning rate to {model.learning_rate:.2e}")
+
+        # allow checkpointing via USR1
+        def melk(*args, **kwargs):
+            # run all checkpoint hooks
+            if trainer.global_rank == 0:
+                print("Summoning checkpoint.")
+                ckpt_path = os.path.join(ckptdir, "last.ckpt")
+                trainer.save_checkpoint(ckpt_path)
+
+        def divein(*args, **kwargs):
+            if trainer.global_rank == 0:
+                import pudb
+                pudb.set_trace()
+
+        import signal
+
+        signal.signal(signal.SIGUSR1, melk)
+        signal.signal(signal.SIGUSR2, divein)
+
+        # run
+        if opt.train:
+            try:
+                trainer.fit(model, data)
+            except Exception:
+                melk()
+                raise
+        # if not opt.no_test and not trainer.interrupted:
+        #     trainer.test(model, data)
+    except Exception:
+        if opt.debug and trainer.global_rank == 0:
+            try:
+                import pudb as debugger
+            except ImportError:
+                import pdb as debugger
+            debugger.post_mortem()
+        raise
+    finally:
+        # move newly created debug project to debug_runs
+        if opt.debug and not opt.resume and trainer.global_rank == 0:
+            dst, name = os.path.split(logdir)
+            dst = os.path.join(dst, "debug_runs", name)
+            os.makedirs(os.path.split(dst)[0], exist_ok=True)
+            os.rename(logdir, dst)
+        if trainer.global_rank == 0:
+            print(trainer.profiler.summary())

examples/images/diffusion/requirements.txt

+albumentations==1.3.0
+opencv-python==4.6.0
+pudb==2019.2
+prefetch_generator
+imageio==2.9.0
+imageio-ffmpeg==0.4.2
+torchmetrics==0.6
+omegaconf==2.1.1
+test-tube>=0.7.5
+streamlit>=0.73.1
+einops==0.3.0
+transformers==4.19.2
+webdataset==0.2.5
+open-clip-torch==2.7.0
+gradio==3.11
+datasets
+colossalai
+-e .

examples/images/diffusion/scripts/download_first_stages.sh

+#!/bin/bash
+wget -O models/first_stage_models/kl-f4/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f4.zip
+wget -O models/first_stage_models/kl-f8/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f8.zip
+wget -O models/first_stage_models/kl-f16/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f16.zip
+wget -O models/first_stage_models/kl-f32/model.zip https://ommer-lab.com/files/latent-diffusion/kl-f32.zip
+wget -O models/first_stage_models/vq-f4/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4.zip
+wget -O models/first_stage_models/vq-f4-noattn/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f4-noattn.zip
+wget -O models/first_stage_models/vq-f8/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8.zip
+wget -O models/first_stage_models/vq-f8-n256/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f8-n256.zip
+wget -O models/first_stage_models/vq-f16/model.zip https://ommer-lab.com/files/latent-diffusion/vq-f16.zip
+
+
+
+cd models/first_stage_models/kl-f4
+unzip -o model.zip
+
+cd ../kl-f8
+unzip -o model.zip
+
+cd ../kl-f16
+unzip -o model.zip
+
+cd ../kl-f32
+unzip -o model.zip
+
+cd ../vq-f4
+unzip -o model.zip
+
+cd ../vq-f4-noattn
+unzip -o model.zip
+
+cd ../vq-f8
+unzip -o model.zip
+
+cd ../vq-f8-n256
+unzip -o model.zip
+
+cd ../vq-f16
+unzip -o model.zip
+
+cd ../..
\ No newline at end of file

examples/images/diffusion/scripts/download_models.sh

+#!/bin/bash
+wget -O models/ldm/celeba256/celeba-256.zip https://ommer-lab.com/files/latent-diffusion/celeba.zip
+wget -O models/ldm/ffhq256/ffhq-256.zip https://ommer-lab.com/files/latent-diffusion/ffhq.zip
+wget -O models/ldm/lsun_churches256/lsun_churches-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_churches.zip
+wget -O models/ldm/lsun_beds256/lsun_beds-256.zip https://ommer-lab.com/files/latent-diffusion/lsun_bedrooms.zip
+wget -O models/ldm/text2img256/model.zip https://ommer-lab.com/files/latent-diffusion/text2img.zip
+wget -O models/ldm/cin256/model.zip https://ommer-lab.com/files/latent-diffusion/cin.zip
+wget -O models/ldm/semantic_synthesis512/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis.zip
+wget -O models/ldm/semantic_synthesis256/model.zip https://ommer-lab.com/files/latent-diffusion/semantic_synthesis256.zip
+wget -O models/ldm/bsr_sr/model.zip https://ommer-lab.com/files/latent-diffusion/sr_bsr.zip
+wget -O models/ldm/layout2img-openimages256/model.zip https://ommer-lab.com/files/latent-diffusion/layout2img_model.zip
+wget -O models/ldm/inpainting_big/model.zip https://ommer-lab.com/files/latent-diffusion/inpainting_big.zip
+
+
+
+cd models/ldm/celeba256
+unzip -o celeba-256.zip
+
+cd ../ffhq256
+unzip -o ffhq-256.zip
+
+cd ../lsun_churches256
+unzip -o lsun_churches-256.zip
+
+cd ../lsun_beds256
+unzip -o lsun_beds-256.zip
+
+cd ../text2img256
+unzip -o model.zip
+
+cd ../cin256
+unzip -o model.zip
+
+cd ../semantic_synthesis512
+unzip -o model.zip
+
+cd ../semantic_synthesis256
+unzip -o model.zip
+
+cd ../bsr_sr
+unzip -o model.zip
+
+cd ../layout2img-openimages256
+unzip -o model.zip
+
+cd ../inpainting_big
+unzip -o model.zip
+
+cd ../..

