# wan2.2 **Repository Path**: brian66237/wan2.2 ## Basic Information - **Project Name**: wan2.2 - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-12-15 - **Last Updated**: 2025-12-15 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README --- pipeline_tag: text-to-video frameworks: - PyTorch hardwares: - NPU - Atlas 800T A2 - Atlas 800I A2 license: apache-2.0 --- # Wan2.2推理指导 ## 一、准备运行环境 **表 1** 版本配套表 | 配套 | 版本 | 环境准备指导 | | ----- | ----- |-----| | Python | 3.11.10 | - | | torch | 2.1.0 | - | ### 1.1 获取CANN&MindIE安装包&环境准备 - 设备支持 [Atlas 800I/800T A2(8*64G)](https://www.hiascend.com/developer/download/community/result?module=pt+ie+cann&product=4&model=32) - [环境准备指导](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/softwareinst/instg/instg_0001.html) ### 1.2 CANN安装 ```shell # 增加软件包可执行权限，{version}表示软件版本号，{arch}表示CPU架构，{soc}表示昇腾AI处理器的版本。 chmod +x ./Ascend-cann-toolkit_{version}_linux-{arch}.run chmod +x ./Ascend-cann-kernels-{soc}_{version}_linux.run chmod +x ./Ascend-cann-nnal_{version}_linux-{arch}.run (若使用稀疏FA) # 校验软件包安装文件的一致性和完整性 ./Ascend-cann-toolkit_{version}_linux-{arch}.run --check ./Ascend-cann-kernels-{soc}_{version}_linux.run --check ./Ascend-cann-nnal{version}_linux-{arch}.run --check (若使用稀疏FA) # 安装 ./Ascend-cann-toolkit_{version}_linux-{arch}.run --install ./Ascend-cann-kernels-{soc}_{version}_linux.run --install ./Ascend-cann-nnal{version}_linux-{arch}.run --torch_atb --install (若使用稀疏FA) # 设置环境变量 source /usr/local/Ascend/ascend-toolkit/set_env.sh source /usr/local/Ascend/nnal/atb/set_env.sh ``` ### 1.3 环境依赖安装 ```shell pip3 install -r requirements.txt ``` ### 1.4 MindIE安装 ```shell # 增加软件包可执行权限，{version}表示软件版本号，{arch}表示CPU架构。 chmod +x ./Ascend-mindie_${version}_linux-${arch}.run ./Ascend-mindie_${version}_linux-${arch}.run --check # 方式一：默认路径安装 ./Ascend-mindie_${version}_linux-${arch}.run --install # 设置环境变量 cd /usr/local/Ascend/mindie && source set_env.sh # 方式二：指定路径安装 ./Ascend-mindie_${version}_linux-${arch}.run --install-path=${AieInstallPath} # 设置环境变量 cd ${AieInstallPath}/mindie && source set_env.sh ``` ### 1.5 Torch_npu安装 下载 pytorch_v{pytorchversion}_py{pythonversion}.tar.gz ```shell tar -xzvf pytorch_v{pytorchversion}_py{pythonversion}.tar.gz # 解压后，会有whl包 pip install torch_npu-{pytorchversion}.xxxx.{arch}.whl ``` ### 1.6 gcc、g++安装 ```shell # 若环境镜像中没有gcc、g++，请用户自行安装 yum install gcc yum install g++ # 导入头文件路径 export CPLUS_INCLUDE_PATH=/usr/include/c++/12/:/usr/include/c++/12/aarch64-openEuler-linux/:$CPLUS_INCLUDE_PATH ``` 注：若使用openeuler镜像，需要配置gcc、g++环境，否则会导致`fatal error: 'stdio.h' file not found` ## 二、下载权重 ### 2.1 Wan2.2 权重及配置文件说明 - Huggingface | 模型 | 链接 | | ------------ | ------------ | | Wan2.2-T2V-A14B | [🤗huggingface](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B/tree/main) | | Wan2.2-I2V-A14B | [🤗huggingface](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B/tree/main) | | Wan2.2-TI2V-5B | [🤗huggingface](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B/tree/main) | - Modelers | 模型 | 链接 | | ------------ | ------------ | | Wan2.2-T2V-A14B | [ Modelers](https://modelers.cn/models/Modelers_Park/Wan2.2-T2V-A14B) | | Wan2.2-I2V-A14B | [ Modelers](https://modelers.cn/models/Modelers_Park/Wan2.2-I2V-A14B ) | | Wan2.2-TI2V-5B | [ Modelers](https://modelers.cn/models/Modelers_Park/Wan2.2-TI2V-5B) | ### 2.2 Wan2.2 支持分辨率说明 | 模型 | 支持分辨率 | | ------------ | ------------ | | Wan2.2-T2V-A14B | 720\*1280, 1280\*720, 480\*832, 832\*480 | | Wan2.2-I2V-A14B | 720\*1280, 1280\*720, 480\*832, 832\*480 | | Wan2.2-TI2V-5B | 704\*1280, 1280\*704 | ## 三、Wan2.2使用 ### 3.1 下载到本地 ```shell git clone https://modelers.cn/MindIE/Wan2.2.git ``` ### 3.2 Wan2.2-T2V-A14B 使用上一步下载的权重 ```shell model_base="./Wan2.2-T2V-A14B/" ``` #### 3.2.1 等价优化 #### 3.2.1.1 8卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=8 --master_port=23459 generate.py \ --task t2v-A14B \ --ckpt_dir ${model_base} \ --size 1280*720 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 4 \ --vae_parallel \ --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \ --base_seed 0 ``` 参数说明： - ALGO: 为0表示默认FA算子; 设置为1表示使用高性能FA算子 - task: 任务类型。 - ckpt_dir: 模型的权重路径 - size: 生成视频的分辨率，支持(1280,720)、(832,480)分辨率 - frame_num: 生成视频的帧数 - sample_steps: 推理步数 - dit_fsdp: dit使能fsdp, 用以降低显存占用 - t5_fsdp: t5使能fsdp, 用以降低显存占用 - cfg_size: cfg并行数 - ulysses_size: ulysses并行数 - vae_parallel: 使能vae并行策略 - prompt: 文本提示词 - base_seed: 随机种子 #### 3.2.1.2 16卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=16 --master_port=23459 generate.py \ --task t2v-A14B \ --ckpt_dir ${model_base} \ --size 1280*720 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 8 \ --vae_parallel \ --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \ --base_seed 0 ``` #### 3.2.2 算法优化-稀疏FA #### 3.2.2.1 8卡性能测试 执行命令： ```shell export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=8 --master_port=23459 generate.py \ --task t2v-A14B \ --ckpt_dir ${model_base} \ --size 1280*720 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 4 \ --vae_parallel \ --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \ --use_rainfusion \ --sparsity 0.64 \ --sparse_start_step 15 \ --base_seed 0 ``` 参数说明： - use_rainfusion: 使能稀疏flash attention计算 - sparsity: 稀疏度，值为[0, 1), 稀疏度越大，加速比越高，相应精度损失更大 - spasre_start_step: 开始稀疏的时间步，通常需要保证不小于1/4的总时间步数 ### 3.3 Wan2.2-I2V-A14B 使用上一步下载的权重 ```shell model_base="./Wan2.2-I2V-A14B/" ``` #### 3.3.1 等价优化 #### 3.3.1.1 8卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=8 generate.py \ --task i2v-A14B \ --ckpt_dir ${model_base} \ --size 1280*720 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 4 \ --vae_parallel \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --base_seed 0 ``` 参数说明： - ALGO: 为0表示默认FA算子; 设置为1表示使用高性能FA算子 - task: 任务类型。 - ckpt_dir: 模型的权重路径 - size: 生成视频的分辨率，支持(1280,720)、(832,480)分辨率 - frame_num: 生成视频的帧数 - sample_steps: 推理步数 - dit_fsdp: dit使能fsdp, 用以降低显存占用 - t5_fsdp: t5使能fsdp, 用以降低显存占用 - cfg_size: cfg并行数 - ulysses_size: ulysses并行数 - vae_parallel: 使能vae并行策略 - image: 输入图片路径 - prompt: 文本提示词 - base_seed: 随机种子 #### 3.3.1.2 16卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=16 --master_port=23459 generate.py \ --task i2v-A14B \ --ckpt_dir ${model_base} \ --size 1280*720 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 8 \ --vae_parallel \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --base_seed 0 ``` #### 3.3.2 算法优化--稀疏FA #### 3.3.2.1 8卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=8 