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[training] CogVideoX-I2V LoRA (#9482) 



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Co-Authored-By: default avataryuan-shenghai <963658029@qq.com>

* add coauthor
Co-Authored-By: default avatarShenghai Yuan <140951558+SHYuanBest@users.noreply.github.com>

* update
Co-Authored-By: default avataryuan-shenghai <963658029@qq.com>

* update

---------
Co-authored-by: default avataryuan-shenghai <963658029@qq.com>
Co-authored-by: default avatarShenghai Yuan <140951558+SHYuanBest@users.noreply.github.com>
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examples/cogvideo/README.md
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...@@ -10,6 +10,11 @@ In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-de ...@@ -10,6 +10,11 @@ In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-de
At the moment, LoRA finetuning has only been tested for [CogVideoX-2b](https://huggingface.co/THUDM/CogVideoX-2b). At the moment, LoRA finetuning has only been tested for [CogVideoX-2b](https://huggingface.co/THUDM/CogVideoX-2b).
> [!NOTE]
> The scripts for CogVideoX come with limited support and may not be fully compatible with different training techniques. They are not feature-rich either and simply serve as minimal examples of finetuning to take inspiration from and improve.
>
> A repository containing memory-optimized finetuning scripts with support for multiple resolutions, dataset preparation, captioning, etc. is available [here](https://github.com/a-r-r-o-w/cogvideox-factory), which will be maintained jointly by the CogVideoX and Diffusers team.
## Data Preparation ## Data Preparation
The training scripts accepts data in two formats. The training scripts accepts data in two formats.
...@@ -132,6 +137,8 @@ Assuming you are training on 50 videos of a similar concept, we have found 1500- ...@@ -132,6 +137,8 @@ Assuming you are training on 50 videos of a similar concept, we have found 1500-
- 1500 steps on 50 videos would correspond to `30` training epochs - 1500 steps on 50 videos would correspond to `30` training epochs
- 4000 steps on 100 videos would correspond to `40` training epochs - 4000 steps on 100 videos would correspond to `40` training epochs
The following bash script launches training for text-to-video lora.
```bash ```bash
#!/bin/bash #!/bin/bash
...@@ -172,6 +179,8 @@ accelerate launch --gpu_ids $GPU_IDS examples/cogvideo/train_cogvideox_lora.py \ ...@@ -172,6 +179,8 @@ accelerate launch --gpu_ids $GPU_IDS examples/cogvideo/train_cogvideox_lora.py \
--report_to wandb --report_to wandb
``` ```
For launching image-to-video finetuning instead, run the `train_cogvideox_image_to_video_lora.py` file instead. Additionally, you will have to pass `--validation_images` as paths to initial images corresponding to `--validation_prompts` for I2V validation to work.
To better track our training experiments, we're using the following flags in the command above: To better track our training experiments, we're using the following flags in the command above:
* `--report_to wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`. * `--report_to wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`.
* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected. * `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
...@@ -197,8 +206,6 @@ Note that setting the `<ID_TOKEN>` is not necessary. From some limited experimen ...@@ -197,8 +206,6 @@ Note that setting the `<ID_TOKEN>` is not necessary. From some limited experimen
> >
> Note that our testing is not exhaustive due to limited time for exploration. Our recommendation would be to play around with the different knobs and dials to find the best settings for your data. > Note that our testing is not exhaustive due to limited time for exploration. Our recommendation would be to play around with the different knobs and dials to find the best settings for your data.
<!-- TODO: Test finetuning with CogVideoX-5b and CogVideoX-5b-I2V and update scripts accordingly -->
## Inference ## Inference
Once you have trained a lora model, the inference can be done simply loading the lora weights into the `CogVideoXPipeline`. Once you have trained a lora model, the inference can be done simply loading the lora weights into the `CogVideoXPipeline`.
...@@ -227,3 +234,5 @@ prompt = ( ...@@ -227,3 +234,5 @@ prompt = (
frames = pipe(prompt, guidance_scale=6, use_dynamic_cfg=True).frames[0] frames = pipe(prompt, guidance_scale=6, use_dynamic_cfg=True).frames[0]
export_to_video(frames, "output.mp4", fps=8) export_to_video(frames, "output.mp4", fps=8)
``` ```
If you've trained a LoRA for `CogVideoXImageToVideoPipeline` instead, everything in the above example remains the same except you must also pass an image as initial condition for generation.
examples/cogvideo/train_cogvideox_image_to_video_lora.py 0 → 100644
View file @ 2ffbb88f
# Copyright 2024 The HuggingFace Team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import math
import os
import random
import shutil
from datetime import timedelta
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
import transformers
from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, InitProcessGroupKwargs, ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, T5EncoderModel, T5Tokenizer
import diffusers
from diffusers import (
AutoencoderKLCogVideoX,
CogVideoXDPMScheduler,
CogVideoXImageToVideoPipeline,
CogVideoXTransformer3DModel,
)
from diffusers.models.embeddings import get_3d_rotary_pos_embed
from diffusers.optimization import get_scheduler
from diffusers.pipelines.cogvideo.pipeline_cogvideox import get_resize_crop_region_for_grid
from diffusers.training_utils import cast_training_params, free_memory
from diffusers.utils import (
check_min_version,
convert_unet_state_dict_to_peft,
export_to_video,
is_wandb_available,
load_image,
)
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.torch_utils import is_compiled_module
if is_wandb_available():
import wandb
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.31.0.dev0")
logger = get_logger(__name__)
def get_args():
parser = argparse.ArgumentParser(description="Simple example of a training script for CogVideoX.")
