default_runner.py

import gc
import requests
from requests.exceptions import RequestException
import torch
import torch.distributed as dist
import torchvision.transforms.functional as TF
from PIL import Image
from lightx2v.utils.profiler import ProfilingContext4Debug, ProfilingContext
from lightx2v.utils.utils import save_videos_grid, cache_video
from lightx2v.utils.generate_task_id import generate_task_id
from lightx2v.utils.envs import *
from lightx2v.utils.service_utils import TensorTransporter, ImageTransporter
from loguru import logger
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()
        self.run_dit = self._run_dit_local
        self.run_vae_decoder = self._run_vae_decoder_local
        if self.config["task"] == "i2v":
            self.run_input_encoder = self._run_input_encoder_local_i2v
        else:
            self.run_input_encoder = self._run_input_encoder_local_t2v

    def set_init_device(self):
        if self.config["parallel_attn_type"]:
            cur_rank = dist.get_rank()
            torch.cuda.set_device(cur_rank)
        if self.config.cpu_offload:
            self.init_device = torch.device("cpu")
        else:
            self.init_device = torch.device("cuda")

    @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()

    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(self):
        total_steps = self.model.scheduler.infer_steps
        for step_index in range(total_steps):
            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"):
                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)

        return self.model.scheduler.latents, self.model.scheduler.generator

    def run_step(self, step_index=0):
        self.init_scheduler()
        self.inputs = self.run_input_encoder()
        self.model.scheduler.prepare(self.inputs["image_encoder_output"])
        self.model.scheduler.step_pre(step_index=step_index)
        self.model.infer(self.inputs)
        self.model.scheduler.step_post()

    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()
            self.model.post_weight.clear()
            del self.model
        torch.cuda.empty_cache()
        gc.collect()

    @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 = Image.open(self.config["image_path"]).convert("RGB")
        clip_encoder_out = self.run_image_encoder(img)
        vae_encode_out, kwargs = self.run_vae_encoder(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 DiT")
    def _run_dit_local(self, kwargs):
        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"])
        latents, generator = self.run()
        self.end_run()
        return latents, generator

    @ProfilingContext("Run VAE Decoder")
    def _run_vae_decoder_local(self, latents, generator):
        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, generator=generator, config=self.config)
        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

    @ProfilingContext("Save video")
    def save_video(self, images):
        if not self.config.parallel_attn_type or (self.config.parallel_attn_type and dist.get_rank() == 0):
            self.save_video_func(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 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()

        kwargs = self.set_target_shape()

        latents, generator = self.run_dit(kwargs)

        images = self.run_vae_decoder(latents, generator)

        if save_video:
            self.save_video(images)

        del latents, generator
        torch.cuda.empty_cache()
        gc.collect()

        return images