| | 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 |
| |
|
| |
|
| | 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.lora_model_id = None |
| | self.base_model_id = None |
| |
|
| | def clear(self) -> None: |
| | self.lora_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(lora_model_id: str) -> bool: |
| | return pathlib.Path(lora_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(lora_model_id: str, |
| | hf_token: str | None = None) -> str: |
| | card = InferencePipeline.get_model_card(lora_model_id, hf_token) |
| | return card.data.base_model |
| |
|
| | def load_pipe(self, lora_model_id: str) -> None: |
| | if lora_model_id == self.lora_model_id: |
| | return |
| | base_model_id = self.get_base_model_info(lora_model_id, self.hf_token) |
| | if base_model_id != self.base_model_id: |
| | if self.device.type == 'cpu': |
| | pipe = DiffusionPipeline.from_pretrained( |
| | base_model_id, use_auth_token=self.hf_token) |
| | else: |
| | pipe = DiffusionPipeline.from_pretrained( |
| | base_model_id, |
| | 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.pipe.unet.load_attn_procs( |
| | lora_model_id, use_auth_token=self.hf_token) |
| |
|
| | self.lora_model_id = lora_model_id |
| | self.base_model_id = base_model_id |
| |
|
| | def run( |
| | self, |
| | lora_model_id: str, |
| | prompt: str, |
| | lora_scale: float, |
| | 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(lora_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, |
| | cross_attention_kwargs={'scale': lora_scale}, |
| | ) |
| | return out.images[0] |
| |
|