# Model information
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
# Dataset information
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--instance_data_root",
type=str,
default=None,
help=("A folder containing the training data."),
)
parser.add_argument(
"--video_column",
type=str,
default="video",
help="The column of the dataset containing videos. Or, the name of the file in `--instance_data_root` folder containing the line-separated path to video data.",
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing the instance prompt for each video. Or, the name of the file in `--instance_data_root` folder containing the line-separated instance prompts.",
)
parser.add_argument(
"--id_token", type=str, default=None, help="Identifier token appended to the start of each prompt if provided."
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
# Validation
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="One or more prompt(s) that is used during validation to verify that the model is learning. Multiple validation prompts should be separated by the '--validation_prompt_seperator' string.",
)
parser.add_argument(
"--validation_images",
type=str,
default=None,
help="One or more image path(s) that is used during validation to verify that the model is learning. Multiple validation paths should be separated by the '--validation_prompt_seperator' string. These should correspond to the order of the validation prompts.",
)
parser.add_argument(
"--validation_prompt_separator",
type=str,
default=":::",
help="String that separates multiple validation prompts",
)
parser.add_argument(
"--num_validation_videos",
type=int,
default=1,
help="Number of videos that should be generated during validation per `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help=(
"Run validation every X epochs. Validation consists of running the prompt `args.validation_prompt` multiple times: `args.num_validation_videos`."
),
)
parser.add_argument(
"--guidance_scale",
type=float,
default=6,
help="The guidance scale to use while sampling validation videos.",
)
parser.add_argument(
"--use_dynamic_cfg",
action="store_true",
default=False,
help="Whether or not to use the default cosine dynamic guidance schedule when sampling validation videos.",
)
# Training information
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--rank",
type=int,
default=128,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--lora_alpha",
type=float,
default=128,
help=("The scaling factor to scale LoRA weight update. The actual scaling factor is `lora_alpha / rank`"),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="cogvideox-i2v-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--height",
type=int,
default=480,
help="All input videos are resized to this height.",
)
parser.add_argument(
"--width",
type=int,
default=720,
help="All input videos are resized to this width.",
)
parser.add_argument("--fps", type=int, default=8, help="All input videos will be used at this FPS.")
parser.add_argument(
"--max_num_frames", type=int, default=49, help="All input videos will be truncated to these many frames."
)
parser.add_argument(
"--skip_frames_start",
type=int,
default=0,
help="Number of frames to skip from the beginning of each input video. Useful if training data contains intro sequences.",
)
parser.add_argument(
"--skip_frames_end",
type=int,
default=0,
help="Number of frames to skip from the end of each input video. Useful if training data contains outro sequences.",
)
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip videos horizontally",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides `--num_train_epochs`.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--enable_slicing",
action="store_true",
default=False,
help="Whether or not to use VAE slicing for saving memory.",
)
parser.add_argument(
"--enable_tiling",
action="store_true",
default=False,
help="Whether or not to use VAE tiling for saving memory.",
)
parser.add_argument(
"--noised_image_dropout",
type=float,
default=0.05,
help="Image condition dropout probability.",
)
# Optimizer
parser.add_argument(
"--optimizer",
type=lambda s: s.lower(),
default="adam",
choices=["adam", "adamw", "prodigy"],
help=("The optimizer type to use."),
)
parser.add_argument(
"--use_8bit_adam",
action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
)
parser.add_argument(
"--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--adam_beta2", type=float, default=0.95, help="The beta2 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--prodigy_beta3",
type=float,
default=None,
help="Coefficients for computing the Prodigy optimizer's stepsize using running averages. If set to None, uses the value of square root of beta2.",
)
parser.add_argument("--prodigy_decouple", action="store_true", help="Use AdamW style decoupled weight decay")
parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
parser.add_argument(
"--adam_epsilon",
type=float,
default=1e-08,
help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
)
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--prodigy_use_bias_correction", action="store_true", help="Turn on Adam's bias correction.")
parser.add_argument(
"--prodigy_safeguard_warmup",
action="store_true",
help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage.",
)
# Other information
parser.add_argument("--tracker_name", type=str, default=None, help="Project tracker name")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help="Directory where logs are stored.",
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default=None,
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--nccl_timeout", type=int, default=600, help="NCCL backend timeout in seconds.")
