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training framework
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Artiprocher
34
examples/wanvideo/model_inference/wan_1.3b_speed_control.py
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34
examples/wanvideo/model_inference/wan_1.3b_speed_control.py
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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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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-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-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-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors", offload_device="cpu"),
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],
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)
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pipe.enable_vram_management()
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# Text-to-video
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video = pipe(
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prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
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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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seed=1, tiled=True,
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motion_bucket_id=0
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)
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save_video(video, "video_slow.mp4", fps=15, quality=5)
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video = pipe(
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prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
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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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seed=1, tiled=True,
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motion_bucket_id=100
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)
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save_video(video, "video_fast.mp4", fps=15, quality=5)
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34
examples/wanvideo/model_inference/wan_1.3b_text_to_video.py
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34
examples/wanvideo/model_inference/wan_1.3b_text_to_video.py
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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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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-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-T2V-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-T2V-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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# Text-to-video
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video = pipe(
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prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
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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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seed=0, tiled=True,
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)
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save_video(video, "video1.mp4", fps=15, quality=5)
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# Video-to-video
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video = VideoData("video1.mp4", height=480, width=832)
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video = pipe(
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prompt="纪实摄影风格画面，一只活泼的小狗戴着黑色墨镜在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，戴着黑色墨镜，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
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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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input_video=video, denoising_strength=0.7,
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seed=1, tiled=True
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)
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save_video(video, "video2.mp4", fps=15, quality=5)
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52
examples/wanvideo/model_inference/wan_1.3b_vace.py
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52
examples/wanvideo/model_inference/wan_1.3b_vace.py
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@@ -0,0 +1,52 @@
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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="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", 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.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.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.mp4", fps=15, quality=5)
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36
examples/wanvideo/model_inference/wan_14b_flf2v.py
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36
examples/wanvideo/model_inference/wan_14b_flf2v.py
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@@ -0,0 +1,36 @@
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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-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.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/first_frame.jpeg", "data/examples/wan/last_frame.jpeg"]
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)
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# First and last frame to video
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video = pipe(
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prompt="写实风格，一个女生手持枯萎的花站在花园中，镜头逐渐拉远，记录下花园的全貌。",
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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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input_image=Image.open("data/examples/wan/first_frame.jpeg").resize((960, 960)),
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end_image=Image.open("data/examples/wan/last_frame.jpeg").resize((960, 960)),
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seed=0, tiled=True,
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height=960, width=960, num_frames=33,
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sigma_shift=16,
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)
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save_video(video, "video.mp4", fps=15, quality=5)
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@@ -0,0 +1,34 @@
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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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|
||||
|
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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-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
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||||
)
|
||||
pipe.enable_vram_management()
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||||
|
||||
dataset_snapshot_download(
|
||||
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
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||||
local_dir="./",
|
||||
allow_file_pattern=f"data/examples/wan/input_image.jpg"
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||||
)
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image = Image.open("data/examples/wan/input_image.jpg")
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||||
|
||||
# Image-to-video
|
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video = pipe(
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prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
|
||||
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",
|
||||
input_image=image,
|
||||
seed=0, tiled=True
|
||||
)
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
@@ -0,0 +1,35 @@
|
||||
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-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
dataset_snapshot_download(
|
||||
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
|
||||
local_dir="./",
|
||||
allow_file_pattern=f"data/examples/wan/input_image.jpg"
|
||||
)
|
||||
image = Image.open("data/examples/wan/input_image.jpg")
|
||||
|
||||
# Image-to-video
|
||||
video = pipe(
|
||||
prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
|
||||
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",
|
||||
input_image=image,
|
||||
seed=0, tiled=True,
|
||||
height=720, width=1280,
|
||||
)
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
24
examples/wanvideo/model_inference/wan_14b_text_to_video.py
Normal file
24
examples/wanvideo/model_inference/wan_14b_text_to_video.py
Normal file
@@ -0,0 +1,24 @@
|
||||
import torch
|
||||
from PIL import Image
|
||||
from diffsynth import save_video, VideoData
|
||||
from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
|
||||
|
||||
|
||||
pipe = WanVideoPipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
# Text-to-video
|
||||
video = pipe(
|
||||
prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。",
|
||||
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",
|
||||
seed=0, tiled=True,
|
||||
)
|
||||
save_video(video, "video1.mp4", fps=15, quality=5)
|
||||
36
examples/wanvideo/model_inference/wan_fun_1.3b_InP.py
Normal file
36
examples/wanvideo/model_inference/wan_fun_1.3b_InP.py
Normal file
@@ -0,0 +1,36 @@
|
||||
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="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
dataset_snapshot_download(
|
||||
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
|
||||
local_dir="./",
|
||||
allow_file_pattern=f"data/examples/wan/input_image.jpg"
|
||||
)
|
||||
image = Image.open("data/examples/wan/input_image.jpg")