examples/images/diffusion/scripts/img2img.py

+"""make variations of input image"""
+
+import argparse, os
+import PIL
+import torch
+import numpy as np
+from omegaconf import OmegaConf
+from PIL import Image
+from tqdm import tqdm, trange
+from itertools import islice
+from einops import rearrange, repeat
+from torchvision.utils import make_grid
+from torch import autocast
+from contextlib import nullcontext
+try:
+    from lightning.pytorch import seed_everything
+except:
+    from pytorch_lightning import seed_everything
+from imwatermark import WatermarkEncoder
+
+
+from scripts.txt2img import put_watermark
+from ldm.util import instantiate_from_config
+from ldm.models.diffusion.ddim import DDIMSampler
+from utils import replace_module, getModelSize
+
+
+def chunk(it, size):
+    it = iter(it)
+    return iter(lambda: tuple(islice(it, size)), ())
+
+
+def load_model_from_config(config, ckpt, verbose=False):
+    print(f"Loading model from {ckpt}")
+    pl_sd = torch.load(ckpt, map_location="cpu")
+    if "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+    sd = pl_sd["state_dict"]
+    model = instantiate_from_config(config.model)
+    m, u = model.load_state_dict(sd, strict=False)
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    model.eval()
+    return model
+
+
+def load_img(path):
+    image = Image.open(path).convert("RGB")
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h}) from {path}")
+    w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
+    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2. * image - 1.
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        nargs="?",
+        default="a painting of a virus monster playing guitar",
+        help="the prompt to render"
+    )
+
+    parser.add_argument(
+        "--init-img",
+        type=str,
+        nargs="?",
+        help="path to the input image"
+    )
+
+    parser.add_argument(
+        "--outdir",
+        type=str,
+        nargs="?",
+        help="dir to write results to",
+        default="outputs/img2img-samples"
+    )
+
+    parser.add_argument(
+        "--ddim_steps",
+        type=int,
+        default=50,
+        help="number of ddim sampling steps",
+    )
+
+    parser.add_argument(
+        "--fixed_code",
+        action='store_true',
+        help="if enabled, uses the same starting code across all samples ",
+    )
+
+    parser.add_argument(
+        "--ddim_eta",
+        type=float,
+        default=0.0,
+        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
+    )
+    parser.add_argument(
+        "--n_iter",
+        type=int,
+        default=1,
+        help="sample this often",
+    )
+
+    parser.add_argument(
+        "--C",
+        type=int,
+        default=4,
+        help="latent channels",
+    )
+    parser.add_argument(
+        "--f",
+        type=int,
+        default=8,
+        help="downsampling factor, most often 8 or 16",
+    )
+
+    parser.add_argument(
+        "--n_samples",
+        type=int,
+        default=2,
+        help="how many samples to produce for each given prompt. A.k.a batch size",
+    )
+
+    parser.add_argument(
+        "--n_rows",
+        type=int,
+        default=0,
+        help="rows in the grid (default: n_samples)",
+    )
+
+    parser.add_argument(
+        "--scale",
+        type=float,
+        default=9.0,
+        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
+    )
+
+    parser.add_argument(
+        "--strength",
+        type=float,
+        default=0.8,
+        help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
+    )
+
+    parser.add_argument(
+        "--from-file",
+        type=str,
+        help="if specified, load prompts from this file",
+    )
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="configs/stable-diffusion/v2-inference.yaml",
+        help="path to config which constructs model",
+    )
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        help="path to checkpoint of model",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="the seed (for reproducible sampling)",
+    )
+    parser.add_argument(
+        "--precision",
+        type=str,
+        help="evaluate at this precision",
+        choices=["full", "autocast"],
+        default="autocast"
+    )
+    parser.add_argument(
+        "--use_int8",
+        type=bool,
+        default=False,
+        help="use int8 for inference",
+    )
+
+    opt = parser.parse_args()
+    seed_everything(opt.seed)
+
+    config = OmegaConf.load(f"{opt.config}")
+    model = load_model_from_config(config, f"{opt.ckpt}")
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = model.to(device)
+
+    # quantize model
+    if opt.use_int8:
+        model = replace_module(model)
+        # # to compute the model size
+        # getModelSize(model)
+    
+    sampler = DDIMSampler(model)
+
+    os.makedirs(opt.outdir, exist_ok=True)
+    outpath = opt.outdir
+
+    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
+    wm = "SDV2"
+    wm_encoder = WatermarkEncoder()
+    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
+
+    batch_size = opt.n_samples
+    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
+    if not opt.from_file:
+        prompt = opt.prompt
+        assert prompt is not None
+        data = [batch_size * [prompt]]
+
+    else:
+        print(f"reading prompts from {opt.from_file}")
+        with open(opt.from_file, "r") as f:
+            data = f.read().splitlines()
+            data = list(chunk(data, batch_size))
+
+    sample_path = os.path.join(outpath, "samples")
+    os.makedirs(sample_path, exist_ok=True)
+    base_count = len(os.listdir(sample_path))
+    grid_count = len(os.listdir(outpath)) - 1
+
+    assert os.path.isfile(opt.init_img)
+    init_image = load_img(opt.init_img).to(device)
+    init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
+    init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))  # move to latent space
+
+    sampler.make_schedule(ddim_num_steps=opt.ddim_steps, ddim_eta=opt.ddim_eta, verbose=False)
+
+    assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
+    t_enc = int(opt.strength * opt.ddim_steps)
+    print(f"target t_enc is {t_enc} steps")
+
+    precision_scope = autocast if opt.precision == "autocast" else nullcontext
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            with model.ema_scope():
+                all_samples = list()
+                for n in trange(opt.n_iter, desc="Sampling"):
+                    for prompts in tqdm(data, desc="data"):
+                        uc = None
+                        if opt.scale != 1.0:
+                            uc = model.get_learned_conditioning(batch_size * [""])
+                        if isinstance(prompts, tuple):
+                            prompts = list(prompts)
+                        c = model.get_learned_conditioning(prompts)
+
+                        # encode (scaled latent)
+                        z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(device))
+                        # decode it
+                        samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
+                                                 unconditional_conditioning=uc, )
+
+                        x_samples = model.decode_first_stage(samples)
+                        x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+                        for x_sample in x_samples:
+                            x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                            img = Image.fromarray(x_sample.astype(np.uint8))
+                            img = put_watermark(img, wm_encoder)
+                            img.save(os.path.join(sample_path, f"{base_count:05}.png"))
+                            base_count += 1
+                        all_samples.append(x_samples)
+
+                # additionally, save as grid
+                grid = torch.stack(all_samples, 0)
+                grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+                grid = make_grid(grid, nrow=n_rows)
+
+                # to image
+                grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
+                grid = Image.fromarray(grid.astype(np.uint8))
+                grid = put_watermark(grid, wm_encoder)
+                grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
+                grid_count += 1
+
+    print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")
+
+
+if __name__ == "__main__":
+    main()
+    # # to compute the mem allocated
+    # print(torch.cuda.max_memory_allocated() / 1024 / 1024)

examples/images/diffusion/scripts/inpaint.py

+import argparse, os, sys, glob
+from omegaconf import OmegaConf
+from PIL import Image
+from tqdm import tqdm
+import numpy as np
+import torch
+from main import instantiate_from_config
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+def make_batch(image, mask, device):
+    image = np.array(Image.open(image).convert("RGB"))
+    image = image.astype(np.float32)/255.0
+    image = image[None].transpose(0,3,1,2)
+    image = torch.from_numpy(image)
+
+    mask = np.array(Image.open(mask).convert("L"))
+    mask = mask.astype(np.float32)/255.0
+    mask = mask[None,None]
+    mask[mask < 0.5] = 0
+    mask[mask >= 0.5] = 1
+    mask = torch.from_numpy(mask)
+
+    masked_image = (1-mask)*image
+
+    batch = {"image": image, "mask": mask, "masked_image": masked_image}
+    for k in batch:
+        batch[k] = batch[k].to(device=device)
+        batch[k] = batch[k]*2.0-1.0
+    return batch
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--indir",
+        type=str,
+        nargs="?",
+        help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",
+    )
+    parser.add_argument(
+        "--outdir",
+        type=str,
+        nargs="?",
+        help="dir to write results to",
+    )
+    parser.add_argument(
+        "--steps",
+        type=int,
+        default=50,
+        help="number of ddim sampling steps",
+    )
+    opt = parser.parse_args()
+
+    masks = sorted(glob.glob(os.path.join(opt.indir, "*_mask.png")))
+    images = [x.replace("_mask.png", ".png") for x in masks]
+    print(f"Found {len(masks)} inputs.")
+
+    config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
+    model = instantiate_from_config(config.model)
+    model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"],
+                          strict=False)
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = model.to(device)
+    sampler = DDIMSampler(model)
+
+    os.makedirs(opt.outdir, exist_ok=True)
+    with torch.no_grad():
+        with model.ema_scope():
+            for image, mask in tqdm(zip(images, masks)):
+                outpath = os.path.join(opt.outdir, os.path.split(image)[1])
+                batch = make_batch(image, mask, device=device)
+
+                # encode masked image and concat downsampled mask
+                c = model.cond_stage_model.encode(batch["masked_image"])
+                cc = torch.nn.functional.interpolate(batch["mask"],
+                                                     size=c.shape[-2:])
+                c = torch.cat((c, cc), dim=1)
+
+                shape = (c.shape[1]-1,)+c.shape[2:]
+                samples_ddim, _ = sampler.sample(S=opt.steps,
+                                                 conditioning=c,
+                                                 batch_size=c.shape[0],
+                                                 shape=shape,
+                                                 verbose=False)
+                x_samples_ddim = model.decode_first_stage(samples_ddim)
+
+                image = torch.clamp((batch["image"]+1.0)/2.0,
+                                    min=0.0, max=1.0)
+                mask = torch.clamp((batch["mask"]+1.0)/2.0,
+                                   min=0.0, max=1.0)
+                predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0,
+                                              min=0.0, max=1.0)
+
+                inpainted = (1-mask)*image+mask*predicted_image
+                inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255
+                Image.fromarray(inpainted.astype(np.uint8)).save(outpath)

