mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 06:39:43 +00:00
652 lines
22 KiB
Python
652 lines
22 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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import numpy as np
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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, is_i2v=False, target_fps=None):
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metadata = pd.read_csv(metadata_path)
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if os.path.exists(os.path.join(base_path, "train")):
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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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else:
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self.path = [os.path.join(base_path, 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.is_i2v = is_i2v
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self.target_fps = target_fps
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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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first_frame = None
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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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if first_frame is None:
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first_frame = np.array(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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if self.is_i2v:
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return frames, first_frame
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else:
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return frames
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def load_video(self, file_path):
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start_frame_id = 0
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if self.target_fps is None:
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frame_interval = self.frame_interval
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else:
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reader = imageio.get_reader(file_path)
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fps = reader.get_meta_data()["fps"]
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reader.close()
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frame_interval = max(round(fps / self.target_fps), 1)
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frames = self.load_frames_using_imageio(file_path, self.max_num_frames, start_frame_id, 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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first_frame = 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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try:
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if self.is_image(path):
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if self.is_i2v:
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raise ValueError(f"{path} is not a video. I2V model doesn't support image-to-image training.")
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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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if self.is_i2v:
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video, first_frame = video
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data = {"text": text, "video": video, "path": path, "first_frame": first_frame}
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else:
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data = {"text": text, "video": video, "path": path}
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except:
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data = None
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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, image_encoder_path=None, tiled=False, tile_size=(34, 34), tile_stride=(18, 16), redirected_tensor_path=None):
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super().__init__()
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model_path = [text_encoder_path, vae_path]
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if image_encoder_path is not None:
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model_path.append(image_encoder_path)
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
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model_manager.load_models(model_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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self.redirected_tensor_path = redirected_tensor_path
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def test_step(self, batch, batch_idx):
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data = batch[0]
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if data is None or data["video"] is None:
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return
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text, video, path = data["text"], data["video"].unsqueeze(0), data["path"]
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self.pipe.device = self.device
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if video is not None:
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# prompt
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prompt_emb = self.pipe.encode_prompt(text)
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# video
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video = video.to(dtype=self.pipe.torch_dtype, device=self.pipe.device)
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latents = self.pipe.encode_video(video, **self.tiler_kwargs)[0]
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# image
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if "first_frame" in batch:
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first_frame = Image.fromarray(batch["first_frame"][0].cpu().numpy())
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_, _, num_frames, height, width = video.shape
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image_emb = self.pipe.encode_image(first_frame, num_frames, height, width)
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else:
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image_emb = {}
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data = {"latents": latents, "prompt_emb": prompt_emb, "image_emb": image_emb}
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if self.redirected_tensor_path is not None:
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path = path.replace("/", "_").replace("\\", "_")
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path = os.path.join(self.redirected_tensor_path, path)
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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=None, steps_per_epoch=1000, redirected_tensor_path=None):
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if os.path.exists(metadata_path):
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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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if redirected_tensor_path is None:
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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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else:
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cached_path = []
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for path in self.path:
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path = path.replace("/", "_").replace("\\", "_")
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path = os.path.join(redirected_tensor_path, path)
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if os.path.exists(path + ".tensors.pth"):
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cached_path.append(path + ".tensors.pth")
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self.path = cached_path
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else:
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print("Cannot find metadata.csv. Trying to search for tensor files.")
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self.path = [os.path.join(base_path, i) for i in os.listdir(base_path) if i.endswith(".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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self.redirected_tensor_path = redirected_tensor_path
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def __getitem__(self, index):
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while True:
