| 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 |
|
|
