FLUX-Prompt-Generator / caption_models.py
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import spaces
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM, Qwen2VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
import numpy as np
import os
from datetime import datetime
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
# Initialize Florence model
florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to(device).eval()
florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True)
# Initialize Qwen2-VL-2B model
qwen_model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype="auto").to(device).eval()
qwen_processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
@spaces.GPU
def florence_caption(image):
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
generated_ids = florence_model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = florence_processor.post_process_generation(
generated_text,
task="<MORE_DETAILED_CAPTION>",
image_size=(image.width, image.height)
)
return parsed_answer["<MORE_DETAILED_CAPTION>"]
def array_to_image_path(image_array):
img = Image.fromarray(np.uint8(image_array))
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"image_{timestamp}.png"
img.save(filename)
full_path = os.path.abspath(filename)
return full_path
@spaces.GPU
def qwen_caption(image):
if not isinstance(image, Image.Image):
image = Image.fromarray(np.uint8(image))
image_path = array_to_image_path(np.array(image))
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{"type": "text", "text": "Describe this image in great detail."},
],
}
]
text = qwen_processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = qwen_processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(device)
generated_ids = qwen_model.generate(**inputs, max_new_tokens=256)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = qwen_processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return output_text[0]