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train_dreambooth_lora.py

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+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from paddlenlp.utils.log import logger
+logger.set_level("WARNING")
+import paddle
+import argparse
+import contextlib
+import gc
+import hashlib
+import math
+import os
+import sys
+import warnings
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import requests
+from huggingface_hub import HfFolder, create_repo, upload_folder, whoami
+from paddle.distributed.fleet.utils.hybrid_parallel_util import (
+    fused_allreduce_gradients,
+)
+from utils import context_nologging, _retry
+from paddle.io import BatchSampler, DataLoader, Dataset, DistributedBatchSampler
+from paddle.optimizer import AdamW
+from paddle.vision import BaseTransform, transforms
+from PIL import Image
+from tqdm.auto import tqdm
+
+from paddlenlp.trainer import set_seed
+from paddlenlp.transformers import AutoTokenizer, PretrainedConfig
+from ppdiffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    UNet2DConditionModel,
+)
+from ppdiffusers.loaders import AttnProcsLayers
+from ppdiffusers.modeling_utils import freeze_params, unwrap_model
+from ppdiffusers.models.cross_attention import LoRACrossAttnProcessor
+from ppdiffusers.optimization import get_scheduler
+from ppdiffusers.utils import image_grid
+
+def str2bool(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("Unsupported value encountered.")
+
+def url_or_path_join(*path_list):
+    return os.path.join(*path_list) if os.path.isdir(os.path.join(*path_list)) else "/".join(path_list)
+
+
+def save_model_card(repo_name, images=None, base_model=str, prompt=str, repo_folder=None):
+    img_str = ""
+    for i, image in enumerate(images):
+        image.save(os.path.join(repo_folder, f"image_{i}.png"))
+        img_str += f"![img_{i}](./image_{i}.png)\n"
+
+    yaml = f"""
+---
+license: creativeml-openrail-m
+base_model: {base_model}
+instance_prompt: {prompt}
+tags:
+- stable-diffusion
+- stable-diffusion-ppdiffusers
+- text-to-image
+- ppdiffusers
+- lora
+inference: false
+---
+    """
+    model_card = f"""
+# LoRA DreamBooth - {repo_name}
+These are LoRA adaption weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \n
+{img_str}
+"""
+    with open(os.path.join(repo_folder, "README.md"), "w") as f:
+        f.write(yaml + model_card)
+
+
+def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
+    try:
+        text_encoder_config = PretrainedConfig.from_pretrained(
+            url_or_path_join(pretrained_model_name_or_path, "text_encoder")
+        )
+        model_class = text_encoder_config.architectures[0]
+    except Exception:
+        model_class = "LDMBertModel"
+    if model_class == "CLIPTextModel":
+        from paddlenlp.transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "RobertaSeriesModelWithTransformation":
+        from ppdiffusers.pipelines.alt_diffusion.modeling_roberta_series import (
+            RobertaSeriesModelWithTransformation,
+        )
+
+        return RobertaSeriesModelWithTransformation
+    elif model_class == "BertModel":
+        from paddlenlp.transformers import BertModel
+
+        return BertModel
+    elif model_class == "LDMBertModel":
+        from ppdiffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import (
+            LDMBertModel,
+        )
+
+        return LDMBertModel
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+class Lambda(BaseTransform):
+    def __init__(self, fn, keys=None):
+        super().__init__(keys)
+        self.fn = fn
+
+    def _apply_image(self, img):
+        return self.fn(img)
+
+
+def get_report_to(args):
+    if args.report_to == "visualdl":
+        from visualdl import LogWriter
+
+        writer = LogWriter(logdir=args.logging_dir)
+    elif args.report_to == "tensorboard":
+        from tensorboardX import SummaryWriter
+
+        writer = SummaryWriter(logdir=args.logging_dir)
+    else:
+        raise ValueError("report_to must be in ['visualdl', 'tensorboard']")
+    return writer
+
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="Simple example of a training dreambooth lora script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--instance_data_dir",
+        type=str,
+        default=None,
+        required=True,
+        help="A folder containing the training data of instance images.",
+    )
+    parser.add_argument(
+        "--class_data_dir",
+        type=str,
+        default=None,
+        required=False,
+        help="A folder containing the training data of class images.",
+    )
+    parser.add_argument(
+        "--instance_prompt",
+        type=str,
+        default=None,
+        required=True,
+        help="The prompt with identifier specifying the instance",
+    )
+    parser.add_argument(
+        "--class_prompt",
+        type=str,
+        default=None,
+        help="The prompt to specify images in the same class as provided instance images.",
+    )
+    parser.add_argument(
+        "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference."
