fix error code (#356)

Co-authored-by: yihuiwen <yihuiwen@sensetime.com>

lightx2v/models/runners/default_runner.py

@@ -170,7 +170,7 @@ class DefaultRunner(BaseRunner):
             img_ori = Image.open(img_path).convert("RGB")
         if GET_RECORDER_MODE():
             width, height = img_ori.size
-            monitor_cli.lightx2v_input_image_len.observe(width*height)
+            monitor_cli.lightx2v_input_image_len.observe(width * height)
         img = TF.to_tensor(img_ori).sub_(0.5).div_(0.5).unsqueeze(0).cuda()
         self.input_info.original_size = img_ori.size
         return img, img_ori
@@ -256,10 +256,12 @@ class DefaultRunner(BaseRunner):
             self.model.select_graph_for_compile(self.input_info)
         for segment_idx in range(self.video_segment_num):
             logger.info(f"🔄 start segment {segment_idx + 1}/{self.video_segment_num}")
-            with ProfilingContext4DebugL1(f"segment end2end {segment_idx + 1}/{self.video_segment_num}", \
-                    recorder_mode=GET_RECORDER_MODE(), \
-                    metrics_func=monitor_cli.lightx2v_run_pre_step_dit_duration, \
-                    metrics_labels=[segment_idx+1, self.video_segment_num]):
+            with ProfilingContext4DebugL1(
+                f"segment end2end {segment_idx + 1}/{self.video_segment_num}",
+                recorder_mode=GET_RECORDER_MODE(),
+                metrics_func=monitor_cli.lightx2v_run_pre_step_dit_duration,
+                metrics_labels=[segment_idx + 1, self.video_segment_num],
+            ):
                 self.check_stop()
                 # 1. default do nothing
                 self.init_run_segment(segment_idx)
@@ -273,12 +275,7 @@ class DefaultRunner(BaseRunner):
         self.end_run()
         return {"video": gen_video_final}
 
-    @ProfilingContext4DebugL1(
-        "Run VAE Decoder",
-        recorder_mode=GET_RECORDER_MODE(),
-        metrics_func=monitor_cli.lightx2v_run_vae_decode_duration,
-        metrics_labels=labels=["DefaultRunner"]
-    )
+    @ProfilingContext4DebugL1("Run VAE Decoder", recorder_mode=GET_RECORDER_MODE(), metrics_func=monitor_cli.lightx2v_run_vae_decode_duration, metrics_labels=["DefaultRunner"])
     def run_vae_decoder(self, latents):
         if self.config.get("lazy_load", False) or self.config.get("unload_modules", False):
             self.vae_decoder = self.load_vae_decoder()
@@ -333,12 +330,7 @@ class DefaultRunner(BaseRunner):
                 logger.info(f"✅ Video saved successfully to: {self.input_info.save_result_path} ✅")
             return {"video": None}
 
-    @ProfilingContext4DebugL1(
-        "RUN pipeline",
-        recorder_mode=GET_RECORDER_MODE(),
-        metrics_func=monitor_cli.lightx2v_worker_request_duration,
-        metrics_labels=["DefaultRunner"]
-    )
+    @ProfilingContext4DebugL1("RUN pipeline", recorder_mode=GET_RECORDER_MODE(), metrics_func=monitor_cli.lightx2v_worker_request_duration, metrics_labels=["DefaultRunner"])
     def run_pipeline(self, input_info):
         if GET_RECORDER_MODE():
             monitor_cli.lightx2v_worker_request_count.inc()

lightx2v/models/runners/qwen_image/qwen_image_runner.py

@@ -10,10 +10,10 @@ from lightx2v.models.networks.qwen_image.model import QwenImageTransformerModel
 from lightx2v.models.runners.default_runner import DefaultRunner
 from lightx2v.models.schedulers.qwen_image.scheduler import QwenImageScheduler
 from lightx2v.models.video_encoders.hf.qwen_image.vae import AutoencoderKLQwenImageVAE
+from lightx2v.server.metrics import monitor_cli
 from lightx2v.utils.envs import *
 from lightx2v.utils.profiler import *
 from lightx2v.utils.registry_factory import RUNNER_REGISTER
-from lightx2v.server.metrics import monitor_cli
 
 
 def calculate_dimensions(target_area, ratio):
@@ -108,12 +108,7 @@ class QwenImageRunner(DefaultRunner):
             "image_encoder_output": image_encoder_output,
         }
 
-    @ProfilingContext4DebugL1(
-        "Run Text Encoder",
-        recorder_mode=GET_RECORDER_MODE(),
-        metrics_func=monitor_cli.lightx2v_run_text_encode_duration,
-        metrics_labels=["QwenImageRunner"]
-    )
+    @ProfilingContext4DebugL1("Run Text Encoder", recorder_mode=GET_RECORDER_MODE(), metrics_func=monitor_cli.lightx2v_run_text_encode_duration, metrics_labels=["QwenImageRunner"])
     def run_text_encoder(self, text, image=None):
         if GET_RECORDER_MODE():
             monitor_cli.lightx2v_input_prompt_len.observe(len(text))
@@ -130,12 +125,7 @@ class QwenImageRunner(DefaultRunner):
             text_encoder_output["image_info"] = image_info
         return text_encoder_output
 
