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https://github.com/modelscope/DiffSynth-Studio.git
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training framework
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Artiprocher
190
diffsynth/trainers/utils.py
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190
diffsynth/trainers/utils.py
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import imageio, os, torch, warnings, torchvision
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from peft import LoraConfig, inject_adapter_in_model
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from PIL import Image
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import pandas as pd
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from tqdm import tqdm
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from accelerate import Accelerator
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class VideoDataset(torch.utils.data.Dataset):
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def __init__(
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self,
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base_path, metadata_path,
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frame_interval=1, num_frames=81,
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dynamic_resolution=True, max_pixels=1920*1080, height=None, width=None,
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height_division_factor=16, width_division_factor=16,
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data_file_keys=("video",),
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image_file_extension=("jpg", "jpeg", "png", "webp"),
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video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"),
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repeat=1,
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):
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metadata = pd.read_csv(metadata_path)
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self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
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self.base_path = base_path
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self.frame_interval = frame_interval
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self.num_frames = num_frames
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self.dynamic_resolution = dynamic_resolution
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self.max_pixels = max_pixels
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self.height = height
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self.width = width
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self.height_division_factor = height_division_factor
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self.width_division_factor = width_division_factor
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self.data_file_keys = data_file_keys
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self.image_file_extension = image_file_extension
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self.video_file_extension = video_file_extension
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self.repeat = repeat
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if height is not None and width is not None and dynamic_resolution == True:
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print("Height and width are fixed. Setting `dynamic_resolution` to False.")
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self.dynamic_resolution = False
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def crop_and_resize(self, image, target_height, target_width):
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width, height = image.size
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scale = max(target_width / width, target_height / height)
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image = torchvision.transforms.functional.resize(
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image,
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(round(height*scale), round(width*scale)),
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interpolation=torchvision.transforms.InterpolationMode.BILINEAR
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)
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image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
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return image
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def get_height_width(self, image):
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if self.dynamic_resolution:
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width, height = image.size
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if width * height > self.max_pixels:
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scale = (width * height / self.max_pixels) ** 0.5
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height, width = int(height / scale), int(width / scale)
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height = height // self.height_division_factor * self.height_division_factor
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width = width // self.width_division_factor * self.width_division_factor
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else:
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height, width = self.height, self.width
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return height, width
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def load_frames_using_imageio(self, file_path, start_frame_id, interval, num_frames):
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reader = imageio.get_reader(file_path)
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if reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval:
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reader.close()
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return None
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frames = []
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for frame_id in range(num_frames):
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frame = reader.get_data(start_frame_id + frame_id * interval)
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frame = Image.fromarray(frame)
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frame = self.crop_and_resize(frame, *self.get_height_width(frame))
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frames.append(frame)
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reader.close()
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return frames
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def load_image(self, file_path):
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image = Image.open(file_path).convert("RGB")
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image = self.crop_and_resize(image, *self.get_height_width(image))
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return image
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def load_video(self, file_path):
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frames = self.load_frames_using_imageio(file_path, 0, self.frame_interval, self.num_frames)
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return frames
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def is_image(self, file_path):
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file_ext_name = file_path.split(".")[-1]
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return file_ext_name.lower() in self.image_file_extension
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def is_video(self, file_path):
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file_ext_name = file_path.split(".")[-1]
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return file_ext_name.lower() in self.video_file_extension
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def load_data(self, file_path):
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if self.is_image(file_path):
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return self.load_image(file_path)
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elif self.is_video(file_path):
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return self.load_video(file_path)
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else:
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return None
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def __getitem__(self, data_id):
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data = self.data[data_id % len(self.data)].copy()
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for key in self.data_file_keys:
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if key in data:
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path = os.path.join(self.base_path, data[key])
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data[key] = self.load_data(path)
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if data[key] is None:
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warnings.warn(f"cannot load file {data[key]}.")
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return None
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return data
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def __len__(self):
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return len(self.data) * self.repeat
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class DiffusionTrainingModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def to(self, *args, **kwargs):
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for name, model in self.named_children():
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model.to(*args, **kwargs)
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return self
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def trainable_modules(self):
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trainable_modules = filter(lambda p: p.requires_grad, self.parameters())
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return trainable_modules
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def trainable_param_names(self):
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trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters()))
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trainable_param_names = set([named_param[0] for named_param in trainable_param_names])
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return trainable_param_names
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def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None):
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if lora_alpha is None:
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lora_alpha = lora_rank
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lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules)
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model = inject_adapter_in_model(lora_config, model)
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return model
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def launch_training_task(model: DiffusionTrainingModule, dataset, learning_rate, num_epochs, output_path, remove_prefix=None):
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dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0])
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optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate)
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scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
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accelerator = Accelerator(gradient_accumulation_steps=1)
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model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
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for epoch in range(num_epochs):
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for data in tqdm(dataloader):
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with accelerator.accumulate(model):
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optimizer.zero_grad()
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loss = model(data)
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accelerator.backward(loss)
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optimizer.step()
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scheduler.step()
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accelerator.wait_for_everyone()
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if accelerator.is_main_process:
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state_dict = accelerator.get_state_dict(model)
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trainable_param_names = model.trainable_param_names()
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state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names}
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if remove_prefix is not None:
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state_dict_ = {}
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for name, param in state_dict.items():
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if name.startswith(remove_prefix):
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name = name[len(remove_prefix):]
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state_dict_[name] = param
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path = os.path.join(output_path, f"epoch-{epoch}")
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accelerator.save(state_dict_, path, safe_serialization=True)
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