DiffSynth-Studio/diffsynth/trainers/utils.py

import imageio, os, torch, warnings, torchvision, argparse, json
from peft import LoraConfig, inject_adapter_in_model
from PIL import Image
import pandas as pd
from tqdm import tqdm
from accelerate import Accelerator



class ImageDataset(torch.utils.data.Dataset):
    def __init__(
        self,
        base_path=None, metadata_path=None,
        max_pixels=1920*1080, height=None, width=None,
        height_division_factor=16, width_division_factor=16,
        data_file_keys=("image",),
        image_file_extension=("jpg", "jpeg", "png", "webp"),
        repeat=1,
        args=None,
    ):
        if args is not None:
            base_path = args.dataset_base_path
            metadata_path = args.dataset_metadata_path
            height = args.height
            width = args.width
            max_pixels = args.max_pixels
            data_file_keys = args.data_file_keys.split(",")
            repeat = args.dataset_repeat
            
        self.base_path = base_path
        self.max_pixels = max_pixels
        self.height = height
        self.width = width
        self.height_division_factor = height_division_factor
        self.width_division_factor = width_division_factor
        self.data_file_keys = data_file_keys
        self.image_file_extension = image_file_extension
        self.repeat = repeat

        if height is not None and width is not None:
            print("Height and width are fixed. Setting `dynamic_resolution` to False.")
            self.dynamic_resolution = False
        elif height is None and width is None:
            print("Height and width are none. Setting `dynamic_resolution` to True.")
            self.dynamic_resolution = True
            
        if metadata_path is None:
            print("No metadata. Trying to generate it.")
            metadata = self.generate_metadata(base_path)
            print(f"{len(metadata)} lines in metadata.")
            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
        elif metadata_path.endswith(".json"):
            with open(metadata_path, "r") as f:
                metadata = json.load(f)
            self.data = metadata
        else:
            metadata = pd.read_csv(metadata_path)
            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]


    def generate_metadata(self, folder):
        image_list, prompt_list = [], []
        file_set = set(os.listdir(folder))
        for file_name in file_set:
            if "." not in file_name:
                continue
            file_ext_name = file_name.split(".")[-1].lower()
            file_base_name = file_name[:-len(file_ext_name)-1]
            if file_ext_name not in self.image_file_extension:
                continue
            prompt_file_name = file_base_name + ".txt"
            if prompt_file_name not in file_set:
                continue
            with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f:
                prompt = f.read().strip()
            image_list.append(file_name)
            prompt_list.append(prompt)
        metadata = pd.DataFrame()
        metadata["image"] = image_list
        metadata["prompt"] = prompt_list
        return metadata
    
    
    def crop_and_resize(self, image, target_height, target_width):
        width, height = image.size
        scale = max(target_width / width, target_height / height)
        image = torchvision.transforms.functional.resize(
            image,
            (round(height*scale), round(width*scale)),
            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
        )
        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
        return image
    
    
    def get_height_width(self, image):
        if self.dynamic_resolution:
            width, height = image.size
            if width * height > self.max_pixels:
                scale = (width * height / self.max_pixels) ** 0.5
                height, width = int(height / scale), int(width / scale)
            height = height // self.height_division_factor * self.height_division_factor
            width = width // self.width_division_factor * self.width_division_factor
        else:
            height, width = self.height, self.width
        return height, width
    
    
    def load_image(self, file_path):
        image = Image.open(file_path).convert("RGB")
        image = self.crop_and_resize(image, *self.get_height_width(image))
        return image
    
    
    def load_data(self, file_path):
        return self.load_image(file_path)


    def __getitem__(self, data_id):
        data = self.data[data_id % len(self.data)].copy()
        for key in self.data_file_keys:
            if key in data:
                path = os.path.join(self.base_path, data[key])
                data[key] = self.load_data(path)
                if data[key] is None:
                    warnings.warn(f"cannot load file {data[key]}.")
                    return None
        return data
    

