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from __future__ import annotations |
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import datetime |
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import os |
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import pathlib |
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import shlex |
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import shutil |
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import subprocess |
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import sys |
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import gradio as gr |
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import slugify |
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import torch |
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from huggingface_hub import HfApi |
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from omegaconf import OmegaConf |
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from app_upload import ModelUploader |
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from utils import save_model_card |
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sys.path.append('Tune-A-Video') |
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URL_TO_JOIN_MODEL_LIBRARY_ORG = 'https://huggingface.co/organizations/Tune-A-Video-library/share/YjTcaNJmKyeHFpMBioHhzBcTzCYddVErEk' |
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class Trainer: |
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def __init__(self, hf_token: str | None = None): |
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self.hf_token = hf_token |
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self.api = HfApi(token=hf_token) |
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self.model_uploader = ModelUploader(hf_token) |
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self.checkpoint_dir = pathlib.Path('checkpoints') |
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self.checkpoint_dir.mkdir(exist_ok=True) |
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def download_base_model(self, base_model_id: str) -> str: |
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model_dir = self.checkpoint_dir / base_model_id |
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if not model_dir.exists(): |
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org_name = base_model_id.split('/')[0] |
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org_dir = self.checkpoint_dir / org_name |
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org_dir.mkdir(exist_ok=True) |
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subprocess.run(shlex.split( |
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f'git clone https://huggingface.co/{base_model_id}'), |
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cwd=org_dir) |
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return model_dir.as_posix() |
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def join_model_library_org(self) -> None: |
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subprocess.run( |
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shlex.split( |
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f'curl -X POST -H "Authorization: Bearer {self.hf_token}" -H "Content-Type: application/json" {URL_TO_JOIN_MODEL_LIBRARY_ORG}' |
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)) |
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def run( |
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self, |
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training_video: str, |
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training_prompt: str, |
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output_model_name: str, |
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overwrite_existing_model: bool, |
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validation_prompt: str, |
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base_model: str, |
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resolution_s: str, |
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n_steps: int, |
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learning_rate: float, |
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gradient_accumulation: int, |
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seed: int, |
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fp16: bool, |
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use_8bit_adam: bool, |
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checkpointing_steps: int, |
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validation_epochs: int, |
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upload_to_hub: bool, |
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use_private_repo: bool, |
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delete_existing_repo: bool, |
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upload_to: str, |
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remove_gpu_after_training: bool, |
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) -> str: |
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if not torch.cuda.is_available(): |
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raise gr.Error('CUDA is not available.') |
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if training_video is None: |
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raise gr.Error('You need to upload a video.') |
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if not training_prompt: |
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raise gr.Error('The training prompt is missing.') |
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if not validation_prompt: |
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raise gr.Error('The validation prompt is missing.') |
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resolution = int(resolution_s) |
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if not output_model_name: |
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timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S') |
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output_model_name = f'tune-a-video-{timestamp}' |
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output_model_name = slugify.slugify(output_model_name) |
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repo_dir = pathlib.Path(__file__).parent |
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output_dir = repo_dir / 'experiments' / output_model_name |
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if overwrite_existing_model or upload_to_hub: |
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shutil.rmtree(output_dir, ignore_errors=True) |
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output_dir.mkdir(parents=True) |
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if upload_to_hub: |
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self.join_model_library_org() |
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config = OmegaConf.load('Tune-A-Video/configs/man-surfing.yaml') |
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config.pretrained_model_path = self.download_base_model(base_model) |
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config.output_dir = output_dir.as_posix() |
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config.train_data.video_path = training_video.name |
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config.train_data.prompt = training_prompt |
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config.train_data.n_sample_frames = 8 |
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config.train_data.width = resolution |
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config.train_data.height = resolution |
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config.train_data.sample_start_idx = 0 |
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config.train_data.sample_frame_rate = 1 |
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config.validation_data.prompts = [validation_prompt] |
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config.validation_data.video_length = 8 |
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config.validation_data.width = resolution |
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config.validation_data.height = resolution |
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config.validation_data.num_inference_steps = 50 |
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config.validation_data.guidance_scale = 7.5 |
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config.learning_rate = learning_rate |
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config.gradient_accumulation_steps = gradient_accumulation |
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config.train_batch_size = 1 |
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config.max_train_steps = n_steps |
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config.checkpointing_steps = checkpointing_steps |
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config.validation_steps = validation_epochs |
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config.seed = seed |
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config.mixed_precision = 'fp16' if fp16 else '' |
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config.use_8bit_adam = use_8bit_adam |
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config_path = output_dir / 'config.yaml' |
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with open(config_path, 'w') as f: |
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OmegaConf.save(config, f) |
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command = f'accelerate launch Tune-A-Video/train_tuneavideo.py --config {config_path}' |
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subprocess.run(shlex.split(command)) |
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save_model_card(save_dir=output_dir, |
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base_model=base_model, |
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training_prompt=training_prompt, |
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test_prompt=validation_prompt, |
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test_image_dir='samples') |
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message = 'Training completed!' |
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print(message) |
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if upload_to_hub: |
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upload_message = self.model_uploader.upload_model( |
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folder_path=output_dir.as_posix(), |
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repo_name=output_model_name, |
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upload_to=upload_to, |
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private=use_private_repo, |
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delete_existing_repo=delete_existing_repo) |
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print(upload_message) |
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message = message + '\n' + upload_message |
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if remove_gpu_after_training: |
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space_id = os.getenv('SPACE_ID') |
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if space_id: |
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self.api.request_space_hardware(repo_id=space_id, |
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hardware='cpu-basic') |
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return message |
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