examples/images/diffusion/scripts/knn2img.py

+import argparse, os, sys, glob
+import clip
+import torch
+import torch.nn as nn
+import numpy as np
+from omegaconf import OmegaConf
+from PIL import Image
+from tqdm import tqdm, trange
+from itertools import islice
+from einops import rearrange, repeat
+from torchvision.utils import make_grid
+import scann
+import time
+from multiprocessing import cpu_count
+
+from ldm.util import instantiate_from_config, parallel_data_prefetch
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder
+
+DATABASES = [
+    "openimages",
+    "artbench-art_nouveau",
+    "artbench-baroque",
+    "artbench-expressionism",
+    "artbench-impressionism",
+    "artbench-post_impressionism",
+    "artbench-realism",
+    "artbench-romanticism",
+    "artbench-renaissance",
+    "artbench-surrealism",
+    "artbench-ukiyo_e",
+]
+
+
+def chunk(it, size):
+    it = iter(it)
+    return iter(lambda: tuple(islice(it, size)), ())
+
+
+def load_model_from_config(config, ckpt, verbose=False):
+    print(f"Loading model from {ckpt}")
+    pl_sd = torch.load(ckpt, map_location="cpu")
+    if "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+    sd = pl_sd["state_dict"]
+    model = instantiate_from_config(config.model)
+    m, u = model.load_state_dict(sd, strict=False)
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    model.cuda()
+    model.eval()
+    return model
+
+
+class Searcher(object):
+    def __init__(self, database, retriever_version='ViT-L/14'):
+        assert database in DATABASES
+        # self.database = self.load_database(database)
+        self.database_name = database
+        self.searcher_savedir = f'data/rdm/searchers/{self.database_name}'
+        self.database_path = f'data/rdm/retrieval_databases/{self.database_name}'
+        self.retriever = self.load_retriever(version=retriever_version)
+        self.database = {'embedding': [],
+                         'img_id': [],
+                         'patch_coords': []}
+        self.load_database()
+        self.load_searcher()
+
+    def train_searcher(self, k,
+                       metric='dot_product',
+                       searcher_savedir=None):
+
+        print('Start training searcher')
+        searcher = scann.scann_ops_pybind.builder(self.database['embedding'] /
+                                                  np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis],
+                                                  k, metric)
+        self.searcher = searcher.score_brute_force().build()
+        print('Finish training searcher')
+
+        if searcher_savedir is not None:
+            print(f'Save trained searcher under "{searcher_savedir}"')
+            os.makedirs(searcher_savedir, exist_ok=True)
+            self.searcher.serialize(searcher_savedir)
+
+    def load_single_file(self, saved_embeddings):
+        compressed = np.load(saved_embeddings)
+        self.database = {key: compressed[key] for key in compressed.files}
+        print('Finished loading of clip embeddings.')
+
+    def load_multi_files(self, data_archive):
+        out_data = {key: [] for key in self.database}
+        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
+            for key in d.files:
+                out_data[key].append(d[key])
+
+        return out_data
+
+    def load_database(self):
+
+        print(f'Load saved patch embedding from "{self.database_path}"')
+        file_content = glob.glob(os.path.join(self.database_path, '*.npz'))
+
+        if len(file_content) == 1:
+            self.load_single_file(file_content[0])
+        elif len(file_content) > 1:
+            data = [np.load(f) for f in file_content]
+            prefetched_data = parallel_data_prefetch(self.load_multi_files, data,
+                                                     n_proc=min(len(data), cpu_count()), target_data_type='dict')
+
+            self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in
+                             self.database}
+        else:
+            raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')
+
+        print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')
+
+    def load_retriever(self, version='ViT-L/14', ):
+        model = FrozenClipImageEmbedder(model=version)
+        if torch.cuda.is_available():
+            model.cuda()
+        model.eval()
+        return model
+
+    def load_searcher(self):
+        print(f'load searcher for database {self.database_name} from {self.searcher_savedir}')
+        self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
+        print('Finished loading searcher.')
+
+    def search(self, x, k):
+        if self.searcher is None and self.database['embedding'].shape[0] < 2e4:
+            self.train_searcher(k)   # quickly fit searcher on the fly for small databases
+        assert self.searcher is not None, 'Cannot search with uninitialized searcher'
+        if isinstance(x, torch.Tensor):
+            x = x.detach().cpu().numpy()
+        if len(x.shape) == 3:
+            x = x[:, 0]
+        query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis]
+
+        start = time.time()
+        nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
+        end = time.time()
+
+        out_embeddings = self.database['embedding'][nns]
+        out_img_ids = self.database['img_id'][nns]
+        out_pc = self.database['patch_coords'][nns]
+
+        out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
+               'img_ids': out_img_ids,
+               'patch_coords': out_pc,
+               'queries': x,
+               'exec_time': end - start,
+               'nns': nns,
+               'q_embeddings': query_embeddings}
+
+        return out
+
+    def __call__(self, x, n):
+        return self.search(x, n)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc)
+    # TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt?
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        nargs="?",
+        default="a painting of a virus monster playing guitar",
+        help="the prompt to render"
+    )
+
+    parser.add_argument(
+        "--outdir",
+        type=str,
+        nargs="?",
+        help="dir to write results to",
+        default="outputs/txt2img-samples"
+    )
+
+    parser.add_argument(
+        "--skip_grid",
+        action='store_true',
+        help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
+    )
+
+    parser.add_argument(
+        "--ddim_steps",
+        type=int,
+        default=50,
