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# this file is only for batch size search and is not used for training
# Define dataset
dataset = dict(
type="VariableVideoTextDataset",
data_path=None,
num_frames=None,
frame_interval=3,
image_size=(None, None),
transform_name="resize_crop",
)
# bucket config format:
# 1. { resolution: {num_frames: (prob, batch_size)} }, in this case batch_size is ignored when searching
# 2. { resolution: {num_frames: (prob, (max_batch_size, ))} }, batch_size is searched in the range [batch_size_start, max_batch_size), batch_size_start is configured via CLI
# 3. { resolution: {num_frames: (prob, (min_batch_size, max_batch_size))} }, batch_size is searched in the range [min_batch_size, max_batch_size)
# 4. { resolution: {num_frames: (prob, (min_batch_size, max_batch_size, step_size))} }, batch_size is searched in the range [min_batch_size, max_batch_size) with step_size (grid search)
# 5. { resolution: {num_frames: (0.0, None)} }, this bucket will not be used
bucket_config = {
# == manual search ==
# "240p": {128: (1.0, 2)}, # 4.28s/it
# "240p": {64: (1.0, 4)},
# "240p": {32: (1.0, 8)}, # 4.6s/it
# "240p": {16: (1.0, 16)}, # 4.6s/it
# "480p": {16: (1.0, 4)}, # 4.6s/it
# "720p": {16: (1.0, 2)}, # 5.89s/it
# "256": {1: (1.0, 256)}, # 4.5s/it
# "512": {1: (1.0, 96)}, # 4.7s/it
# "512": {1: (1.0, 128)}, # 6.3s/it
# "480p": {1: (1.0, 50)}, # 4.0s/it
# "1024": {1: (1.0, 32)}, # 6.8s/it
# "1024": {1: (1.0, 20)}, # 4.3s/it
# "1080p": {1: (1.0, 16)}, # 8.6s/it
# "1080p": {1: (1.0, 8)}, # 4.4s/it
# == stage 2 ==
# "240p": {
# 16: (1.0, (2, 32)),
# 32: (1.0, (2, 16)),
# 64: (1.0, (2, 8)),
# 128: (1.0, (2, 6)),
# },
# "256": {1: (1.0, (128, 300))},
# "512": {1: (0.5, (64, 128))},
# "480p": {1: (0.4, (32, 128)), 16: (0.4, (2, 32)), 32: (0.0, None)},
# "720p": {16: (0.1, (2, 16)), 32: (0.0, None)}, # No examples now
# "1024": {1: (0.3, (8, 64))},
# "1080p": {1: (0.3, (2, 32))},
# == stage 3 ==
"720p": {1: (20, 40), 32: (0.5, (2, 4)), 64: (0.5, (1, 1))},
}
# Define acceleration
num_workers = 4
num_bucket_build_workers = 16
dtype = "bf16"
grad_checkpoint = True
plugin = "zero2"
sp_size = 1
# Define model
model = dict(
type="STDiT2-XL/2",
from_pretrained=None,
input_sq_size=512, # pretrained model is trained on 512x512
qk_norm=True,
qk_norm_legacy=True,
enable_flash_attn=True,
enable_layernorm_kernel=True,
)
vae = dict(
type="VideoAutoencoderKL",
from_pretrained="stabilityai/sd-vae-ft-ema",
micro_batch_size=4,
local_files_only=True,
)
text_encoder = dict(
type="t5",
from_pretrained="DeepFloyd/t5-v1_1-xxl",
model_max_length=200,
shardformer=True,
local_files_only=True,
)
scheduler = dict(
type="iddpm",
timestep_respacing="",
)
# Others
seed = 42
outputs = "outputs"
wandb = False
epochs = 1000
log_every = 10
ckpt_every = 1000
load = None
batch_size = None
lr = 2e-5
grad_clip = 1.0
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