default_runner.py

import gc

import requests
import torch
import torch.distributed as dist
import torchvision.transforms.functional as TF
from PIL import Image
from loguru import logger
from requests.exceptions import RequestException

from lightx2v.server.metrics import monitor_cli
from lightx2v.utils.envs import *
from lightx2v.utils.generate_task_id import generate_task_id
from lightx2v.utils.global_paras import CALIB
from lightx2v.utils.memory_profiler import peak_memory_decorator
from lightx2v.utils.profiler import *
from lightx2v.utils.utils import save_to_video, vae_to_comfyui_image
from lightx2v_platform.base.global_var import AI_DEVICE

from .base_runner import BaseRunner


class DefaultRunner(BaseRunner):
    def __init__(self, config):
        super().__init__(config)
        self.has_prompt_enhancer = False
        self.progress_callback = None
        if self.config["task"] == "t2v" and self.config.get("sub_servers", {}).get("prompt_enhancer") is not None:
            self.has_prompt_enhancer = True
            if not self.check_sub_servers("prompt_enhancer"):
                self.has_prompt_enhancer = False
                logger.warning("No prompt enhancer server available, disable prompt enhancer.")
        if not self.has_prompt_enhancer:
            self.config["use_prompt_enhancer"] = False
        self.set_init_device()
        self.init_scheduler()

    def init_modules(self):
        logger.info("Initializing runner modules...")
        if not self.config.get("lazy_load", False) and not self.config.get("unload_modules", False):
            self.load_model()
        elif self.config.get("lazy_load", False):
            assert self.config.get("cpu_offload", False)
        if hasattr(self, "model"):
            self.model.set_scheduler(self.scheduler)  # set scheduler to model
        if self.config["task"] == "i2v":
            self.run_input_encoder = self._run_input_encoder_local_i2v
        elif self.config["task"] == "flf2v":
            self.run_input_encoder = self._run_input_encoder_local_flf2v
        elif self.config["task"] == "t2v":
            self.run_input_encoder = self._run_input_encoder_local_t2v
        elif self.config["task"] == "vace":
            self.run_input_encoder = self._run_input_encoder_local_vace
        elif self.config["task"] == "animate":
            self.run_input_encoder = self._run_input_encoder_local_animate
        elif self.config["task"] == "s2v":
            self.run_input_encoder = self._run_input_encoder_local_s2v
        self.config.lock()  # lock config to avoid modification
        if self.config.get("compile", False) and hasattr(self.model, "comple"):
            logger.info(f"[Compile] Compile all shapes: {self.config.get('compile_shapes', [])}")
            self.model.compile(self.config.get("compile_shapes", []))

    def set_init_device(self):
        if self.config["cpu_offload"]:
            self.init_device = torch.device("cpu")
        else:
            self.init_device = torch.device(AI_DEVICE)

    def load_vfi_model(self):
        if self.config["video_frame_interpolation"].get("algo", None) == "rife":
            from lightx2v.models.vfi.rife.rife_comfyui_wrapper import RIFEWrapper

            logger.info("Loading RIFE model...")
            return RIFEWrapper(self.config["video_frame_interpolation"]["model_path"])
        else:
            raise ValueError(f"Unsupported VFI model: {self.config['video_frame_interpolation']['algo']}")

    def load_vsr_model(self):
        if "video_super_resolution" in self.config:
            from lightx2v.models.runners.vsr.vsr_wrapper import VSRWrapper

            logger.info("Loading VSR model...")
            return VSRWrapper(self.config["video_super_resolution"]["model_path"])
        else:
            return None

