Merge pull request #317 from THUDM/CogVideoX_dev

Finetune and Readme update

README.md

@@ -277,6 +277,7 @@ pipe.vae.enable_tiling()
 We highly welcome contributions from the community and actively contribute to the open-source community. The following
 works have already been adapted for CogVideoX, and we invite everyone to use them:
 
++ [CogVideoX-Fun](https://github.com/aigc-apps/CogVideoX-Fun): CogVideoX-Fun is a modified pipeline based on the CogVideoX architecture, supporting flexible resolutions and multiple launch methods.
 + [Xorbits Inference](https://github.com/xorbitsai/inference): A powerful and comprehensive distributed inference
   framework, allowing you to easily deploy your own models or the latest cutting-edge open-source models with just one
   click.

README_ja.md

@@ -261,6 +261,7 @@ pipe.vae.enable_tiling()
 
 コミュニティからの貢献を大歓迎し、私たちもオープンソースコミュニティに積極的に貢献しています。以下の作品はすでにCogVideoXに対応しており、ぜひご利用ください：
 
++ [CogVideoX-Fun](https://github.com/aigc-apps/CogVideoX-Fun): CogVideoX-Funは、CogVideoXアーキテクチャを基にした改良パイプラインで、自由な解像度と複数の起動方法をサポートしています。
 + [Xorbits Inference](https://github.com/xorbitsai/inference):
   強力で包括的な分散推論フレームワークであり、ワンクリックで独自のモデルや最新のオープンソースモデルを簡単にデプロイできます。
 + [ComfyUI-CogVideoXWrapper](https://github.com/kijai/ComfyUI-CogVideoXWrapper)

README_zh.md

@@ -248,7 +248,7 @@ pipe.vae.enable_tiling()
 ## 友情链接
 
 我们非常欢迎来自社区的贡献，并积极的贡献开源社区。以下作品已经对CogVideoX进行了适配，欢迎大家使用:
-
++ [CogVideoX-Fun](https://github.com/aigc-apps/CogVideoX-Fun): CogVideoX-Fun是一个基于CogVideoX结构修改后的的pipeline，支持自由的分辨率，多种启动方式。
 + [Xorbits Inference](https://github.com/xorbitsai/inference): 性能强大且功能全面的分布式推理框架，轻松一键部署你自己的模型或内置的前沿开源模型。
 + [ComfyUI-CogVideoXWrapper](https://github.com/kijai/ComfyUI-CogVideoXWrapper) 使用ComfyUI框架，将CogVideoX加入到你的工作流中。
 + [VideoSys](https://github.com/NUS-HPC-AI-Lab/VideoSys): VideoSys 提供了易用且高性能的视频生成基础设施，支持完整的管道，并持续集成最新的模型和技术。

finetune/README.md

@@ -9,9 +9,9 @@ see [here](../sat/README_zh.md). The dataset format is different from this versi
 
 ## Hardware Requirements
 
-+ CogVideoX-2B LoRA: 1 * A100
-+ CogVideoX-2B SFT:  8 * A100
-+ CogVideoX-5B/5B-I2V is not supported yet.
++ CogVideoX-2B / 5B LoRA: 1 * A100 (5B need to use `--use_8bit_adam`)
++ CogVideoX-2B SFT:  8 * A100 (Working)
++ CogVideoX-5B-I2V is not supported yet.
 
 ## Install Dependencies
 
@@ -20,8 +20,7 @@ diffusers branch. Please follow the steps below to install dependencies:
 
 ```shell
 git clone https://github.com/huggingface/diffusers.git
-cd diffusers
-git checkout cogvideox-lora-and-training
+cd diffusers # Now in Main branch
 pip install -e .
 ```
 
@@ -124,13 +123,13 @@ accelerate launch --config_file accelerate_config_machine_single.yaml --multi_gp
 Single GPU fine-tuning:
 
 ```shell
-bash finetune_single_gpu.sh
+bash finetune_single_rank.sh
 ```
 
 Multi-GPU fine-tuning:
 
 ```shell
-bash finetune_multi_gpus_1.sh # Needs to be run on each node
+bash finetune_multi_rank.sh # Needs to be run on each node
 ```
 