+        help="number of ddim sampling steps",
+    )
+
+    parser.add_argument(
+        "--n_repeat",
+        type=int,
+        default=1,
+        help="number of repeats in CLIP latent space",
+    )
+
+    parser.add_argument(
+        "--plms",
+        action='store_true',
+        help="use plms sampling",
+    )
+
+    parser.add_argument(
+        "--ddim_eta",
+        type=float,
+        default=0.0,
+        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
+    )
+    parser.add_argument(
+        "--n_iter",
+        type=int,
+        default=1,
+        help="sample this often",
+    )
+
+    parser.add_argument(
+        "--H",
+        type=int,
+        default=768,
+        help="image height, in pixel space",
+    )
+
+    parser.add_argument(
+        "--W",
+        type=int,
+        default=768,
+        help="image width, in pixel space",
+    )
+
+    parser.add_argument(
+        "--n_samples",
+        type=int,
+        default=3,
+        help="how many samples to produce for each given prompt. A.k.a batch size",
+    )
+
+    parser.add_argument(
+        "--n_rows",
+        type=int,
+        default=0,
+        help="rows in the grid (default: n_samples)",
+    )
+
+    parser.add_argument(
+        "--scale",
+        type=float,
+        default=5.0,
+        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
+    )
+
+    parser.add_argument(
+        "--from-file",
+        type=str,
+        help="if specified, load prompts from this file",
+    )
+
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="configs/retrieval-augmented-diffusion/768x768.yaml",
+        help="path to config which constructs model",
+    )
+
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="models/rdm/rdm768x768/model.ckpt",
+        help="path to checkpoint of model",
+    )
+
+    parser.add_argument(
+        "--clip_type",
+        type=str,
+        default="ViT-L/14",
+        help="which CLIP model to use for retrieval and NN encoding",
+    )
+    parser.add_argument(
+        "--database",
+        type=str,
+        default='artbench-surrealism',
+        choices=DATABASES,
+        help="The database used for the search, only applied when --use_neighbors=True",
+    )
+    parser.add_argument(
+        "--use_neighbors",
+        default=False,
+        action='store_true',
+        help="Include neighbors in addition to text prompt for conditioning",
+    )
+    parser.add_argument(
+        "--knn",
+        default=10,
+        type=int,
+        help="The number of included neighbors, only applied when --use_neighbors=True",
+    )
+
+    opt = parser.parse_args()
+
+    config = OmegaConf.load(f"{opt.config}")
+    model = load_model_from_config(config, f"{opt.ckpt}")
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = model.to(device)
+
+    clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device)
+
+    if opt.plms:
+        sampler = PLMSSampler(model)
+    else:
+        sampler = DDIMSampler(model)
+
+    os.makedirs(opt.outdir, exist_ok=True)
+    outpath = opt.outdir
+
+    batch_size = opt.n_samples
+    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
+    if not opt.from_file:
+        prompt = opt.prompt
+        assert prompt is not None
+        data = [batch_size * [prompt]]
+
+    else:
+        print(f"reading prompts from {opt.from_file}")
+        with open(opt.from_file, "r") as f:
+            data = f.read().splitlines()
+            data = list(chunk(data, batch_size))
+
+    sample_path = os.path.join(outpath, "samples")
+    os.makedirs(sample_path, exist_ok=True)
+    base_count = len(os.listdir(sample_path))
+    grid_count = len(os.listdir(outpath)) - 1
+
+    print(f"sampling scale for cfg is {opt.scale:.2f}")
+
+    searcher = None
+    if opt.use_neighbors:
+        searcher = Searcher(opt.database)
+
+    with torch.no_grad():
+        with model.ema_scope():
+            for n in trange(opt.n_iter, desc="Sampling"):
+                all_samples = list()
+                for prompts in tqdm(data, desc="data"):
+                    print("sampling prompts:", prompts)
+                    if isinstance(prompts, tuple):
+                        prompts = list(prompts)
+                    c = clip_text_encoder.encode(prompts)
+                    uc = None
+                    if searcher is not None:
+                        nn_dict = searcher(c, opt.knn)
+                        c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1)
+                    if opt.scale != 1.0:
+                        uc = torch.zeros_like(c)
+                    if isinstance(prompts, tuple):
+                        prompts = list(prompts)
+                    shape = [16, opt.H // 16, opt.W // 16]  # note: currently hardcoded for f16 model
+                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
+                                                     conditioning=c,
+                                                     batch_size=c.shape[0],
+                                                     shape=shape,
+                                                     verbose=False,
+                                                     unconditional_guidance_scale=opt.scale,
+                                                     unconditional_conditioning=uc,
+                                                     eta=opt.ddim_eta,
+                                                     )
+
+                    x_samples_ddim = model.decode_first_stage(samples_ddim)
+                    x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+
+                    for x_sample in x_samples_ddim:
+                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                        Image.fromarray(x_sample.astype(np.uint8)).save(
+                            os.path.join(sample_path, f"{base_count:05}.png"))
+                        base_count += 1
+                    all_samples.append(x_samples_ddim)
+
+                if not opt.skip_grid:
+                    # additionally, save as grid
+                    grid = torch.stack(all_samples, 0)
+                    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+                    grid = make_grid(grid, nrow=n_rows)
+
+                    # to image
+                    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
+                    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
+                    grid_count += 1
+
+    print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")

