mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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34a5ef8c15
Included .jpeg extension for image type detection, preventing an error trying to the read image as a video format
528 lines
18 KiB
Python
528 lines
18 KiB
Python
import torch, os, imageio, argparse
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from torchvision.transforms import v2
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from einops import rearrange
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import lightning as pl
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import pandas as pd
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from diffsynth import WanVideoPipeline, ModelManager, load_state_dict
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from peft import LoraConfig, inject_adapter_in_model
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import torchvision
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from PIL import Image
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class TextVideoDataset(torch.utils.data.Dataset):
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def __init__(self, base_path, metadata_path, max_num_frames=81, frame_interval=1, num_frames=81, height=480, width=832):
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metadata = pd.read_csv(metadata_path)
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self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]]
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self.text = metadata["text"].to_list()
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self.max_num_frames = max_num_frames
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self.frame_interval = frame_interval
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self.num_frames = num_frames
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self.height = height
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self.width = width
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self.frame_process = v2.Compose([
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v2.CenterCrop(size=(height, width)),
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v2.Resize(size=(height, width), antialias=True),
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v2.ToTensor(),
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v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
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])
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def crop_and_resize(self, image):
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width, height = image.size
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scale = max(self.width / width, self.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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return image
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def load_frames_using_imageio(self, file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process):
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reader = imageio.get_reader(file_path)
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if reader.count_frames() < max_num_frames or 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)
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frame = frame_process(frame)
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frames.append(frame)
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reader.close()
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frames = torch.stack(frames, dim=0)
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frames = rearrange(frames, "T C H W -> C T H W")
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return frames
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def load_video(self, file_path):
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start_frame_id = torch.randint(0, self.max_num_frames - (self.num_frames - 1) * self.frame_interval, (1,))[0]
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frames = self.load_frames_using_imageio(file_path, self.max_num_frames, start_frame_id, self.frame_interval, self.num_frames, self.frame_process)
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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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if file_ext_name.lower() in ["jpg", "jpeg", "png", "webp"]:
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return True
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return False
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def load_image(self, file_path):
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frame = Image.open(file_path).convert("RGB")
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frame = self.crop_and_resize(frame)
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frame = self.frame_process(frame)
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frame = rearrange(frame, "C H W -> C 1 H W")
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return frame
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def __getitem__(self, data_id):
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text = self.text[data_id]
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path = self.path[data_id]
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if self.is_image(path):
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video = self.load_image(path)
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else:
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video = self.load_video(path)
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data = {"text": text, "video": video, "path": path}
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return data
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def __len__(self):
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return len(self.path)
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class LightningModelForDataProcess(pl.LightningModule):
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def __init__(self, text_encoder_path, vae_path, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
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super().__init__()
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
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model_manager.load_models([text_encoder_path, vae_path])
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self.pipe = WanVideoPipeline.from_model_manager(model_manager)
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self.tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
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def test_step(self, batch, batch_idx):
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text, video, path = batch["text"][0], batch["video"], batch["path"][0]
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self.pipe.device = self.device
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if video is not None:
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prompt_emb = self.pipe.encode_prompt(text)
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latents = self.pipe.encode_video(video, **self.tiler_kwargs)[0]
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data = {"latents": latents, "prompt_emb": prompt_emb}
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torch.save(data, path + ".tensors.pth")
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class TensorDataset(torch.utils.data.Dataset):
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def __init__(self, base_path, metadata_path, steps_per_epoch):
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metadata = pd.read_csv(metadata_path)
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self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]]
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print(len(self.path), "videos in metadata.")
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self.path = [i + ".tensors.pth" for i in self.path if os.path.exists(i + ".tensors.pth")]
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print(len(self.path), "tensors cached in metadata.")
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assert len(self.path) > 0
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self.steps_per_epoch = steps_per_epoch
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def __getitem__(self, index):
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data_id = torch.randint(0, len(self.path), (1,))[0]
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data_id = (data_id + index) % len(self.path) # For fixed seed.
