Update docker && configs

Dockerfile

-FROM nvidia/cuda:12.8.1-cudnn-devel-ubuntu24.04 AS base
+FROM pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel AS base
 
 WORKDIR /app
 
@@ -12,19 +12,8 @@ RUN apt-get update && apt-get install -y vim tmux zip unzip bzip2 wget git git-l
     libsoup2.4-dev libnice-dev libopus-dev libvpx-dev libx264-dev libsrtp2-dev libglib2.0-dev libdrm-dev libjpeg-dev libpng-dev \
     && apt-get clean && rm -rf /var/lib/apt/lists/* && git lfs install
 
-# install miniconda with Python 3.12
-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py312_25.9.1-1-Linux-x86_64.sh -O /tmp/miniconda.sh && \
-    bash /tmp/miniconda.sh -b -p /app/miniconda && \
-    rm /tmp/miniconda.sh
-ENV PATH=/app/miniconda/bin:$PATH
-RUN conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
-RUN conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
-
 RUN conda install conda-forge::ffmpeg=8.0.0 -y && conda clean -all -y
 
-# install torch
-RUN pip install --no-cache-dir torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
-
 RUN pip install --no-cache-dir packaging ninja cmake scikit-build-core uv meson ruff pre-commit fastapi uvicorn requests -U
 
 RUN git clone https://github.com/vllm-project/vllm.git && cd vllm \

Dockerfile_5090

-FROM nvidia/cuda:12.8.1-cudnn-devel-ubuntu24.04 AS base
+FROM pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel AS base
 
 WORKDIR /app
 
@@ -12,19 +12,8 @@ RUN apt-get update && apt-get install -y vim tmux zip unzip bzip2 wget git git-l
     libsoup2.4-dev libnice-dev libopus-dev libvpx-dev libx264-dev libsrtp2-dev libglib2.0-dev libdrm-dev libjpeg-dev libpng-dev \
     && apt-get clean && rm -rf /var/lib/apt/lists/* && git lfs install
 
-# install miniconda with Python 3.12
-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-py312_25.9.1-1-Linux-x86_64.sh -O /tmp/miniconda.sh && \
-    bash /tmp/miniconda.sh -b -p /app/miniconda && \
-    rm /tmp/miniconda.sh
-ENV PATH=/app/miniconda/bin:$PATH
-RUN conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main
-RUN conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
-
 RUN conda install conda-forge::ffmpeg=8.0.0 -y && conda clean -all -y
 
-# install torch
-RUN pip install --no-cache-dir torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
-
 RUN pip install --no-cache-dir packaging ninja cmake scikit-build-core uv meson ruff pre-commit fastapi uvicorn requests -U
 
 RUN git clone https://github.com/vllm-project/vllm.git && cd vllm \

Dockerfile_deploy

@@ -5,7 +5,7 @@ RUN cd /opt/lightx2v/deploy/server/frontend \
     && npm install \
     && npm run build
 
-FROM lightx2v/lightx2v:25111001-cu128 AS base
+FROM lightx2v/lightx2v:25111101-cu128 AS base
 
 RUN mkdir /workspace/LightX2V
 WORKDIR /workspace/LightX2V

configs/seko_talk/seko_talk_16_fp8_dist_compile.json

@@ -17,6 +17,8 @@
         "seq_p_size": 8,
         "seq_p_attn_type": "ulysses"
     },
+    "clip_quantized": true,
+    "clip_quant_scheme": "fp8-sgl",
     "dit_quantized": true,
     "dit_quant_scheme": "fp8-sgl",
     "adapter_quantized": true,

configs/seko_talk/seko_talk_24_fp8_dist_compile_nbhd_attn.json

@@ -6,8 +6,11 @@
     "target_video_length": 81,
     "resize_mode": "adaptive",
     "self_attn_1_type": "nbhd_attn",
-    "cross_attn_1_type": "sage_attn2",
-    "cross_attn_2_type": "sage_attn2",
+    "nbhd_attn_setting": {
+        "coefficient": [1.0, 0.25, 0.056]
+    },
+    "cross_attn_1_type": "flash_attn3",
+    "cross_attn_2_type": "flash_attn3",
     "sample_guide_scale": 1.0,
     "sample_shift": 5,
     "enable_cfg": false,

docs/EN/source/getting_started/quickstart.md

@@ -27,10 +27,10 @@ We strongly recommend using the Docker environment, which is the simplest and fa
 
 #### 1. Pull Image
 
-Visit LightX2V's [Docker Hub](https://hub.docker.com/r/lightx2v/lightx2v/tags), select a tag with the latest date, such as `25111001-cu128`:
+Visit LightX2V's [Docker Hub](https://hub.docker.com/r/lightx2v/lightx2v/tags), select a tag with the latest date, such as `25111101-cu128`:
 
 ```bash
-docker pull lightx2v/lightx2v:25111001-cu128
+docker pull lightx2v/lightx2v:25111101-cu128
 ```
 
 We recommend using the `cuda128` environment for faster inference speed. If you need to use the `cuda124` environment, you can use image versions with the `-cu124` suffix:
@@ -51,7 +51,7 @@ For mainland China, if the network is unstable when pulling images, you can pull
 
 ```bash
 # cuda128
-docker pull registry.cn-hangzhou.aliyuncs.com/yongyang/lightx2v:25111001-cu128
+docker pull registry.cn-hangzhou.aliyuncs.com/yongyang/lightx2v:25111101-cu128
 
 # cuda124
 docker pull registry.cn-hangzhou.aliyuncs.com/yongyang/lightx2v:25101501-cu124

docs/ZH_CN/source/getting_started/quickstart.md

@@ -27,10 +27,10 @@
 
 #### 1. 拉取镜像
 
-访问 LightX2V 的 [Docker Hub](https://hub.docker.com/r/lightx2v/lightx2v/tags)，选择一个最新日期的 tag，比如 `25111001-cu128`：
+访问 LightX2V 的 [Docker Hub](https://hub.docker.com/r/lightx2v/lightx2v/tags)，选择一个最新日期的 tag，比如 `25111101-cu128`：
 
 ```bash
-docker pull lightx2v/lightx2v:25111001-cu128
+docker pull lightx2v/lightx2v:25111101-cu128
 ```
 
 我们推荐使用`cuda128`环境，以获得更快的推理速度，若需要使用`cuda124`环境，可以使用带`-cu124`后缀的镜像版本：
@@ -51,7 +51,7 @@ docker run --gpus all -itd --ipc=host --name [容器名] -v [挂载设置] --ent
 
 ```bash
 # cuda128
-docker pull registry.cn-hangzhou.aliyuncs.com/yongyang/lightx2v:25111001-cu128
+docker pull registry.cn-hangzhou.aliyuncs.com/yongyang/lightx2v:25111101-cu128
 
 # cuda124
 docker pull registry.cn-hangzhou.aliyuncs.com/yongyang/lightx2v:25101501-cu124