Spaces:
Sleeping
Sleeping
from __future__ import annotations | |
import gc | |
import pathlib | |
import spaces | |
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' 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) | |
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}') | |
def check_if_model_is_local(lora_model_id: str) -> bool: | |
return pathlib.Path(lora_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(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.') | |
print(f"running with {self.device}, {self.pipe.device}") | |
self.load_pipe(content_lora_model_id, style_lora_model_id, content_alpha, style_alpha) | |
generator = torch.Generator().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 | |