Open-Sora / configs /dit /inference /1x256x256-class.py
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num_frames = 1
fps = 1
image_size = (256, 256)
# Define model
model = dict(
type="DiT-XL/2",
no_temporal_pos_emb=True,
condition="label_1000",
from_pretrained="DiT-XL-2-256x256.pt",
)
vae = dict(
type="VideoAutoencoderKL",
from_pretrained="stabilityai/sd-vae-ft-ema",
)
text_encoder = dict(
type="classes",
num_classes=1000,
)
scheduler = dict(
type="dpm-solver",
num_sampling_steps=20,
cfg_scale=4.0,
)
dtype = "fp16"
# Others
batch_size = 2
seed = 42
prompt_path = "./assets/texts/imagenet_id.txt"
save_dir = "./outputs/samples/"