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A newer version of the Gradio SDK is available:
5.9.1
Repo & Config Structure
Repo Structure
Open-Sora
├── README.md
├── docs
│ ├── acceleration.md -> Acceleration & Speed benchmark
│ ├── command.md -> Commands for training & inference
│ ├── datasets.md -> Datasets used in this project
│ ├── structure.md -> This file
│ └── report_v1.md -> Report for Open-Sora v1
├── scripts
│ ├── train.py -> diffusion training script
│ └── inference.py -> Report for Open-Sora v1
├── configs -> Configs for training & inference
├── opensora
│ ├── __init__.py
│ ├── registry.py -> Registry helper
│ ├── acceleration -> Acceleration related code
│ ├── dataset -> Dataset related code
│ ├── models
│ │ ├── layers -> Common layers
│ │ ├── vae -> VAE as image encoder
│ │ ├── text_encoder -> Text encoder
│ │ │ ├── classes.py -> Class id encoder (inference only)
│ │ │ ├── clip.py -> CLIP encoder
│ │ │ └── t5.py -> T5 encoder
│ │ ├── dit
│ │ ├── latte
│ │ ├── pixart
│ │ └── stdit -> Our STDiT related code
│ ├── schedulers -> Diffusion shedulers
│ │ ├── iddpm -> IDDPM for training and inference
│ │ └── dpms -> DPM-Solver for fast inference
│ └── utils
└── tools -> Tools for data processing and more
Configs
Our config files follows MMEgine. MMEngine will reads the config file (a .py file) and parse it into a dictionary-like object.
Open-Sora
└── configs -> Configs for training & inference
├── opensora -> STDiT related configs
│ ├── inference
│ │ ├── 16x256x256.py -> Sample videos 16 frames 256x256
│ │ ├── 16x512x512.py -> Sample videos 16 frames 512x512
│ │ └── 64x512x512.py -> Sample videos 64 frames 512x512
│ └── train
│ ├── 16x256x256.py -> Train on videos 16 frames 256x256
│ ├── 16x256x256.py -> Train on videos 16 frames 256x256
│ └── 64x512x512.py -> Train on videos 64 frames 512x512
├── dit -> DiT related configs
│ ├── inference
│ │ ├── 1x256x256-class.py -> Sample images with ckpts from DiT
│ │ ├── 1x256x256.py -> Sample images with clip condition
│ │ └── 16x256x256.py -> Sample videos
│ └── train
│ ├── 1x256x256.py -> Train on images with clip condition
│ └── 16x256x256.py -> Train on videos
├── latte -> Latte related configs
└── pixart -> PixArt related configs
Inference config demos
To change the inference settings, you can directly modify the corresponding config file. Or you can pass arguments to overwrite the config file (config_utils.py). To change sampling prompts, you should modify the .txt file passed to the --prompt_path argument.
--prompt_path ./assets/texts/t2v_samples.txt -> prompt_path
--ckpt-path ./path/to/your/ckpt.pth -> model["from_pretrained"]
The explanation of each field is provided below.
# Define sampling size
num_frames = 64 # number of frames
fps = 24 // 2 # frames per second (divided by 2 for frame_interval=2)
image_size = (512, 512) # image size (height, width)
# Define model
model = dict(
type="STDiT-XL/2", # Select model type (STDiT-XL/2, DiT-XL/2, etc.)
space_scale=1.0, # (Optional) Space positional encoding scale (new height / old height)
time_scale=2 / 3, # (Optional) Time positional encoding scale (new frame_interval / old frame_interval)
enable_flashattn=True, # (Optional) Speed up training and inference with flash attention
enable_layernorm_kernel=True, # (Optional) Speed up training and inference with fused kernel
from_pretrained="PRETRAINED_MODEL", # (Optional) Load from pretrained model
no_temporal_pos_emb=True, # (Optional) Disable temporal positional encoding (for image)
)
vae = dict(
type="VideoAutoencoderKL", # Select VAE type
from_pretrained="stabilityai/sd-vae-ft-ema", # Load from pretrained VAE
micro_batch_size=128, # VAE with micro batch size to save memory
)
text_encoder = dict(
type="t5", # Select text encoder type (t5, clip)
from_pretrained="./pretrained_models/t5_ckpts", # Load from pretrained text encoder
model_max_length=120, # Maximum length of input text
)
scheduler = dict(
type="iddpm", # Select scheduler type (iddpm, dpm-solver)
num_sampling_steps=100, # Number of sampling steps
cfg_scale=7.0, # hyper-parameter for classifier-free diffusion
)
dtype = "fp16" # Computation type (fp16, fp32, bf16)
# Other settings
batch_size = 1 # batch size
seed = 42 # random seed
prompt_path = "./assets/texts/t2v_samples.txt" # path to prompt file
save_dir = "./samples" # path to save samples
Training config demos
# Define sampling size
num_frames = 64
frame_interval = 2 # sample every 2 frames
image_size = (512, 512)
# Define dataset
root = None # root path to the dataset
data_path = "CSV_PATH" # path to the csv file
use_image_transform = False # True if training on images
num_workers = 4 # number of workers for dataloader
# Define acceleration
dtype = "bf16" # Computation type (fp16, bf16)
grad_checkpoint = True # Use gradient checkpointing
plugin = "zero2" # Plugin for distributed training (zero2, zero2-seq)
sp_size = 1 # Sequence parallelism size (1 for no sequence parallelism)
# Define model
model = dict(
type="STDiT-XL/2",
space_scale=1.0,
time_scale=2 / 3,
from_pretrained="YOUR_PRETRAINED_MODEL",
enable_flashattn=True, # Enable flash attention
enable_layernorm_kernel=True, # Enable layernorm kernel
)
vae = dict(
type="VideoAutoencoderKL",
from_pretrained="stabilityai/sd-vae-ft-ema",
micro_batch_size=128,
)
text_encoder = dict(
type="t5",
from_pretrained="./pretrained_models/t5_ckpts",
model_max_length=120,
shardformer=True, # Enable shardformer for T5 acceleration
)
scheduler = dict(
type="iddpm",
timestep_respacing="", # Default 1000 timesteps
)
# Others
seed = 42
outputs = "outputs" # path to save checkpoints
wandb = False # Use wandb for logging
epochs = 1000 # number of epochs (just large enough, kill when satisfied)
log_every = 10
ckpt_every = 250
load = None # path to resume training
batch_size = 4
lr = 2e-5
grad_clip = 1.0 # gradient clipping