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https://github.com/modelscope/DiffSynth-Studio.git
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camera
This commit is contained in:
CD22104
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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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from dchen.camera_compute import process_pose_file
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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="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", 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/control_video.mp4", "data/examples/wan/reference_image_girl.png"]
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)
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# Control video
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control_video = None
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reference_image = None
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control_camera_text = "/mnt/nas2/dchen/Work/add_0609/DiffSynth-Studio/dchen/camera_information.txt"
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input_image = Image.open("/mnt/nas2/dchen/Work/add_0609/DiffSynth-Studio/dchen/7.png")
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sigma_shift = 3
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height = 480
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width = 832
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control_camera_video = process_pose_file(control_camera_text, width, height)
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video = pipe(
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prompt="一个小女孩正在户外玩耍。她穿着一件蓝色的短袖上衣和粉色的短裤，头发扎成一个可爱的辫子。她的脚上没有穿鞋，显得非常自然和随意。她正用一把红色的小铲子在泥土里挖土，似乎在进行某种有趣的活动，可能是种花或是挖掘宝藏。地上有一根长长的水管，可能是用来浇水的。背景是一片草地和一些绿色植物，阳光明媚，整个场景充满了童趣和生机。小女孩专注的表情和认真的动作让人感受到她的快乐和好奇心。",
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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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control_video=control_video, reference_image=reference_image,
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height=height, width=width, num_frames=81,
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seed=1, tiled=True,
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control_camera_video = control_camera_video,
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input_image = input_image,
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sigma_shift = sigma_shift,
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)
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save_video(video, "video.mp4", fps=15, quality=5)
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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="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", 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=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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# First and last frame 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。",
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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,
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seed=0, tiled=True
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# You can input `end_image=xxx` to control the last frame of the video.
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# The model will automatically generate the dynamic content between `input_image` and `end_image`.
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)
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save_video(video, "video.mp4", fps=15, quality=5)
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@@ -0,0 +1,50 @@
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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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from dchen.camera_compute import process_pose_file
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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="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", 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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print("success!")
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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/control_video.mp4", "data/examples/wan/reference_image_girl.png"]
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)
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# Control video
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control_video = None
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reference_image = None
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control_camera_text = "/mnt/nas2/dchen/Work/add_0609/DiffSynth-Studio/dchen/camera_information.txt"
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input_image = Image.open("/mnt/nas2/dchen/Work/add_0609/DiffSynth-Studio/dchen/7.png")
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sigma_shift = 3
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height = 480
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width = 832
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control_camera_video = process_pose_file(control_camera_text, width, height)
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video = pipe(
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prompt="一个小女孩正在户外玩耍。她穿着一件蓝色的短袖上衣和粉色的短裤，头发扎成一个可爱的辫子。她的脚上没有穿鞋，显得非常自然和随意。她正用一把红色的小铲子在泥土里挖土，似乎在进行某种有趣的活动，可能是种花或是挖掘宝藏。地上有一根长长的水管，可能是用来浇水的。背景是一片草地和一些绿色植物，阳光明媚，整个场景充满了童趣和生机。小女孩专注的表情和认真的动作让人感受到她的快乐和好奇心。",
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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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control_video=control_video, reference_image=reference_image,
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height=height, width=width, num_frames=81,
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seed=1, tiled=True,
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control_camera_video = control_camera_video,
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input_image = input_image,
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sigma_shift = sigma_shift,
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)
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save_video(video, "video2.mp4", fps=15, quality=5)
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36
examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py
Normal file
36
examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py
Normal file
@@ -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="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", 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=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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# First and last frame 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。",
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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,
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seed=0, tiled=True
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# You can input `end_image=xxx` to control the last frame of the video.
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# The model will automatically generate the dynamic content between `input_image` and `end_image`.
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)
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save_video(video, "video.mp4", fps=15, quality=5)
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@@ -0,0 +1,14 @@
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accelerate launch examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-5 \
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--num_epochs 2 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-InP_full" \
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--trainable_models "dit" \
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--input_contains_input_image \
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--input_contains_end_image
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@@ -0,0 +1,14 @@
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accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
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--dataset_base_path data/example_video_dataset \
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--dataset_metadata_path data/example_video_dataset/metadata.csv \
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--height 480 \
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--width 832 \
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--dataset_repeat 100 \
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--model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \
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--learning_rate 1e-5 \
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--num_epochs 2 \
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--remove_prefix_in_ckpt "pipe.dit." \
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--output_path "./models/train/Wan2.1-Fun-V1.1-14B-InP_full" \
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--trainable_models "dit" \
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--input_contains_input_image \
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--input_contains_end_image
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@@ -0,0 +1,31 @@
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData, load_state_dict
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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="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
|
||||
)
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||||
state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-1.3B-InP_full/epoch-1.safetensors")
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||||
pipe.dit.load_state_dict(state_dict)
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||||
pipe.enable_vram_management()
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||||
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video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)
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# First and last frame to video
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||||
video = pipe(
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prompt="from sunset to night, a small town, light, house, river",
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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",
|
||||
input_image=video[0], end_image=video[80],
|
||||
seed=0, tiled=True
|
||||
)
|
||||
save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-InP.mp4", fps=15, quality=5)
|
||||
@@ -0,0 +1,31 @@
|
||||
import torch
|
||||
from PIL import Image
|
||||
from diffsynth import save_video, VideoData, load_state_dict
|
||||
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-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-14B-InP_full/epoch-1.safetensors")
|
||||
pipe.dit.load_state_dict(state_dict)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)
|
||||
|
||||
# First and last frame to video
|
||||
video = pipe(
|
||||
prompt="from sunset to night, a small town, light, house, river",
|
||||
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=video[0], end_image=video[80],
|
||||
seed=0, tiled=True
|
||||
)
|
||||
save_video(video, "video_Wan2.1-Fun-V1.1-14B-InP.mp4", fps=15, quality=5)
|
||||
Reference in New Issue
Block a user