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path = self.path[data_id]
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data = torch.load(path, weights_only=True, map_location="cpu")
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return data
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def __len__(self):
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return self.steps_per_epoch
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class LightningModelForTrain(pl.LightningModule):
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def __init__(self, dit_path, learning_rate=1e-5, lora_rank=4, lora_alpha=4, train_architecture="lora", lora_target_modules="q,k,v,o,ffn.0,ffn.2", init_lora_weights="kaiming", use_gradient_checkpointing=True, pretrained_lora_path=None):
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super().__init__()
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
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model_manager.load_models([dit_path])
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self.pipe = WanVideoPipeline.from_model_manager(model_manager)
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self.pipe.scheduler.set_timesteps(1000, training=True)
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self.freeze_parameters()
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if train_architecture == "lora":
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self.add_lora_to_model(
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self.pipe.denoising_model(),
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lora_rank=lora_rank,
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lora_alpha=lora_alpha,
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lora_target_modules=lora_target_modules,
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init_lora_weights=init_lora_weights,
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pretrained_lora_path=pretrained_lora_path,
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)
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else:
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self.pipe.denoising_model().requires_grad_(True)
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self.learning_rate = learning_rate
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self.use_gradient_checkpointing = use_gradient_checkpointing
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def freeze_parameters(self):
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# Freeze parameters
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self.pipe.requires_grad_(False)
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self.pipe.eval()
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self.pipe.denoising_model().train()
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def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_target_modules="q,k,v,o,ffn.0,ffn.2", init_lora_weights="kaiming", pretrained_lora_path=None, state_dict_converter=None):
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# Add LoRA to UNet
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self.lora_alpha = lora_alpha
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if init_lora_weights == "kaiming":
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init_lora_weights = True
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lora_config = LoraConfig(
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r=lora_rank,
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lora_alpha=lora_alpha,
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init_lora_weights=init_lora_weights,
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target_modules=lora_target_modules.split(","),
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)
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model = inject_adapter_in_model(lora_config, model)
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for param in model.parameters():
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# Upcast LoRA parameters into fp32
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if param.requires_grad:
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param.data = param.to(torch.float32)
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# Lora pretrained lora weights
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if pretrained_lora_path is not None:
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state_dict = load_state_dict(pretrained_lora_path)
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if state_dict_converter is not None:
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state_dict = state_dict_converter(state_dict)
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missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
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all_keys = [i for i, _ in model.named_parameters()]
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num_updated_keys = len(all_keys) - len(missing_keys)
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num_unexpected_keys = len(unexpected_keys)
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print(f"{num_updated_keys} parameters are loaded from {pretrained_lora_path}. {num_unexpected_keys} parameters are unexpected.")
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def training_step(self, batch, batch_idx):
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# Data
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latents = batch["latents"].to(self.device)
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prompt_emb = batch["prompt_emb"]
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prompt_emb["context"] = [prompt_emb["context"][0][0].to(self.device)]
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# Loss
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noise = torch.randn_like(latents)
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timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,))
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timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device)
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extra_input = self.pipe.prepare_extra_input(latents)
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noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep)
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training_target = self.pipe.scheduler.training_target(latents, noise, timestep)
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# Compute loss
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with torch.amp.autocast(dtype=torch.bfloat16, device_type=torch.device(self.device).type):
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noise_pred = self.pipe.denoising_model()(
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noisy_latents, timestep=timestep, **prompt_emb, **extra_input,
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use_gradient_checkpointing=self.use_gradient_checkpointing
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)
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loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
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loss = loss * self.pipe.scheduler.training_weight(timestep)
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# Record log
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self.log("train_loss", loss, prog_bar=True)
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return loss
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def configure_optimizers(self):
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trainable_modules = filter(lambda p: p.requires_grad, self.pipe.denoising_model().parameters())
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optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate)
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return optimizer
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def on_save_checkpoint(self, checkpoint):
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checkpoint.clear()
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trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.denoising_model().named_parameters()))
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trainable_param_names = set([named_param[0] for named_param in trainable_param_names])
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state_dict = self.pipe.denoising_model().state_dict()
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lora_state_dict = {}
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for name, param in state_dict.items():
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if name in trainable_param_names:
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lora_state_dict[name] = param
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checkpoint.update(lora_state_dict)
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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"--task",
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type=str,
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default="data_process",
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required=True,
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choices=["data_process", "train"],
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help="Task. `data_process` or `train`.",
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)
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parser.add_argument(
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"--dataset_path",
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type=str,
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default=None,
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required=True,
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help="The path of the Dataset.",
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)
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parser.add_argument(
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"--output_path",
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type=str,
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default="./",
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help="Path to save the model.",
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)
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parser.add_argument(
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"--text_encoder_path",
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type=str,
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default=None,
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help="Path of text encoder.",
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)
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parser.add_argument(
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"--vae_path",
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type=str,
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default=None,
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help="Path of VAE.",
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)
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parser.add_argument(
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"--dit_path",
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type=str,
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default=None,
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help="Path of DiT.",
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)
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parser.add_argument(
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"--tiled",
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default=False,
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action="store_true",
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help="Whether enable tile encode in VAE. This option can reduce VRAM required.",
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)
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parser.add_argument(
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"--tile_size_height",
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type=int,
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default=34,
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help="Tile size (height) in VAE.",
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)
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parser.add_argument(
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"--tile_size_width",
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type=int,
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default=34,
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help="Tile size (width) in VAE.",
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)
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parser.add_argument(
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"--tile_stride_height",