 ## Loading the Fine-tuned Model

finetune/README_ja.md

@@ -9,9 +9,9 @@
 
 ## ハードウェア要件
 
-+ CogVideoX-2B LORA: 1 * A100
-+ CogVideoX-2B SFT:  8 * A100
-+ CogVideoX-5B/5B-I2V まだサポートしていません
++ CogVideoX-2B / 5B T2V LORA: 1 * A100  (5B need to use `--use_8bit_adam`)
++ CogVideoX-2B SFT:  8 * A100 （動作確認済み）
++ CogVideoX-5B-I2V まだサポートしていません
 
 ## 依存関係のインストール
 
@@ -19,8 +19,7 @@
 
 ```shell
 git clone https://github.com/huggingface/diffusers.git
-cd diffusers
-git checkout cogvideox-lora-and-training
+cd diffusers # Now in Main branch
 pip install -e .
 ```
 
@@ -120,13 +119,13 @@ accelerate launch --config_file accelerate_config_machine_single.yaml --multi_gp
 単一GPU微調整：
 
 ```shell
-bash finetune_single_gpu.sh
+bash finetune_single_rank.sh
 ```
 
 複数GPU微調整：
 
 ```shell
-bash finetune_multi_gpus_1.sh # 各ノードで実行する必要があります。
+bash finetune_multi_rank.sh # 各ノードで実行する必要があります。
 ```
 
 ## 微調整済みモデルのロード

finetune/README_zh.md

@@ -8,9 +8,9 @@
 
 ## 硬件要求
 
-+ CogVideoX-2B LORA: 1 * A100
-+ CogVideoX-2B SFT:  8 * A100
-+ CogVideoX-5B/5B-I2V 暂未支持
++ CogVideoX-2B / 5B T2V LORA: 1 * A100  (5B need to use `--use_8bit_adam`)
++ CogVideoX-2B SFT:  8 * A100 (制作中)
++ CogVideoX-5B-I2V 暂未支持
 
 ## 安装依赖
 
@@ -18,8 +18,7 @@
 
 ```shell
 git clone https://github.com/huggingface/diffusers.git
-cd diffusers
-git checkout cogvideox-lora-and-training
+cd diffusers # Now in Main branch
 pip install -e .
 ```
 
@@ -150,13 +149,13 @@ accelerate launch --config_file accelerate_config_machine_single.yaml --multi_gp
 单卡微调：
 
 ```shell
-bash finetune_single_gpu.sh
+bash finetune_single_rank.sh
 ```
 
 多卡微调：
 
 ```shell
-bash finetune_multi_gpus_1.sh #需要在每个节点运行
+bash finetune_multi_rank.sh #需要在每个节点运行
 ```
 
 ## 载入微调的模型

finetune/finetune_multi_rank.sh

@@ -9,6 +9,7 @@ export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
 
 accelerate launch --config_file accelerate_config_machine_single.yaml --multi_gpu  --machine_rank 0 \
   train_cogvideox_lora.py \
+  --gradient_checkpointing \
   --pretrained_model_name_or_path $MODEL_PATH \
   --cache_dir $CACHE_PATH \
   --enable_tiling \

finetune/finetune_single_rank.sh

@@ -3,12 +3,17 @@
 export MODEL_PATH="THUDM/CogVideoX-2b"
 export CACHE_PATH="~/.cache"
 export DATASET_PATH="Disney-VideoGeneration-Dataset"
-export OUTPUT_PATH="cogvideox-lora-single-gpu"
+export OUTPUT_PATH="cogvideox-lora-multi-gpu"
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
 export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
 
+
+#  --use_8bit_adam is necessary for CogVideoX-5B-I2V
+# if you are not using wth 8 gus, change `accelerate_config_machine_single.yaml` num_processes as your gpu number
 accelerate launch --config_file accelerate_config_machine_single.yaml --multi_gpu \
   train_cogvideox_lora.py \
+  --gradient_checkpointing \
+  --use_8bit_adam  \
   --pretrained_model_name_or_path $MODEL_PATH \
   --cache_dir $CACHE_PATH \
   --enable_tiling \

finetune/train_cogvideox_lora.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import argparse
-import itertools
 import logging
 import math
 import os
@@ -32,7 +31,7 @@ from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dic
 from torch.utils.data import DataLoader, Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
-from transformers import T5EncoderModel, T5Tokenizer
+from transformers import AutoTokenizer, T5EncoderModel, T5Tokenizer
 
 import diffusers
 from diffusers import AutoencoderKLCogVideoX, CogVideoXDPMScheduler, CogVideoXPipeline, CogVideoXTransformer3DModel
@@ -40,7 +39,6 @@ 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 (
-    _set_state_dict_into_text_encoder,
     cast_training_params,
     clear_objs_and_retain_memory,
 )
@@ -48,6 +46,7 @@ from diffusers.utils import check_min_version, convert_unet_state_dict_to_peft,
 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
 
