EasyAnimate 腾讯云 GPU 部署指南：AI 视频生成实战

1nvidia-smi

1sudo apt-get update && sudo apt-get install -y ubuntu-drivers-common
2sudo ubuntu-drivers autoinstall   # 自动装推荐驱动
3sudo reboot

1# Docker（腾讯云镜像源加速）
2sudo apt-get update && sudo apt-get install -y ca-certificates curl gnupg lsb-release
3curl -fsSL https://mirrors.cloud.tencent.com/docker-ce/linux/ubuntu/gpg -o docker.gpg
4sudo install -m 0755 -d /etc/apt/keyrings && sudo cp docker.gpg /etc/apt/keyrings/docker.gpg
5sudo chmod a+r /etc/apt/keyrings/docker.gpg
6echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://mirrors.cloud.tencent.com/docker-ce/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
7sudo apt-get update
8sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-compose-plugin
9
10# 配置 Docker Hub 镜像加速器（关键！否则拉镜像极慢）
11sudo mkdir -p /etc/docker
12sudo tee /etc/docker/daemon.json <<-'EOF'
13{
14  "registry-mirrors": ["https://mirror.ccs.tencentyun.com"]
15}
16EOF
17sudo systemctl daemon-reload && sudo systemctl restart docker && sudo systemctl enable docker
18docker --version && docker compose version

1# 验证容器内能看到 GPU（最关键一步）
2docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu22.04 nvidia-smi

1sudo apt-get install -y ffmpeg && ffmpeg -version

1# 拉取含 CUDA+PyTorch 的基础镜像（国内走 Docker Hub 加速）
2docker pull pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel
3
4# 启动容器并挂载目录（注意 --gpus all 让 GPU 可见）
5mkdir -p ~/easyanimate/{models,output}
6docker run -itd --name easyanimate --gpus all \
7  -p 7860:7860 \
8  -v ~/easyanimate/models:/root/EasyAnimate/models \
9  -v ~/easyanimate/output:/root/EasyAnimate/samples \
10  --shm-size 16g \
11  pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel bash

1docker exec -it easyanimate bash
2
3cd /root
4git clone https://github.com/aigc-apps/EasyAnimate.git
5cd EasyAnimate
6
7# pip 国内清华源加速
8pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
9
10# 安装依赖
11pip install -r requirements.txt
12
13# 安装 attention 加速库（关键性能！三选一，优先 SageAttention）
14# 推荐：SageAttention（比 xformers 更快且省显存）
15pip install git+https://github.com/thu-ml/SageAttention.git
16# 备选：xformers
17# pip install xformers==0.0.22
18
19# Triton 相关（若 SageAttention 需要）
20pip install triton

1# 方式1：HF 镜像加速（推荐）
2export HF_ENDPOINT=https://hf-mirror.com
3huggingface-cli download alibaba-pai/EasyAnimateV5-7b-zh-InP --local-dir models/Diffusion_Transformer/EasyAnimateV5-7b-zh-InP
4
5# 方式2：ModelScope（魔搭，国内最快最稳）
6pip install modelscope
7python3 -c "
8from modelscope import snapshot_download
9snapshot_download('PAI/EasyAnimateV5-7b-zh-InP', cache_dir='models/Diffusion_Transformer/EasyAnimateV5-7b-zh-InP')
10"

1cd /root/EasyAnimate
2# 启用 SageAttention 加速 + FP16 省显存
3python app.py --share False --server-name 0.0.0.0 --server-port 7860

1# 安装 miniconda
2wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
3bash Miniconda3-latest-Linux-x86_64.sh -b
4
5source ~/miniconda3/bin/activate
6
7# 建独立环境
8conda create -n easyanimate python=3.10 -y
9conda activate easyanimate
10
11# PyTorch（匹配你的 CUDA 版本，以 11.8 为例；用国内 conda 源加速）
12conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
13conda install pytorch==2.1.0 torchvision==0.16.0 pytorch-cuda=11.8 -c pytorch -c nvidia
14
15pip install -r requirements.txt
16pip install git+https://github.com/thu-ml/SageAttention.git