return parser.parse_args()
class VideoDataset(Dataset):
def __init__(
self,
instance_data_root: Optional[str] = None,
dataset_name: Optional[str] = None,
dataset_config_name: Optional[str] = None,
caption_column: str = "text",
video_column: str = "video",
height: int = 480,
width: int = 720,
fps: int = 8,
max_num_frames: int = 49,
skip_frames_start: int = 0,
skip_frames_end: int = 0,
cache_dir: Optional[str] = None,
id_token: Optional[str] = None,
) -> None:
super().__init__()
self.instance_data_root = Path(instance_data_root) if instance_data_root is not None else None
self.dataset_name = dataset_name
self.dataset_config_name = dataset_config_name
self.caption_column = caption_column
self.video_column = video_column
self.height = height
self.width = width
self.fps = fps
self.max_num_frames = max_num_frames
self.skip_frames_start = skip_frames_start
self.skip_frames_end = skip_frames_end
self.cache_dir = cache_dir
self.id_token = id_token or ""
if dataset_name is not None:
self.instance_prompts, self.instance_video_paths = self._load_dataset_from_hub()
else:
self.instance_prompts, self.instance_video_paths = self._load_dataset_from_local_path()
self.instance_prompts = [self.id_token + prompt for prompt in self.instance_prompts]
self.num_instance_videos = len(self.instance_video_paths)
if self.num_instance_videos != len(self.instance_prompts):
raise ValueError(
f"Expected length of instance prompts and videos to be the same but found {len(self.instance_prompts)=} and {len(self.instance_video_paths)=}. Please ensure that the number of caption prompts and videos match in your dataset."
)
self.instance_videos = self._preprocess_data()
def __len__(self):
return self.num_instance_videos
def __getitem__(self, index):
return {
"instance_prompt": self.instance_prompts[index],
"instance_video": self.instance_videos[index],
}
def _load_dataset_from_hub(self):
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"You are trying to load your data using the datasets library. If you wish to train using custom "
"captions please install the datasets library: `pip install datasets`. If you wish to load a "
"local folder containing images only, specify --instance_data_root instead."
)
# Downloading and loading a dataset from the hub. See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
dataset = load_dataset(
self.dataset_name,
self.dataset_config_name,
cache_dir=self.cache_dir,
)
column_names = dataset["train"].column_names
if self.video_column is None:
video_column = column_names[0]
logger.info(f"`video_column` defaulting to {video_column}")
else:
video_column = self.video_column
if video_column not in column_names:
raise ValueError(
f"`--video_column` value '{video_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
if self.caption_column is None:
caption_column = column_names[1]
logger.info(f"`caption_column` defaulting to {caption_column}")
else:
caption_column = self.caption_column
if self.caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{self.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
instance_prompts = dataset["train"][caption_column]
instance_videos = [Path(self.instance_data_root, filepath) for filepath in dataset["train"][video_column]]
return instance_prompts, instance_videos
def _load_dataset_from_local_path(self):
if not self.instance_data_root.exists():
raise ValueError("Instance videos root folder does not exist")
prompt_path = self.instance_data_root.joinpath(self.caption_column)
video_path = self.instance_data_root.joinpath(self.video_column)
if not prompt_path.exists() or not prompt_path.is_file():
raise ValueError(
"Expected `--caption_column` to be path to a file in `--instance_data_root` containing line-separated text prompts."
)
if not video_path.exists() or not video_path.is_file():
raise ValueError(
"Expected `--video_column` to be path to a file in `--instance_data_root` containing line-separated paths to video data in the same directory."
)
with open(prompt_path, "r", encoding="utf-8") as file:
instance_prompts = [line.strip() for line in file.readlines() if len(line.strip()) > 0]
with open(video_path, "r", encoding="utf-8") as file:
instance_videos = [
self.instance_data_root.joinpath(line.strip()) for line in file.readlines() if len(line.strip()) > 0
]
if any(not path.is_file() for path in instance_videos):
raise ValueError(
"Expected '--video_column' to be a path to a file in `--instance_data_root` containing line-separated paths to video data but found atleast one path that is not a valid file."
)
return instance_prompts, instance_videos
def _preprocess_data(self):
try:
import decord
except ImportError:
raise ImportError(
"The `decord` package is required for loading the video dataset. Install with `pip install decord`"
)
decord.bridge.set_bridge("torch")
videos = []
train_transforms = transforms.Compose(
[
transforms.Lambda(lambda x: x / 255.0 * 2.0 - 1.0),
]
)
for filename in self.instance_video_paths:
video_reader = decord.VideoReader(uri=filename.as_posix(), width=self.width, height=self.height)
video_num_frames = len(video_reader)
start_frame = min(self.skip_frames_start, video_num_frames)
end_frame = max(0, video_num_frames - self.skip_frames_end)
if end_frame <= start_frame:
frames = video_reader.get_batch([start_frame])
elif end_frame - start_frame <= self.max_num_frames:
frames = video_reader.get_batch(list(range(start_frame, end_frame)))
else:
indices = list(range(start_frame, end_frame, (end_frame - start_frame) // self.max_num_frames))
frames = video_reader.get_batch(indices)
# Ensure that we don't go over the limit
frames = frames[: self.max_num_frames]
selected_num_frames = frames.shape[0]
# Choose first (4k + 1) frames as this is how many is required by the VAE
remainder = (3 + (selected_num_frames % 4)) % 4
if remainder != 0:
frames = frames[:-remainder]
selected_num_frames = frames.shape[0]
assert (selected_num_frames - 1) % 4 == 0
# Training transforms
frames = frames.float()
frames = torch.stack([train_transforms(frame) for frame in frames], dim=0)
videos.append(frames.permute(0, 3, 1, 2).contiguous()) # [F, C, H, W]
return videos
def save_model_card(
repo_id: str,
videos=None,
base_model: str = None,
validation_prompt=None,
repo_folder=None,
fps=8,
):
widget_dict = []
if videos is not None:
for i, video in enumerate(videos):
video_path = f"final_video_{i}.mp4"
export_to_video(video, os.path.join(repo_folder, video_path, fps=fps))
widget_dict.append(
{"text": validation_prompt if validation_prompt else " ", "output": {"url": video_path}},
)
model_description = f"""
# CogVideoX LoRA - {repo_id}
<Gallery />
## Model description
These are {repo_id} LoRA weights for {base_model}.