-    @ProfilingContext4DebugL1(
-        "Run VAE Encoder",
-        recorder_mode=GET_RECORDER_MODE(),
-        metrics_func=monitor_cli.lightx2v_run_vae_encode_duration,
-        metrics_labels=["QwenImageRunner"]
-    )
+    @ProfilingContext4DebugL1("Run VAE Encoder", recorder_mode=GET_RECORDER_MODE(), metrics_func=monitor_cli.lightx2v_run_vae_encode_duration, metrics_labels=["QwenImageRunner"])
     def run_vae_encoder(self, image):
         image_latents = self.vae.encode_vae_image(image)
         return {"image_latents": image_latents}

lightx2v/models/runners/wan/wan_animate_runner.py

@@ -18,11 +18,11 @@ from lightx2v.models.input_encoders.hf.animate.face_encoder import FaceEncoder
 from lightx2v.models.input_encoders.hf.animate.motion_encoder import Generator
 from lightx2v.models.networks.wan.animate_model import WanAnimateModel
 from lightx2v.models.runners.wan.wan_runner import WanRunner
+from lightx2v.server.metrics import monitor_cli
 from lightx2v.utils.envs import *
 from lightx2v.utils.profiler import *
 from lightx2v.utils.registry_factory import RUNNER_REGISTER
 from lightx2v.utils.utils import load_weights, remove_substrings_from_keys
-from lightx2v.server.metrics import monitor_cli
 
 
 @RUNNER_REGISTER("wan2.2_animate")

lightx2v/models/runners/wan/wan_runner.py

@@ -24,12 +24,12 @@ from lightx2v.models.schedulers.wan.scheduler import WanScheduler
 from lightx2v.models.video_encoders.hf.wan.vae import WanVAE
 from lightx2v.models.video_encoders.hf.wan.vae_2_2 import Wan2_2_VAE
 from lightx2v.models.video_encoders.hf.wan.vae_tiny import Wan2_2_VAE_tiny, WanVAE_tiny
+from lightx2v.server.metrics import monitor_cli
 from lightx2v.utils.envs import *
 from lightx2v.utils.profiler import *
 from lightx2v.utils.registry_factory import RUNNER_REGISTER
 from lightx2v.utils.utils import *
 from lightx2v.utils.utils import best_output_size
-from lightx2v.server.metrics import monitor_cli
 
 
 @RUNNER_REGISTER("wan2.1")

lightx2v/models/runners/wan/wan_vace_runner.py

@@ -8,10 +8,10 @@ from PIL import Image
 from lightx2v.models.input_encoders.hf.vace.vace_processor import VaceVideoProcessor
 from lightx2v.models.networks.wan.vace_model import WanVaceModel
 from lightx2v.models.runners.wan.wan_runner import WanRunner
+from lightx2v.server.metrics import monitor_cli
 from lightx2v.utils.envs import *
 from lightx2v.utils.profiler import *
 from lightx2v.utils.registry_factory import RUNNER_REGISTER
-from lightx2v.server.metrics import monitor_cli
 
 
 @RUNNER_REGISTER("wan2.1_vace")

lightx2v/server/metrics/__init__.py

 # -*-coding=utf-8-*-
 
 from .metrics import server_process
-
 from .monitor import Monitor
 
 monitor_cli = Monitor()

lightx2v/server/metrics/metrics.py

 # -*-coding=utf-8-*-
-from prometheus_client import start_http_server, Counter, Gauge, Histogram
-from prometheus_client.metrics import MetricWrapperBase
-import time
-from loguru import logger
 import threading
+from typing import List, Tuple
+
+from loguru import logger
+from prometheus_client import Counter, Gauge, Histogram, start_http_server
 from pydantic import BaseModel
-from typing import Optional, List, Dict, Any, Tuple
-from functools import wraps
 
 
 class MetricsConfig(BaseModel):
@@ -129,15 +127,11 @@ class MetricsClient:
             if config.type_ == "counter":
                 self.register_counter(config.name, config.desc, config.labels)
             elif config.type_ == "histogram":
-                self.register_histogram(
-                    config.name, config.desc, config.labels, buckets=config.buckets
-                )
+                self.register_histogram(config.name, config.desc, config.labels, buckets=config.buckets)
             elif config.type_ == "gauge":
                 self.register_gauge(config.name, config.desc, config.labels)
             else:
-                logger.warning(
-                    f"Unsupported metric type: {config.type_} for {metric_name}"
-                )
+                logger.warning(f"Unsupported metric type: {config.type_} for {metric_name}")
 
     def register_counter(self, name, desc, labels):
         metric_instance = Counter(name, desc, labels)

lightx2v/server/metrics/monitor.py

 # -*-coding=utf-8-*-
 import threading
+
 from .metrics import MetricsClient
 
 

lightx2v/utils/envs.py

@@ -42,9 +42,8 @@ def GET_SENSITIVE_DTYPE():
         return GET_DTYPE()
     return DTYPE_MAP[RUNNING_FLAG]
 
+
 @lru_cache(maxsize=None)
 def GET_RECORDER_MODE():
     RECORDER_MODE = int(os.getenv("RECORDER_MODE", "0"))
     return RECORDER_MODE
-
-