+    )
+    parser.add_argument(
+        "--num_validation_images",
+        type=int,
+        default=4,
+        help="Number of images that should be generated during validation with `validation_prompt`.",
+    )
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=50,
+        help=(
+            "Run dreambooth validation every X global steps. Dreambooth validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`."
+        ),
+    )
+    parser.add_argument(
+        "--with_prior_preservation",
+        default=False,
+        action="store_true",
+        help="Flag to add prior preservation loss.",
+    )
+    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
+    parser.add_argument(
+        "--num_class_images",
+        type=int,
+        default=100,
+        help=(
+            "Minimal class images for prior preservation loss. If there are not enough images already present in"
+            " class_data_dir, additional images will be sampled with class_prompt."
+        ),
+    )
+    parser.add_argument(
+        "--lora_rank",
+        type=int,
+        default=4,
+        help=(
+            "lora_rank"
+        ),
+    )  
+
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lora-dreambooth-model",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=None,
+        help=(
+            "The height for input images, all the images in the train/validation dataset will be resized to this"
+            " height"
+        ),
+    )
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=None,
+        help=(
+            "The width for input images, all the images in the train/validation dataset will be resized to this"
+            " width"
+        ),
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--center_crop",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+    parser.add_argument(
+        "--random_flip",
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
+        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=1)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=500,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=100,
+        help=("Save a checkpoint of the training state every X updates."),
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--lr_num_cycles",
+        type=int,
+        default=1,
+        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
+    )
+    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--push_to_hub", type=str2bool, nargs="?", const=False, help="Whether or not to push the model to the Hub.")
+    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--hub_model_id",
+        type=str,
+        default=None,
+        help="The name of the repository to keep in sync with the local `output_dir`.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) or [VisualDL](https://www.paddlepaddle.org.cn/paddle/visualdl) log directory. Will default to"
+            "*output_dir/logs"
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="visualdl",
+        choices=["tensorboard", "visualdl"],
+        help="Log writer type.",
+    )
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    if args.instance_data_dir is None:
+        raise ValueError("You must specify a train data directory.")
+
+    if args.with_prior_preservation:
+        if args.class_data_dir is None:
+            raise ValueError("You must specify a data directory for class images.")
+        if args.class_prompt is None:
+            raise ValueError("You must specify prompt for class images.")
+    else:
+        # logger is not available yet
+        if args.class_data_dir is not None:
+            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
+        if args.class_prompt is not None:
+            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
+
+    args.logging_dir = os.path.join(args.output_dir, args.logging_dir)
+    if args.height is None or args.width is None and args.resolution is not None:
+        args.height = args.width = args.resolution
+
+    return args
+
+
+class DreamBoothDataset(Dataset):
+    """
+    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
+    It pre-processes the images and the tokenizes prompts.
+    """
+
+    def __init__(
+        self,
+        instance_data_root,
+        instance_prompt,
+        tokenizer,
+        class_data_root=None,
+        class_prompt=None,
+        height=512,
+        width=512,
+        center_crop=False,
+        interpolation="bilinear",
+        random_flip=False,
+    ):
+        self.height = height
+        self.width = width
+        self.center_crop = center_crop
+        self.tokenizer = tokenizer
+
+        self.instance_data_root = Path(instance_data_root)
+        if not self.instance_data_root.exists():
+            raise ValueError("Instance images root doesn't exists.")