The weights were trained using the [CogVideoX Diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/cogvideo/train_cogvideox_image_to_video_lora.py).
Was LoRA for the text encoder enabled? No.
## Download model
[Download the *.safetensors LoRA]({repo_id}/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
import torch
from diffusers import CogVideoXImageToVideoPipeline
from diffusers.utils import load_image, export_to_video
pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("{repo_id}", weight_name="pytorch_lora_weights.safetensors", adapter_name=["cogvideox-i2v-lora"])
# The LoRA adapter weights are determined by what was used for training.
# In this case, we assume `--lora_alpha` is 32 and `--rank` is 64.
# It can be made lower or higher from what was used in training to decrease or amplify the effect
# of the LoRA upto a tolerance, beyond which one might notice no effect at all or overflows.
pipe.set_adapters(["cogvideox-i2v-lora"], [32 / 64])
image = load_image("/path/to/image")
video = pipe(image=image, "{validation_prompt}", guidance_scale=6, use_dynamic_cfg=True).frames[0]
export_to_video(video, "output.mp4", fps=8)
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/THUDM/CogVideoX-5b-I2V/blob/main/LICENSE).
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="other",
base_model=base_model,
prompt=validation_prompt,
model_description=model_description,
widget=widget_dict,
)
tags = [
"image-to-video",
"diffusers-training",
"diffusers",
"lora",
"cogvideox",
"cogvideox-diffusers",
"template:sd-lora",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
def log_validation(
pipe,
args,
accelerator,
pipeline_args,
epoch,
is_final_validation: bool = False,
):
logger.info(
f"Running validation... \n Generating {args.num_validation_videos} videos with prompt: {pipeline_args['prompt']}."
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
scheduler_args = {}
if "variance_type" in pipe.scheduler.config:
variance_type = pipe.scheduler.config.variance_type
if variance_type in ["learned", "learned_range"]:
variance_type = "fixed_small"
scheduler_args["variance_type"] = variance_type
pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, **scheduler_args)
pipe = pipe.to(accelerator.device)
# pipe.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
videos = []
for _ in range(args.num_validation_videos):
video = pipe(**pipeline_args, generator=generator, output_type="np").frames[0]
videos.append(video)
for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
if tracker.name == "wandb":
video_filenames = []
for i, video in enumerate(videos):
prompt = (
pipeline_args["prompt"][:25]
.replace(" ", "_")
.replace(" ", "_")
.replace("'", "_")
.replace('"', "_")
.replace("/", "_")
)
filename = os.path.join(args.output_dir, f"{phase_name}_video_{i}_{prompt}.mp4")
export_to_video(video, filename, fps=8)
video_filenames.append(filename)
tracker.log(
{
phase_name: [
wandb.Video(filename, caption=f"{i}: {pipeline_args['prompt']}")
for i, filename in enumerate(video_filenames)
]
}
)
del pipe
free_memory()
return videos
def _get_t5_prompt_embeds(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("`text_input_ids` must be provided when the tokenizer is not specified.")
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
return prompt_embeds
def encode_prompt(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_embeds = _get_t5_prompt_embeds(
tokenizer,
text_encoder,
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
text_input_ids=text_input_ids,
)
return prompt_embeds
def compute_prompt_embeddings(
tokenizer, text_encoder, prompt, max_sequence_length, device, dtype, requires_grad: bool = False
):
if requires_grad:
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
else:
with torch.no_grad():
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds
def prepare_rotary_positional_embeddings(
height: int,
width: int,
num_frames: int,
vae_scale_factor_spatial: int = 8,
patch_size: int = 2,
attention_head_dim: int = 64,
device: Optional[torch.device] = None,
base_height: int = 480,
base_width: int = 720,
) -> Tuple[torch.Tensor, torch.Tensor]:
grid_height = height // (vae_scale_factor_spatial * patch_size)
grid_width = width // (vae_scale_factor_spatial * patch_size)
base_size_width = base_width // (vae_scale_factor_spatial * patch_size)
base_size_height = base_height // (vae_scale_factor_spatial * patch_size)
grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size_width, base_size_height)
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
embed_dim=attention_head_dim,
crops_coords=grid_crops_coords,
grid_size=(grid_height, grid_width),
temporal_size=num_frames,
)
freqs_cos = freqs_cos.to(device=device)
freqs_sin = freqs_sin.to(device=device)
return freqs_cos, freqs_sin
def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
# Use DeepSpeed optimzer
if use_deepspeed:
from accelerate.utils import DummyOptim
return DummyOptim(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
# Optimizer creation
supported_optimizers = ["adam", "adamw", "prodigy"]
if args.optimizer not in supported_optimizers:
logger.warning(
f"Unsupported choice of optimizer: {args.optimizer}. Supported optimizers include {supported_optimizers}. Defaulting to AdamW"
)
args.optimizer = "adamw"
if args.use_8bit_adam and args.optimizer.lower() not in ["adam", "adamw"]:
logger.warning(
f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was "
f"set to {args.optimizer.lower()}"
)
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
if args.optimizer.lower() == "adamw":
optimizer_class = bnb.optim.AdamW8bit if args.use_8bit_adam else torch.optim.AdamW
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
elif args.optimizer.lower() == "adam":
optimizer_class = bnb.optim.Adam8bit if args.use_8bit_adam else torch.optim.Adam
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
eps=args.adam_epsilon,
weight_decay=args.adam_weight_decay,
)
elif args.optimizer.lower() == "prodigy":
try:
import prodigyopt
except ImportError:
raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
optimizer_class = prodigyopt.Prodigy
if args.learning_rate <= 0.1:
logger.warning(
"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
)
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
beta3=args.prodigy_beta3,
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
decouple=args.prodigy_decouple,
use_bias_correction=args.prodigy_use_bias_correction,
safeguard_warmup=args.prodigy_safeguard_warmup,
)
return optimizer
def main(args):
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `huggingface-cli login` to authenticate with the Hub."
