from __future__ import annotations import gc import pathlib import sys import tempfile import gradio as gr import imageio import PIL.Image import torch from diffusers.utils.import_utils import is_xformers_available from einops import rearrange from huggingface_hub import ModelCard sys.path.append('Video-P2P') sys.path.append('Tune-A-Video') from tuneavideo.models.unet import UNet3DConditionModel from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline class InferencePipeline: def __init__(self, hf_token: str | None = None): self.hf_token = hf_token self.pipe = None self.device = torch.device( 'cuda:0' if torch.cuda.is_available() else 'cpu') self.model_id = None def clear(self) -> None: self.model_id = None del self.pipe self.pipe = None torch.cuda.empty_cache() gc.collect() @staticmethod def check_if_model_is_local(model_id: str) -> bool: return pathlib.Path(model_id).exists() @staticmethod def get_model_card(model_id: str, hf_token: str | None = None) -> ModelCard: if InferencePipeline.check_if_model_is_local(model_id): card_path = (pathlib.Path(model_id) / 'README.md').as_posix() else: card_path = model_id return ModelCard.load(card_path, token=hf_token) @staticmethod def get_base_model_info(model_id: str, hf_token: str | None = None) -> str: card = InferencePipeline.get_model_card(model_id, hf_token) return card.data.base_model def load_pipe(self, model_id: str) -> None: if model_id == self.model_id: return base_model_id = self.get_base_model_info(model_id, self.hf_token) unet = UNet3DConditionModel.from_pretrained( model_id, subfolder='unet', torch_dtype=torch.float16, use_auth_token=self.hf_token) pipe = TuneAVideoPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, use_auth_token=self.hf_token) pipe = pipe.to(self.device) if is_xformers_available(): pipe.unet.enable_xformers_memory_efficient_attention() self.pipe = pipe self.model_id = model_id # type: ignore def run( self, model_id: str, prompt: str, video_length: int, fps: int, seed: int, n_steps: int, guidance_scale: float, ) -> PIL.Image.Image: if not torch.cuda.is_available(): raise gr.Error('CUDA is not available.') self.load_pipe(model_id) generator = torch.Generator(device=self.device).manual_seed(seed) out = self.pipe( prompt, video_length=video_length, width=512, height=512, num_inference_steps=n_steps, guidance_scale=guidance_scale, generator=generator, ) # type: ignore frames = rearrange(out.videos[0], 'c t h w -> t h w c') frames = (frames * 255).to(torch.uint8).numpy() out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) writer = imageio.get_writer(out_file.name, fps=fps) for frame in frames: writer.append_data(frame) writer.close() return out_file.name