access wan-vace-14b model and add refernce example code

This commit is contained in:
lzw478614@alibaba-inc.com
2025-06-09 10:21:38 +08:00
parent 6e977e1181
commit 98016b2a76
5 changed files with 125 additions and 4 deletions

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|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|基础模型|||||||
|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|基础模型|||||||
|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|适配器|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./model_training/full/VACE-Wan2.1-1.3B-Preview.sh)|[code](./model_training/validate_full/VACE-Wan2.1-1.3B-Preview.py)|[code](./model_training/lora/VACE-Wan2.1-1.3B-Preview.sh)|[code](./model_training/validate_lora/VACE-Wan2.1-1.3B-Preview.py)|
|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|适配器|`vace_control_video`, `vace_reference_image`||||||
|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|适配器|`vace_control_video`, `vace_reference_image`||||||
|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|适配器|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B.py)|||||
|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|适配器|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-14B.py)|||||
|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|适配器|`motion_bucket_id`|[code](./model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
## 模型推理

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import torch
from PIL import Image
from diffsynth import save_video, VideoData
from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
from modelscope import dataset_snapshot_download
pipe = WanVideoPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
],
)
pipe.enable_vram_management()
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
local_dir="./",
allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"]
)
# Depth video -> Video
control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832)
video = pipe(
prompt="两只可爱的橘猫戴上拳击手套,站在一个拳击台上搏斗。",
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",
vace_video=control_video,
seed=1, tiled=True
)
save_video(video, "video1_14b.mp4", fps=15, quality=5)
# Reference image -> Video
video = pipe(
prompt="两只可爱的橘猫戴上拳击手套,站在一个拳击台上搏斗。",
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",
vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
seed=1, tiled=True
)
save_video(video, "video2_14b.mp4", fps=15, quality=5)
# Depth video + Reference image -> Video
video = pipe(
prompt="两只可爱的橘猫戴上拳击手套,站在一个拳击台上搏斗。",
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",
vace_video=control_video,
vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
seed=1, tiled=True
)
save_video(video, "video3_14b.mp4", fps=15, quality=5)

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import torch
from PIL import Image
from diffsynth import save_video, VideoData
from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
from modelscope import dataset_snapshot_download
pipe = WanVideoPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
],
)
pipe.enable_vram_management()
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
local_dir="./",
allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"]
)
# Depth video -> Video
control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832)
video = pipe(
prompt="两只可爱的橘猫戴上拳击手套,站在一个拳击台上搏斗。",
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",
vace_video=control_video,
seed=1, tiled=True
)
save_video(video, "video1_14b.mp4", fps=15, quality=5)
# Reference image -> Video
video = pipe(
prompt="两只可爱的橘猫戴上拳击手套,站在一个拳击台上搏斗。",
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",
vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
seed=1, tiled=True
)
save_video(video, "video2_14b.mp4", fps=15, quality=5)
# Depth video + Reference image -> Video
video = pipe(
prompt="两只可爱的橘猫戴上拳击手套,站在一个拳击台上搏斗。",
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",
vace_video=control_video,
vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
seed=1, tiled=True
)
save_video(video, "video3_14b.mp4", fps=15, quality=5)