finetrainers 🧪

cogvideox-factory was renamed to finetrainers. If you're looking to train CogVideoX or Mochi with the legacy training scripts, please refer to this README instead. Everything in the training/ directory will be eventually moved and supported under finetrainers.

FineTrainers is a work-in-progress library to support training of video models. The first priority is to support lora training for all models in Diffusers, and eventually other methods like controlnets, control-loras, distillation, etc.

CogVideoX-LoRA.mp4

News

• 🔥 2024-12-20: Support for LoRA finetuning of Hunyuan Video added! We would like to thank @SHYuanBest for his work on a training script here.
• 🔥 2024-12-18: Support for LoRA finetuning of LTX Video added!

Quickstart

Clone the repository and make sure the requirements are installed: pip install -r requirements.txt and install diffusers from source by pip install git+https://github.com/huggingface/diffusers.

Then download a dataset:

# install `huggingface_hub`
huggingface-cli download \
  --repo-type dataset Wild-Heart/Disney-VideoGeneration-Dataset \
  --local-dir video-dataset-disney

Then launch LoRA fine-tuning. For CogVideoX and Mochi, refer to this and this.

LTX Video

Training:

#!/bin/bash

# export TORCH_LOGS="+dynamo,recompiles,graph_breaks"
# export TORCHDYNAMO_VERBOSE=1
export WANDB_MODE="offline"
export NCCL_P2P_DISABLE=1
export TORCH_NCCL_ENABLE_MONITORING=0
export FINETRAINERS_LOG_LEVEL=DEBUG

GPU_IDS="0,1"

DATA_ROOT="/raid/aryan/video-dataset-disney"
CAPTION_COLUMN="prompts.txt"
VIDEO_COLUMN="videos.txt"
OUTPUT_DIR="/path/to/output/directory/ltx-video/ltxv_disney"

# Model arguments
model_cmd="--model_name ltx_video \
  --pretrained_model_name_or_path Lightricks/LTX-Video"

# Dataset arguments
dataset_cmd="--data_root $DATA_ROOT \
  --video_column $VIDEO_COLUMN \
  --caption_column $CAPTION_COLUMN \
  --id_token BW_STYLE \
  --video_resolution_buckets 49x512x768 \
  --caption_dropout_p 0.05"

# Dataloader arguments
dataloader_cmd="--dataloader_num_workers 0"

# Diffusion arguments
diffusion_cmd="--flow_resolution_shifting"

# Training arguments
training_cmd="--training_type lora \
  --seed 42 \
  --mixed_precision bf16 \
  --batch_size 1 \
  --train_steps 1200 \
  --rank 128 \
  --lora_alpha 128 \
  --target_modules to_q to_k to_v to_out.0 \
  --gradient_accumulation_steps 1 \
  --gradient_checkpointing \
  --checkpointing_steps 500 \
  --checkpointing_limit 2 \
  --enable_slicing \
  --enable_tiling"

# Optimizer arguments
optimizer_cmd="--optimizer adamw \
  --lr 3e-5 \
  --lr_scheduler constant_with_warmup \
  --lr_warmup_steps 100 \
  --lr_num_cycles 1 \
  --beta1 0.9 \
  --beta2 0.95 \
  --weight_decay 1e-4 \
  --epsilon 1e-8 \
  --max_grad_norm 1.0"

# Validation arguments
validation_cmd="--validation_prompts \"afkx A black and white animated scene unfolds with an anthropomorphic goat surrounded by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The goat then engages with Mickey, who bends down to converse or react. The dynamics shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a minimalistic background. The scene captures the evolving relationship between the two characters in a whimsical, animated setting, emphasizing their interactions and emotions.@@@49x512x768:::A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage@@@49x512x768\" \
  --num_validation_videos 1 \
  --validation_steps 100"

# Miscellaneous arguments
miscellaneous_cmd="--tracker_name finetrainers-ltxv \
  --output_dir $OUTPUT_DIR \
  --nccl_timeout 1800 \
  --report_to wandb"

cmd="accelerate launch --config_file accelerate_configs/uncompiled_2.yaml --gpu_ids $GPU_IDS train.py \
  $model_cmd \
  $dataset_cmd \
  $dataloader_cmd \
  $diffusion_cmd \
  $training_cmd \
  $optimizer_cmd \
  $validation_cmd \
  $miscellaneous_cmd"

echo "Running command: $cmd"
eval $cmd
echo -ne "-------------------- Finished executing script --------------------\n\n"

Inference:

Assuming your LoRA is saved and pushed to the HF Hub, and named my-awesome-name/my-awesome-lora, we can now use the finetuned model for inference:

import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video

pipe = LTXPipeline.from_pretrained(
    "Lightricks/LTX-Video", torch_dtype=torch.bfloat16
).to("cuda")
+ pipe.load_lora_weights("my-awesome-name/my-awesome-lora", adapter_name="ltxv-lora")
+ pipe.set_adapters(["ltxv-lora"], [0.75])

video = pipe("<my-awesome-prompt>").frames[0]
export_to_video(video, "output.mp4", fps=8)

Memory Usage

LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, 49x512x768 resolution, without precomputation:

Training configuration: {
    "trainable parameters": 117440512,
    "total samples": 69,
    "train epochs": 1,
    "train steps": 10,
    "batches per device": 1,
    "total batches observed per epoch": 69,
    "train batch size": 1,
    "gradient accumulation steps": 1
}

stage	memory_allocated	max_memory_reserved
before training start	13.486	13.879
before validation start	14.146	17.623
after validation end	14.146	17.623
after epoch 1	14.146	17.623
after training end	4.461	17.623

Note: requires about 18 GB of VRAM without precomputation.

LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, 49x512x768 resolution, with precomputation:

Training configuration: {
    "trainable parameters": 117440512,
    "total samples": 1,
    "train epochs": 10,
    "train steps": 10,
    "batches per device": 1,
    "total batches observed per epoch": 1,
    "train batch size": 1,
    "gradient accumulation steps": 1
}

stage	memory_allocated	max_memory_reserved
after precomputing conditions	8.88	8.920
after precomputing latents	9.684	11.613
before training start	3.809	10.010
after epoch 1	4.26	10.916
before validation start	4.26	10.916
after validation end	13.924	17.262
after training end	4.26	14.314

Note: requires about 17.5 GB of VRAM with precomputation. If validation is not performed, the memory usage is reduced to 11 GB.

Hunyuan Video

Training:

#!/bin/bash

# export TORCH_LOGS="+dynamo,recompiles,graph_breaks"
# export TORCHDYNAMO_VERBOSE=1
export WANDB_MODE="offline"
export NCCL_P2P_DISABLE=1
export TORCH_NCCL_ENABLE_MONITORING=0
export FINETRAINERS_LOG_LEVEL=DEBUG

GPU_IDS="0,1,2,3,4,5,6,7"

DATA_ROOT="/path/to/dataset"
CAPTION_COLUMN="prompts.txt"
VIDEO_COLUMN="videos.txt"
OUTPUT_DIR="/path/to/models/hunyuan-video/hunyuan-video-loras/hunyuan-video_cakify_500_3e-5_constant_with_warmup"

# Model arguments
model_cmd="--model_name hunyuan_video \
  --pretrained_model_name_or_path hunyuanvideo-community/HunyuanVideo"

# Dataset arguments
dataset_cmd="--data_root $DATA_ROOT \
  --video_column $VIDEO_COLUMN \
  --caption_column $CAPTION_COLUMN \
  --id_token afkx \
  --video_resolution_buckets 17x512x768 49x512x768 61x512x768 129x512x768 \
  --caption_dropout_p 0.05"

# Dataloader arguments
dataloader_cmd="--dataloader_num_workers 0"

# Diffusion arguments
diffusion_cmd=""

# Training arguments
training_cmd="--training_type lora \
  --seed 42 \
  --mixed_precision bf16 \
  --batch_size 1 \
  --train_steps 500 \
  --rank 128 \
  --lora_alpha 128 \
  --target_modules to_q to_k to_v to_out.0 \
  --gradient_accumulation_steps 1 \
  --gradient_checkpointing \
  --checkpointing_steps 500 \
  --checkpointing_limit 2 \
  --enable_slicing \
  --enable_tiling"

# Optimizer arguments
optimizer_cmd="--optimizer adamw \
  --lr 2e-5 \
  --lr_scheduler constant_with_warmup \
  --lr_warmup_steps 100 \
  --lr_num_cycles 1 \
  --beta1 0.9 \
  --beta2 0.95 \
  --weight_decay 1e-4 \
  --epsilon 1e-8 \
  --max_grad_norm 1.0"

