| import os
|
| import torch
|
| import ldm_patched.modules.model_management as model_management
|
|
|
| from torchvision import transforms
|
| from torchvision.transforms.functional import InterpolationMode
|
| from modules.model_loader import load_file_from_url
|
| from modules.config import path_clip_vision
|
| from ldm_patched.modules.model_patcher import ModelPatcher
|
| from extras.BLIP.models.blip import blip_decoder
|
|
|
|
|
| blip_image_eval_size = 384
|
| blip_repo_root = os.path.join(os.path.dirname(__file__), 'BLIP')
|
|
|
|
|
| class Interrogator:
|
| def __init__(self):
|
| self.blip_model = None
|
| self.load_device = torch.device('cpu')
|
| self.offload_device = torch.device('cpu')
|
| self.dtype = torch.float32
|
|
|
| @torch.no_grad()
|
| @torch.inference_mode()
|
| def interrogate(self, img_rgb):
|
| if self.blip_model is None:
|
| filename = load_file_from_url(
|
| url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth',
|
| model_dir=path_clip_vision,
|
| file_name='model_base_caption_capfilt_large.pth',
|
| )
|
|
|
| model = blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base',
|
| med_config=os.path.join(blip_repo_root, "configs", "med_config.json"))
|
| model.eval()
|
|
|
| self.load_device = model_management.text_encoder_device()
|
| self.offload_device = model_management.text_encoder_offload_device()
|
| self.dtype = torch.float32
|
|
|
| model.to(self.offload_device)
|
|
|
| if model_management.should_use_fp16(device=self.load_device):
|
| model.half()
|
| self.dtype = torch.float16
|
|
|
| self.blip_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
|
|
|
| model_management.load_model_gpu(self.blip_model)
|
|
|
| gpu_image = transforms.Compose([
|
| transforms.ToTensor(),
|
| transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
|
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
|
| ])(img_rgb).unsqueeze(0).to(device=self.load_device, dtype=self.dtype)
|
|
|
| caption = self.blip_model.model.generate(gpu_image, sample=True, num_beams=1, max_length=75)[0]
|
|
|
| return caption
|
|
|
|
|
| default_interrogator = Interrogator().interrogate
|
|
|
