import torch
from PIL import Image
from transformers import AutoProcessor, LlavaForConditionalGeneration
IMAGE_PATH = "image.jpg"
PROMPT = "Write a long descriptive caption for this image in a formal tone."
MODEL_NAME = "1038lab/llama-joycaption-beta-one"
# Load JoyCaption
# bfloat16 is the native dtype of the LLM used in JoyCaption (Llama 3.1)
# device_map=0 loads the model into the first GPU
processor = AutoProcessor.from_pretrained(MODEL_NAME)
llava_model = LlavaForConditionalGeneration.from_pretrained(MODEL_NAME, torch_dtype="bfloat16", device_map=0)
llava_model.eval()
with torch.no_grad():
# Load image
image = Image.open(IMAGE_PATH)
# Build the conversation
convo = [
{
"role": "system",
"content": "You are a helpful image captioner.",
},
{
"role": "user",
"content": PROMPT,
},
]
# Format the conversation
# WARNING: HF's handling of chat's on Llava models is very fragile. This specific combination of processor.apply_chat_template(), and processor() works
# but if using other combinations always inspect the final input_ids to ensure they are correct. Often times you will end up with multiple <bos> tokens
# if not careful, which can make the model perform poorly.
convo_string = processor.apply_chat_template(convo, tokenize = False, add_generation_prompt = True)
assert isinstance(convo_string, str)
# Process the inputs
inputs = processor(text=[convo_string], images=[image], return_tensors="pt").to('cuda')
inputs['pixel_values'] = inputs['pixel_values'].to(torch.bfloat16)
# Generate the captions
generate_ids = llava_model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
suppress_tokens=None,
use_cache=True,
temperature=0.6,
top_k=None,
top_p=0.9,
)[0]
# Trim off the prompt
generate_ids = generate_ids[inputs['input_ids'].shape[1]:]
# Decode the caption
caption = processor.tokenizer.decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
caption = caption.strip()
print(caption)