examples/images/diffusion/scripts/sample_diffusion.py

+import argparse, os, sys, glob, datetime, yaml
+import torch
+import time
+import numpy as np
+from tqdm import trange
+
+from omegaconf import OmegaConf
+from PIL import Image
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config
+
+rescale = lambda x: (x + 1.) / 2.
+
+def custom_to_pil(x):
+    x = x.detach().cpu()
+    x = torch.clamp(x, -1., 1.)
+    x = (x + 1.) / 2.
+    x = x.permute(1, 2, 0).numpy()
+    x = (255 * x).astype(np.uint8)
+    x = Image.fromarray(x)
+    if not x.mode == "RGB":
+        x = x.convert("RGB")
+    return x
+
+
+def custom_to_np(x):
+    # saves the batch in adm style as in https://github.com/openai/guided-diffusion/blob/main/scripts/image_sample.py
+    sample = x.detach().cpu()
+    sample = ((sample + 1) * 127.5).clamp(0, 255).to(torch.uint8)
+    sample = sample.permute(0, 2, 3, 1)
+    sample = sample.contiguous()
+    return sample
+
+
+def logs2pil(logs, keys=["sample"]):
+    imgs = dict()
+    for k in logs:
+        try:
+            if len(logs[k].shape) == 4:
+                img = custom_to_pil(logs[k][0, ...])
+            elif len(logs[k].shape) == 3:
+                img = custom_to_pil(logs[k])
+            else:
+                print(f"Unknown format for key {k}. ")
+                img = None
+        except:
+            img = None
+        imgs[k] = img
+    return imgs
+
+
+@torch.no_grad()
+def convsample(model, shape, return_intermediates=True,
+               verbose=True,
+               make_prog_row=False):
+
+
+    if not make_prog_row:
+        return model.p_sample_loop(None, shape,
+                                   return_intermediates=return_intermediates, verbose=verbose)
+    else:
+        return model.progressive_denoising(
+            None, shape, verbose=True
+        )
+
+
+@torch.no_grad()
+def convsample_ddim(model, steps, shape, eta=1.0
+                    ):
+    ddim = DDIMSampler(model)
+    bs = shape[0]
+    shape = shape[1:]
+    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, eta=eta, verbose=False,)
+    return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(model, batch_size, vanilla=False, custom_steps=None, eta=1.0,):
+
+
+    log = dict()
+
+    shape = [batch_size,
+             model.model.diffusion_model.in_channels,
+             model.model.diffusion_model.image_size,
+             model.model.diffusion_model.image_size]
+
+    with model.ema_scope("Plotting"):
+        t0 = time.time()
+        if vanilla:
+            sample, progrow = convsample(model, shape,
+                                         make_prog_row=True)
+        else:
+            sample, intermediates = convsample_ddim(model,  steps=custom_steps, shape=shape,
+                                                    eta=eta)
+
+        t1 = time.time()
+
+    x_sample = model.decode_first_stage(sample)
+
+    log["sample"] = x_sample
+    log["time"] = t1 - t0
+    log['throughput'] = sample.shape[0] / (t1 - t0)
+    print(f'Throughput for this batch: {log["throughput"]}')
+    return log
+
+def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, eta=None, n_samples=50000, nplog=None):
+    if vanilla:
+        print(f'Using Vanilla DDPM sampling with {model.num_timesteps} sampling steps.')
+    else:
+        print(f'Using DDIM sampling with {custom_steps} sampling steps and eta={eta}')
+
+
+    tstart = time.time()
+    n_saved = len(glob.glob(os.path.join(logdir,'*.png')))-1
+    # path = logdir
+    if model.cond_stage_model is None:
+        all_images = []
+
+        print(f"Running unconditional sampling for {n_samples} samples")
+        for _ in trange(n_samples // batch_size, desc="Sampling Batches (unconditional)"):
+            logs = make_convolutional_sample(model, batch_size=batch_size,
+                                             vanilla=vanilla, custom_steps=custom_steps,
+                                             eta=eta)
+            n_saved = save_logs(logs, logdir, n_saved=n_saved, key="sample")
+            all_images.extend([custom_to_np(logs["sample"])])
+            if n_saved >= n_samples:
+                print(f'Finish after generating {n_saved} samples')
+                break
+        all_img = np.concatenate(all_images, axis=0)
+        all_img = all_img[:n_samples]
+        shape_str = "x".join([str(x) for x in all_img.shape])
+        nppath = os.path.join(nplog, f"{shape_str}-samples.npz")
+        np.savez(nppath, all_img)
+
+    else:
+       raise NotImplementedError('Currently only sampling for unconditional models supported.')
+
+    print(f"sampling of {n_saved} images finished in {(time.time() - tstart) / 60.:.2f} minutes.")
+
+
+def save_logs(logs, path, n_saved=0, key="sample", np_path=None):
+    for k in logs:
+        if k == key:
+            batch = logs[key]
+            if np_path is None:
+                for x in batch:
+                    img = custom_to_pil(x)
+                    imgpath = os.path.join(path, f"{key}_{n_saved:06}.png")
+                    img.save(imgpath)
+                    n_saved += 1
+            else:
+                npbatch = custom_to_np(batch)
+                shape_str = "x".join([str(x) for x in npbatch.shape])
+                nppath = os.path.join(np_path, f"{n_saved}-{shape_str}-samples.npz")
+                np.savez(nppath, npbatch)
+                n_saved += npbatch.shape[0]
+    return n_saved
+
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-r",
+        "--resume",
+        type=str,
+        nargs="?",
+        help="load from logdir or checkpoint in logdir",
+    )
+    parser.add_argument(
+        "-n",
+        "--n_samples",
+        type=int,
+        nargs="?",
+        help="number of samples to draw",
+        default=50000
+    )
+    parser.add_argument(
+        "-e",
+        "--eta",
+        type=float,
+        nargs="?",
+        help="eta for ddim sampling (0.0 yields deterministic sampling)",
+        default=1.0
+    )
+    parser.add_argument(
+        "-v",
+        "--vanilla_sample",
+        default=False,
+        action='store_true',
+        help="vanilla sampling (default option is DDIM sampling)?",
+    )
+    parser.add_argument(
+        "-l",
+        "--logdir",
+        type=str,
+        nargs="?",
+        help="extra logdir",
+        default="none"
+    )
+    parser.add_argument(
+        "-c",
+        "--custom_steps",
+        type=int,
+        nargs="?",
+        help="number of steps for ddim and fastdpm sampling",
+        default=50
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        nargs="?",
+        help="the bs",
+        default=10
+    )
+    return parser
+
+
+def load_model_from_config(config, sd):
+    model = instantiate_from_config(config)
+    model.load_state_dict(sd,strict=False)
+    model.cuda()
+    model.eval()
+    return model
+
+
+def load_model(config, ckpt, gpu, eval_mode):
+    if ckpt:
+        print(f"Loading model from {ckpt}")
+        pl_sd = torch.load(ckpt, map_location="cpu")
+        global_step = pl_sd["global_step"]
+    else:
+        pl_sd = {"state_dict": None}
+        global_step = None
+    model = load_model_from_config(config.model,
+                                   pl_sd["state_dict"])
+
+    return model, global_step
+
+
+if __name__ == "__main__":
+    now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+    sys.path.append(os.getcwd())
+    command = " ".join(sys.argv)
+
+    parser = get_parser()
+    opt, unknown = parser.parse_known_args()
+    ckpt = None
+
+    if not os.path.exists(opt.resume):
+        raise ValueError("Cannot find {}".format(opt.resume))
+    if os.path.isfile(opt.resume):
+        # paths = opt.resume.split("/")
+        try:
+            logdir = '/'.join(opt.resume.split('/')[:-1])
+            # idx = len(paths)-paths[::-1].index("logs")+1
+            print(f'Logdir is {logdir}')
+        except ValueError:
+            paths = opt.resume.split("/")
+            idx = -2  # take a guess: path/to/logdir/checkpoints/model.ckpt
+            logdir = "/".join(paths[:idx])
+        ckpt = opt.resume
+    else:
+        assert os.path.isdir(opt.resume), f"{opt.resume} is not a directory"
+        logdir = opt.resume.rstrip("/")
+        ckpt = os.path.join(logdir, "model.ckpt")
+
+    base_configs = sorted(glob.glob(os.path.join(logdir, "config.yaml")))
+    opt.base = base_configs
+
+    configs = [OmegaConf.load(cfg) for cfg in opt.base]
+    cli = OmegaConf.from_dotlist(unknown)
+    config = OmegaConf.merge(*configs, cli)
+
+    gpu = True
+    eval_mode = True
+
+    if opt.logdir != "none":
+        locallog = logdir.split(os.sep)[-1]
+        if locallog == "": locallog = logdir.split(os.sep)[-2]
+        print(f"Switching logdir from '{logdir}' to '{os.path.join(opt.logdir, locallog)}'")
+        logdir = os.path.join(opt.logdir, locallog)
+
+    print(config)
+
+    model, global_step = load_model(config, ckpt, gpu, eval_mode)
+    print(f"global step: {global_step}")
+    print(75 * "=")
+    print("logging to:")
+    logdir = os.path.join(logdir, "samples", f"{global_step:08}", now)
+    imglogdir = os.path.join(logdir, "img")
+    numpylogdir = os.path.join(logdir, "numpy")
+
+    os.makedirs(imglogdir)
+    os.makedirs(numpylogdir)
+    print(logdir)
+    print(75 * "=")
+
+    # write config out
+    sampling_file = os.path.join(logdir, "sampling_config.yaml")
+    sampling_conf = vars(opt)
+
+    with open(sampling_file, 'w') as f:
+        yaml.dump(sampling_conf, f, default_flow_style=False)
+    print(sampling_conf)
+
+
+    run(model, imglogdir, eta=opt.eta,
+        vanilla=opt.vanilla_sample,  n_samples=opt.n_samples, custom_steps=opt.custom_steps,
+        batch_size=opt.batch_size, nplog=numpylogdir)
+
+    print("done.")