)
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
init_kwargs = InitProcessGroupKwargs(backend="nccl", timeout=timedelta(seconds=args.nccl_timeout))
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
kwargs_handlers=[ddp_kwargs, init_kwargs],
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name,
exist_ok=True,
).repo_id
# Prepare models and scheduler
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
text_encoder = T5EncoderModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
# CogVideoX-2b weights are stored in float16
# CogVideoX-5b and CogVideoX-5b-I2V weights are stored in bfloat16
load_dtype = torch.bfloat16 if "5b" in args.pretrained_model_name_or_path.lower() else torch.float16
transformer = CogVideoXTransformer3DModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=load_dtype,
revision=args.revision,
variant=args.variant,
)
vae = AutoencoderKLCogVideoX.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
)
scheduler = CogVideoXDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
if args.enable_slicing:
vae.enable_slicing()
if args.enable_tiling:
vae.enable_tiling()
# We only train the additional adapter LoRA layers
text_encoder.requires_grad_(False)
transformer.requires_grad_(False)
vae.requires_grad_(False)
# For mixed precision training we cast all non-trainable weights (vae, text_encoder and transformer) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.state.deepspeed_plugin:
# DeepSpeed is handling precision, use what's in the DeepSpeed config
if (
"fp16" in accelerator.state.deepspeed_plugin.deepspeed_config
and accelerator.state.deepspeed_plugin.deepspeed_config["fp16"]["enabled"]
):
weight_dtype = torch.float16
if (
"bf16" in accelerator.state.deepspeed_plugin.deepspeed_config
and accelerator.state.deepspeed_plugin.deepspeed_config["bf16"]["enabled"]
):
weight_dtype = torch.float16
else:
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
# due to pytorch#99272, MPS does not yet support bfloat16.
raise ValueError(
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
text_encoder.to(accelerator.device, dtype=weight_dtype)
transformer.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
if args.gradient_checkpointing:
transformer.enable_gradient_checkpointing()
# now we will add new LoRA weights to the attention layers
transformer_lora_config = LoraConfig(
r=args.rank,
lora_alpha=args.lora_alpha,
init_lora_weights=True,
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)
transformer.add_adapter(transformer_lora_config)
def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
transformer_lora_layers_to_save = None
for model in models:
if isinstance(model, type(unwrap_model(transformer))):
transformer_lora_layers_to_save = get_peft_model_state_dict(model)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
CogVideoXImageToVideoPipeline.save_lora_weights(
output_dir,
transformer_lora_layers=transformer_lora_layers_to_save,
)
def load_model_hook(models, input_dir):
transformer_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(unwrap_model(transformer))):
transformer_ = model
else:
raise ValueError(f"Unexpected save model: {model.__class__}")
lora_state_dict = CogVideoXImageToVideoPipeline.lora_state_dict(input_dir)
transformer_state_dict = {
f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.")
}
transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")
if incompatible_keys is not None:
# check only for unexpected keys
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
logger.warning(
f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
f" {unexpected_keys}. "
)
# Make sure the trainable params are in float32. This is again needed since the base models
# are in `weight_dtype`. More details:
# https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
if args.mixed_precision == "fp16":
# only upcast trainable parameters (LoRA) into fp32
cast_training_params([transformer_])
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32 and torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
