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from __future__ import annotations
import gc
import pathlib
import gradio as gr
import PIL.Image
import torch
from diffusers import StableDiffusionXLPipeline
from huggingface_hub import ModelCard
from blora_utils import BLOCKS, filter_lora, scale_lora
class InferencePipeline:
def __init__(self, hf_token: str | None = None):
self.hf_token = hf_token
self.base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
if self.device.type == 'cpu':
self.pipe = StableDiffusionXLPipeline.from_pretrained(
self.base_model_id, use_auth_token=self.hf_token, cache_dir='./cache')
else:
self.pipe = StableDiffusionXLPipeline.from_pretrained(
self.base_model_id,
torch_dtype=torch.float16,
use_auth_token=self.hf_token)
self.pipe = self.pipe.to(self.device)
self.content_lora_model_id = None
self.style_lora_model_id = None
def clear(self) -> None:
self.content_lora_model_id = None
self.style_lora_model_id = None
del self.pipe
self.pipe = None
torch.cuda.empty_cache()
gc.collect()
def load_b_lora_to_unet(self, content_lora_model_id: str, style_lora_model_id: str, content_alpha: float,
style_alpha: float) -> None:
try:
# Get Content B-LoRA SD
if content_lora_model_id and content_lora_model_id != 'None':
content_B_LoRA_sd, _ = self.pipe.lora_state_dict(content_lora_model_id, use_auth_token=self.hf_token)
content_B_LoRA = filter_lora(content_B_LoRA_sd, BLOCKS['content'])
content_B_LoRA = scale_lora(content_B_LoRA, content_alpha)
else:
content_B_LoRA = {}
# Get Style B-LoRA SD
if style_lora_model_id and style_lora_model_id != 'None':
style_B_LoRA_sd, _ = self.pipe.lora_state_dict(style_lora_model_id, use_auth_token=self.hf_token)
style_B_LoRA = filter_lora(style_B_LoRA_sd, BLOCKS['style'])
style_B_LoRA = scale_lora(style_B_LoRA, style_alpha)
else:
style_B_LoRA = {}
# Merge B-LoRAs SD
res_lora = {**content_B_LoRA, **style_B_LoRA}
# Load
self.pipe.load_lora_into_unet(res_lora, None, self.pipe.unet)
except Exception as e:
raise type(e)(f'failed to load_b_lora_to_unet, due to: {e}')
@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, content_lora_model_id: str, style_lora_model_id: str, content_alpha: float,
style_alpha: float) -> None:
if content_lora_model_id == self.content_lora_model_id and style_lora_model_id == self.style_lora_model_id:
return
self.pipe.unload_lora_weights()
self.load_b_lora_to_unet(content_lora_model_id, style_lora_model_id, content_alpha, style_alpha)
self.content_lora_model_id = content_lora_model_id
self.style_lora_model_id = style_lora_model_id
def run(
self,
content_lora_model_id: str,
style_lora_model_id: str,
prompt: str,
content_alpha: float,
style_alpha: float,
seed: int,
n_steps: int,
guidance_scale: float,
num_images_per_prompt: int = 1
) -> PIL.Image.Image:
if not torch.cuda.is_available():
raise gr.Error('CUDA is not available.')
self.load_pipe(content_lora_model_id, style_lora_model_id, content_alpha, style_alpha)
generator = torch.Generator(device=self.device).manual_seed(seed)
out = self.pipe(
prompt,
num_inference_steps=n_steps,
guidance_scale=guidance_scale,
generator=generator,
num_images_per_prompt=num_images_per_prompt,
) # type: ignore
return out.images
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