# Validation arguments
validation_cmd="--validation_prompts \"afkx A baker carefully cuts a green bell pepper cake on a white plate against a bright yellow background, followed by a strawberry cake with a similar slice of cake being cut before the interior of the bell pepper cake is revealed with the surrounding cake-to-object sequence.@@@49x512x768:::afkx A cake shaped like a Nutella container is carefully sliced, revealing a light interior, amidst a Nutella-themed setup, showcasing deliberate cutting and preserved details for an appetizing dessert presentation on a white base with accompanying jello and cutlery, highlighting culinary skills and creative cake designs.@@@49x512x768:::afkx A cake shaped like a Nutella container is carefully sliced, revealing a light interior, amidst a Nutella-themed setup, showcasing deliberate cutting and preserved details for an appetizing dessert presentation on a white base with accompanying jello and cutlery, highlighting culinary skills and creative cake designs.@@@61x512x768:::afkx A vibrant orange cake disguised as a Nike packaging box sits on a dark surface, meticulous in its detail and design, complete with a white swoosh and 'NIKE' logo. A person's hands, holding a knife, hover over the cake, ready to make a precise cut, amidst a simple and clean background.@@@61x512x768:::afkx A vibrant orange cake disguised as a Nike packaging box sits on a dark surface, meticulous in its detail and design, complete with a white swoosh and 'NIKE' logo. A person's hands, holding a knife, hover over the cake, ready to make a precise cut, amidst a simple and clean background.@@@97x512x768:::afkx A vibrant orange cake disguised as a Nike packaging box sits on a dark surface, meticulous in its detail and design, complete with a white swoosh and 'NIKE' logo. A person's hands, holding a knife, hover over the cake, ready to make a precise cut, amidst a simple and clean background.@@@129x512x768:::A person with gloved hands carefully cuts a cake shaped like a Skittles bottle, beginning with a precise incision at the lid, followed by careful sequential cuts around the neck, eventually detaching the lid from the body, revealing the chocolate interior of the cake while showcasing the layered design's detail.@@@61x512x768:::afkx A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage@@@61x512x768\" \
  --num_validation_videos 1 \
  --validation_steps 100"

# Miscellaneous arguments
miscellaneous_cmd="--tracker_name finetrainers-hunyuan-video \
  --output_dir $OUTPUT_DIR \
  --nccl_timeout 1800 \
  --report_to wandb"

cmd="accelerate launch --config_file accelerate_configs/uncompiled_8.yaml --gpu_ids $GPU_IDS train.py \
  $model_cmd \
  $dataset_cmd \
  $dataloader_cmd \
  $diffusion_cmd \
  $training_cmd \
  $optimizer_cmd \
  $validation_cmd \
  $miscellaneous_cmd"

echo "Running command: $cmd"
eval $cmd
echo -ne "-------------------- Finished executing script --------------------\n\n"

Inference:

Assuming your LoRA is saved and pushed to the HF Hub, and named my-awesome-name/my-awesome-lora, we can now use the finetuned model for inference:

import torch
from diffusers import HunyuanVideoPipeline

import torch
from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
from diffusers.utils import export_to_video

model_id = "hunyuanvideo-community/HunyuanVideo"
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
    model_id, subfolder="transformer", torch_dtype=torch.bfloat16
)
pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.float16)
pipe.load_lora_weights("my-awesome-name/my-awesome-lora", adapter_name="hunyuanvideo-lora")
pipe.set_adapters(["hunyuanvideo-lora"], [0.6])
pipe.vae.enable_tiling()
pipe.to("cuda")

output = pipe(
    prompt="A cat walks on the grass, realistic",
    height=320,
    width=512,
    num_frames=61,
    num_inference_steps=30,
).frames[0]
export_to_video(output, "output.mp4", fps=15)

Memory Usage

LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, 49x512x768 resolutions, without precomputation:

Training configuration: {
    "trainable parameters": 163577856,
    "total samples": 69,
    "train epochs": 1,
    "train steps": 10,
    "batches per device": 1,
    "total batches observed per epoch": 69,
    "train batch size": 1,
    "gradient accumulation steps": 1
}

stage	memory_allocated	max_memory_reserved
before training start	38.889	39.020
before validation start	39.747	56.266
after validation end	39.748	58.385
after epoch 1	39.748	40.910
after training end	25.288	40.910

Note: requires about 59 GB of VRAM without precomputation.

LoRA with rank 128, batch size 1, gradient checkpointing, optimizer adamw, 49x512x768 resolutions, with precomputation:

Training configuration: {
    "trainable parameters": 163577856,
    "total samples": 1,
    "train epochs": 10,
    "train steps": 10,
    "batches per device": 1,
    "total batches observed per epoch": 1,
    "train batch size": 1,
    "gradient accumulation steps": 1
}

stage	memory_allocated	max_memory_reserved
after precomputing conditions	14.232	14.461
after precomputing latents	14.717	17.244
before training start	24.195	26.039
after epoch 1	24.83	42.387
before validation start	24.842	42.387
after validation end	39.558	46.947
after training end	24.842	41.039

Note: requires about 47 GB of VRAM with precomputation. If validation is not performed, the memory usage is reduced to about 42 GB.

