DiffSynth-Studio 2.0 major update

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2025-12-04 16:33:07 +08:00
parent afd101f345
commit 72af7122b3
758 changed files with 26462 additions and 2221398 deletions

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import torch
from diffsynth.utils.data import save_video
from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
from PIL import Image
from modelscope import dataset_snapshot_download
vram_config = {
"offload_dtype": "disk",
"offload_device": "disk",
"onload_dtype": torch.bfloat16,
"onload_device": "cpu",
"preparing_dtype": torch.bfloat16,
"preparing_device": "cuda",
"computation_dtype": torch.bfloat16,
"computation_device": "cuda",
}
pipe = WanVideoPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", **vram_config),
ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", **vram_config),
ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", **vram_config),
ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", **vram_config),
],
tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
)
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_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5)