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.utils.envs import *
from lightx2v.utils.generate_task_id import generate_task_id
from lightx2v.utils.profiler import ProfilingContext, ProfilingContext4Debug
from lightx2v.utils.utils import save_to_video, vae_to_comfyui_image

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()

    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 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

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

    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']}")

    @ProfilingContext("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

    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.config["prompt"] = inputs.get("prompt", "")
        self.config["use_prompt_enhancer"] = False
        if self.has_prompt_enhancer:
            self.config["use_prompt_enhancer"] = inputs.get("use_prompt_enhancer", False)  # Reset use_prompt_enhancer from clinet side.
        self.config["negative_prompt"] = inputs.get("negative_prompt", "")
        self.config["image_path"] = inputs.get("image_path", "")
        self.config["save_video_path"] = inputs.get("save_video_path", "")
        self.config["infer_steps"] = inputs.get("infer_steps", self.config.get("infer_steps", 5))
        self.config["target_video_length"] = inputs.get("target_video_length", self.config.get("target_video_length", 81))
        self.config["seed"] = inputs.get("seed", self.config.get("seed", 42))
        self.config["audio_path"] = inputs.get("audio_path", "")  # for wan-audio
        self.config["video_duration"] = inputs.get("video_duration", 5)  # for wan-audio

        # self.config["sample_shift"] = inputs.get("sample_shift", self.config.get("sample_shift", 5))
        # self.config["sample_guide_scale"] = inputs.get("sample_guide_scale", self.config.get("sample_guide_scale", 5))

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

    def run_segment(self, total_steps=None):
        if total_steps is None:
            total_steps = self.model.scheduler.infer_steps
        for step_index in range(total_steps):
            # only for single segment, check stop signal every step
            if self.video_segment_num == 1:
                self.check_stop()
            logger.info(f"==> step_index: {step_index + 1} / {total_steps}")

            with ProfilingContext4Debug("step_pre"):
                self.model.scheduler.step_pre(step_index=step_index)

            with ProfilingContext4Debug("🚀 infer_main"):
                self.model.infer(self.inputs)

            with ProfilingContext4Debug("step_post"):
                self.model.scheduler.step_post()

            if self.progress_callback:
                self.progress_callback(((step_index + 1) / total_steps) * 100, 100)

        return self.model.scheduler.latents

    def run_step(self):
        self.inputs = self.run_input_encoder()
        self.run_main(total_steps=1)

    def end_run(self):
        self.model.scheduler.clear()
        del self.inputs, self.model.scheduler
        if self.config.get("lazy_load", False) or self.config.get("unload_modules", False):
            if hasattr(self.model.transformer_infer, "weights_stream_mgr"):
                self.model.transformer_infer.weights_stream_mgr.clear()
            if hasattr(self.model.transformer_weights, "clear"):
                self.model.transformer_weights.clear()
            self.model.pre_weight.clear()
            del self.model
        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")
        img = TF.to_tensor(img_ori).sub_(0.5).div_(0.5).unsqueeze(0).cuda()
        return img, img_ori

    @ProfilingContext("Run Encoders")
    def _run_input_encoder_local_i2v(self):
        prompt = self.config["prompt_enhanced"] if self.config["use_prompt_enhancer"] else self.config["prompt"]
        img, img_ori = self.read_image_input(self.config["image_path"])
        clip_encoder_out = self.run_image_encoder(img) if self.config.get("use_image_encoder", True) else None
        vae_encode_out = self.run_vae_encoder(img_ori if self.vae_encoder_need_img_original else img)
        text_encoder_output = self.run_text_encoder(prompt, img)
        torch.cuda.empty_cache()
        gc.collect()
        return self.get_encoder_output_i2v(clip_encoder_out, vae_encode_out, text_encoder_output, img)

    @ProfilingContext("Run Encoders")
    def _run_input_encoder_local_t2v(self):
        prompt = self.config["prompt_enhanced"] if self.config["use_prompt_enhancer"] else self.config["prompt"]
        text_encoder_output = self.run_text_encoder(prompt, None)
        torch.cuda.empty_cache()
        gc.collect()
        return {
            "text_encoder_output": text_encoder_output,
            "image_encoder_output": None,
        }

    @ProfilingContext("Run Encoders")
    def _run_input_encoder_local_flf2v(self):
        prompt = self.config["prompt_enhanced"] if self.config["use_prompt_enhancer"] else self.config["prompt"]
        first_frame, _ = self.read_image_input(self.config["image_path"])
        last_frame, _ = self.read_image_input(self.config["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 = self.run_vae_encoder(first_frame, last_frame)
        text_encoder_output = self.run_text_encoder(prompt, first_frame)
        torch.cuda.empty_cache()
        gc.collect()
        return self.get_encoder_output_i2v(clip_encoder_out, vae_encode_out, text_encoder_output)

    @ProfilingContext("Run Encoders")
    def _run_input_encoder_local_vace(self):
        prompt = self.config["prompt_enhanced"] if self.config["use_prompt_enhancer"] else self.config["prompt"]
        src_video = self.config.get("src_video", None)
        src_mask = self.config.get("src_mask", None)
        src_ref_images = self.config.get("src_ref_images", None)
        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 = self.run_vae_encoder(src_video, src_ref_images, src_mask)
        text_encoder_output = self.run_text_encoder(prompt)
        torch.cuda.empty_cache()
        gc.collect()
        return self.get_encoder_output_i2v(None, vae_encoder_out, text_encoder_output)

    def init_run(self):
        self.set_target_shape()
        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.init_scheduler()
        self.model.scheduler.prepare(self.inputs["image_encoder_output"])
        if self.config.get("model_cls") == "wan2.2" and self.config["task"] == "i2v":
            self.inputs["image_encoder_output"]["vae_encoder_out"] = None

    @ProfilingContext("Run DiT")
    def run_main(self, total_steps=None):
        self.init_run()
        for segment_idx in range(self.video_segment_num):
            logger.info(f"🔄 segment_idx: {segment_idx + 1}/{self.video_segment_num}")
            with ProfilingContext(f"segment end2end {segment_idx + 1}/{self.video_segment_num}"):
                self.check_stop()
                # 1. default do nothing
                self.init_run_segment(segment_idx)
                # 2. main inference loop
                latents = self.run_segment(total_steps=total_steps)
                # 3. vae decoder
                self.gen_video = self.run_vae_decoder(latents)
                # 4. default do nothing
                self.end_run_segment()
        self.end_run()

    @ProfilingContext("Run VAE Decoder")
    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, save_video=True):
        self.gen_video = vae_to_comfyui_image(self.gen_video)

        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 = self.vfi_model.interpolate_frames(
                self.gen_video,
                source_fps=self.config.get("fps", 16),
                target_fps=target_fps,
            )

        if save_video:
            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, self.config.save_video_path, fps=fps, method="ffmpeg")
                logger.info(f"✅ Video saved successfully to: {self.config.save_video_path} ✅")
        return {"video": self.gen_video}

    def run_pipeline(self, save_video=True):
        if self.config["use_prompt_enhancer"]:
            self.config["prompt_enhanced"] = self.post_prompt_enhancer()

        self.inputs = self.run_input_encoder()

        self.run_main()

        gen_video = self.process_images_after_vae_decoder(save_video=save_video)

        torch.cuda.empty_cache()
        gc.collect()

        return gen_video