    def __len__(self):
        return len(self.data) * self.repeat



class VideoDataset(torch.utils.data.Dataset):
    def __init__(
        self,
        base_path=None, metadata_path=None,
        num_frames=81,
        time_division_factor=4, time_division_remainder=1,
        max_pixels=1920*1080, height=None, width=None,
        height_division_factor=16, width_division_factor=16,
        data_file_keys=("video",),
        image_file_extension=("jpg", "jpeg", "png", "webp"),
        video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"),
        repeat=1,
        args=None,
    ):
        if args is not None:
            base_path = args.dataset_base_path
            metadata_path = args.dataset_metadata_path
            height = args.height
            width = args.width
            max_pixels = args.max_pixels
            num_frames = args.num_frames
            data_file_keys = args.data_file_keys.split(",")
            repeat = args.dataset_repeat
        
        self.base_path = base_path
        self.num_frames = num_frames
        self.time_division_factor = time_division_factor
        self.time_division_remainder = time_division_remainder
        self.max_pixels = max_pixels
        self.height = height
        self.width = width
        self.height_division_factor = height_division_factor
        self.width_division_factor = width_division_factor
        self.data_file_keys = data_file_keys
        self.image_file_extension = image_file_extension
        self.video_file_extension = video_file_extension
        self.repeat = repeat
        
        if height is not None and width is not None:
            print("Height and width are fixed. Setting `dynamic_resolution` to False.")
            self.dynamic_resolution = False
        elif height is None and width is None:
            print("Height and width are none. Setting `dynamic_resolution` to True.")
            self.dynamic_resolution = True
            
        if metadata_path is None:
            print("No metadata. Trying to generate it.")
            metadata = self.generate_metadata(base_path)
            print(f"{len(metadata)} lines in metadata.")
            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
        elif metadata_path.endswith(".json"):
            with open(metadata_path, "r") as f:
                metadata = json.load(f)
            self.data = metadata
        else:
            metadata = pd.read_csv(metadata_path)
            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
            
    
    def generate_metadata(self, folder):
        video_list, prompt_list = [], []
        file_set = set(os.listdir(folder))
        for file_name in file_set:
            if "." not in file_name:
                continue
            file_ext_name = file_name.split(".")[-1].lower()
            file_base_name = file_name[:-len(file_ext_name)-1]
            if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension:
                continue
            prompt_file_name = file_base_name + ".txt"
            if prompt_file_name not in file_set:
                continue
            with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f:
                prompt = f.read().strip()
            video_list.append(file_name)
            prompt_list.append(prompt)
        metadata = pd.DataFrame()
        metadata["video"] = video_list
        metadata["prompt"] = prompt_list
        return metadata
        
        
    def crop_and_resize(self, image, target_height, target_width):
        width, height = image.size
        scale = max(target_width / width, target_height / height)
        image = torchvision.transforms.functional.resize(
            image,
            (round(height*scale), round(width*scale)),
            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
        )
        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
        return image
    
    
    def get_height_width(self, image):
        if self.dynamic_resolution:
            width, height = image.size
            if width * height > self.max_pixels:
                scale = (width * height / self.max_pixels) ** 0.5
                height, width = int(height / scale), int(width / scale)
            height = height // self.height_division_factor * self.height_division_factor
            width = width // self.width_division_factor * self.width_division_factor
        else:
            height, width = self.height, self.width
        return height, width
    
    
    def get_num_frames(self, reader):
        num_frames = self.num_frames
        if int(reader.count_frames()) < num_frames:
            num_frames = int(reader.count_frames())
            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
                num_frames -= 1
        return num_frames
    

    def load_video(self, file_path):
        reader = imageio.get_reader(file_path)
        num_frames = self.get_num_frames(reader)
        frames = []
        for frame_id in range(num_frames):
            frame = reader.get_data(frame_id)
            frame = Image.fromarray(frame)
            frame = self.crop_and_resize(frame, *self.get_height_width(frame))
            frames.append(frame)
        reader.close()
        return frames
    
    
    def load_image(self, file_path):
        image = Image.open(file_path).convert("RGB")
        image = self.crop_and_resize(image, *self.get_height_width(image))
        frames = [image]
        return frames
    
    
    def is_image(self, file_path):
        file_ext_name = file_path.split(".")[-1]
        return file_ext_name.lower() in self.image_file_extension
    
