from transformers import AutoProcessor, BlipForQuestionAnswering from transformers.utils import logging class Inference: def __init__(self): self.blip_processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base") self.blip_model_saffal = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_saffal_fashion_finetuning") self.blip_model_control_net = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_control_net_fashion_finetuning") logging.set_verbosity_info() self.logger = logging.get_logger("transformers") def inference(self, selected, image, text): self.logger.info(f"selected model {selected}, question {text.value}") if selected == "Blip Saffal": return self.__inference_saffal_blip(image, text) elif selected == "Blip CN": return self.__inference_control_net_blip(image, text) else: self.logger.warning("Please select a model to make the inference..") def __inference_saffal_blip(self, image, text): encoding = self.blip_processor(image, text, return_tensors="pt") out = self.blip_model_saffal.generate(**encoding, max_new_tokens=100) generated_text = self.blip_processor.decode(out[0], skip_special_tokens=True) return f"{generated_text}" def __inference_control_net_blip(self, image, text): encoding = self.blip_processor(image, text, return_tensors="pt") out = self.blip_model_control_net.generate(**encoding, max_new_tokens=100) generated_text = self.blip_processor.decode(out[0], skip_special_tokens=True) return f"{generated_text}"