@@ -239,11 +238,6 @@ def get_args():
         action="store_true",
         help="whether to randomly flip videos horizontally",
     )
-    parser.add_argument(
-        "--train_text_encoder",
-        action="store_true",
-        help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
-    )
     parser.add_argument(
         "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
     )
@@ -296,12 +290,6 @@ def get_args():
         default=1e-4,
         help="Initial learning rate (after the potential warmup period) to use.",
     )
-    parser.add_argument(
-        "--text_encoder_lr",
-        type=float,
-        default=5e-6,
-        help="Text encoder learning rate to use.",
-    )
     parser.add_argument(
         "--scale_lr",
         action="store_true",
@@ -367,9 +355,6 @@ def get_args():
     )
     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_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
-    )
     parser.add_argument(
         "--adam_epsilon",
         type=float,
@@ -423,20 +408,20 @@ def get_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,
+        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__()
 
@@ -555,7 +540,7 @@ class VideoDataset(Dataset):
             import decord
         except ImportError:
             raise ImportError(
-                "The `decord` package is required for loading the video dataset. Install with `pip install dataset`"
+                "The `decord` package is required for loading the video dataset. Install with `pip install decord`"
             )
 
         decord.bridge.set_bridge("torch")
@@ -602,13 +587,12 @@ class VideoDataset(Dataset):
 
 
 def save_model_card(
-        repo_id: str,
-        videos=None,
-        base_model: str = None,
-        train_text_encoder=False,
-        validation_prompt=None,
-        repo_folder=None,
-        fps=8,
+    repo_id: str,
+    videos=None,
+    base_model: str = None,
+    validation_prompt=None,
+    repo_folder=None,
+    fps=8,
 ):
     widget_dict = []
     if videos is not None:
@@ -627,9 +611,9 @@ def save_model_card(
 
 These are {repo_id} LoRA weights for {base_model}.
 
-The weights were trained using the [CogVideoX Diffusers trainer](TODO).
+The weights were trained using the [CogVideoX Diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/cogvideo/train_cogvideox_lora.py).
 
-Was LoRA for the text encoder enabled? {train_text_encoder}.
+Was LoRA for the text encoder enabled? No.
 