1import torch
2from diffusers import EasyAnimateInpaintPipeline
3from diffusers.pipelines.easyanimate.pipeline_easyanimate_inpaint import EasyAnimatePipeline
4from diffusers.utils import export_to_video
5
6pipe = EasyAnimatePipeline.from_pretrained(
7    "models/Diffusion_Transformer/EasyAnimateV5-7b-zh-InP",
8    torch_dtype=torch.float16
9)
10
11# SageAttention 替换默认 attention（省显存+提速）
12from sageattention import sageattn
13pipe.transformer.set_attn_processor(sageattn)
14
15# 显存不够时开启 CPU offload（会变慢但能跑）
16pipe.enable_model_cpu_offload()
17
18prompt = "一只可爱的小狗在草地上奔跑，阳光明媚，4k 高清"
19video = pipe(
20    prompt=prompt,
21    num_frames=40,             # 帧数越多视频越长，显存消耗越大
22    height=512,
23    width=512,
24    num_inference_steps=50,
25    guidance_scale=7.5,
26).frames
27
28export_to_video(video, "output/dog.mp4", fps=8)
29print("生成完成: output/dog.mp4")

1cd ~/ComfyUI/custom_nodes
2git clone https://github.com/aigc-apps/ComfyUI-EasyAnimate.git
3cd EasyAnimate && pip install -r requirements.txt

1docker exec -it easyanimate env python3 -c \
2  "import torch;print(torch.cuda.is_available(),torch.cuda.get_device_name(0))"

1# 看资源占用
2docker stats --no-stream
3
4# 实测推理速度（tokens/秒）
5time docker exec -it easyanimate python3 - <<-'EOF'
6import time, torch
7from diffusers import EasyAnimatePipeline
8from diffusers.utils import export_to_video
9pipe = EasyAnimatePipeline.from_pretrained("models/Diffusion_Transformer/EasyAnimateV5-7b-zh-InP", torch_dtype=torch.float16).to("cuda")
10pipe.transformer.set_attn_processor(sageattn)
11t=time.time(); v=pipe("小狗奔跑", num_frames=24, height=384, width=672, num_inference_steps=30).frames
12print("耗时秒数:", time.time()-t)
13export_to_video(v, "output/speedtest.mp4", fps=8)
14EOF

1prompts = ["小狗草地奔跑","未来城市飞行器","海边日落海浪"]
2for i,p in enumerate(prompts):
3    v = pipe(prompt=p, num_frames=32, height=384, width=672, num_inference_steps=40).frames
4    export_to_video(v, f"output/batch_{i}.mp4", fps=8)
5    torch.cuda.empty_cache()   # 关键！清空显存防累积 OOM

1pipe.load_lora_weights("./lora_out", weight_name="pytorch_lora_weights.safetensors")
2pipe.fuse_lora()   # 或保持未融合以随时调整 scale

1sudo apt install -y nginx certbot python3-certbot-nginx
2sudo tee /etc/nginx/sites-available/easyanimate <<-'EOF'
3server {
4    listen 80;
5    server_name video.yourcompany.com;
6    location / {
7        proxy_pass http://127.0.0.1:7860;
8        proxy_http_version 1.1;
9        proxy_set_header Upgrade $http_upgrade;
10        proxy_set_header Connection "upgrade";
11        proxy_set_header Host $host;
12        proxy_set_header X-Real-IP $remote_addr;
13    }
14}
15EOF
16sudo ln -s /etc/nginx/sites-available/easyanimate /etc/nginx/sites-enabled/
17sudo nginx -t && sudo systemctl reload nginx
18sudo certbot --nginx -d video.yourcompany.com

1# 同步到腾讯云 COS（内网免流量）
2# coscli sync ~/easyanimate/models cos://your-bucket/easyanimate-models/
3# coscli sync ~/easyanimate/output cos://your-bucket/easyanimate-out/