# Make sure the trainable params are in float32.
if args.mixed_precision == "fp16":
# only upcast trainable parameters (LoRA) into fp32
cast_training_params([transformer], dtype=torch.float32)
transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
# Optimization parameters
transformer_parameters_with_lr = {"params": transformer_lora_parameters, "lr": args.learning_rate}
params_to_optimize = [transformer_parameters_with_lr]
use_deepspeed_optimizer = (
accelerator.state.deepspeed_plugin is not None
and "optimizer" in accelerator.state.deepspeed_plugin.deepspeed_config
)
use_deepspeed_scheduler = (
accelerator.state.deepspeed_plugin is not None
and "scheduler" in accelerator.state.deepspeed_plugin.deepspeed_config
)
optimizer = get_optimizer(args, params_to_optimize, use_deepspeed=use_deepspeed_optimizer)
# Dataset and DataLoader
train_dataset = VideoDataset(
instance_data_root=args.instance_data_root,
dataset_name=args.dataset_name,
dataset_config_name=args.dataset_config_name,
caption_column=args.caption_column,
video_column=args.video_column,
height=args.height,
width=args.width,
fps=args.fps,
max_num_frames=args.max_num_frames,
skip_frames_start=args.skip_frames_start,
skip_frames_end=args.skip_frames_end,
cache_dir=args.cache_dir,
id_token=args.id_token,
)
def encode_video(video):
video = video.to(accelerator.device, dtype=vae.dtype).unsqueeze(0)
video = video.permute(0, 2, 1, 3, 4) # [B, C, F, H, W]
image = video[:, :, :1].clone()
latent_dist = vae.encode(video).latent_dist
image_noise_sigma = torch.normal(mean=-3.0, std=0.5, size=(1,), device=image.device)
image_noise_sigma = torch.exp(image_noise_sigma).to(dtype=image.dtype)
noisy_image = image + torch.randn_like(image) * image_noise_sigma[:, None, None, None, None]
image_latent_dist = vae.encode(noisy_image).latent_dist
return latent_dist, image_latent_dist
train_dataset.instance_prompts = [
compute_prompt_embeddings(
tokenizer,
text_encoder,
[prompt],
transformer.config.max_text_seq_length,
accelerator.device,
weight_dtype,
requires_grad=False,
)
for prompt in train_dataset.instance_prompts
]
train_dataset.instance_videos = [encode_video(video) for video in train_dataset.instance_videos]
def collate_fn(examples):
videos = []
images = []
for example in examples:
latent_dist, image_latent_dist = example["instance_video"]
video_latents = latent_dist.sample() * vae.config.scaling_factor
image_latents = image_latent_dist.sample() * vae.config.scaling_factor
video_latents = video_latents.permute(0, 2, 1, 3, 4)
image_latents = image_latents.permute(0, 2, 1, 3, 4)
padding_shape = (video_latents.shape[0], video_latents.shape[1] - 1, *video_latents.shape[2:])
latent_padding = image_latents.new_zeros(padding_shape)
image_latents = torch.cat([image_latents, latent_padding], dim=1)
if random.random() < args.noised_image_dropout:
image_latents = torch.zeros_like(image_latents)
videos.append(video_latents)
images.append(image_latents)
videos = torch.cat(videos)
images = torch.cat(images)
videos = videos.to(memory_format=torch.contiguous_format).float()
images = images.to(memory_format=torch.contiguous_format).float()
prompts = [example["instance_prompt"] for example in examples]
prompts = torch.cat(prompts)
return {
"videos": (videos, images),
"prompts": prompts,
}
train_dataloader = DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
if use_deepspeed_scheduler:
from accelerate.utils import DummyScheduler
lr_scheduler = DummyScheduler(
name=args.lr_scheduler,
optimizer=optimizer,
total_num_steps=args.max_train_steps * accelerator.num_processes,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
)
else:
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
transformer, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_name = args.tracker_name or "cogvideox-i2v-lora"
accelerator.init_trackers(tracker_name, config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
num_trainable_parameters = sum(param.numel() for model in params_to_optimize for param in model["params"])
logger.info("***** Running training *****")
logger.info(f" Num trainable parameters = {num_trainable_parameters}")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if not args.resume_from_checkpoint:
initial_global_step = 0
else:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
vae_scale_factor_spatial = 2 ** (len(vae.config.block_out_channels) - 1)
# For DeepSpeed training
model_config = transformer.module.config if hasattr(transformer, "module") else transformer.config
for epoch in range(first_epoch, args.num_train_epochs):
transformer.train()
for step, batch in enumerate(train_dataloader):
models_to_accumulate = [transformer]
with accelerator.accumulate(models_to_accumulate):
video_latents, image_latents = batch["videos"]
prompt_embeds = batch["prompts"]
video_latents = video_latents.to(dtype=weight_dtype) # [B, F, C, H, W]
image_latents = image_latents.to(dtype=weight_dtype) # [B, F, C, H, W]
batch_size, num_frames, num_channels, height, width = video_latents.shape
# Sample a random timestep for each image
timesteps = torch.randint(
0, scheduler.config.num_train_timesteps, (batch_size,), device=video_latents.device
)
timesteps = timesteps.long()
# Sample noise that will be added to the latents
noise = torch.randn_like(video_latents)
# Add noise to the model input according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_video_latents = scheduler.add_noise(video_latents, noise, timesteps)
noisy_model_input = torch.cat([noisy_video_latents, image_latents], dim=2)
# Prepare rotary embeds
image_rotary_emb = (
prepare_rotary_positional_embeddings(
height=args.height,
width=args.width,
num_frames=num_frames,
vae_scale_factor_spatial=vae_scale_factor_spatial,
patch_size=model_config.patch_size,
attention_head_dim=model_config.attention_head_dim,
device=accelerator.device,
)
if model_config.use_rotary_positional_embeddings
else None
)
# Predict the noise residual
model_output = transformer(
