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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('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
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