If you would like to use a custom dataset, refer to the dataset preparation guide here.

Note

To lower memory requirements:

• Pass --precompute_conditions when launching training.
• Pass --gradient_checkpointing when launching training.
• Do not perform validation/testing. This saves a significant amount of memory, which can be used to focus solely on training if you're on smaller VRAM GPUs.

Memory requirements

CogVideoX LoRA Finetuning
THUDM/CogVideoX-2b	THUDM/CogVideoX-5b
CogVideoX Full Finetuning
THUDM/CogVideoX-2b	THUDM/CogVideoX-5b

Supported and verified memory optimizations for training include:

• CPUOffloadOptimizer from torchao. You can read about its capabilities and limitations here. In short, it allows you to use the CPU for storing trainable parameters and gradients. This results in the optimizer step happening on the CPU, which requires a fast CPU optimizer, such as torch.optim.AdamW(fused=True) or applying torch.compile on the optimizer step. Additionally, it is recommended not to torch.compile your model for training. Gradient clipping and accumulation is not supported yet either.
• Low-bit optimizers from bitsandbytes. TODO: to test and make torchao ones work
• DeepSpeed Zero2: Since we rely on accelerate, follow this guide to configure your accelerate installation to enable training with DeepSpeed Zero2 optimizations.

Important

The memory requirements are reported after running the training/prepare_dataset.py, which converts the videos and captions to latents and embeddings. During training, we directly load the latents and embeddings, and do not require the VAE or the T5 text encoder. However, if you perform validation/testing, these must be loaded and increase the amount of required memory. Not performing validation/testing saves a significant amount of memory, which can be used to focus solely on training if you're on smaller VRAM GPUs.

If you choose to run validation/testing, you can save some memory on lower VRAM GPUs by specifying --enable_model_cpu_offload.

LoRA finetuning

Note

The memory requirements for image-to-video lora finetuning are similar to that of text-to-video on THUDM/CogVideoX-5b, so it hasn't been reported explicitly.

Additionally, to prepare test images for I2V finetuning, you could either generate them on-the-fly by modifying the script, or extract some frames from your training data using: ffmpeg -i input.mp4 -frames:v 1 frame.png, or provide a URL to a valid and accessible image.

AdamW

Note: Trying to run CogVideoX-5b without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.

With train_batch_size = 1:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	False	12.945	43.764	46.918	24.234
THUDM/CogVideoX-2b	16	True	12.945	12.945	21.121	24.234
THUDM/CogVideoX-2b	64	False	13.035	44.314	47.469	24.469
THUDM/CogVideoX-2b	64	True	13.036	13.035	21.564	24.500
THUDM/CogVideoX-2b	256	False	13.095	45.826	48.990	25.543
THUDM/CogVideoX-2b	256	True	13.094	13.095	22.344	25.537
THUDM/CogVideoX-5b	16	True	19.742	19.742	28.746	38.123
THUDM/CogVideoX-5b	64	True	20.006	20.818	30.338	38.738
THUDM/CogVideoX-5b	256	True	20.771	22.119	31.939	41.537

With train_batch_size = 4:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	True	12.945	21.803	21.814	24.322
THUDM/CogVideoX-2b	64	True	13.035	22.254	22.254	24.572
THUDM/CogVideoX-2b	256	True	13.094	22.020	22.033	25.574
THUDM/CogVideoX-5b	16	True	19.742	46.492	46.492	38.197
THUDM/CogVideoX-5b	64	True	20.006	47.805	47.805	39.365
THUDM/CogVideoX-5b	256	True	20.771	47.268	47.332	41.008

AdamW (8-bit bitsandbytes)

Note: Trying to run CogVideoX-5b without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.

With train_batch_size = 1:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	False	12.945	43.732	46.887	24.195
THUDM/CogVideoX-2b	16	True	12.945	12.945	21.430	24.195
THUDM/CogVideoX-2b	64	False	13.035	44.004	47.158	24.369
THUDM/CogVideoX-2b	64	True	13.035	13.035	21.297	24.357
THUDM/CogVideoX-2b	256	False	13.035	45.291	48.455	24.836
THUDM/CogVideoX-2b	256	True	13.035	13.035	21.625	24.869
THUDM/CogVideoX-5b	16	True	19.742	19.742	28.602	38.049
THUDM/CogVideoX-5b	64	True	20.006	20.818	29.359	38.520
THUDM/CogVideoX-5b	256	True	20.771	21.352	30.727	39.596

With train_batch_size = 4:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	True	12.945	21.734	21.775	24.281
THUDM/CogVideoX-2b	64	True	13.036	21.941	21.941	24.445
THUDM/CogVideoX-2b	256	True	13.094	22.020	22.266	24.943
THUDM/CogVideoX-5b	16	True	19.742	46.320	46.326	38.104
THUDM/CogVideoX-5b	64	True	20.006	46.820	46.820	38.588
THUDM/CogVideoX-5b	256	True	20.771	47.920	47.980	40.002

AdamW + CPUOffloadOptimizer (with gradient offloading)

Note: Trying to run CogVideoX-5b without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.