+        ext = ["png", "jpg", "jpeg", "bmp", "PNG", "JPG", "JPEG", "BMP"]
+        self.instance_images_path = []
+        for p in Path(instance_data_root).iterdir():
+            if any(suffix in p.name for suffix in ext):
+                self.instance_images_path.append(p)
+        self.num_instance_images = len(self.instance_images_path)
+        self.instance_prompt = instance_prompt
+        self._length = self.num_instance_images
+
+        if class_data_root is not None:
+            self.class_data_root = Path(class_data_root)
+            self.class_data_root.mkdir(parents=True, exist_ok=True)
+            self.class_images_path = []
+            for p in Path(class_data_root).iterdir():
+                if any(suffix in p.name for suffix in ext):
+                    self.class_images_path.append(p)
+            self.num_class_images = len(self.class_images_path)
+            self._length = max(self.num_class_images, self.num_instance_images)
+            self.class_prompt = class_prompt
+        else:
+            self.class_data_root = None
+
+        self.image_transforms = transforms.Compose(
+            [
+                transforms.Resize((height, width), interpolation=interpolation),
+                transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
+                transforms.RandomHorizontalFlip() if random_flip else Lambda(lambda x: x),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, index):
+        example = {}
+        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
+        if not instance_image.mode == "RGB":
+            instance_image = instance_image.convert("RGB")
+        example["instance_images"] = self.image_transforms(instance_image)
+        example["instance_prompt_ids"] = self.tokenizer(
+            self.instance_prompt,
+            padding="do_not_pad",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_attention_mask=False,
+        ).input_ids
+
+        if self.class_data_root:
+            class_image = Image.open(self.class_images_path[index % self.num_class_images])
+            if not class_image.mode == "RGB":
+                class_image = class_image.convert("RGB")
+            example["class_images"] = self.image_transforms(class_image)
+            example["class_prompt_ids"] = self.tokenizer(
+                self.class_prompt,
+                padding="do_not_pad",
+                truncation=True,
+                max_length=self.tokenizer.model_max_length,
+                return_attention_mask=False,
+            ).input_ids
+
+        return example
+
+
+class PromptDataset(Dataset):
+    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
+
+    def __init__(self, prompt, num_samples):
+        self.prompt = prompt
+        self.num_samples = num_samples
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, index):
+        example = {}
+        example["prompt"] = self.prompt
+        example["index"] = index
+        return example
+
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
+
+
+def main():
+    paddle.randn((1,))
+    args = parse_args()
+    rank = paddle.distributed.get_rank()
+    is_main_process = rank == 0
+    num_processes = paddle.distributed.get_world_size()
+    if num_processes > 1:
+        paddle.distributed.init_parallel_env()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Generate class images if prior preservation is enabled.
+    if args.with_prior_preservation:
+        class_images_dir = Path(args.class_data_dir)
+        if not class_images_dir.exists():
+            class_images_dir.mkdir(parents=True)
+        cur_class_images = len(list(class_images_dir.iterdir()))
+
+        if cur_class_images < args.num_class_images:
+            with context_nologging():
+                pipeline = DiffusionPipeline.from_pretrained(
+                    args.pretrained_model_name_or_path,
+                    safety_checker=None,
+                )
+                pipeline.set_progress_bar_config(disable=True)
+
+            num_new_images = args.num_class_images - cur_class_images
+            logger.info(f"Number of class images to sample: {num_new_images}.")
+
+            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
+            batch_sampler = (
+                DistributedBatchSampler(sample_dataset, batch_size=args.sample_batch_size, shuffle=False)
+                if num_processes > 1
+                else BatchSampler(sample_dataset, batch_size=args.sample_batch_size, shuffle=False)
+            )
+            sample_dataloader = DataLoader(
+                sample_dataset, batch_sampler=batch_sampler, num_workers=args.dataloader_num_workers
+            )
+
+            for example in tqdm(sample_dataloader, desc="Generating class images", disable=not is_main_process, ncols=100):
+                images = pipeline(example["prompt"]).images
+
+                for i, image in enumerate(images):
+                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
+                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
+                    image.save(image_filename)
+            pipeline.to("cpu")
+            del pipeline
+            gc.collect()
+
+    if is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    print("正在下载模型权重，请耐心等待。。。。。。。。。。")
+    with context_nologging():
+        # Load the tokenizer
+        if args.tokenizer_name:
+            tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name)
+        elif args.pretrained_model_name_or_path:
+            tokenizer = AutoTokenizer.from_pretrained(url_or_path_join(args.pretrained_model_name_or_path, "tokenizer"))
+
+        # import correct text encoder class
+        text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)
+
+        # Load scheduler and models
+        noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+        text_encoder = text_encoder_cls.from_pretrained(
+            url_or_path_join(args.pretrained_model_name_or_path, "text_encoder")
+        )
+        text_config = text_encoder.config if isinstance(text_encoder.config, dict) else text_encoder.config.to_dict()
+        if text_config.get("use_attention_mask", None) is not None and text_config["use_attention_mask"]:
+            use_attention_mask = True
+        else:
+            use_attention_mask = False
+        vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+        unet = UNet2DConditionModel.from_pretrained(
+            args.pretrained_model_name_or_path,
+            subfolder="unet",
+        )
+
+    # We only train the additional adapter LoRA layers
+    freeze_params(vae.parameters())
+    freeze_params(text_encoder.parameters())
+    freeze_params(unet.parameters())
+
+    # now we will add new LoRA weights to the attention layers
+    # It's important to realize here how many attention weights will be added and of which sizes
+    # The sizes of the attention layers consist only of two different variables:
+    # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
+    # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
+
+    # Let's first see how many attention processors we will have to set.