examples/images/diffusion/scripts/tests/test_checkpoint.py

+import os
+import sys
+from copy import deepcopy
+
+import yaml
+from datetime import datetime
+
+from diffusers import StableDiffusionPipeline
+import torch
+from ldm.util import instantiate_from_config
+from main import get_parser
+
+if __name__ == "__main__":
+    with torch.no_grad():
+        yaml_path = "../../train_colossalai.yaml"
+        with open(yaml_path, 'r', encoding='utf-8') as f:
+            config = f.read()
+        base_config = yaml.load(config, Loader=yaml.FullLoader)
+        unet_config = base_config['model']['params']['unet_config']
+        diffusion_model = instantiate_from_config(unet_config).to("cuda:0")
+
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "/data/scratch/diffuser/stable-diffusion-v1-4"
+        ).to("cuda:0")
+        dif_model_2 = pipe.unet
+
+        random_input_ = torch.rand((4, 4, 32, 32)).to("cuda:0")
+        random_input_2 = torch.clone(random_input_).to("cuda:0")
+        time_stamp = torch.randint(20, (4,)).to("cuda:0")
+        time_stamp2 = torch.clone(time_stamp).to("cuda:0")
+        context_ = torch.rand((4, 77, 768)).to("cuda:0")
+        context_2 = torch.clone(context_).to("cuda:0")
+
+        out_1 = diffusion_model(random_input_, time_stamp, context_)
+        out_2 = dif_model_2(random_input_2, time_stamp2, context_2)
+        print(out_1.shape)
+        print(out_2['sample'].shape)
\ No newline at end of file

examples/images/diffusion/scripts/tests/test_watermark.py

+import cv2
+import fire
+from imwatermark import WatermarkDecoder
+
+
+def testit(img_path):
+    bgr = cv2.imread(img_path)
+    decoder = WatermarkDecoder('bytes', 136)
+    watermark = decoder.decode(bgr, 'dwtDct')
+    try:
+        dec = watermark.decode('utf-8')
+    except:
+        dec = "null"
+    print(dec)
+
+
+if __name__ == "__main__":
+    fire.Fire(testit)
\ No newline at end of file

examples/images/diffusion/scripts/train_searcher.py

+import os, sys
+import numpy as np
+import scann
+import argparse
+import glob
+from multiprocessing import cpu_count
+from tqdm import tqdm
+
+from ldm.util import parallel_data_prefetch
+
+
+def search_bruteforce(searcher):
+    return searcher.score_brute_force().build()
+
+
+def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k,
+                        partioning_trainsize, num_leaves, num_leaves_to_search):
+    return searcher.tree(num_leaves=num_leaves,
+                         num_leaves_to_search=num_leaves_to_search,
+                         training_sample_size=partioning_trainsize). \
+        score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
+
+
+def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):
+    return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(
+        reorder_k).build()
+
+def load_datapool(dpath):
+
+
+    def load_single_file(saved_embeddings):
+        compressed = np.load(saved_embeddings)
+        database = {key: compressed[key] for key in compressed.files}
+        return database
+
+    def load_multi_files(data_archive):
+        database = {key: [] for key in data_archive[0].files}
+        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
+            for key in d.files:
+                database[key].append(d[key])
+
+        return database
+
+    print(f'Load saved patch embedding from "{dpath}"')
+    file_content = glob.glob(os.path.join(dpath, '*.npz'))
+
+    if len(file_content) == 1:
+        data_pool = load_single_file(file_content[0])
+    elif len(file_content) > 1:
+        data = [np.load(f) for f in file_content]
+        prefetched_data = parallel_data_prefetch(load_multi_files, data,
+                                                 n_proc=min(len(data), cpu_count()), target_data_type='dict')
+
+        data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()}
+    else:
+        raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?')
+
+    print(f'Finished loading of retrieval database of length {data_pool["embedding"].shape[0]}.')
+    return data_pool
+
+
+def train_searcher(opt,
+                   metric='dot_product',
+                   partioning_trainsize=None,
+                   reorder_k=None,
+                   # todo tune
+                   aiq_thld=0.2,
+                   dims_per_block=2,
+                   num_leaves=None,
+                   num_leaves_to_search=None,):
+
+    data_pool = load_datapool(opt.database)
+    k = opt.knn
+
+    if not reorder_k:
+        reorder_k = 2 * k
+
+    # normalize
+    # embeddings =
+    searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric)
+    pool_size = data_pool['embedding'].shape[0]
+
+    print(*(['#'] * 100))
+    print('Initializing scaNN searcher with the following values:')
+    print(f'k: {k}')
+    print(f'metric: {metric}')
+    print(f'reorder_k: {reorder_k}')
+    print(f'anisotropic_quantization_threshold: {aiq_thld}')
+    print(f'dims_per_block: {dims_per_block}')
+    print(*(['#'] * 100))
+    print('Start training searcher....')
+    print(f'N samples in pool is {pool_size}')
+
+    # this reflects the recommended design choices proposed at
+    # https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md
+    if pool_size < 2e4:
+        print('Using brute force search.')
+        searcher = search_bruteforce(searcher)
+    elif 2e4 <= pool_size and pool_size < 1e5:
+        print('Using asymmetric hashing search and reordering.')
+        searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
+    else:
+        print('Using using partioning, asymmetric hashing search and reordering.')
+
+        if not partioning_trainsize:
+            partioning_trainsize = data_pool['embedding'].shape[0] // 10
+        if not num_leaves:
+            num_leaves = int(np.sqrt(pool_size))
+
+        if not num_leaves_to_search:
+            num_leaves_to_search = max(num_leaves // 20, 1)
+
+        print('Partitioning params:')
+        print(f'num_leaves: {num_leaves}')
+        print(f'num_leaves_to_search: {num_leaves_to_search}')
+        # self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
+        searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k,
+                                                 partioning_trainsize, num_leaves, num_leaves_to_search)
+
+    print('Finish training searcher')
+    searcher_savedir = opt.target_path
+    os.makedirs(searcher_savedir, exist_ok=True)
+    searcher.serialize(searcher_savedir)
+    print(f'Saved trained searcher under "{searcher_savedir}"')
+
+if __name__ == '__main__':
+    sys.path.append(os.getcwd())
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--database',
+                        '-d',
+                        default='data/rdm/retrieval_databases/openimages',
+                        type=str,
+                        help='path to folder containing the clip feature of the database')
+    parser.add_argument('--target_path',
+                        '-t',
+                        default='data/rdm/searchers/openimages',
+                        type=str,
+                        help='path to the target folder where the searcher shall be stored.')
+    parser.add_argument('--knn',
+                        '-k',
+                        default=20,
+                        type=int,
+                        help='number of nearest neighbors, for which the searcher shall be optimized')
+
+    opt, _  = parser.parse_known_args()
+
+    train_searcher(opt,)
\ No newline at end of file

