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Merge pull request #599 from lzws/wan-refactor
access wan-vace-14b model and add refernce example code
This commit is contained in:
Zhongjie Duan
GitHub
@@ -134,6 +134,7 @@ model_loader_configs = [
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(None, "70ddad9d3a133785da5ea371aae09504", ["wan_video_dit"], [WanModel], "civitai"),
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(None, "26bde73488a92e64cc20b0a7485b9e5b", ["wan_video_dit"], [WanModel], "civitai"),
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(None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
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(None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
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(None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"),
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(None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"),
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(None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"),
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@@ -1,6 +1,6 @@
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import torch
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from .wan_video_dit import DiTBlock
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from .utils import hash_state_dict_keys
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class VaceWanAttentionBlock(DiTBlock):
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def __init__(self, has_image_input, dim, num_heads, ffn_dim, eps=1e-6, block_id=0):
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@@ -74,4 +74,17 @@ class VaceWanModelDictConverter:
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def from_civitai(self, state_dict):
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state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("vace")}
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return state_dict_
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if hash_state_dict_keys(state_dict_) == '3b2726384e4f64837bdf216eea3f310d': # vace 14B
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config = {
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"vace_layers": (0, 5, 10, 15, 20, 25, 30, 35),
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"vace_in_dim": 96,
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"patch_size": (1, 2, 2),
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"has_image_input": False,
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"dim": 5120,
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"num_heads": 40,
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"ffn_dim": 13824,
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"eps": 1e-06,
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}
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else:
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config = {}
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return state_dict_, config
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@@ -18,8 +18,8 @@
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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|||||||
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|[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|||||||
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|[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)|
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|[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`||||||
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|[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`||||||
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|[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)|||||
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|[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)|||||
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|[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)|
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## 模型推理
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53
examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py
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53
examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py
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@@ -0,0 +1,53 @@
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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from modelscope import dataset_snapshot_download
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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pipe.enable_vram_management()
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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/examples_in_diffsynth",
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local_dir="./",
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allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"]
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)
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# Depth video -> Video
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control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832)
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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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",
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vace_video=control_video,
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seed=1, tiled=True
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)
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save_video(video, "video1_14b.mp4", fps=15, quality=5)
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# Reference image -> Video
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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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",
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vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
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seed=1, tiled=True
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)
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save_video(video, "video2_14b.mp4", fps=15, quality=5)
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# Depth video + Reference image -> Video
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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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",
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vace_video=control_video,
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vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
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seed=1, tiled=True
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)
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save_video(video, "video3_14b.mp4", fps=15, quality=5)
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54
examples/wanvideo/model_inference/Wan2.1-VACE-14B.py
Normal file
54
examples/wanvideo/model_inference/Wan2.1-VACE-14B.py
Normal file
@@ -0,0 +1,54 @@
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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from modelscope import dataset_snapshot_download
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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],
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)
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pipe.enable_vram_management()
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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/examples_in_diffsynth",
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local_dir="./",
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allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"]
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)
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# Depth video -> Video
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control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832)
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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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",
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vace_video=control_video,
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seed=1, tiled=True
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)
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save_video(video, "video1_14b.mp4", fps=15, quality=5)
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# Reference image -> Video
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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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",
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vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
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seed=1, tiled=True
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)
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save_video(video, "video2_14b.mp4", fps=15, quality=5)
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# Depth video + Reference image -> Video
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video = pipe(
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prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。",
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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",
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vace_video=control_video,
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vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)),
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seed=1, tiled=True
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)
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save_video(video, "video3_14b.mp4", fps=15, quality=5)
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