+    # For Stable Diffusion, it should be equal to:
+    # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
+    # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
+    # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
+    # => 32 layers
+
+    # Set correct lora layers
+    lora_attn_procs = {}
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+
+        lora_attn_procs[name] = LoRACrossAttnProcessor(
+            hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=args.lora_rank
+        )
+
+    unet.set_attn_processor(lora_attn_procs)
+    lora_layers = AttnProcsLayers(unet.attn_processors)
+
+    # Dataset and DataLoaders creation:
+    train_dataset = DreamBoothDataset(
+        instance_data_root=args.instance_data_dir,
+        instance_prompt=args.instance_prompt,
+        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
+        class_prompt=args.class_prompt,
+        tokenizer=tokenizer,
+        height=args.height,
+        width=args.width,
+        center_crop=args.center_crop,
+        interpolation="bilinear",
+        random_flip=args.random_flip,
+    )
+
+    def collate_fn(examples):
+        input_ids = [example["instance_prompt_ids"] for example in examples]
+        pixel_values = [example["instance_images"] for example in examples]
+
+        # Concat class and instance examples for prior preservation.
+        # We do this to avoid doing two forward passes.
+        if args.with_prior_preservation:
+            input_ids += [example["class_prompt_ids"] for example in examples]
+            pixel_values += [example["class_images"] for example in examples]
+
+        pixel_values = paddle.stack(pixel_values).astype("float32")
+
+        input_ids = tokenizer.pad(
+            {"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pd"
+        ).input_ids
+
+        return {
+            "input_ids": input_ids,
+            "pixel_values": pixel_values,
+        }
+
+    train_sampler = (
+        DistributedBatchSampler(train_dataset, batch_size=args.train_batch_size, shuffle=True)
+        if num_processes > 1
+        else BatchSampler(train_dataset, batch_size=args.train_batch_size, shuffle=True)
+    )
+    train_dataloader = DataLoader(
+        train_dataset, batch_sampler=train_sampler, collate_fn=collate_fn, num_workers=args.dataloader_num_workers
+    )
+
+    # Scheduler and math around the number of training steps.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * num_processes
+        )
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        learning_rate=args.learning_rate,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+        num_cycles=args.lr_num_cycles,
+        power=args.lr_power,
+    )
+
+    # Optimizer creation
+    optimizer = AdamW(
+        learning_rate=lr_scheduler,
+        parameters=lora_layers.parameters(),
+        beta1=args.adam_beta1,
+        beta2=args.adam_beta2,
+        weight_decay=args.adam_weight_decay,
+        epsilon=args.adam_epsilon,
+        grad_clip=nn.ClipGradByGlobalNorm(args.max_grad_norm) if args.max_grad_norm > 0 else None,
+    )
+
+    if num_processes > 1:
+        unet = paddle.DataParallel(unet)
+
+    if is_main_process:
+        logger.info("-----------  Configuration Arguments -----------")
+        for arg, value in sorted(vars(args).items()):
+            logger.info("%s: %s" % (arg, value))
+        logger.info("------------------------------------------------")
+        writer = get_report_to(args)
+
+    # Train!