With train_batch_size = 1:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	False	12.945	43.705	46.859	24.180
THUDM/CogVideoX-2b	16	True	12.945	12.945	21.395	24.180
THUDM/CogVideoX-2b	64	False	13.035	43.916	47.070	24.234
THUDM/CogVideoX-2b	64	True	13.035	13.035	20.887	24.266
THUDM/CogVideoX-2b	256	False	13.095	44.947	48.111	24.607
THUDM/CogVideoX-2b	256	True	13.095	13.095	21.391	24.635
THUDM/CogVideoX-5b	16	True	19.742	19.742	28.533	38.002
THUDM/CogVideoX-5b	64	True	20.006	20.006	29.107	38.785
THUDM/CogVideoX-5b	256	True	20.771	20.771	30.078	39.559

With train_batch_size = 4:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	True	12.945	21.709	21.762	24.254
THUDM/CogVideoX-2b	64	True	13.035	21.844	21.855	24.338
THUDM/CogVideoX-2b	256	True	13.094	22.020	22.031	24.709
THUDM/CogVideoX-5b	16	True	19.742	46.262	46.297	38.400
THUDM/CogVideoX-5b	64	True	20.006	46.561	46.574	38.840
THUDM/CogVideoX-5b	256	True	20.771	47.268	47.332	39.623

DeepSpeed (AdamW + CPU/Parameter offloading)

Note: Results are reported with gradient_checkpointing enabled, running on a 2x A100.

With train_batch_size = 1:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.141	13.141	21.070	24.602
THUDM/CogVideoX-5b	20.170	20.170	28.662	38.957

With train_batch_size = 4:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.141	19.854	20.836	24.709
THUDM/CogVideoX-5b	20.170	40.635	40.699	39.027

Full finetuning

Note

The memory requirements for image-to-video full finetuning are similar to that of text-to-video on THUDM/CogVideoX-5b, so it hasn't been reported explicitly.

Additionally, to prepare test images for I2V finetuning, you could either generate them on-the-fly by modifying the script, or extract some frames from your training data using: ffmpeg -i input.mp4 -frames:v 1 frame.png, or provide a URL to a valid and accessible image.

Note

Trying to run full finetuning without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.

AdamW

With train_batch_size = 1:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	33.934	43.848	37.520
THUDM/CogVideoX-5b	True	30.061	OOM	OOM	OOM

With train_batch_size = 4:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	38.281	48.341	37.544
THUDM/CogVideoX-5b	True	30.061	OOM	OOM	OOM

AdamW (8-bit bitsandbytes)

With train_batch_size = 1:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	16.447	27.555	27.156
THUDM/CogVideoX-5b	True	30.061	52.826	58.570	49.541

With train_batch_size = 4:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	27.930	27.990	27.326
THUDM/CogVideoX-5b	True	16.396	66.648	66.705	48.828

AdamW + CPUOffloadOptimizer (with gradient offloading)

With train_batch_size = 1:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	16.396	26.100	23.832
THUDM/CogVideoX-5b	True	30.061	39.359	48.307	37.947

With train_batch_size = 4:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	27.916	27.975	23.936
THUDM/CogVideoX-5b	True	30.061	66.607	66.668	38.061

DeepSpeed (AdamW + CPU/Parameter offloading)

Note: Results are reported with gradient_checkpointing enabled, running on a 2x A100.

With train_batch_size = 1:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.111	13.111	20.328	23.867
THUDM/CogVideoX-5b	19.762	19.998	27.697	38.018

With train_batch_size = 4:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.111	21.188	21.254	23.869
THUDM/CogVideoX-5b	19.762	43.465	43.531	38.082

Note

• memory_after_validation is indicative of the peak memory required for training. This is because apart from the activations, parameters and gradients stored for training, you also need to load the vae and text encoder in memory and spend some memory to perform inference. In order to reduce total memory required to perform training, one can choose not to perform validation/testing as part of the training script.

• memory_before_validation is the true indicator of the peak memory required for training if you choose to not perform validation/testing.

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