optimize Qwen text encoder

configs/qwen_image/qwen_image_i2i_2511_kernel.json

@@ -0,0 +1,17 @@
+{
+    "infer_steps": 40,
+    "prompt_template_encode": "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n",
+    "prompt_template_encode_start_idx": 64,
+    "resize_mode": "adaptive",
+    "attn_type": "flash_attn3",
+    "enable_cfg": true,
+    "sample_guide_scale": 4.0,
+    "vae_scale_factor": 8,
+    "CONDITION_IMAGE_SIZE": 147456,
+    "USE_IMAGE_ID_IN_PROMPT": true,
+    "text_encoder_type": "lightllm_kernel",
+    "lightllm_config": {
+        "use_flash_attention_kernel": true,
+        "use_rmsnorm_kernel": true
+    }
+}

configs/qwen_image/qwen_image_i2i_2511_service.json

@@ -0,0 +1,19 @@
+{
+    "infer_steps": 40,
+    "prompt_template_encode": "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n",
+    "prompt_template_encode_start_idx": 64,
+    "resize_mode": "adaptive",
+    "attn_type": "flash_attn3",
+    "enable_cfg": true,
+    "sample_guide_scale": 4.0,
+    "vae_scale_factor": 8,
+    "CONDITION_IMAGE_SIZE": 147456,
+    "USE_IMAGE_ID_IN_PROMPT": true,
+    "text_encoder_type": "lightllm_service",
+    "lightllm_config": {
+        "service_url": "http://localhost:8010",
+        "service_timeout": 30,
+        "service_retry": 3,
+        "use_shm": true
+    }
+}

lightx2v/models/input_encoders/lightllm/qwen25_text_encoder_kernel.py

@@ -2,17 +2,8 @@
 Kernel-Optimized Text Encoder
 
 Key optimizations:
-1. Flash Attention (No-Padding) - ~40% of inference time
-2. Fused RMSNorm - frequent operation
-
-Performance target:
-- Speed: 1.13x faster than Baseline (81.23ms vs 92.23ms)
-- Precision: >0.99 cosine similarity
-- Memory: Similar to Lite (~125MB VRAM)
-
-Usage:
-    encoder = LightLLMKernelTextEncoder(config)
-    hidden_states, mask, image_info = encoder.infer(text, image_list)
+1. Flash Attention
+2. Fused RMSNorm - frequent operation (sgl_kernel version)
 """
 
 import math

lightx2v/models/input_encoders/lightllm/qwen25_text_encoder_service.py

@@ -1,12 +1,6 @@
 """
 LightLLM Service-based Text Encoder
 使用 LightLLM 服务提供的 hidden states 作为 text encoder 输出
-
-优势:
-1. 显存节省: 本地不需要加载 text encoder (~7-8GB)
-2. 计算卸载: text encoder 推理在服务端完成
-3. 服务复用: 多个实例可共享同一服务
-4. 硬件优化: 服务端使用专用优化 (Flash Attention 3, CUDA Graph)
 """
 
 import math
@@ -55,17 +49,19 @@ def __init__(self, config, device=None):
             self.resolution = config.get("resolution", 640)
             self.VAE_IMAGE_SIZE = self.resolution * self.resolution
 
-        # LightLLM 服务配置 (支持新旧格式)
-        # 新格式: lightllm_config.service_url
-        # 旧格式: lightllm_service_url (向后兼容)
-        self.service_url = config.get("service_url", config.get("lightllm_service_url", "http://localhost:8010"))
-        self.timeout = config.get("service_timeout", config.get("lightllm_service_timeout", 30))  # 超时时间（秒）
-        self.retry_times = config.get("service_retry", config.get("lightllm_service_retry", 3))  # 重试次数
+        # LightLLM 服务配置
+        self.service_url = config.get("service_url", "http://localhost:8010")
+        self.timeout = config.get("service_timeout", 30)  # 超时时间（秒）
+        self.retry_times = config.get("service_retry", 3)  # 重试次数
+
+        # Shared Memory 模式配置（默认开启，仅在同机部署时有效）
+        self.use_shm = config.get("use_shm", True)
 
         logger.info(f"Initializing LightLLM Service Text Encoder")
         logger.info(f"  Service URL: {self.service_url}")
         logger.info(f"  Timeout: {self.timeout}s")
         logger.info(f"  Device: {self.device}")
+        logger.info(f"  Use Shared Memory: {self.use_shm}")
 