+    total_batch_size = args.train_batch_size * num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(args.max_train_steps), disable=not is_main_process, ncols=100)
+    progress_bar.set_description("Train Steps")
+    global_step = 0
+    vae.eval()
+    text_encoder.eval()
+
+    for epoch in range(args.num_train_epochs):
+        unet.train()
+        for step, batch in enumerate(train_dataloader):
+            # Convert images to latent space
+            latents = vae.encode(batch["pixel_values"]).latent_dist.sample()
+            latents = latents * 0.18215
+
+            # Sample noise that we'll add to the latents
+            noise = paddle.randn(latents.shape)
+            batch_size = latents.shape[0]
+            # Sample a random timestep for each image
+            timesteps = paddle.randint(0, noise_scheduler.config.num_train_timesteps, (batch_size,)).cast("int64")
+
+            # Add noise to the latents according to the noise magnitude at each timestep
+            # (this is the forward diffusion process)
+            noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+            if num_processes > 1 and (
+                args.gradient_checkpointing or ((step + 1) % args.gradient_accumulation_steps != 0)
+            ):
+                # grad acc, no_sync when (step + 1) % args.gradient_accumulation_steps != 0:
+                # gradient_checkpointing, no_sync every where
+                # gradient_checkpointing + grad_acc, no_sync every where
+                unet_ctx_manager = unet.no_sync()
+            else:
+                unet_ctx_manager = contextlib.nullcontext() if sys.version_info >= (3, 7) else contextlib.suppress()
+
+            if use_attention_mask:
+                attention_mask = (batch["input_ids"] != tokenizer.pad_token_id).cast("int64")
+            else:
+                attention_mask = None
+            encoder_hidden_states = text_encoder(batch["input_ids"], attention_mask=attention_mask)[0]
+
+            with unet_ctx_manager:
+                # Predict the noise residual / sample
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                # Get the target for loss depending on the prediction type
+                if noise_scheduler.config.prediction_type == "epsilon":
+                    target = noise
+                elif noise_scheduler.config.prediction_type == "v_prediction":
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+                if args.with_prior_preservation:
+                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
+                    model_pred, model_pred_prior = model_pred.chunk(2, axis=0)
+                    target, target_prior = target.chunk(2, axis=0)
+
+                    # Compute instance loss
+                    loss = F.mse_loss(model_pred, target, reduction="mean")
+
+                    # Compute prior loss
+                    prior_loss = F.mse_loss(model_pred_prior, target_prior, reduction="mean")
+
+                    # Add the prior loss to the instance loss.
+                    loss = loss + args.prior_loss_weight * prior_loss
+                else:
+                    loss = F.mse_loss(model_pred, target, reduction="mean")
+
+                if args.gradient_accumulation_steps > 1:
+                    loss = loss / args.gradient_accumulation_steps
+                loss.backward()
+
+            if (step + 1) % args.gradient_accumulation_steps == 0:
+                if num_processes > 1 and args.gradient_checkpointing:
+                    fused_allreduce_gradients(lora_layers.parameters(), None)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.clear_grad()
+                progress_bar.update(1)
+                global_step += 1
+                step_loss = loss.item() * args.gradient_accumulation_steps
+                logs = {
+                    "epoch": str(epoch).zfill(4),
+                    "step_loss": round(step_loss, 10),
+                    "lr": lr_scheduler.get_lr(),
+                }
+                progress_bar.set_postfix(**logs)
+
+                if is_main_process:
+                    for name, val in logs.items():
+                        if name == "epoch":
+                            continue
+                        writer.add_scalar(f"train/{name}", val, step=global_step)
+
+                    if global_step % args.checkpointing_steps == 0:
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        with context_nologging():
+                            unwrap_model(unet).save_attn_procs(save_path)
+                        print(f"\n Saved lora weights to {save_path}")
+
+                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
+                        with context_nologging():
+                            logger.info(
+                                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+                                    f" {args.validation_prompt}."