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try:
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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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except:
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continue
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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__(
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self,
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dit_path,
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learning_rate=1e-5,
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lora_rank=4, lora_alpha=4, train_architecture="lora", lora_target_modules="q,k,v,o,ffn.0,ffn.2", init_lora_weights="kaiming",
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use_gradient_checkpointing=True, use_gradient_checkpointing_offload=False,
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pretrained_lora_path=None
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):
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super().__init__()
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
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if os.path.isfile(dit_path):
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model_manager.load_models([dit_path])
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else:
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dit_path = dit_path.split(",")
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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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self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
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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].to(self.device)
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image_emb = batch["image_emb"]
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if "clip_feature" in image_emb:
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image_emb["clip_feature"] = image_emb["clip_feature"][0].to(self.device)
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if "y" in image_emb:
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image_emb["y"] = image_emb["y"][0].to(self.device)
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# Loss
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self.pipe.device = self.device
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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(dtype=self.pipe.torch_dtype, device=self.pipe.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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noise_pred = self.pipe.denoising_model()(
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noisy_latents, timestep=timestep, **prompt_emb, **extra_input, **image_emb,
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use_gradient_checkpointing=self.use_gradient_checkpointing,
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use_gradient_checkpointing_offload=self.use_gradient_checkpointing_offload
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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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"--metadata_path",
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type=str,
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default=None,
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help="Path to metadata.csv.",
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)
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parser.add_argument(
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"--redirected_tensor_path",
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type=str,
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default=None,
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help="Path to save cached tensors.",
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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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"--image_encoder_path",
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type=str,
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default=None,
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help="Path of image 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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"--target_fps",
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type=int,
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default=None,
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help="Expected FPS for sampling 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.",
|
|
)
|
|
parser.add_argument(
|
|
"--max_epochs",
|
|
type=int,
|
|
default=1,
|
|
help="Number of epochs.",
|
|
)
|
|
parser.add_argument(
|
|
"--lora_target_modules",
|
|
type=str,
|
|
default="q,k,v,o,ffn.0,ffn.2",
|
|
help="Layers with LoRA modules.",
|
|
)
|
|
parser.add_argument(
|
|
"--init_lora_weights",
|
|
type=str,
|
|
default="kaiming",
|
|
choices=["gaussian", "kaiming"],
|
|
help="The initializing method of LoRA weight.",
|
|
)
|
|
parser.add_argument(
|
|
"--training_strategy",
|
|
type=str,
|
|
default="auto",
|
|
choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"],
|
|
help="Training strategy",
|
|
)
|
|
parser.add_argument(
|
|
"--lora_rank",
|
|
type=int,
|
|
default=4,
|
|
help="The dimension of the LoRA update matrices.",
|
|
)
|
|
parser.add_argument(
|
|
"--lora_alpha",
|
|
type=float,
|
|
default=4.0,
|
|
help="The weight of the LoRA update matrices.",
|
|
)
|
|
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 use gradient checkpointing offload.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_architecture",
|
|
type=str,
|
|
default="lora",
|
|
choices=["lora", "full"],
|
|
help="Model structure to train. LoRA training or full training.",
|
|
)
|
|
parser.add_argument(
|
|
"--pretrained_lora_path",
|
|
type=str,
|
|
default=None,
|
|
help="Pretrained LoRA path. Required if the training is resumed.",
|
|
)
|
|
parser.add_argument(
|
|
"--use_swanlab",
|
|
default=False,
|
|
action="store_true",
|
|
help="Whether to use SwanLab logger.",
|
|
)
|
|
parser.add_argument(
|
|
"--swanlab_mode",
|
|
default=None,
|
|
help="SwanLab mode (cloud or local).",
|
|
)
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
def data_process(args):
|
|
dataset = TextVideoDataset(
|
|
args.dataset_path,
|
|
os.path.join(args.dataset_path, "metadata.csv") if args.metadata_path is None else args.metadata_path,
|
|
max_num_frames=args.num_frames,
|
|
frame_interval=1,
|
|
num_frames=args.num_frames,
|
|
height=args.height,
|
|
width=args.width,
|
|
is_i2v=args.image_encoder_path is not None,
|
|
target_fps=args.target_fps,
|
|
)
|
|
dataloader = torch.utils.data.DataLoader(
|
|
dataset,
|
|
shuffle=False,
|
|
batch_size=1,
|
|
num_workers=args.dataloader_num_workers,
|
|
collate_fn=lambda x: x,
|
|
)
|
|
model = LightningModelForDataProcess(
|
|
text_encoder_path=args.text_encoder_path,
|
|
image_encoder_path=args.image_encoder_path,
|
|
vae_path=args.vae_path,
|
|
tiled=args.tiled,
|
|
tile_size=(args.tile_size_height, args.tile_size_width),
|
|
tile_stride=(args.tile_stride_height, args.tile_stride_width),
|
|
redirected_tensor_path=args.redirected_tensor_path,
|
|
)
|
|
trainer = pl.Trainer(
|
|
accelerator="gpu",
|
|
devices="auto",
|
|
default_root_dir=args.output_path,
|
|
)
|
|
trainer.test(model, dataloader)
|
|
|
|
|
|
def train(args):
|
|
dataset = TensorDataset(
|
|
args.dataset_path,
|
|
os.path.join(args.dataset_path, "metadata.csv") if args.metadata_path is None else args.metadata_path,
|
|
steps_per_epoch=args.steps_per_epoch,
|
|
redirected_tensor_path=args.redirected_tensor_path,
|
|
)
|
|
dataloader = torch.utils.data.DataLoader(
|
|
dataset,
|
|
shuffle=True,
|
|
batch_size=1,
|
|
num_workers=args.dataloader_num_workers
|
|
)
|
|
model = LightningModelForTrain(
|
|
dit_path=args.dit_path,
|
|
learning_rate=args.learning_rate,
|
|
train_architecture=args.train_architecture,
|
|
lora_rank=args.lora_rank,
|
|
lora_alpha=args.lora_alpha,
|
|
lora_target_modules=args.lora_target_modules,
|
|
init_lora_weights=args.init_lora_weights,
|
|
use_gradient_checkpointing=args.use_gradient_checkpointing,
|
|
use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
|
|
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=os.path.join(args.output_path, "swanlog"),
|
|
)
|
|
logger = [swanlab_logger]
|
|
else:
|
|
logger = None
|
|
trainer = pl.Trainer(
|
|
max_epochs=args.max_epochs,
|
|
accelerator="gpu",
|
|
devices="auto",
|
|
precision="bf16",
|
|
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)
|