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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()
@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()
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]
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