    @ProfilingContext4DebugL2("Load models")
    def load_model(self):
        self.model = self.load_transformer()
        self.text_encoders = self.load_text_encoder()
        self.image_encoder = self.load_image_encoder()
        self.vae_encoder, self.vae_decoder = self.load_vae()
        self.vfi_model = self.load_vfi_model() if "video_frame_interpolation" in self.config else None
        self.vsr_model = self.load_vsr_model() if "video_super_resolution" in self.config else None

    def check_sub_servers(self, task_type):
        urls = self.config.get("sub_servers", {}).get(task_type, [])
        available_servers = []
        for url in urls:
            try:
                status_url = f"{url}/v1/local/{task_type}/generate/service_status"
                response = requests.get(status_url, timeout=2)
                if response.status_code == 200:
                    available_servers.append(url)
                else:
                    logger.warning(f"Service {url} returned status code {response.status_code}")

            except RequestException as e:
                logger.warning(f"Failed to connect to {url}: {str(e)}")
                continue
        logger.info(f"{task_type} available servers: {available_servers}")
        self.config["sub_servers"][task_type] = available_servers
        return len(available_servers) > 0

    def set_inputs(self, inputs):
        self.input_info.seed = inputs.get("seed", 42)
        self.input_info.prompt = inputs.get("prompt", "")
        if self.config["use_prompt_enhancer"]:
            self.input_info.prompt_enhanced = inputs.get("prompt_enhanced", "")
        self.input_info.negative_prompt = inputs.get("negative_prompt", "")
        if "image_path" in self.input_info.__dataclass_fields__:
            self.input_info.image_path = inputs.get("image_path", "")
        if "audio_path" in self.input_info.__dataclass_fields__:
            self.input_info.audio_path = inputs.get("audio_path", "")
        if "video_path" in self.input_info.__dataclass_fields__:
            self.input_info.video_path = inputs.get("video_path", "")
        self.input_info.save_result_path = inputs.get("save_result_path", "")

    def set_config(self, config_modify):
        logger.info(f"modify config: {config_modify}")
        with self.config.temporarily_unlocked():
            self.config.update(config_modify)

    def set_progress_callback(self, callback):
        self.progress_callback = callback

    @peak_memory_decorator
    def run_segment(self, segment_idx=0):
        infer_steps = self.model.scheduler.infer_steps

        for step_index in range(infer_steps):
            # only for single segment, check stop signal every step
            with ProfilingContext4DebugL1(
                f"Run Dit every step",
                recorder_mode=GET_RECORDER_MODE(),
                metrics_func=monitor_cli.lightx2v_run_per_step_dit_duration,
                metrics_labels=[step_index + 1, infer_steps],
            ):
                if self.video_segment_num == 1:
                    self.check_stop()
                logger.info(f"==> step_index: {step_index + 1} / {infer_steps}")

                with ProfilingContext4DebugL1("step_pre"):
                    self.model.scheduler.step_pre(step_index=step_index)

                with ProfilingContext4DebugL1("🚀 infer_main"):
                    self.model.infer(self.inputs)

                with ProfilingContext4DebugL1("step_post"):
                    self.model.scheduler.step_post()

                if self.progress_callback:
                    current_step = segment_idx * infer_steps + step_index + 1
                    total_all_steps = self.video_segment_num * infer_steps
                    self.progress_callback((current_step / total_all_steps) * 100, 100)

        if segment_idx is not None and segment_idx == self.video_segment_num - 1:
            del self.inputs
            torch.cuda.empty_cache()

        return self.model.scheduler.latents

    def run_step(self):
        self.inputs = self.run_input_encoder()
        if hasattr(self, "sr_version") and self.sr_version is not None is not None:
            self.config_sr["is_sr_running"] = True
            self.inputs_sr = self.run_input_encoder()
            self.config_sr["is_sr_running"] = False

        self.run_main(total_steps=1)

    def end_run(self):
        self.model.scheduler.clear()
        if hasattr(self, "inputs"):
            del self.inputs
        self.input_info = None
        if self.config.get("lazy_load", False) or self.config.get("unload_modules", False):
            del self.model
        if self.config.get("do_mm_calib", False):
            calib_path = os.path.join(os.getcwd(), "calib.pt")
            torch.save(CALIB, calib_path)
            logger.info(f"[CALIB] Saved calibration data successfully to: {calib_path}")
        torch.cuda.empty_cache()
        gc.collect()

    def read_image_input(self, img_path):
        if isinstance(img_path, Image.Image):
            img_ori = img_path
        else:
            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)
        img = TF.to_tensor(img_ori).sub_(0.5).div_(0.5).unsqueeze(0).to(self.init_device)
        self.input_info.original_size = img_ori.size
        return img, img_ori