examples/images/diffusion/scripts/txt2img.py

+import argparse, os
+import cv2
+import torch
+import numpy as np
+from omegaconf import OmegaConf
+from PIL import Image
+from tqdm import tqdm, trange
+from itertools import islice
+from einops import rearrange
+from torchvision.utils import make_grid
+try:
+    from lightning.pytorch import seed_everything
+except:
+    from pytorch_lightning import seed_everything
+from torch import autocast
+from contextlib import nullcontext
+from imwatermark import WatermarkEncoder
+
+from ldm.util import instantiate_from_config
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.dpm_solver import DPMSolverSampler
+from utils import replace_module, getModelSize
+
+torch.set_grad_enabled(False)
+
+def chunk(it, size):
+    it = iter(it)
+    return iter(lambda: tuple(islice(it, size)), ())
+
+
+def load_model_from_config(config, ckpt, verbose=False):
+    print(f"Loading model from {ckpt}")
+    pl_sd = torch.load(ckpt, map_location="cpu")
+    if "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+    sd = pl_sd["state_dict"]
+    model = instantiate_from_config(config.model)
+    m, u = model.load_state_dict(sd, strict=False)
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    model.eval()
+    return model
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        nargs="?",
+        default="a professional photograph of an astronaut riding a triceratops",
+        help="the prompt to render"
+    )
+    parser.add_argument(
+        "--outdir",
+        type=str,
+        nargs="?",
+        help="dir to write results to",
+        default="outputs/txt2img-samples"
+    )
+    parser.add_argument(
+        "--steps",
+        type=int,
+        default=50,
+        help="number of ddim sampling steps",
+    )
+    parser.add_argument(
+        "--plms",
+        action='store_true',
+        help="use plms sampling",
+    )
+    parser.add_argument(
+        "--dpm",
+        action='store_true',
+        help="use DPM (2) sampler",
+    )
+    parser.add_argument(
+        "--fixed_code",
+        action='store_true',
+        help="if enabled, uses the same starting code across all samples ",
+    )
+    parser.add_argument(
+        "--ddim_eta",
+        type=float,
+        default=0.0,
+        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
+    )
+    parser.add_argument(
+        "--n_iter",
+        type=int,
+        default=3,
+        help="sample this often",
+    )
+    parser.add_argument(
+        "--H",
+        type=int,
+        default=512,
+        help="image height, in pixel space",
+    )
+    parser.add_argument(
+        "--W",
+        type=int,
+        default=512,
+        help="image width, in pixel space",
+    )
+    parser.add_argument(
+        "--C",
+        type=int,
+        default=4,
+        help="latent channels",
+    )
+    parser.add_argument(
+        "--f",
+        type=int,
+        default=8,
+        help="downsampling factor, most often 8 or 16",
+    )
+    parser.add_argument(
+        "--n_samples",
+        type=int,
+        default=3,
+        help="how many samples to produce for each given prompt. A.k.a batch size",
+    )
+    parser.add_argument(
+        "--n_rows",
+        type=int,
+        default=0,
+        help="rows in the grid (default: n_samples)",
+    )
+    parser.add_argument(
+        "--scale",
+        type=float,
+        default=9.0,
+        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
+    )
+    parser.add_argument(
+        "--from-file",
+        type=str,
+        help="if specified, load prompts from this file, separated by newlines",
+    )
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="configs/stable-diffusion/v2-inference.yaml",
+        help="path to config which constructs model",
+    )
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        help="path to checkpoint of model",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="the seed (for reproducible sampling)",
+    )
+    parser.add_argument(
+        "--precision",
+        type=str,
+        help="evaluate at this precision",
+        choices=["full", "autocast"],
+        default="autocast"
+    )
+    parser.add_argument(
+        "--repeat",
+        type=int,
+        default=1,
+        help="repeat each prompt in file this often",
+    )
+    parser.add_argument(
+        "--use_int8",
+        type=bool,
+        default=False,
+        help="use int8 for inference",
+    )
+    opt = parser.parse_args()
+    return opt
+
+
+def put_watermark(img, wm_encoder=None):
+    if wm_encoder is not None:
+        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+        img = wm_encoder.encode(img, 'dwtDct')
+        img = Image.fromarray(img[:, :, ::-1])
+    return img
+
+
+def main(opt):
+    seed_everything(opt.seed)
+
+    config = OmegaConf.load(f"{opt.config}")
+    model = load_model_from_config(config, f"{opt.ckpt}")
+    
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+    model = model.to(device)
+    
+    # quantize model
+    if opt.use_int8:
+        model = replace_module(model)
+        # # to compute the model size
+        # getModelSize(model)
+    
+    if opt.plms:
+        sampler = PLMSSampler(model)
+    elif opt.dpm:
+        sampler = DPMSolverSampler(model)
+    else:
+        sampler = DDIMSampler(model)
+
+    os.makedirs(opt.outdir, exist_ok=True)
+    outpath = opt.outdir
+
+    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
+    wm = "SDV2"
+    wm_encoder = WatermarkEncoder()
+    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))
+
+    batch_size = opt.n_samples
+    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
+    if not opt.from_file:
+        prompt = opt.prompt
+        assert prompt is not None
+        data = [batch_size * [prompt]]
+
+    else:
+        print(f"reading prompts from {opt.from_file}")
+        with open(opt.from_file, "r") as f:
+            data = f.read().splitlines()
+            data = [p for p in data for i in range(opt.repeat)]
+            data = list(chunk(data, batch_size))
+
+    sample_path = os.path.join(outpath, "samples")
+    os.makedirs(sample_path, exist_ok=True)
+    sample_count = 0
+    base_count = len(os.listdir(sample_path))
+    grid_count = len(os.listdir(outpath)) - 1
+
+    start_code = None
+    if opt.fixed_code:
+        start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
+
+    precision_scope = autocast if opt.precision == "autocast" else nullcontext
+    with torch.no_grad(), \
+        precision_scope("cuda"), \
+        model.ema_scope():
+            all_samples = list()
+            for n in trange(opt.n_iter, desc="Sampling"):
+                for prompts in tqdm(data, desc="data"):
+                    uc = None
+                    if opt.scale != 1.0:
+                        uc = model.get_learned_conditioning(batch_size * [""])
+                    if isinstance(prompts, tuple):
+                        prompts = list(prompts)
+                    c = model.get_learned_conditioning(prompts)
+                    shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
+                    samples, _ = sampler.sample(S=opt.steps,
+                                                     conditioning=c,
+                                                     batch_size=opt.n_samples,
+                                                     shape=shape,
+                                                     verbose=False,
+                                                     unconditional_guidance_scale=opt.scale,
+                                                     unconditional_conditioning=uc,
+                                                     eta=opt.ddim_eta,
+                                                     x_T=start_code)
+
+                    x_samples = model.decode_first_stage(samples)
+                    x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+                    for x_sample in x_samples:
+                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                        img = Image.fromarray(x_sample.astype(np.uint8))
+                        img = put_watermark(img, wm_encoder)
+                        img.save(os.path.join(sample_path, f"{base_count:05}.png"))
+                        base_count += 1
+                        sample_count += 1
+
+                    all_samples.append(x_samples)
+
+            # additionally, save as grid
+            grid = torch.stack(all_samples, 0)
+            grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+            grid = make_grid(grid, nrow=n_rows)
+
+            # to image
+            grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
+            grid = Image.fromarray(grid.astype(np.uint8))
+            grid = put_watermark(grid, wm_encoder)
+            grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
+            grid_count += 1
+
+    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
+          f" \nEnjoy.")
+
+
+if __name__ == "__main__":
+    opt = parse_args()
+    main(opt)
+    # # to compute the mem allocated
+    # print(torch.cuda.max_memory_allocated() / 1024 / 1024)

