Load the model into memory to make running multiple predictions efficient
(self)
| 43 | |
| 44 | class Predictor(BasePredictor): |
| 45 | def setup(self) -> None: |
| 46 | """Load the model into memory to make running multiple predictions efficient""" |
| 47 | pretrained_path = "pretrained_models" |
| 48 | torch.set_grad_enabled(False) |
| 49 | self.device = "cuda:0" |
| 50 | |
| 51 | # ============ base model ============ |
| 52 | args_base = OmegaConf.load("base/configs/sample.yaml") |
| 53 | sd_path = pretrained_path + "/stable-diffusion-v1-4" |
| 54 | self.unet = get_models_base(args_base, sd_path).to( |
| 55 | self.device, dtype=torch.float16 |
| 56 | ) |
| 57 | state_dict = find_model(pretrained_path + "/lavie_base.pt") |
| 58 | self.unet.load_state_dict(state_dict) |
| 59 | |
| 60 | self.vae = AutoencoderKL.from_pretrained( |
| 61 | sd_path, subfolder="vae", torch_dtype=torch.float16 |
| 62 | ).to(self.device) |
| 63 | self.tokenizer_one = CLIPTokenizer.from_pretrained( |
| 64 | sd_path, subfolder="tokenizer" |
| 65 | ) |
| 66 | self.text_encoder_one = CLIPTextModel.from_pretrained( |
| 67 | sd_path, subfolder="text_encoder", torch_dtype=torch.float16 |
| 68 | ).to(self.device) |
| 69 | |
| 70 | self.unet.eval() |
| 71 | self.vae.eval() |
| 72 | self.text_encoder_one.eval() |
| 73 | |
| 74 | self.schedulers = { |
| 75 | "ddim": DDIMScheduler.from_pretrained( |
| 76 | sd_path, |
| 77 | subfolder="scheduler", |
| 78 | beta_start=args_base.beta_start, |
| 79 | beta_end=args_base.beta_end, |
| 80 | beta_schedule=args_base.beta_schedule, |
| 81 | ), |
| 82 | "eulerdiscrete": EulerDiscreteScheduler.from_pretrained( |
| 83 | sd_path, |
| 84 | subfolder="scheduler", |
| 85 | beta_start=args_base.beta_start, |
| 86 | beta_end=args_base.beta_end, |
| 87 | beta_schedule=args_base.beta_schedule, |
| 88 | ), |
| 89 | "ddpm": DDPMScheduler.from_pretrained( |
| 90 | sd_path, |
| 91 | subfolder="scheduler", |
| 92 | beta_start=args_base.beta_start, |
| 93 | beta_end=args_base.beta_end, |
| 94 | beta_schedule=args_base.beta_schedule, |
| 95 | ), |
| 96 | } |
| 97 | |
| 98 | # ============ interpolation model ============ |
| 99 | interpolation_ckpt_path = pretrained_path + "/lavie_interpolation.pt" |
| 100 | self.args_interpolation = OmegaConf.load( |
| 101 | "interpolation/configs/sample.yaml" |
| 102 | ).args |
nothing calls this directly
no test coverage detected