|
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 |
|
|
|
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, |
|
) |
|
|
|
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 |
|
|