+                                )
+                            # create pipeline
+                            pipeline = DiffusionPipeline.from_pretrained(
+                                args.pretrained_model_name_or_path,
+                                unet=unwrap_model(unet),
+                                safety_checker=None,
+                            )
+                            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+                            pipeline.set_progress_bar_config(disable=True)
+
+                        # run inference
+                        generator = paddle.Generator().manual_seed(args.seed) if args.seed else None
+                        images = [
+                            pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
+                            for _ in range(args.num_validation_images)
+                        ]
+                        png_save_path = os.path.join(args.output_dir, "validation_images")
+                        os.makedirs(png_save_path, exist_ok=True)
+                        if len(images) == 1:
+                            gird_image = images[0]
+                        elif len(images) == 2:
+                            gird_image = image_grid(images, 1, 2)
+                        else:
+                            display_images = 2 * (len(images) // 2)
+                            gird_image = image_grid(images[:display_images], 2, display_images // 2)
+                        gird_image.save(os.path.join(png_save_path, f"{global_step}.png"))
+
+                        np_images = np.stack([np.asarray(img) for img in images])
+
+                        if args.report_to == "tensorboard":
+                            writer.add_images("test", np_images, epoch, dataformats="NHWC")
+                        else:
+                            writer.add_image("test", np_images, epoch, dataformats="NHWC")
+                        del pipeline
+                        gc.collect()
+
+                if global_step >= args.max_train_steps:
+                    break
+    # Save the lora layers
+    if is_main_process:
+        unet = unwrap_model(unet)
+        unet.save_attn_procs(args.output_dir)
+
+        # Final inference
+        # Load previous pipeline
+        with context_nologging():
+            pipeline = DiffusionPipeline.from_pretrained(args.pretrained_model_name_or_path, safety_checker=None)
+            pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+            pipeline.set_progress_bar_config(disable=True)
+        # load attention processors
+        pipeline.unet.load_attn_procs(args.output_dir)
+
+        # run inference
+        if args.validation_prompt and args.num_validation_images > 0:
+            generator = paddle.Generator().manual_seed(args.seed) if args.seed else None
+            images = [
+                pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
+                for _ in range(args.num_validation_images)
+            ]
+            np_images = np.stack([np.asarray(img) for img in images])
+
+            if args.report_to == "tensorboard":
+                writer.add_images("test", np_images, epoch, dataformats="NHWC")
+            else:
+                writer.add_image("test", np_images, epoch, dataformats="NHWC")
+
+        writer.close()
+
+        # logic to push to HF Hub
+        if args.push_to_hub:
+            if args.hub_model_id is None:
+                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+
+            _retry(
+                create_repo,
+                func_kwargs={"repo_id": repo_name, "exist_ok": True, "token": args.hub_token},
+                base_wait_time=1.0,
+                max_retries=5,
+                max_wait_time=10.0,
+            )
+
+            save_model_card(
+                repo_name,
+                images=images,
+                base_model=args.pretrained_model_name_or_path,
+                prompt=args.instance_prompt,
+                repo_folder=args.output_dir,
+            )
+            # Upload model
+            logger.info(f"Pushing to {repo_name}")
+            _retry(
+                upload_folder,
+                func_kwargs={
+                    "repo_id": repo_name,
+                    "repo_type": "model",
+                    "folder_path": args.output_dir,
+                    "commit_message": "End of training",
+                    "token": args.hub_token,
+                    "ignore_patterns": ["checkpoint-*/*", "logs/*"],
+                },
+                base_wait_time=1.0,
+                max_retries=5,
+                max_wait_time=20.0,
+            )
+
+
+if __name__ == "__main__":
+    main()

untitled.streamlit.py

@@ -0,0 +1,32 @@
+import os
+os.system("pip install paddlenlp==2.5.2")
+os.system("pip install ppdiffusers==0.11.1")
+
+from ppdiffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+import paddle
+import streamlit as st
+
+st.header("用LoRA 和 DreamBooth画出你眼中的HomeTown")
+st.image("lora_outputs/validation_images/700.png")
+
+st.subheader("设置Prompt")
+
+st.write("输入你眼中家乡的几个关键句吧!看看能不能绘制出你心中的家乡!")
+
+pr1 = st.text_input('你心中的家乡：',"")
+
+test = st.button("⚡⚡开始生成⚡⚡")
+if test:
+  pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+  pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+  prompt = pr1
+  image = pipe(prompt).images[0]
+
+  image.save("demo.png")
+  st.image("demo.png")
+
+  st.success('推理完毕！！', icon="✅")
+  st.write("移动端长按图片可保存，PC端右键图片可另存到本地。")
+
+

utils.py

@@ -0,0 +1,133 @@
+from paddlenlp.utils.serialization import load_torch
+import paddle
+import safetensors.numpy
+import os
+import ppdiffusers
+from contextlib import contextmanager
+
+@contextmanager
+def context_nologging():
+    ppdiffusers.utils.logging.set_verbosity_error()
+    try:
+        yield
+    finally:
+        ppdiffusers.utils.logging.set_verbosity_info()
+
+
+__all__ = ['convert_paddle_lora_to_safetensor_lora', 'convert_pytorch_lora_to_paddle_lora']
+
+def convert_paddle_lora_to_safetensor_lora(paddle_file, safe_file=None):
+    if not os.path.exists(paddle_file):
+        print(f"{paddle_file} 文件不存在！")
+        return 
+    if safe_file is None:
+        safe_file = paddle_file.replace("paddle_lora_weights.pdparams", "pytorch_lora_weights.safetensors")
+        
+    tensors = paddle.load(paddle_file)
+    new_tensors = {}
+    for k, v in tensors.items():
+        new_tensors[k] = v.cpu().numpy().T
+    safetensors.numpy.save_file(new_tensors, safe_file)
+    print(f"文件已经保存到{safe_file}!")