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type=int,
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default=18,
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help="Tile stride (height) in VAE.",
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)
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parser.add_argument(
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"--tile_stride_width",
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type=int,
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default=16,
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help="Tile stride (width) in VAE.",
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)
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parser.add_argument(
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"--steps_per_epoch",
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type=int,
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default=500,
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help="Number of steps per epoch.",
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)
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parser.add_argument(
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"--num_frames",
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type=int,
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default=81,
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help="Number of frames.",
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)
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parser.add_argument(
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"--height",
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type=int,
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default=480,
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help="Image height.",
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)
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parser.add_argument(
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"--width",
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type=int,
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default=832,
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help="Image width.",
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)
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=1,
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help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-5,
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help="Learning rate.",
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)
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parser.add_argument(
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"--accumulate_grad_batches",
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type=int,
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default=1,
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help="The number of batches in gradient accumulation.",
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)
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parser.add_argument(
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"--max_epochs",
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type=int,
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default=1,
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help="Number of epochs.",
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)
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parser.add_argument(
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"--lora_target_modules",
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type=str,
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default="q,k,v,o,ffn.0,ffn.2",
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help="Layers with LoRA modules.",
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)
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parser.add_argument(
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"--init_lora_weights",
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type=str,
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default="kaiming",
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choices=["gaussian", "kaiming"],
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help="The initializing method of LoRA weight.",
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)
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parser.add_argument(
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"--training_strategy",
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type=str,
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default="auto",
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choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"],
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help="Training strategy",
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)
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parser.add_argument(
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"--lora_rank",
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type=int,
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default=4,
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help="The dimension of the LoRA update matrices.",
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)
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parser.add_argument(
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"--lora_alpha",
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type=float,
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default=4.0,
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help="The weight of the LoRA update matrices.",
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)
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parser.add_argument(
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"--use_gradient_checkpointing",
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default=False,
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action="store_true",
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help="Whether to use gradient checkpointing.",
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)
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parser.add_argument(
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"--train_architecture",
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type=str,
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default="lora",
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choices=["lora", "full"],
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help="Model structure to train. LoRA training or full training.",
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)
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parser.add_argument(
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"--pretrained_lora_path",
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type=str,
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default=None,
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help="Pretrained LoRA path. Required if the training is resumed.",
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)
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parser.add_argument(
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"--use_swanlab",
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default=False,
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action="store_true",
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help="Whether to use SwanLab logger.",
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)
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parser.add_argument(
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"--swanlab_mode",
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default=None,
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help="SwanLab mode (cloud or local).",
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)
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args = parser.parse_args()
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return args
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def data_process(args):
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dataset = TextVideoDataset(
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args.dataset_path,
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os.path.join(args.dataset_path, "metadata.csv"),
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max_num_frames=args.num_frames,
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frame_interval=1,
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num_frames=args.num_frames,
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height=args.height,
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width=args.width
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)
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dataloader = torch.utils.data.DataLoader(
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dataset,
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shuffle=False,
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batch_size=1,
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num_workers=args.dataloader_num_workers
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)
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model = LightningModelForDataProcess(
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text_encoder_path=args.text_encoder_path,
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vae_path=args.vae_path,
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tiled=args.tiled,
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tile_size=(args.tile_size_height, args.tile_size_width),
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tile_stride=(args.tile_stride_height, args.tile_stride_width),
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)
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trainer = pl.Trainer(
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accelerator="gpu",
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devices="auto",
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default_root_dir=args.output_path,
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)
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trainer.test(model, dataloader)
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def train(args):
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dataset = TensorDataset(
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args.dataset_path,
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os.path.join(args.dataset_path, "metadata.csv"),
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steps_per_epoch=args.steps_per_epoch,
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)
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dataloader = torch.utils.data.DataLoader(
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dataset,
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shuffle=True,
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batch_size=1,
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num_workers=args.dataloader_num_workers
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)
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model = LightningModelForTrain(
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dit_path=args.dit_path,
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learning_rate=args.learning_rate,
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train_architecture=args.train_architecture,
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lora_rank=args.lora_rank,
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lora_alpha=args.lora_alpha,
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lora_target_modules=args.lora_target_modules,
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init_lora_weights=args.init_lora_weights,
|
|
use_gradient_checkpointing=args.use_gradient_checkpointing,
|
|
pretrained_lora_path=args.pretrained_lora_path,
|
|
)
|
|
if args.use_swanlab:
|
|
from swanlab.integration.pytorch_lightning import SwanLabLogger
|
|
swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"}
|
|
swanlab_config.update(vars(args))
|
|
swanlab_logger = SwanLabLogger(
|
|
project="wan",
|
|
name="wan",
|
|
config=swanlab_config,
|
|
mode=args.swanlab_mode,
|
|
logdir=args.output_path,
|
|
)
|
|
logger = [swanlab_logger]
|
|
else:
|
|
logger = None
|
|
trainer = pl.Trainer(
|
|
max_epochs=args.max_epochs,
|
|
accelerator="gpu",
|
|
devices="auto",
|
|
strategy=args.training_strategy,
|
|
default_root_dir=args.output_path,
|
|
accumulate_grad_batches=args.accumulate_grad_batches,
|
|
callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)],
|
|
logger=logger,
|
|
)
|
|
trainer.fit(model, dataloader)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_args()
|
|
if args.task == "data_process":
|
|
data_process(args)
|
|
elif args.task == "train":
|
|
train(args)
|