caption_models.py

import spaces
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM, Qwen2VLForConditionalGeneration, AutoModel, AutoTokenizer, AutoModelForCausalLM
from qwen_vl_utils import process_vision_info
import numpy as np
import os
from datetime import datetime
import subprocess
import torch.nn as nn

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, check=True, shell=True)

import sys
import importlib.util
from unittest.mock import MagicMock

# Create a fake spec object
class FakeFlashAttnSpec:
    name = 'flash_attn'
    loader = None
    origin = None
    submodule_search_locations = []
    
fake_spec = FakeFlashAttnSpec()

# Create mock modules with proper __spec__ attributes
flash_attn_mock = MagicMock()
flash_attn_mock.__spec__ = fake_spec
flash_attn_mock.__version__ = "0.0.0"  # Force version check to fail

sys.modules['flash_attn'] = flash_attn_mock
sys.modules['flash_attn.flash_attn_interface'] = MagicMock()
sys.modules['flash_attn.bert_padding'] = MagicMock()

# Patch find_spec to return our fake spec
_original_find_spec = importlib.util.find_spec

def _patched_find_spec(name, package=None):
    if name == 'flash_attn' or name.startswith('flash_attn.'):
        return fake_spec
    return _original_find_spec(name, package)

importlib.util.find_spec = _patched_find_spec

device = "cuda" if torch.cuda.is_available() else "cpu"

HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN", None)

# Initialize Florence model
florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True, attn_implementation="sdpa", revision="00d2f1570b00c6dea5df998f5635db96840436bc").to(device).eval()
florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True, attn_implementation="sdpa", revision="00d2f1570b00c6dea5df998f5635db96840436bc")

# 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)

# Add these new imports and constants
CLIP_PATH = "google/siglip-so400m-patch14-384"
VLM_PROMPT = "A descriptive caption for this image:\n"
MODEL_PATH = "meta-llama/Meta-Llama-3.1-8B"
CHECKPOINT_PATH = "wpkklhc6"

class ImageAdapter(nn.Module):
    def __init__(self, input_features: int, output_features: int):
        super().__init__()
        self.linear1 = nn.Linear(input_features, output_features)
        self.activation = nn.GELU()
        self.linear2 = nn.Linear(output_features, output_features)
    
    def forward(self, vision_outputs: torch.Tensor):
        x = self.linear1(vision_outputs)
        x = self.activation(x)
        x = self.linear2(x)
        return x

# Load CLIP
clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
clip_model = AutoModel.from_pretrained(CLIP_PATH).vision_model
clip_model.eval()
clip_model.requires_grad_(False)
clip_model.to(device)

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_fast=False, token=HF_TOKEN)

# LLM
text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto", torch_dtype=torch.bfloat16, token=HF_TOKEN)
text_model.eval()

# Image Adapter
image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size)
image_adapter.load_state_dict(torch.load(f"{CHECKPOINT_PATH}/image_adapter.pt", map_location="cpu"))
image_adapter.eval()
image_adapter.to(device)

@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 in one paragraph."},
            ],
        }
    ]
    
    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]

@spaces.GPU
@torch.no_grad()
def joycaption(image):
    if not isinstance(image, Image.Image):
        image = Image.fromarray(np.uint8(image))
    
    # Preprocess image
    image = clip_processor(images=image, return_tensors='pt').pixel_values
    image = image.to(device)

    # Tokenize the prompt
    prompt = tokenizer.encode(VLM_PROMPT, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)

    # Embed image
    with torch.amp.autocast_mode.autocast(device_type='cuda', enabled=True):
        vision_outputs = clip_model(pixel_values=image, output_hidden_states=True)
        image_features = vision_outputs.hidden_states[-2]
        embedded_images = image_adapter(image_features)
        embedded_images = embedded_images.to(device)
    
    # Embed prompt
    prompt_embeds = text_model.model.embed_tokens(prompt.to(device))
    embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=device, dtype=torch.int64))

    # Construct prompts
    inputs_embeds = torch.cat([
        embedded_bos.expand(embedded_images.shape[0], -1, -1),
        embedded_images.to(dtype=embedded_bos.dtype),
        prompt_embeds.expand(embedded_images.shape[0], -1, -1),
    ], dim=1)

    input_ids = torch.cat([
        torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),
        torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),
        prompt,
    ], dim=1).to(device)
    attention_mask = torch.ones_like(input_ids)

    generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, top_k=10, temperature=0.5, suppress_tokens=None)

    # Trim off the prompt
    generate_ids = generate_ids[:, input_ids.shape[1]:]
    if generate_ids[0][-1] == tokenizer.eos_token_id:
        generate_ids = generate_ids[:, :-1]

    caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]

    return caption.strip()