openeuler/lightx2v repository overview

# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci0
# Update the lightx2v image
export IMAGE=openeuler/lightx2v:0.6.0-torch_npu2.9.0-cann9.1.0-python3.12-oe2403sp4
docker run --rm \
--name my-lightx2v \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash

python3 -c "import torch, torch_npu; print('torch=', torch.__version__); print('torch_npu=', torch_npu.__version__); print('is_available:', torch.npu.is_available())"
python3 -c "from importlib.metadata import version; print('lightx2v', version('lightx2v'))"

import os
import time

# LightX2V selects the backend through the PLATFORM environment variable,
# which must be set before importing lightx2v (the image sets it already).
os.environ["PLATFORM"] = "ascend_npu"

from modelscope import snapshot_download

from lightx2v import LightX2VPipeline

model_path = None
for attempt in range(3):
    try:
        model_path = snapshot_download('Wan-AI/Wan2.1-T2V-1.3B')
        break
    except Exception as exc:
        print('download attempt %d/3 failed: %s' % (attempt + 1, exc))
        time.sleep(10)
assert model_path, 'Wan2.1-T2V-1.3B download failed after 3 attempts'

pipe = LightX2VPipeline(
    model_path=model_path,
    model_cls='wan2.1',
    task='t2v',
)
pipe.create_generator(config_json='/opt/LightX2V/configs/platforms/ascend_npu/wan_t2v.json')

prompt = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
save_result_path = "save_results/output_lightx2v_wan_t2v.mp4"

pipe.generate(
    seed=42,
    prompt=prompt,
    negative_prompt=negative_prompt,
    save_result_path=save_result_path,
)
print(f'saved: {save_result_path}')

export HF_ENDPOINT=https://hf-mirror.com
# or
pip install modelscope