examples/images/diffusion/scripts/txt2img.sh

+python scripts/txt2img.py --prompt "Teyvat, Name:Layla, Element: Cryo, Weapon:Sword, Region:Sumeru, Model type:Medium Female, Description:a woman in a blue outfit holding a sword" --plms \
+    --outdir ./output \
+    --config /home/lcmql/data2/Genshin/2022-11-18T16-38-46_train_colossalai_teyvattest/checkpoints/last.ckpt \
+    --ckpt /home/lcmql/data2/Genshin/2022-11-18T16-38-46_train_colossalai_teyvattest/configs/2022-11-18T16-38-46-project.yaml  \
+    --n_samples 4
+

examples/images/diffusion/scripts/utils.py

+import bitsandbytes as bnb
+import torch.nn as nn
+import torch
+
+class Linear8bit(nn.Linear):
+    def __init__(
+        self,
+        input_features,
+        output_features,
+        bias=True,
+        has_fp16_weights=False,
+        memory_efficient_backward=False,
+        threshold=6.0,
+        weight_data=None,
+        bias_data=None
+    ):
+        super(Linear8bit, self).__init__(
+            input_features, output_features, bias
+        )
+        self.state = bnb.MatmulLtState()
+        self.bias = bias_data
+        self.state.threshold = threshold
+        self.state.has_fp16_weights = has_fp16_weights
+        self.state.memory_efficient_backward = memory_efficient_backward
+        if threshold > 0.0 and not has_fp16_weights:
+            self.state.use_pool = True
+            
+        self.register_parameter("SCB", nn.Parameter(torch.empty(0), requires_grad=False))
+        self.weight = weight_data
+        self.quant()
+        
+
+    def quant(self):  
+        weight = self.weight.data.contiguous().half().cuda()
+        CB, _, SCB, _, _ = bnb.functional.double_quant(weight)
+        delattr(self, "weight")
+        setattr(self, "weight", nn.Parameter(CB, requires_grad=False))
+        delattr(self, "SCB")
+        setattr(self, "SCB", nn.Parameter(SCB, requires_grad=False))
+        del weight
+
+    def forward(self, x):
+        self.state.is_training = self.training
+        
+        if self.bias is not None and self.bias.dtype != torch.float16:
+            self.bias.data = self.bias.data.half()
+        
+        self.state.CB = self.weight.data
+        self.state.SCB = self.SCB.data
+        
+        out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
+        del self.state.CxB
+        return out
+
+def replace_module(model):
+    for name, module in model.named_children():
+        if len(list(module.children())) > 0:
+            replace_module(module)
+
+        if isinstance(module, nn.Linear) and "out_proj" not in name:    
+            model._modules[name] = Linear8bit(
+                        input_features=module.in_features,
+                        output_features=module.out_features,
+                        threshold=6.0,
+                        weight_data=module.weight,
+                        bias_data=module.bias,
+                )
+    return model
+
+def getModelSize(model):
+    param_size = 0
+    param_sum = 0
+    for param in model.parameters():
+        param_size += param.nelement() * param.element_size()
+        param_sum += param.nelement()
+    buffer_size = 0
+    buffer_sum = 0
+    for buffer in model.buffers():
+        buffer_size += buffer.nelement() * buffer.element_size()
+        buffer_sum += buffer.nelement()
+    all_size = (param_size + buffer_size) / 1024 / 1024
+    print('Model Size: {:.3f}MB'.format(all_size))
+    return (param_size, param_sum, buffer_size, buffer_sum, all_size)

examples/images/diffusion/setup.py

+from setuptools import setup, find_packages
+
+setup(
+    name='latent-diffusion',
+    version='0.0.1',
+    description='',
+    packages=find_packages(),
+    install_requires=[
+        'torch',
+        'numpy',
+        'tqdm',
+    ],
+)
\ No newline at end of file

examples/images/diffusion/train_colossalai.sh

+HF_DATASETS_OFFLINE=1 
+TRANSFORMERS_OFFLINE=1 
+DIFFUSERS_OFFLINE=1 
+
+python main.py --logdir /tmp  -t -b /configs/train_colossalai.yaml

examples/images/diffusion/train_ddp.sh

+HF_DATASETS_OFFLINE=1 
+TRANSFORMERS_OFFLINE=1 
+DIFFUSERS_OFFLINE=1 
+
+python main.py --logdir /tmp  -t -b /configs/train_ddp.yaml

examples/images/dreambooth/README.md

+# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)
+
+[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
+The `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion.
+
+By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel.
+
+## Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+```bash
+pip install -r requirements_colossalai.txt
+```
+
+### Install [colossalai](https://github.com/hpcaitech/ColossalAI.git)
+
+```bash
+pip install colossalai==0.2.0+torch1.12cu11.3 -f https://release.colossalai.org
+```
+
+**From source**
+
+```bash
+git clone https://github.com/hpcaitech/ColossalAI.git
+python setup.py install
+```
+
+## Dataset for Teyvat BLIP captions
+Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion).
+
+BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2).
+
+For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided.
+
+The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
+
+## Training
+
+The arguement `placement` can be `cpu`, `auto`, `cuda`, with `cpu` the GPU RAM required can be minimized to 4GB but will deceleration, with `cuda` you can also reduce GPU memory by half but accelerated training， with `auto` a more balanced solution for speed and memory can be obtained。
+
+**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export INSTANCE_DIR="path-to-instance-images"
+export OUTPUT_DIR="path-to-save-model"
+
+torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --instance_prompt="a photo of sks dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --learning_rate=5e-6 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=400 \
+  --placement="cuda"
+```
+
+
+### Training with prior-preservation loss
+
+Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
+According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export INSTANCE_DIR="path-to-instance-images"
+export CLASS_DIR="path-to-class-images"
+export OUTPUT_DIR="path-to-save-model"
+
+torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --class_data_dir=$CLASS_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --with_prior_preservation --prior_loss_weight=1.0 \
+  --instance_prompt="a photo of sks dog" \
+  --class_prompt="a photo of dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --learning_rate=5e-6 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=800 \
+  --placement="cuda"
+```
+
+## Inference
+
+Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.
+
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = "path-to-save-model"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
+
+prompt = "A photo of sks dog in a bucket"
+image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
+
+image.save("dog-bucket.png")
+```