generate.py \ --task i2v-A14B \ --ckpt_dir ${model_base} \ --size 1280*720 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 4 \ --vae_parallel \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --use_rainfusion \ --sparsity 0.64 \ --sparse_start_step 15 \ --base_seed 0 ``` 参数说明： - use_rainfusion: 使能稀疏flash attention计算 - sparsity: 稀疏度，值为[0, 1), 稀疏度越大，加速比越高，相应精度损失更大 - spasre_start_step: 开始稀疏的时间步，通常需要保证不小于1/4的总时间步数 ### 3.4 Wan2.2-TI2V-5B 使用上一步下载的权重 ```shell model_base="./Wan2.2-TI2V-5B/" ``` #### 3.4.1 等价优化 #### 3.4.1.1 单卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false python generate.py \ --task ti2v-5B \ --ckpt_dir ${model_base} \ --size 1280*704 \ --frame_num 121 \ --sample_steps 50 \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --offload_model False \ --base_seed 0 ``` 参数说明： - ALGO: 为0表示默认FA算子；设置为1表示使用高性能FA算子 - task: 任务类型。 - ckpt_dir: 模型的权重路径 - size: 生成视频的分辨率，支持(1280,720)、(832,480)分辨率 - frame_num: 生成视频的帧数 - sample_steps: 推理步数 - image: 输入图片路径 - prompt: 文本提示词 - offload_model: 是否开启cpu offload，单卡默认开启 - base_seed: 随机种子 #### 3.4.1.2 8卡性能测试 执行命令: ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=8 generate.py \ --task ti2v-5B \ --ckpt_dir ${model_base} \ --size 1280*704 \ --frame_num 121 \ --sample_steps 50 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 4 \ --vae_parallel \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --base_seed 0 ``` 参数说明： - ALGO: 为0表示默认FA算子；设置为1表示使用高性能FA算子 - task: 任务类型。 - ckpt_dir: 模型的权重路径 - size: 生成视频的分辨率，支持(1280,720)、(832,480)分辨率 - frame_num: 生成视频的帧数 - sample_steps: 推理步数 - dit_fsdp: dit使能fsdp, 用以降低显存占用 - t5_fsdp: t5使能fsdp, 用以降低显存占用 - cfg_size: cfg并行数 - ulysses_size: ulysses并行数 - vae_parallel: 使能vae并行策略 - image: 输入图片路径 - prompt: 文本提示词 - base_seed: 随机种子 #### 3.4.1.3 16卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=16 --master_port=23459 generate.py \ --task ti2v-5B \ --ckpt_dir ${model_base} \ --size 1280*704 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 8 \ --vae_parallel \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --base_seed 0 ``` #### 3.4.2 算法优化 #### 3.4.2.1 单卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false python generate.py \ --task ti2v-5B \ --ckpt_dir ${model_base} \ --size 1280*704 \ --frame_num 121 \ --sample_steps 50 \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --base_seed 0 \ --use_attentioncache \ --start_step 20 \ --attentioncache_interval 2 \ --end_step 47 ``` 参数说明： - ALGO: 为0表示默认FA算子；设置为1表示使用高性能FA算子 - use_attentioncache: 使能attentioncache策略 - start_step: cache开始的step - attentioncache_interval: cache重计算间隔 - end_step: cache结束的step #### 3.4.2.2 8卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=8 generate.py \ --task ti2v-5B \ --ckpt_dir ${model_base} \ --size 1280*704 \ --frame_num 121 \ --sample_steps 50 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 4 \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --vae_parallel \ --base_seed 0 \ --use_attentioncache \ --start_step 20 \ --attentioncache_interval 2 \ --end_step 47 ``` #### 3.4.2.1 16卡性能测试 执行命令： ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false torchrun --nproc_per_node=16 --master_port=23459 generate.py \ --task ti2v-5B \ --ckpt_dir ${model_base} \ --size 1280*704 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 2 \ --ulysses_size 8 \ --vae_parallel \ --image examples/i2v_input.JPG \ --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \ --base_seed 0 \ --use_attentioncache \ --start_step 20 \ --attentioncache_interval 2 \ --end_step 47 ``` ## 四、量化功能支持 新增Wan2.2-T2V、Wan2.2-I2V、Wan2.2-TI2V的W8A8_dynamic量化支持，针对DiT模型进行量化，降低现存占用，提高模型推理性能 ### 4.1 安装量化工具msModelSlim 下载并安装msmodelslim工具 ```shell git clone https://gitcode.com/Ascend/msit cd msit/msmodelslim bash install.sh ``` ### 4.2 导出量化权重 以Wan2.2-T2V-A14B模型为例，导出DiT的W8A8量化权重及描述文件 ```shell cd /path/to/Wan2.2 model_base="./Wan2.2-T2V-A14B/" python quant_wan22.py \ --task t2v-A14B \ --ckpt_dir ${model_base} \ --quant_dit_path ./quant_w8a8_dynamic \ --quant_type W8A8 \ --is_dynamic ``` 参数说明： - task: 生成任务类型，t2v-A14B、i2v-A14B、ti2v-5B - ckpt_dir: 浮点模型权重路径 - quant_dit_path：导出量化DiT模型权重及描述文件的保存路径 - quant_type：量化类型，当前仅支持W8A8 - is_dynamic：使能动态量化 执行后，`quant_w8a8_dynamic`目录下会生成两个文件夹： - `high_noise_model` - `quant_model_description_w8a8_dynamic.json`：量化配置描述文件 - `quant_model_weight_w8a8_dynamic.safetensors`：量化后的权重文件 - `low_noise_model` - `quant_model_description_w8a8_dynamic.json`：量化配置描述文件 - `quant_model_weight_w8a8_dynamic.safetensors`：量化后的权重文件 ### 4.3 量化模型推理 以Wan2.2-T2V-A14B模型为例，执行量化推理 ```shell export ALGO=0 export PYTORCH_NPU_ALLOC_CONF='expandable_segments:True' export TASK_QUEUE_ENABLE=2 export CPU_AFFINITY_CONF=1 export TOKENIZERS_PARALLELISM=false model_base="./Wan2.2-T2V-A14B/" quant_dit_path="./quant_w8a8_dynamic/" torchrun --nproc_per_node=8 --master_port=23459 generate.py \ --task t2v-A14B \ --ckpt_dir ${model_base} \ --quant_dit_path ${quant_dit_path} \ --size 1280*720 \ --frame_num 81 \ --sample_steps 40 \ --dit_fsdp \ --t5_fsdp \ --cfg_size 1 \ --ulysses_size 8 \ --vae_parallel \ --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \ --base_seed 0 ``` 参数说明： - quant_dit_path：量化DiT模型权重的路径，传入该参数则使能量化 ## 五、推理结果参考 ### Atlas 800I A2(8*64G) 64核(arm)性能数据 (ALGO=1) | 模型 | 分辨率 | 帧数 | 迭代次数 | 卡数 | E2E耗时| | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | | Wan2.2-T2V-A14B | 1280×720 | 81 | 40 | 8 | 435.99s | | Wan2.2-I2V-A14B | 1280×720 | 81 | 40 | 8 | 436.42s | | Wan2.2-TI2V-5B | 1280×704 | 121 | 50 | 8 |72.21s | ## 声明 - 本代码仓提到的数据集和模型仅作为示例，这些数据集和模型仅供您用于非商业目的，如您使用这些数据集和模型来完成示例，请您特别注意应遵守对应数据集和模型的License，如您因使用数据集或模型而产生侵权纠纷，华为不承担任何责任。 - 如您在使用本代码仓的过程中，发现任何问题（包括但不限于功能问题、合规问题），请在本代码仓提交issue，我们将及时审视并解答。 ## 六、常见问题 1. 若出现OOM, 可添加环境变量 `export T5_LOAD_CPU=1`，以降低显存占用 2. 当前仅TI2V支持attentioncache 3. 若遇到报错: `Directory operation failed. Reason: Directory [/usr/local/Ascend/mindie/latest/mindie-rt/aoe] does not exist`,请设置环境变量`unset TUNE_BANK_PATH` 4. 若使用openeuler镜像, 若没有配置gcc、g++环境，会遇到报错：`fatal error: 'stdio.h' file not found`，请参考`1.6 gcc、g++安装` 5. 若循环跑纯模型推理，可能会因为HCCL端口未及时释放，导致因端口被占用而推理失败，报错：`Failed to bind the IP port. Reason: The IP address and port have been bound already.` `HCCL function error :HcclGetRootInfo(&hcclID), error code is 7`: 请配置`export HCCL_HOST_SOCKET_PORT_RANGE="auto"`不指定端口 `HCCL function error :HcclGetRootInfo(&hcclID), error code is 11`: 请配置`sysctl -w net.ipv4.ip_local_reserved_ports=60000-60015`预留端口 6. 当前版本A3设备上暂不原生支持ALGO=1, 即将支持，敬请期待