    @ProfilingContext4DebugL2("Run Encoders")
    def _run_input_encoder_local_i2v(self):
        img, img_ori = self.read_image_input(self.input_info.image_path)
        clip_encoder_out = self.run_image_encoder(img) if self.config.get("use_image_encoder", True) else None
        vae_encode_out, latent_shape = self.run_vae_encoder(img_ori if self.vae_encoder_need_img_original else img)
        self.input_info.latent_shape = latent_shape  # Important: set latent_shape in input_info
        text_encoder_output = self.run_text_encoder(self.input_info)
        torch.cuda.empty_cache()
        gc.collect()
        return self.get_encoder_output_i2v(clip_encoder_out, vae_encode_out, text_encoder_output, img)

    @ProfilingContext4DebugL2("Run Encoders")
    def _run_input_encoder_local_t2v(self):
        self.input_info.latent_shape = self.get_latent_shape_with_target_hw()  # Important: set latent_shape in input_info
        text_encoder_output = self.run_text_encoder(self.input_info)
        torch.cuda.empty_cache()
        gc.collect()
        return {
            "text_encoder_output": text_encoder_output,
            "image_encoder_output": None,
        }

    @ProfilingContext4DebugL2("Run Encoders")
    def _run_input_encoder_local_flf2v(self):
        first_frame, _ = self.read_image_input(self.input_info.image_path)
        last_frame, _ = self.read_image_input(self.input_info.last_frame_path)
        clip_encoder_out = self.run_image_encoder(first_frame, last_frame) if self.config.get("use_image_encoder", True) else None
        vae_encode_out, latent_shape = self.run_vae_encoder(first_frame, last_frame)
        self.input_info.latent_shape = latent_shape  # Important: set latent_shape in input_info
        text_encoder_output = self.run_text_encoder(self.input_info)
        torch.cuda.empty_cache()
        gc.collect()
        return self.get_encoder_output_i2v(clip_encoder_out, vae_encode_out, text_encoder_output)

    @ProfilingContext4DebugL2("Run Encoders")
    def _run_input_encoder_local_vace(self):
        src_video = self.input_info.src_video
        src_mask = self.input_info.src_mask
        src_ref_images = self.input_info.src_ref_images
        src_video, src_mask, src_ref_images = self.prepare_source(
            [src_video],
            [src_mask],
            [None if src_ref_images is None else src_ref_images.split(",")],
            (self.config["target_width"], self.config["target_height"]),
        )
        self.src_ref_images = src_ref_images

        vae_encoder_out, latent_shape = self.run_vae_encoder(src_video, src_ref_images, src_mask)
        self.input_info.latent_shape = latent_shape  # Important: set latent_shape in input_info
        text_encoder_output = self.run_text_encoder(self.input_info)
        torch.cuda.empty_cache()
        gc.collect()
        return self.get_encoder_output_i2v(None, vae_encoder_out, text_encoder_output)

    @ProfilingContext4DebugL2("Run Text Encoder")
    def _run_input_encoder_local_animate(self):
        text_encoder_output = self.run_text_encoder(self.input_info)
        torch.cuda.empty_cache()
        gc.collect()
        return self.get_encoder_output_i2v(None, None, text_encoder_output, None)

    def _run_input_encoder_local_s2v(self):
        pass

    def init_run(self):
        self.gen_video_final = None
        self.get_video_segment_num()

        if self.config.get("lazy_load", False) or self.config.get("unload_modules", False):
            self.model = self.load_transformer()
            self.model.set_scheduler(self.scheduler)

