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# Define dataset
# dataset = dict(
# type="VariableVideoTextDataset",
# data_path=None,
# num_frames=None,
# frame_interval=3,
# image_size=(None, None),
# transform_name="resize_crop",
# )
dataset = dict(
type="VideoTextDataset",
data_path=None,
num_frames=1,
frame_interval=1,
image_size=(256, 256),
transform_name="center",
)
bucket_config = { # 6s/it
"256": {1: (1.0, 256)},
"512": {1: (1.0, 80)},
"480p": {1: (1.0, 52)},
"1024": {1: (1.0, 20)},
"1080p": {1: (1.0, 8)},
}
# Define acceleration
num_workers = 16
dtype = "bf16"
grad_checkpoint = True
plugin = "zero2"
sp_size = 1
# Define model
# model = dict(
# type="DiT-XL/2",
# from_pretrained="/home/zhaowangbo/wangbo/PixArt-alpha/pretrained_models/PixArt-XL-2-512x512.pth",
# # input_sq_size=512, # pretrained model is trained on 512x512
# enable_flash_attn=True,
# enable_layernorm_kernel=True,
# )
model = dict(
type="PixArt-XL/2",
space_scale=1.0,
time_scale=1.0,
no_temporal_pos_emb=True,
from_pretrained="PixArt-XL-2-512x512.pth",
enable_flash_attn=True,
enable_layernorm_kernel=True,
)
# model = dict(
# type="DiT-XL/2",
# # space_scale=1.0,
# # time_scale=1.0,
# no_temporal_pos_emb=True,
# # from_pretrained="PixArt-XL-2-512x512.pth",
# from_pretrained="/home/zhaowangbo/wangbo/PixArt-alpha/pretrained_models/PixArt-XL-2-512x512.pth",
# enable_flash_attn=True,
# enable_layernorm_kernel=True,
# )
vae = dict(
type="VideoAutoencoderKL",
from_pretrained="stabilityai/sd-vae-ft-ema",
micro_batch_size=4,
)
text_encoder = dict(
type="t5",
from_pretrained="DeepFloyd/t5-v1_1-xxl",
model_max_length=200,
shardformer=True,
)
scheduler = dict(
type="rflow",
# timestep_respacing="",
)
# Others
seed = 42
outputs = "outputs"
wandb = False
epochs = 10
log_every = 10
ckpt_every = 500
load = None
batch_size = 100 # only for logging
lr = 2e-5
grad_clip = 1.0
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