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on
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Running
on
Zero
File size: 2,082 Bytes
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import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
MODEL_ID = "ducviet00/Florence-2-large-hf"
# Global variables for lazy loading
_model = None
_processor = None
def _load_model():
"""Load model and processor lazily"""
global _model, _processor
if _model is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
print(f"Loading model {MODEL_ID} on {device}...")
_model = AutoModelForCausalLM.from_pretrained(MODEL_ID, trust_remote_code=True)
_processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
print("Model loaded successfully!")
return _model, _processor
def get_task_response(task_prompt: str, image: Image.Image, text_input=None):
"""Return associated task response
Task can be:
'<MORE_DETAILED_CAPTION>'
'<DETAILED_CAPTION>'
'<CAPTION>'
"""
# Lazy load model only when needed
model, processor = _load_model()
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
# Ensure image is in RGB mode
if image.mode != "RGB":
image = image.convert("RGB")
if processor is None:
raise ValueError("processor is None")
# Process inputs using the correct API
inputs = processor(text=prompt, images=image, return_tensors="pt")
# Move inputs to device if model is on CUDA
device = next(model.parameters()).device
inputs = {k: v.to(device) for k, v in inputs.items()}
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(
generated_text, task=task_prompt, image_size=(image.width, image.height)
)
return parsed_answer[task_prompt]
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