 ## Download model
 
@@ -642,8 +626,15 @@ from diffusers import CogVideoXPipeline
 import torch
 
 pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cuda")
-pipe.load_lora_weights("{repo_id}", weight_name="pytorch_lora_weights.safetensors")
-video = pipe("{validation_prompt}").frames[0]
+pipe.load_lora_weights("{repo_id}", weight_name="pytorch_lora_weights.safetensors", adapter_name=["cogvideox-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-lora"], [32 / 64])
+
+video = pipe("{validation_prompt}", guidance_scale=6, use_dynamic_cfg=True).frames[0]
 ```
 
 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)
@@ -676,12 +667,12 @@ Please adhere to the licensing terms as described [here](https://huggingface.co/
 
 
 def log_validation(
-        pipe,
-        args,
-        accelerator,
-        pipeline_args,
-        epoch,
-        is_final_validation: bool = False,
+    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']}."
@@ -741,14 +732,14 @@ def log_validation(
 
 
 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,
+    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)
@@ -779,14 +770,14 @@ def _get_t5_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,
+    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(
@@ -802,13 +793,16 @@ def encode_prompt(
     return prompt_embeds
 
 
-def compute_prompt_embeddings(tokenizer, text_encoder, prompt, device, dtype, requires_grad: bool = False):
+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,
         )
@@ -819,6 +813,7 @@ def compute_prompt_embeddings(tokenizer, text_encoder, prompt, device, dtype, re
                 text_encoder,
                 prompt,
                 num_videos_per_prompt=1,
+                max_sequence_length=max_sequence_length,
                 device=device,
                 dtype=dtype,
             )
@@ -826,15 +821,15 @@ def compute_prompt_embeddings(tokenizer, text_encoder, prompt, device, dtype, re
 
 
 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,
+    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)
@@ -854,10 +849,22 @@ def prepare_rotary_positional_embeddings(
     return freqs_cos, freqs_sin
 
 
-def get_optimizer(args, params_to_optimize):
+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 ["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"
         )
@@ -907,14 +914,6 @@ def get_optimizer(args, params_to_optimize):
             logger.warning(
                 "Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
             )
-        if args.train_text_encoder and args.text_encoder_lr:
-            logger.warning(
-                f"Learning rates were provided both for the transformer and the text encoder - e.g. text_encoder_lr:"
-                f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. "
-                f"When using prodigy only learning_rate is used as the initial learning rate."
-            )
-            # Changes the learning rate of text_encoder_parameters to be --learning_rate
-            params_to_optimize[1]["lr"] = args.learning_rate
 
         optimizer = optimizer_class(
             params_to_optimize,
@@ -994,7 +993,7 @@ def main(args):
             ).repo_id
 
     # Prepare models and scheduler
-    tokenizer = T5Tokenizer.from_pretrained(
+    tokenizer = AutoTokenizer.from_pretrained(
         args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
     )
 
@@ -1035,13 +1034,13 @@ def main(args):
     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"]
+            "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"]
+            "bf16" in accelerator.state.deepspeed_plugin.deepspeed_config
+            and accelerator.state.deepspeed_plugin.deepspeed_config["bf16"]["enabled"]
         ):
             weight_dtype = torch.float16
     else:
@@ -1062,8 +1061,6 @@ def main(args):
 
     if args.gradient_checkpointing:
         transformer.enable_gradient_checkpointing()
-        if args.train_text_encoder:
-            text_encoder.gradient_checkpointing_enable()
 
     # now we will add new LoRA weights to the attention layers
     transformer_lora_config = LoraConfig(
@@ -1073,14 +1070,6 @@ def main(args):
         target_modules=["to_k", "to_q", "to_v", "to_out.0"],
     )
     transformer.add_adapter(transformer_lora_config)
-    if args.train_text_encoder:
-        text_lora_config = LoraConfig(
-            r=args.rank,
-            lora_alpha=args.lora_alpha,
-            init_lora_weights=True,
-            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
-        )
-        text_encoder.add_adapter(text_lora_config)
 
     def unwrap_model(model):
         model = accelerator.unwrap_model(model)
@@ -1091,13 +1080,10 @@ def main(args):
     def save_model_hook(models, weights, output_dir):
         if accelerator.is_main_process:
             transformer_lora_layers_to_save = None
-            text_encoder_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)
-                elif isinstance(model, type(unwrap_model(text_encoder))):
-                    text_encoder_lora_layers_to_save = get_peft_model_state_dict(model)
                 else:
                     raise ValueError(f"unexpected save model: {model.__class__}")
 
@@ -1107,22 +1093,18 @@ def main(args):
             CogVideoXPipeline.save_lora_weights(
                 output_dir,
                 transformer_lora_layers=transformer_lora_layers_to_save,
-                text_encoder_lora_layers=text_encoder_lora_layers_to_save,
             )
 
     def load_model_hook(models, input_dir):
         transformer_ = None
-        text_encoder_ = None
 
         while len(models) > 0:
             model = models.pop()
 
             if isinstance(model, type(unwrap_model(transformer))):
                 transformer_ = model
-            elif isinstance(model, type(unwrap_model(text_encoder))):
-                text_encoder_ = model
             else:
-                raise ValueError(f"unexpected save model: {model.__class__}")
+                raise ValueError(f"Unexpected save model: {model.__class__}")
 
         lora_state_dict = CogVideoXPipeline.lora_state_dict(input_dir)
 
@@ -1139,19 +1121,13 @@ def main(args):
                     f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
                     f" {unexpected_keys}. "
                 )
-        if args.train_text_encoder:
-            # Do we need to call `scale_lora_layers()` here?
-            _set_state_dict_into_text_encoder(lora_state_dict, prefix="text_encoder.", text_encoder=text_encoder_)
 