|
||||
|
||||
# First and last frame to video
|
||||
video = pipe(
|
||||
prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
|
||||
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",
|
||||
input_image=image,
|
||||
seed=0, tiled=True
|
||||
# You can input `end_image=xxx` to control the last frame of the video.
|
||||
# The model will automatically generate the dynamic content between `input_image` and `end_image`.
|
||||
)
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
34
examples/wanvideo/model_inference/wan_fun_1.3b_control.py
Normal file
34
examples/wanvideo/model_inference/wan_fun_1.3b_control.py
Normal file
@@ -0,0 +1,34 @@
|
||||
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="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
dataset_snapshot_download(
|
||||
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
|
||||
local_dir="./",
|
||||
allow_file_pattern=f"data/examples/wan/control_video.mp4"
|
||||
)
|
||||
|
||||
# Control video
|
||||
control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576)
|
||||
video = pipe(
|
||||
prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。",
|
||||
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",
|
||||
control_video=control_video, height=832, width=576, num_frames=49,
|
||||
seed=1, tiled=True
|
||||
)
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
36
examples/wanvideo/model_inference/wan_fun_14b_InP.py
Normal file
36
examples/wanvideo/model_inference/wan_fun_14b_InP.py
Normal file
@@ -0,0 +1,36 @@
|
||||
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="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
dataset_snapshot_download(
|
||||
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
|
||||
local_dir="./",
|
||||
allow_file_pattern=f"data/examples/wan/input_image.jpg"
|
||||
)
|
||||
image = Image.open("data/examples/wan/input_image.jpg")
|
||||
|
||||
# First and last frame to video
|
||||
video = pipe(
|
||||
prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
|
||||
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",
|
||||
input_image=image,
|
||||
seed=0, tiled=True
|
||||
# You can input `end_image=xxx` to control the last frame of the video.
|
||||
# The model will automatically generate the dynamic content between `input_image` and `end_image`.
|
||||
)
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
34
examples/wanvideo/model_inference/wan_fun_14b_control.py
Normal file
34
examples/wanvideo/model_inference/wan_fun_14b_control.py
Normal file
@@ -0,0 +1,34 @@
|
||||
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="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
dataset_snapshot_download(
|
||||
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
|
||||
local_dir="./",
|
||||
allow_file_pattern=f"data/examples/wan/control_video.mp4"
|
||||
)
|
||||
|
||||
# Control video
|
||||
control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576)
|
||||
video = pipe(
|
||||
prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。",
|
||||
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",
|
||||
control_video=control_video, height=832, width=576, num_frames=49,
|
||||
seed=1, tiled=True
|
||||
)
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
@@ -0,0 +1,36 @@
|
||||
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="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.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/control_video.mp4", "data/examples/wan/reference_image_girl.png"]
|
||||
)
|
||||
|
||||
# Control video
|
||||
control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576)
|
||||
reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832))
|
||||
video = pipe(
|
||||
prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。",
|
||||
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",
|
||||
control_video=control_video, reference_image=reference_image,
|
||||
height=832, width=576, num_frames=49,
|
||||
seed=1, tiled=True
|
||||
)
|
||||
save_video(video, "video.mp4", fps=15, quality=5)
|
||||
@@ -0,0 +1,36 @@
|
||||
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="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.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/control_video.mp4", "data/examples/wan/reference_image_girl.png"]
|
||||
)
|
||||
|
||||
# Control video
|
||||
control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576)
|
||||
reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832))
|
||||
video = pipe(
|
||||
prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。",
|
||||
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",
|
||||
control_video=control_video, reference_image=reference_image,
|
||||
height=832, width=576, num_frames=49,
|
||||
seed=1, tiled=True
|
||||
)
|
||||
save_video(video, "video1.mp4", fps=15, quality=5)
|
||||
Reference in New Issue
Block a user