    
    def is_video(self, file_path):
        file_ext_name = file_path.split(".")[-1]
        return file_ext_name.lower() in self.video_file_extension
    
    
    def load_data(self, file_path):
        if self.is_image(file_path):
            return self.load_image(file_path)
        elif self.is_video(file_path):
            return self.load_video(file_path)
        else:
            return None


    def __getitem__(self, data_id):
        data = self.data[data_id % len(self.data)].copy()
        for key in self.data_file_keys:
            if key in data:
                path = os.path.join(self.base_path, data[key])
                data[key] = self.load_data(path)
                if data[key] is None:
                    warnings.warn(f"cannot load file {data[key]}.")
                    return None
        return data
    

    def __len__(self):
        return len(self.data) * self.repeat



class DiffusionTrainingModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        
        
    def to(self, *args, **kwargs):
        for name, model in self.named_children():
            model.to(*args, **kwargs)
        return self
        
        
    def trainable_modules(self):
        trainable_modules = filter(lambda p: p.requires_grad, self.parameters())
        return trainable_modules
    
    
    def trainable_param_names(self):
        trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters()))
        trainable_param_names = set([named_param[0] for named_param in trainable_param_names])
        return trainable_param_names
    
    
    def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None):
        if lora_alpha is None:
            lora_alpha = lora_rank
        lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules)
        model = inject_adapter_in_model(lora_config, model)
        return model
    
    
    def export_trainable_state_dict(self, state_dict, remove_prefix=None):
        trainable_param_names = self.trainable_param_names()
        state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names}
        if remove_prefix is not None:
            state_dict_ = {}
            for name, param in state_dict.items():
                if name.startswith(remove_prefix):
                    name = name[len(remove_prefix):]
                state_dict_[name] = param
            state_dict = state_dict_
        return state_dict



class ModelLogger:
    def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x):
        self.output_path = output_path
        self.remove_prefix_in_ckpt = remove_prefix_in_ckpt
        self.state_dict_converter = state_dict_converter
        
    
    def on_step_end(self, loss):
        pass
    
    
    def on_epoch_end(self, accelerator, model, epoch_id):
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            state_dict = accelerator.get_state_dict(model)
            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
            state_dict = self.state_dict_converter(state_dict)
            os.makedirs(self.output_path, exist_ok=True)
            path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors")
            accelerator.save(state_dict, path, safe_serialization=True)



def launch_training_task(
    dataset: torch.utils.data.Dataset,
    model: DiffusionTrainingModule,
    model_logger: ModelLogger,
    optimizer: torch.optim.Optimizer,
    scheduler: torch.optim.lr_scheduler.LRScheduler,
    num_epochs: int = 1,
    gradient_accumulation_steps: int = 1,
):
    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0])
    accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps)
    model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
    
    for epoch_id in range(num_epochs):
        for data in tqdm(dataloader):
            with accelerator.accumulate(model):
                optimizer.zero_grad()
                loss = model(data)
                accelerator.backward(loss)
                optimizer.step()
                model_logger.on_step_end(loss)
                scheduler.step()
        model_logger.on_epoch_end(accelerator, model, epoch_id)



def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"):
    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0])
    accelerator = Accelerator()
    model, dataloader = accelerator.prepare(model, dataloader)
    os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True)
    for data_id, data in enumerate(tqdm(dataloader)):
        with torch.no_grad():
            inputs = model.forward_preprocess(data)
            inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs}
            torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth"))



def wan_parser():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.")
    parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.")
    parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..")
    parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.")
    parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.")
    parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.")
    parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.")
    parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.")
    parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.")
    parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.")
    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.")
    parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.")
    parser.add_argument("--output_path", type=str, default="./models", help="Output save path.")
    parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.")
    parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.")
    parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
    parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
    parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
    parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
    parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
    parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).")
    parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).")
    return parser



def flux_parser():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.")
    parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.")
    parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..")
    parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.")
    parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.")
    parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.")
    parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.")
    parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.")
    parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.")
    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.")
    parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.")
    parser.add_argument("--output_path", type=str, default="./models", help="Output save path.")
    parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.")
    parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.")
    parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
    parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
    parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
    parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
    parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.")
    parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
    parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
    return parser