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, load_text_encoder_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() @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 = 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() if self.device.type != 'cpu': unet = unet.to(self.device, dtype=torch.float16) text_encoder = load_text_encoder_for_svdiff(base_model_id, spectral_shifts_ckpt=model_id, subfolder="text_encoder") if self.device.type != 'cpu': text_encoder = text_encoder.to(self.device, dtype=torch.float16) else: text_encoder = text_encoder.to(self.device) if base_model_id != self.base_model_id: if self.device.type == 'cpu': pipe = DiffusionPipeline.from_pretrained( base_model_id, unet=unet, text_encoder=text_encoder, use_auth_token=self.hf_token ) else: pipe = DiffusionPipeline.from_pretrained( base_model_id, unet=unet, text_encoder=text_encoder, 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]