         # 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":
-            models = [transformer_]
-            if args.train_text_encoder:
-                models.extend([text_encoder_])
             # only upcast trainable parameters (LoRA) into fp32
-            cast_training_params(models)
+            cast_training_params([transformer_])
 
     accelerator.register_save_state_pre_hook(save_model_hook)
     accelerator.register_load_state_pre_hook(load_model_hook)
@@ -1163,38 +1139,30 @@ def main(args):
 
     if args.scale_lr:
         args.learning_rate = (
-                args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+            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":
-        models = [transformer]
-        if args.train_text_encoder:
-            models.extend([text_encoder])
         # only upcast trainable parameters (LoRA) into fp32
-        cast_training_params(models, dtype=torch.float32)
+        cast_training_params([transformer], dtype=torch.float32)
 
     transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
-    if args.train_text_encoder:
-        text_encoder_lora_parameters = list(filter(lambda p: p.requires_grad, text_encoder.parameters()))
 
     # Optimization parameters
     transformer_parameters_with_lr = {"params": transformer_lora_parameters, "lr": args.learning_rate}
-    if args.train_text_encoder:
-        # different learning rate for text encoder and unet
-        text_encoder_parameters_with_lr = {
-            "params": text_encoder_lora_parameters,
-            "weight_decay": args.adam_weight_decay_text_encoder,
-            "lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
-        }
-        params_to_optimize = [
-            transformer_parameters_with_lr,
-            text_encoder_parameters_with_lr,
-        ]
-    else:
-        params_to_optimize = [transformer_parameters_with_lr]
+    params_to_optimize = [transformer_parameters_with_lr]
 
-    optimizer = get_optimizer(args, params_to_optimize)
+    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" not 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(
@@ -1248,35 +1216,30 @@ def main(args):
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
 
-    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,
-    )
+    if use_deepspeed_scheduler:
+        from accelerate.utils import DummyScheduler
 
-    # Prepare everything with our `accelerator`.
-    if args.train_text_encoder:
-        (
-            transformer,
-            text_encoder,
-            optimizer,
-            train_dataloader,
-            lr_scheduler,
-        ) = accelerator.prepare(
-            transformer,
-            text_encoder,
-            optimizer,
-            train_dataloader,
-            lr_scheduler,
+        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:
-        transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            transformer, optimizer, train_dataloader, lr_scheduler
+        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:
@@ -1347,15 +1310,9 @@ def main(args):
 
     for epoch in range(first_epoch, args.num_train_epochs):
         transformer.train()
-        if args.train_text_encoder:
-            text_encoder.train()
-            # set top parameter requires_grad = True for gradient checkpointing works
-            accelerator.unwrap_model(text_encoder).text_model.embeddings.requires_grad_(True)
 
         for step, batch in enumerate(train_dataloader):
             models_to_accumulate = [transformer]
-            if args.train_text_encoder:
-                models_to_accumulate.extend([text_encoder])
 
             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]
@@ -1366,9 +1323,10 @@ def main(args):
                     tokenizer,
                     text_encoder,
                     prompts,
+                    model_config.max_text_seq_length,
                     accelerator.device,
                     weight_dtype,
-                    requires_grad=args.train_text_encoder,
+                    requires_grad=False,
                 )
 
                 # Sample noise that will be added to the latents
@@ -1422,16 +1380,14 @@ def main(args):
                 accelerator.backward(loss)
 
                 if accelerator.sync_gradients:
-                    params_to_clip = (
-                        itertools.chain(transformer.parameters(), text_encoder.parameters())
-                        if args.train_text_encoder
-                        else transformer.parameters()
-                    )
+                    params_to_clip = transformer.parameters()
                     accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
 
-                optimizer.step()
+                if accelerator.state.deepspeed_plugin is None:
+                    optimizer.step()
+                    optimizer.zero_grad()
+
                 lr_scheduler.step()
-                optimizer.zero_grad()
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
@@ -1507,7 +1463,6 @@ def main(args):
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
         transformer = unwrap_model(transformer)
-        # transformer = transformer.to(torch.float32)
         dtype = (
             torch.float16
             if args.mixed_precision == "fp16"
@@ -1518,16 +1473,9 @@ def main(args):
         transformer = transformer.to(dtype)
         transformer_lora_layers = get_peft_model_state_dict(transformer)
 
-        if args.train_text_encoder:
-            text_encoder = unwrap_model(text_encoder)
-            text_encoder_lora_layers = get_peft_model_state_dict(text_encoder.to(dtype))
-        else:
-            text_encoder_lora_layers = None
-
         CogVideoXPipeline.save_lora_weights(
             save_directory=args.output_dir,
             transformer_lora_layers=transformer_lora_layers,
-            text_encoder_lora_layers=text_encoder_lora_layers,
         )
 
         # Final test inference
@@ -1539,6 +1487,11 @@ def main(args):
         )
         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-lora")
@@ -1572,7 +1525,6 @@ def main(args):
                 repo_id,
                 videos=validation_outputs,
                 base_model=args.pretrained_model_name_or_path,
-                train_text_encoder=args.train_text_encoder,
                 validation_prompt=args.validation_prompt,
                 repo_folder=args.output_dir,
                 fps=args.fps,