hidden_states=noisy_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timesteps,
image_rotary_emb=image_rotary_emb,
return_dict=False,
)[0]
model_pred = scheduler.get_velocity(model_output, noisy_video_latents, timesteps)
alphas_cumprod = scheduler.alphas_cumprod[timesteps]
weights = 1 / (1 - alphas_cumprod)
while len(weights.shape) < len(model_pred.shape):
weights = weights.unsqueeze(-1)
target = video_latents
loss = torch.mean((weights * (model_pred - target) ** 2).reshape(batch_size, -1), dim=1)
loss = loss.mean()
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = transformer.parameters()
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
if accelerator.state.deepspeed_plugin is None:
optimizer.step()
optimizer.zero_grad()
lr_scheduler.step()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process or accelerator.distributed_type == DistributedType.DEEPSPEED:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"Removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompt is not None and (epoch + 1) % args.validation_epochs == 0:
# Create pipeline
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
args.pretrained_model_name_or_path,
transformer=unwrap_model(transformer),
scheduler=scheduler,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
validation_prompts = args.validation_prompt.split(args.validation_prompt_separator)
validation_images = args.validation_images.split(args.validation_prompt_separator)
for validation_image, validation_prompt in zip(validation_images, validation_prompts):
pipeline_args = {
"image": load_image(validation_image),
"prompt": validation_prompt,
"guidance_scale": args.guidance_scale,
"use_dynamic_cfg": args.use_dynamic_cfg,
"height": args.height,
"width": args.width,
}
validation_outputs = log_validation(
pipe=pipe,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
)
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
transformer = unwrap_model(transformer)
dtype = (
torch.float16
if args.mixed_precision == "fp16"
else torch.bfloat16
if args.mixed_precision == "bf16"
else torch.float32
)
transformer = transformer.to(dtype)
transformer_lora_layers = get_peft_model_state_dict(transformer)
CogVideoXImageToVideoPipeline.save_lora_weights(
save_directory=args.output_dir,
transformer_lora_layers=transformer_lora_layers,
)
# Cleanup trained models to save memory
del transformer
free_memory()
# Final test inference
pipe = CogVideoXImageToVideoPipeline.from_pretrained(
args.pretrained_model_name_or_path,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config)
if args.enable_slicing:
pipe.vae.enable_slicing()
if args.enable_tiling:
pipe.vae.enable_tiling()
# Load LoRA weights
lora_scaling = args.lora_alpha / args.rank
pipe.load_lora_weights(args.output_dir, adapter_name="cogvideox-i2v-lora")
pipe.set_adapters(["cogvideox-i2v-lora"], [lora_scaling])
# Run inference
validation_outputs = []
if args.validation_prompt and args.num_validation_videos > 0:
validation_prompts = args.validation_prompt.split(args.validation_prompt_separator)
validation_images = args.validation_images.split(args.validation_prompt_separator)
for validation_image, validation_prompt in zip(validation_images, validation_prompts):
pipeline_args = {
"image": load_image(validation_image),
"prompt": validation_prompt,
"guidance_scale": args.guidance_scale,
"use_dynamic_cfg": args.use_dynamic_cfg,
"height": args.height,
"width": args.width,
}
video = log_validation(
pipe=pipe,
args=args,
accelerator=accelerator,
pipeline_args=pipeline_args,
epoch=epoch,
is_final_validation=True,
)
validation_outputs.extend(video)
if args.push_to_hub:
validation_prompt = args.validation_prompt or ""
validation_prompt = validation_prompt.split(args.validation_prompt_separator)[0]
save_model_card(
repo_id,
videos=validation_outputs,
base_model=args.pretrained_model_name_or_path,
validation_prompt=validation_prompt,
repo_folder=args.output_dir,
fps=args.fps,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = get_args()
main(args)
examples/cogvideo/train_cogvideox_lora.py
View file @ 2ffbb88f
...@@ -25,7 +25,7 @@ import numpy as np ...@@ -25,7 +25,7 @@ import numpy as np
import torch import torch
import torchvision.transforms as TT import torchvision.transforms as TT
import transformers import transformers
from accelerate import Accelerator from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder from huggingface_hub import create_repo, upload_folder
...@@ -922,7 +922,7 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False): ...@@ -922,7 +922,7 @@ def get_optimizer(args, params_to_optimize, use_deepspeed: bool = False):
) )
args.optimizer = "adamw" args.optimizer = "adamw"
if args.use_8bit_adam and not (args.optimizer.lower() not in ["adam", "adamw"]): if args.use_8bit_adam and args.optimizer.lower() not in ["adam", "adamw"]:
logger.warning( logger.warning(
f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was " f"use_8bit_adam is ignored when optimizer is not set to 'Adam' or 'AdamW'. Optimizer was "
f"set to {args.optimizer.lower()}" f"set to {args.optimizer.lower()}"
...@@ -1211,7 +1211,7 @@ def main(args): ...@@ -1211,7 +1211,7 @@ def main(args):
) )
use_deepspeed_scheduler = ( use_deepspeed_scheduler = (
accelerator.state.deepspeed_plugin is not None accelerator.state.deepspeed_plugin is not None
and "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config and "scheduler" in accelerator.state.deepspeed_plugin.deepspeed_config
) )
optimizer = get_optimizer(args, params_to_optimize, use_deepspeed=use_deepspeed_optimizer) optimizer = get_optimizer(args, params_to_optimize, use_deepspeed=use_deepspeed_optimizer)
...@@ -1255,6 +1255,7 @@ def main(args): ...@@ -1255,6 +1255,7 @@ def main(args):
prompts = [example["instance_prompt"] for example in examples] prompts = [example["instance_prompt"] for example in examples]
videos = torch.cat(videos) videos = torch.cat(videos)
videos = videos.permute(0, 2, 1, 3, 4)