         # 加载必要的组件
         self.load()
@@ -153,12 +149,9 @@ def _call_service(self, text: str, images: Optional[List[Image.Image]] = None) -
             image_items = []
             for idx, img in enumerate(images):
                 buffered = BytesIO()
-                # JPEG 编码比 PNG 快 3-5x，且文件更小
-                # 使用高质量 (95) 以保持图像质量
-                if img.mode == "RGBA":
-                    # JPEG 不支持透明通道，转换为 RGB
-                    img = img.convert("RGB")
-                img.save(buffered, format="JPEG", quality=95, optimize=False)
+                # BMP 格式：无损且编码极快 (也就是直接内存拷贝)，适合 Localhost 高带宽场景
+                # 相比 PNG (CPU 压缩慢) 和 JPEG (有损)，BMP 是由于 Service Mode 的最佳选择
+                img.save(buffered, format="BMP")
                 img_str = base64.b64encode(buffered.getvalue()).decode()
                 image_items.append({"type": "base64", "data": img_str})
                 logger.debug(f"Encoded image {idx + 1}/{len(images)}: {len(img_str)} chars base64 (JPEG)")
@@ -293,7 +286,31 @@ def infer(self, text: List[str], image_list: Optional[List] = None) -> Tuple:
         result = self._call_service(txt, condition_image_list)
 
         # 解析返回的 hidden states
-        if "hidden_states_base64" in result:
+        # 优先使用 Shared Memory 模式（零拷贝，最快）
+        if self.use_shm and "shm_hidden_states_name" in result:
+            from .shm_client import get_shm_client
+
+            shm_name = result["shm_hidden_states_name"]
+            shape = tuple(result["shm_hidden_states_shape"])
+            try:
+                shm_client = get_shm_client()
+                hidden_states_np = shm_client.read_hidden_states(shm_name, shape, dtype=np.uint8)
+                hidden_states = torch.from_numpy(hidden_states_np).to(device=self.device, non_blocking=True)
+                logger.debug(f"✓ Read hidden states from shared memory: {shm_name}")
+            except Exception as e:
+                logger.warning(f"Failed to read from shared memory '{shm_name}': {e}, falling back to HTTP mode")
+                # Fallback to base64 mode
+                if "hidden_states_base64" in result:
+                    import base64
+
+                    data_bytes = base64.b64decode(result["hidden_states_base64"])
+                    shape = result["hidden_states_shape"]
+                    hidden_states_np = np.frombuffer(data_bytes, dtype=np.uint8).reshape(shape)
+                    hidden_states = torch.from_numpy(hidden_states_np).to(device=self.device, non_blocking=True)
+                else:
+                    raise
+
+        elif "hidden_states_base64" in result:
             import base64
 
