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| # General | |
| import sys | |
| from pathlib import Path | |
| import torch | |
| from pytorch_lightning import LightningDataModule | |
| # For Stage-1 | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter | |
| from diffusers import StableVideoDiffusionPipeline, AutoPipelineForText2Image | |
| # For Stage-2 | |
| import tempfile | |
| import yaml | |
| from t2v_enhanced.model.video_ldm import VideoLDM | |
| from t2v_enhanced.model.callbacks import SaveConfigCallback | |
| from t2v_enhanced.inference_utils import legacy_transformation, remove_value, CustomCLI | |
| # For Stage-3 | |
| from modelscope.pipelines import pipeline | |
| # Initialize Stage-1 model1. | |
| def init_modelscope(device="cuda"): | |
| pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16") | |
| # pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
| # pipe.set_progress_bar_config(disable=True) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_model_cpu_offload() | |
| pipe.enable_vae_slicing() | |
| pipe.set_progress_bar_config(disable=True) | |
| return pipe.to(device) | |
| def init_zeroscope(device="cuda"): | |
| pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) | |
| pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_model_cpu_offload() | |
| return pipe.to(device) | |
| def init_animatediff(device="cuda"): | |
| adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16) | |
| model_id = "SG161222/Realistic_Vision_V5.1_noVAE" | |
| pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16) | |
| scheduler = DDIMScheduler.from_pretrained( | |
| model_id, | |
| subfolder="scheduler", | |
| clip_sample=False, | |
| timestep_spacing="linspace", | |
| beta_schedule="linear", | |
| steps_offset=1, | |
| ) | |
| pipe.scheduler = scheduler | |
| pipe.enable_vae_slicing() | |
| pipe.enable_model_cpu_offload() | |
| return pipe.to(device) | |
| def init_sdxl(device="cuda"): | |
| pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
| # pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) | |
| return pipe.to(device) | |
| def init_svd(device="cuda"): | |
| pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16") | |
| pipe.enable_model_cpu_offload() | |
| return pipe.to(device) | |
| # Initialize StreamingT2V model. | |
| def init_streamingt2v_model(ckpt_file, result_fol): | |
| config_file = "t2v_enhanced/configs/text_to_video/config.yaml" | |
| sys.argv = sys.argv[:1] | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| storage_fol = Path(tmpdirname) | |
| with open(config_file, "r") as yaml_handle: | |
| yaml_obj = yaml.safe_load(yaml_handle) | |
| yaml_obj_orig_data_cfg = legacy_transformation(yaml_obj) | |
| yaml_obj_orig_data_cfg = remove_value(yaml_obj_orig_data_cfg, "video_dataset") | |
| with open(storage_fol / 'config.yaml', 'w') as outfile: | |
| yaml.dump(yaml_obj_orig_data_cfg, outfile, default_flow_style=False) | |
| sys.argv.append("--config") | |
| sys.argv.append((storage_fol / 'config.yaml').as_posix()) | |
| sys.argv.append("--ckpt") | |
| sys.argv.append(ckpt_file.as_posix()) | |
| sys.argv.append("--result_fol") | |
| sys.argv.append(result_fol.as_posix()) | |
| sys.argv.append("--config") | |
| sys.argv.append("t2v_enhanced/configs/inference/inference_long_video.yaml") | |
| sys.argv.append("--data.prompt_cfg.type=prompt") | |
| sys.argv.append(f"--data.prompt_cfg.content='test prompt for initialization'") | |
| sys.argv.append("--trainer.devices=1") | |
| sys.argv.append("--trainer.num_nodes=1") | |
| sys.argv.append(f"--model.inference_params.num_inference_steps=50") | |
| sys.argv.append(f"--model.inference_params.n_autoregressive_generations=4") | |
| sys.argv.append("--model.inference_params.concat_video=True") | |
| sys.argv.append("--model.inference_params.result_formats=[eval_mp4]") | |
| cli = CustomCLI(VideoLDM, LightningDataModule, run=False, subclass_mode_data=True, | |
| auto_configure_optimizers=False, parser_kwargs={"parser_mode": "omegaconf"}, save_config_callback=SaveConfigCallback, save_config_kwargs={"log_dir": result_fol, "overwrite": True}) | |
| model = cli.model | |
| model.load_state_dict(torch.load( | |
| cli.config["ckpt"].as_posix(), map_location=torch.device('cpu'))["state_dict"], strict=False) | |
| return cli, model | |
| # Initialize Stage-3 model. | |
| def init_v2v_model(cfg): | |
| model_id = cfg['model_id'] | |
| pipe_enhance = pipeline(task="video-to-video", model=model_id, model_revision='v1.1.0', device='cuda') | |
| return pipe_enhance | |