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from .caption import default_long_prompt, default_short_prompt, default_replacements, clean_caption |
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import torch |
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from PIL import Image, ImageOps |
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from transformers import AutoTokenizer, BitsAndBytesConfig, CLIPImageProcessor |
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img_ext = ['.jpg', '.jpeg', '.png', '.webp'] |
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class LLaVAImageProcessor: |
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def __init__(self, device='cuda'): |
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try: |
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from llava.model import LlavaLlamaForCausalLM |
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except ImportError: |
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print( |
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"You need to manually install llava -> pip install --no-deps git+https://github.com/haotian-liu/LLaVA.git") |
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raise |
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self.device = device |
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self.model: LlavaLlamaForCausalLM = None |
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self.tokenizer: AutoTokenizer = None |
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self.image_processor: CLIPImageProcessor = None |
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self.is_loaded = False |
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def load_model(self): |
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from llava.model import LlavaLlamaForCausalLM |
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model_path = "4bit/llava-v1.5-13b-3GB" |
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kwargs = {"device_map": self.device} |
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kwargs['load_in_4bit'] = True |
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kwargs['quantization_config'] = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_compute_dtype=torch.float16, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type='nf4' |
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) |
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self.model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) |
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self.tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) |
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vision_tower = self.model.get_vision_tower() |
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if not vision_tower.is_loaded: |
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vision_tower.load_model() |
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vision_tower.to(device=self.device) |
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self.image_processor = vision_tower.image_processor |
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self.is_loaded = True |
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def generate_caption( |
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self, image: |
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Image, prompt: str = default_long_prompt, |
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replacements=default_replacements, |
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max_new_tokens=512 |
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): |
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from llava.conversation import conv_templates, SeparatorStyle |
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from llava.utils import disable_torch_init |
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from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN |
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from llava.mm_utils import tokenizer_image_token, KeywordsStoppingCriteria |
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disable_torch_init() |
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conv_mode = "llava_v0" |
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conv = conv_templates[conv_mode].copy() |
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roles = conv.roles |
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image_tensor = self.image_processor.preprocess([image], return_tensors='pt')['pixel_values'].half().cuda() |
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inp = f"{roles[0]}: {prompt}" |
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inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + inp |
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conv.append_message(conv.roles[0], inp) |
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conv.append_message(conv.roles[1], None) |
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raw_prompt = conv.get_prompt() |
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input_ids = tokenizer_image_token(raw_prompt, self.tokenizer, IMAGE_TOKEN_INDEX, |
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return_tensors='pt').unsqueeze(0).cuda() |
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stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 |
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keywords = [stop_str] |
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stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids) |
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with torch.inference_mode(): |
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output_ids = self.model.generate( |
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input_ids, images=image_tensor, do_sample=True, temperature=0.1, |
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max_new_tokens=max_new_tokens, use_cache=True, stopping_criteria=[stopping_criteria], |
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top_p=0.8 |
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) |
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outputs = self.tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() |
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conv.messages[-1][-1] = outputs |
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output = outputs.rsplit('</s>', 1)[0] |
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return clean_caption(output, replacements=replacements) |
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