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