File size: 1,536 Bytes
3c11df8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | import torch
import json
from diffsynth.utils.data import save_video
from diffsynth.pipelines.lingbot_video import LingBotVideoPipeline, ModelConfig
from modelscope import dataset_snapshot_download
pipe = LingBotVideoPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="transformer/diffusion_pytorch_model.safetensors"),
ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern="*.safetensors"),
ModelConfig(model_id="Robbyant/lingbot-video-dense-1.3b", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
processor_config=ModelConfig(model_id="Qwen/Qwen3-VL-4B-Instruct", origin_file_pattern=""),
)
pipe.load_lora(pipe.dit, './models/train/lingbot-video-dense-1.3b_t2v_split/epoch-4.safetensors', alpha=1)
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
local_dir="data/diffsynth_example_dataset",
allow_file_pattern="lingbot_video/lingbot-video-dense-1.3b_t2v/*",
)
with open("data/diffsynth_example_dataset/lingbot_video/lingbot-video-dense-1.3b_t2v/t2v_example_1.json", "r", encoding="utf-8") as f:
caption = json.load(f)
video = pipe(
prompt=caption,
negative_prompt=pipe.default_negative_prompt,
height=480, width=832, num_frames=81,
num_inference_steps=40, cfg_scale=3.0,
seed=0,
)
save_video(video, 'split_training_lingbot-video-dense-1.3b_t2v.mp4', fps=15, quality=10)
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