        self.model.scheduler.prepare(seed=self.input_info.seed, latent_shape=self.input_info.latent_shape, image_encoder_output=self.inputs["image_encoder_output"])
        if self.config.get("model_cls") == "wan2.2" and self.config["task"] in ["i2v", "s2v"]:
            self.inputs["image_encoder_output"]["vae_encoder_out"] = None

        if hasattr(self, "sr_version") and self.sr_version is not None:
            self.lq_latents_shape = self.model.scheduler.latents.shape
            self.model_sr.set_scheduler(self.scheduler_sr)
            self.config_sr["is_sr_running"] = True
            self.inputs_sr = self.run_input_encoder()
            self.config_sr["is_sr_running"] = False

    @ProfilingContext4DebugL2("Run DiT")
    def run_main(self):
        self.init_run()
        if self.config.get("compile", False) and hasattr(self.model, "comple"):
            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_segments_end2end_duration,
                metrics_labels=["DefaultRunner"],
            ):
                self.check_stop()
                # 1. default do nothing
                self.init_run_segment(segment_idx)
                # 2. main inference loop
                latents = self.run_segment(segment_idx)
                # 3. vae decoder
                self.gen_video = self.run_vae_decoder(latents)
                # 4. default do nothing
                self.end_run_segment(segment_idx)
        gen_video_final = self.process_images_after_vae_decoder()
        self.end_run()
        return gen_video_final

    @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()
        images = self.vae_decoder.decode(latents.to(GET_DTYPE()))
        if self.config.get("lazy_load", False) or self.config.get("unload_modules", False):
            del self.vae_decoder
            torch.cuda.empty_cache()
            gc.collect()
        return images

    def post_prompt_enhancer(self):
        while True:
            for url in self.config["sub_servers"]["prompt_enhancer"]:
                response = requests.get(f"{url}/v1/local/prompt_enhancer/generate/service_status").json()
                if response["service_status"] == "idle":
                    response = requests.post(
                        f"{url}/v1/local/prompt_enhancer/generate",
                        json={
                            "task_id": generate_task_id(),
                            "prompt": self.config["prompt"],
                        },
                    )
                    enhanced_prompt = response.json()["output"]
                    logger.info(f"Enhanced prompt: {enhanced_prompt}")
                    return enhanced_prompt

    def process_images_after_vae_decoder(self):
        self.gen_video_final = vae_to_comfyui_image(self.gen_video_final)

        if "video_frame_interpolation" in self.config:
            assert self.vfi_model is not None and self.config["video_frame_interpolation"].get("target_fps", None) is not None
            target_fps = self.config["video_frame_interpolation"]["target_fps"]
            logger.info(f"Interpolating frames from {self.config.get('fps', 16)} to {target_fps}")
            self.gen_video_final = self.vfi_model.interpolate_frames(
                self.gen_video_final,
                source_fps=self.config.get("fps", 16),
                target_fps=target_fps,
            )

        if self.input_info.return_result_tensor:
            return {"video": self.gen_video_final}
        elif self.input_info.save_result_path is not None:
            if "video_frame_interpolation" in self.config and self.config["video_frame_interpolation"].get("target_fps"):
                fps = self.config["video_frame_interpolation"]["target_fps"]
            else:
                fps = self.config.get("fps", 16)

            if not dist.is_initialized() or dist.get_rank() == 0:
                logger.info(f"🎬 Start to save video 🎬")

                save_to_video(self.gen_video_final, self.input_info.save_result_path, fps=fps, method="ffmpeg")
                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"])
    def run_pipeline(self, input_info):
        if GET_RECORDER_MODE():
            monitor_cli.lightx2v_worker_request_count.inc()
        self.input_info = input_info

        if self.config["use_prompt_enhancer"]:
            self.input_info.prompt_enhanced = self.post_prompt_enhancer()

        self.inputs = self.run_input_encoder()

        gen_video_final = self.run_main()

        if GET_RECORDER_MODE():
            monitor_cli.lightx2v_worker_request_success.inc()
        return gen_video_final