             # Decode base64 to bytes

lightx2v/models/input_encoders/lightllm/shm_client.py

@@ -0,0 +1,112 @@
+"""
+Shared Memory Client for LightLLM Hidden States
+
+支持从 LightLLM 服务的共享内存中直接读取 hidden states，
+实现零拷贝数据传输，显著降低通信延迟。
+"""
+
+from multiprocessing import shared_memory
+from typing import Optional, Tuple
+
+import numpy as np
+from loguru import logger
+
+
+class ShmClient:
+    """共享内存客户端，用于读取 LightLLM 服务的 hidden states"""
+
+    def __init__(self):
+        self._cache = {}  # 缓存已打开的共享内存对象
+
+    def read_hidden_states(
+        self,
+        shm_name: str,
+        shape: Tuple[int, ...],
+        dtype: np.dtype = np.uint8,
+    ) -> np.ndarray:
+        """
+        从共享内存读取 hidden states 数据
+
+        Args:
+            shm_name: 共享内存名称
+            shape: 数据形状
+            dtype: 数据类型（默认 uint8，需要后续 view 为 bfloat16）
+
+        Returns:
+            numpy 数组（数据的副本，可安全使用）
+        """
+        try:
+            # 打开共享内存
+            shm = shared_memory.SharedMemory(name=shm_name)
+
+            # 创建 numpy 数组视图
+            arr = np.ndarray(shape, dtype=dtype, buffer=shm.buf)
+
+            # 复制数据（确保数据独立，不依赖共享内存生命周期）
+            result = arr.copy()
+
+            # 关闭共享内存（不 unlink，因为服务端负责管理生命周期）
+            shm.close()
+
+            logger.debug(f"Read hidden states from shm '{shm_name}': shape={shape}")
+            return result
+
+        except FileNotFoundError:
+            logger.error(f"Shared memory '{shm_name}' not found")
+            raise
+        except Exception as e:
+            logger.error(f"Failed to read from shared memory '{shm_name}': {e}")
+            raise
+
+    def read_hidden_states_zero_copy(
+        self,
+        shm_name: str,
+        shape: Tuple[int, ...],
+        dtype: np.dtype = np.uint8,
+    ) -> Tuple[np.ndarray, shared_memory.SharedMemory]:
+        """
+        从共享内存读取 hidden states 数据（零拷贝模式）
+
+        注意：此模式返回的数组直接引用共享内存，调用者需要负责：
+        1. 在使用完数据后调用 shm.close()
+        2. 不要在共享内存关闭后继续使用数组
+
+        Args:
+            shm_name: 共享内存名称
+            shape: 数据形状
+            dtype: 数据类型
+
+        Returns:
+            (numpy 数组, SharedMemory 对象) - 调用者需要管理 shm 对象的生命周期
+        """
+        try:
+            shm = shared_memory.SharedMemory(name=shm_name)
+            arr = np.ndarray(shape, dtype=dtype, buffer=shm.buf)
+            logger.debug(f"Zero-copy read from shm '{shm_name}': shape={shape}")
+            return arr, shm
+        except Exception as e:
+            logger.error(f"Failed to zero-copy read from shared memory '{shm_name}': {e}")
+            raise
+
+    def is_shm_available(self, shm_name: str) -> bool:
+        """检查共享内存是否可用"""
+        try:
+            shm = shared_memory.SharedMemory(name=shm_name)
+            shm.close()
+            return True
+        except FileNotFoundError:
+            return False
+        except Exception:
+            return False
+
+
+# 全局单例
+_shm_client: Optional[ShmClient] = None
+
+
+def get_shm_client() -> ShmClient:
+    """获取共享内存客户端单例"""
+    global _shm_client
+    if _shm_client is None:
+        _shm_client = ShmClient()
+    return _shm_client

scripts/qwen_image/qwen_image_i2i_2511_kernel.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# set path and first
+export lightx2v_path=
+export model_path=
+
+export CUDA_VISIBLE_DEVICES=0
+
+# set environment variables
+source ${lightx2v_path}/scripts/base/base.sh
+
+python -m lightx2v.infer \
+    --model_cls qwen_image \
+    --task i2i \
+    --model_path $model_path \
+    --config_json ${lightx2v_path}/configs/qwen_image/qwen_image_i2i_2511_kernel.json \
+    --prompt "Make the girl from Image 1 wear the black dress from Image 2 and sit in the pose from Image 3." \
+    --negative_prompt " " \
+    --image_path "1.png,2.png,3.png" \
+    --save_result_path ${lightx2v_path}/save_results/qwen_image_i2i_2511_kernel.png \
+    --seed 0

scripts/qwen_image/qwen_image_i2i_2511_service.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# set path and first
+export lightx2v_path=
+export model_path=
+
+export CUDA_VISIBLE_DEVICES=0
+
+# set environment variables
+source ${lightx2v_path}/scripts/base/base.sh
+
+python -m lightx2v.infer \
+    --model_cls qwen_image \
+    --task i2i \
+    --model_path $model_path \
+    --config_json ${lightx2v_path}/configs/qwen_image/qwen_image_i2i_2511_service.json \
+    --prompt "Make the girl from Image 1 wear the black dress from Image 2 and sit in the pose from Image 3." \
+    --negative_prompt " " \
+    --image_path "1.png,2.png,3.png" \
+    --save_result_path ${lightx2v_path}/save_results/qwen_image_i2i_2511_service.png \
+    --seed 0