+    
+def convert_pytorch_lora_to_paddle_lora(pytorch_file, paddle_file=None):
+    if not os.path.exists(pytorch_file):
+        print(f"{pytorch_file} 文件不存在！")
+        return 
+    if paddle_file is None:
+        paddle_file = pytorch_file.replace("pytorch_lora_weights.bin", "paddle_lora_weights.pdparams")
+        
+    tensors = load_torch(pytorch_file)
+    new_tensors = {}
+    for k, v in tensors.items():
+        new_tensors[k] = v.T
+    paddle.save(new_tensors, paddle_file)
+    print(f"文件已经保存到{paddle_file}!")
+
+
+
+import time
+from typing import Optional, Type
+import paddle
+import requests
+from huggingface_hub import create_repo, upload_folder, get_full_repo_name
+
+# Since HF sometimes timeout, we need to retry uploads
+# Credit: https://github.com/huggingface/datasets/blob/06ae3f678651bfbb3ca7dd3274ee2f38e0e0237e/src/datasets/utils/file_utils.py#L265
+def _retry(
+    func,
+    func_args: Optional[tuple] = None,
+    func_kwargs: Optional[dict] = None,
+    exceptions: Type[requests.exceptions.RequestException] = requests.exceptions.RequestException,
+    max_retries: int = 0,
+    base_wait_time: float = 0.5,
+    max_wait_time: float = 2,
+):
+    func_args = func_args or ()
+    func_kwargs = func_kwargs or {}
+    retry = 0
+    while True:
+        try:
+            return func(*func_args, **func_kwargs)
+        except exceptions as err:
+            if retry >= max_retries:
+                raise err
+            else:
+                sleep_time = min(max_wait_time, base_wait_time * 2**retry)  # Exponential backoff
+                print(f"{func} timed out, retrying in {sleep_time}s... [{retry/max_retries}]")
+                time.sleep(sleep_time)
+                retry += 1
+                
+def upload_lora_folder(upload_dir, repo_name, pretrained_model_name_or_path, prompt, hub_token=None):
+    repo_name = get_full_repo_name(repo_name, token=hub_token)
+    _retry(
+        create_repo,
+        func_kwargs={"repo_id": repo_name, "exist_ok": True, "token": hub_token},
+        base_wait_time=1.0,
+        max_retries=5,
+        max_wait_time=10.0,
+    )
+    save_model_card(
+        repo_name,
+        base_model=pretrained_model_name_or_path,
+        prompt=prompt,
+        repo_folder=upload_dir,
+    )
+    # Upload model
+    print(f"Pushing to {repo_name}")
+    _retry(
+        upload_folder,
+        func_kwargs={
+            "repo_id": repo_name,
+            "repo_type": "model",
+            "folder_path": upload_dir,
+            "commit_message": "submit best ckpt",
+            "token": hub_token,
+            "ignore_patterns": ["checkpoint-*/*", "logs/*", "validation_images/*"],
+        },
+        base_wait_time=1.0,
+        max_retries=5,
+        max_wait_time=20.0,
+        )
+    
+def save_model_card(repo_name, base_model=str, prompt=str, repo_folder=None):
+    yaml = f"""
+---
+license: creativeml-openrail-m
+base_model: {base_model}
+instance_prompt: {prompt}
+tags:
+- stable-diffusion
+- stable-diffusion-ppdiffusers
+- text-to-image
+- ppdiffusers
+- lora
+inference: false
+---
+    """
+    model_card = f"""
+# LoRA DreamBooth - {repo_name}
+本仓库的 LoRA 权重是基于 {base_model} 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 {prompt} 文本进行了训练。 
+"""
+    with open(os.path.join(repo_folder, "README.md"), "w") as f:
+        f.write(yaml + model_card)