Spaces:
Sleeping
Sleeping
| from __future__ import annotations | |
| import gc | |
| import pathlib | |
| import gradio as gr | |
| import PIL.Image | |
| import torch | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from huggingface_hub import ModelCard | |
| from svdiff_pytorch import load_unet_for_svdiff, SCHEDULER_MAPPING, image_grid | |
| 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 | |
| self.base_model_id = None | |
| def clear(self) -> None: | |
| self.model_id = None | |
| self.base_model_id = None | |
| del self.pipe | |
| self.pipe = None | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| def check_if_model_is_local(model_id: str) -> bool: | |
| return pathlib.Path(model_id).exists() | |
| 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) | |
| 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 = load_unet_for_svdiff(base_model_id, spectral_shifts_ckpt=model_id, subfolder="unet").to(self.device) | |
| # first perform svd and cache | |
| for module in unet.modules(): | |
| if hasattr(module, "perform_svd"): | |
| module.perform_svd() | |
| unet = unet.to(self.device, dtype=torch.float16) | |
| if base_model_id != self.base_model_id: | |
| if self.device.type == 'cpu': | |
| pipe = DiffusionPipeline.from_pretrained( | |
| base_model_id, | |
| unet=unet, | |
| use_auth_token=self.hf_token | |
| ) | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained( | |
| base_model_id, | |
| unet=unet, | |
| torch_dtype=torch.float16, | |
| use_auth_token=self.hf_token | |
| ) | |
| pipe = pipe.to(self.device) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| self.pipe = pipe | |
| self.model_id = model_id # type: ignore | |
| self.base_model_id = base_model_id # type: ignore | |
| def run( | |
| self, | |
| model_id: str, | |
| prompt: str, | |
| 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, | |
| num_inference_steps=n_steps, | |
| guidance_scale=guidance_scale, | |
| generator=generator, | |
| ) # type: ignore | |
| return out.images[0] | |