videos = videos.to(memory_format=torch.contiguous_format).float() videos = videos.to(memory_format=torch.contiguous_format).float()
return { return {
...@@ -1376,7 +1377,7 @@ def main(args): ...@@ -1376,7 +1377,7 @@ def main(args):
models_to_accumulate = [transformer] models_to_accumulate = [transformer]
with accelerator.accumulate(models_to_accumulate): with accelerator.accumulate(models_to_accumulate):
model_input = batch["videos"].permute(0, 2, 1, 3, 4).to(dtype=weight_dtype) # [B, F, C, H, W] model_input = batch["videos"].to(dtype=weight_dtype) # [B, F, C, H, W]
prompts = batch["prompts"] prompts = batch["prompts"]
# encode prompts # encode prompts
...@@ -1455,7 +1456,7 @@ def main(args): ...@@ -1455,7 +1456,7 @@ def main(args):
progress_bar.update(1) progress_bar.update(1)
global_step += 1 global_step += 1
if accelerator.is_main_process: if accelerator.is_main_process or accelerator.distributed_type == DistributedType.DEEPSPEED:
if global_step % args.checkpointing_steps == 0: if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit` # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None: if args.checkpoints_total_limit is not None:
...@@ -1495,7 +1496,6 @@ def main(args): ...@@ -1495,7 +1496,6 @@ def main(args):
args.pretrained_model_name_or_path, args.pretrained_model_name_or_path,
transformer=unwrap_model(transformer), transformer=unwrap_model(transformer),
text_encoder=unwrap_model(text_encoder), text_encoder=unwrap_model(text_encoder),
vae=unwrap_model(vae),
scheduler=scheduler, scheduler=scheduler,
revision=args.revision, revision=args.revision,
variant=args.variant, variant=args.variant,
...@@ -1539,6 +1539,10 @@ def main(args): ...@@ -1539,6 +1539,10 @@ def main(args):
transformer_lora_layers=transformer_lora_layers, transformer_lora_layers=transformer_lora_layers,
) )
# Cleanup trained models to save memory
del transformer
free_memory()
# Final test inference # Final test inference
pipe = CogVideoXPipeline.from_pretrained( pipe = CogVideoXPipeline.from_pretrained(
args.pretrained_model_name_or_path, args.pretrained_model_name_or_path,
......
src/diffusers/pipelines/cogvideo/pipeline_cogvideox_image2video.py
View file @ 2ffbb88f
...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
import inspect import inspect
import math import math
from typing import Callable, Dict, List, Optional, Tuple, Union from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import PIL import PIL
import torch import torch
...@@ -23,6 +23,7 @@ from transformers import T5EncoderModel, T5Tokenizer ...@@ -23,6 +23,7 @@ from transformers import T5EncoderModel, T5Tokenizer
from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...callbacks import MultiPipelineCallbacks, PipelineCallback
from ...image_processor import PipelineImageInput from ...image_processor import PipelineImageInput
from ...loaders import CogVideoXLoraLoaderMixin
from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel from ...models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
from ...models.embeddings import get_3d_rotary_pos_embed from ...models.embeddings import get_3d_rotary_pos_embed
from ...pipelines.pipeline_utils import DiffusionPipeline from ...pipelines.pipeline_utils import DiffusionPipeline
...@@ -152,7 +153,7 @@ def retrieve_latents( ...@@ -152,7 +153,7 @@ def retrieve_latents(
raise AttributeError("Could not access latents of provided encoder_output") raise AttributeError("Could not access latents of provided encoder_output")
class CogVideoXImageToVideoPipeline(DiffusionPipeline): class CogVideoXImageToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
r""" r"""
Pipeline for image-to-video generation using CogVideoX. Pipeline for image-to-video generation using CogVideoX.
...@@ -546,6 +547,10 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline): ...@@ -546,6 +547,10 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline):
def num_timesteps(self): def num_timesteps(self):
return self._num_timesteps return self._num_timesteps
@property
def attention_kwargs(self):
return self._attention_kwargs
@property @property
def interrupt(self): def interrupt(self):
return self._interrupt return self._interrupt
...@@ -572,6 +577,7 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline): ...@@ -572,6 +577,7 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline):
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: str = "pil", output_type: str = "pil",
return_dict: bool = True, return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[ callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None, ] = None,
...@@ -635,6 +641,10 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline): ...@@ -635,6 +641,10 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline):
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
of a plain tuple. of a plain tuple.
attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end (`Callable`, *optional*): callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
...@@ -679,6 +689,7 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline): ...@@ -679,6 +689,7 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline):
negative_prompt_embeds=negative_prompt_embeds, negative_prompt_embeds=negative_prompt_embeds,
) )
self._guidance_scale = guidance_scale self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._interrupt = False self._interrupt = False
# 2. Default call parameters # 2. Default call parameters
...@@ -768,6 +779,7 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline): ...@@ -768,6 +779,7 @@ class CogVideoXImageToVideoPipeline(DiffusionPipeline):
encoder_hidden_states=prompt_embeds, encoder_hidden_states=prompt_embeds,
timestep=timestep, timestep=timestep,
image_rotary_emb=image_rotary_emb, image_rotary_emb=image_rotary_emb,
attention_kwargs=attention_kwargs,
return_dict=False, return_dict=False,
)[0] )[0]
noise_pred = noise_pred.float() noise_pred = noise_pred.float()
......
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