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import asyncio
import base64
import warnings
warnings.simplefilter(action='ignore', category=UserWarning)
from torch import nn
from io import BytesIO
from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, \
AutoModelForCausalLM
import torch
import torch.amp.autocast_mode
from PIL import Image
import numpy as np
from io import BytesIO
from ..base_config import init_instance , setup_logger
from ..locales import _
llm_logger = setup_logger('[LLM-Caption]')
class JoyPipeline:
def __init__(self):
self.clip_model = None
self.clip_processor = None
self.tokenizer = None
self.text_model = None
self.image_adapter = None
self.parent = None
def clearCache(self):
self.clip_model = None
self.clip_processor = None
self.tokenizer = None
self.text_model = None
self.image_adapter = None
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
class Joy_caption_load:
def __init__(self):
self.model = None
self.pipeline = JoyPipeline()
self.pipeline.parent = self
self.config = init_instance.config
pass
def loadCheckPoint(self):
# 清除一波
if self.pipeline != None:
self.pipeline.clearCache()
# clip
model_id = self.config.server_settings['llm_caption']['clip']
model = AutoModel.from_pretrained(model_id)
clip_processor = AutoProcessor.from_pretrained(model_id)
clip_model = AutoModel.from_pretrained(
model_id,
trust_remote_code=True
)
clip_model = clip_model.vision_model
clip_model.eval()
clip_model.requires_grad_(False)
clip_model.to("cuda")
# LLM
model_path_llm = self.config.server_settings['llm_caption']['llm']
tokenizer = AutoTokenizer.from_pretrained(model_path_llm, use_fast=False)
assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer,
PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}"
text_model = AutoModelForCausalLM.from_pretrained(model_path_llm, device_map="auto", trust_remote_code=True)
text_model.eval()
# Image Adapte
image_adapter = ImageAdapter(clip_model.config.hidden_size,
text_model.config.hidden_size) # ImageAdapter(clip_model.config.hidden_size, 4096)
image_adapter.load_state_dict(torch.load(self.config.server_settings['llm_caption']['image_adapter'], map_location="cpu", weights_only=True))
adjusted_adapter = image_adapter # AdjustedImageAdapter(image_adapter, text_model.config.hidden_size)
adjusted_adapter.eval()
adjusted_adapter.to("cuda")
self.pipeline.clip_model = clip_model
self.pipeline.clip_processor = clip_processor
self.pipeline.tokenizer = tokenizer
self.pipeline.text_model = text_model
self.pipeline.image_adapter = adjusted_adapter
def clearCache(self):
if self.pipeline != None:
self.pipeline.clearCache()
def gen(self, model):
if self.model == None or self.model != model or self.pipeline == None:
self.model = model
self.loadCheckPoint()
return (self.pipeline,)
class Joy_caption:
def __init__(self):
pass
@staticmethod
def tensor2pil(t_image: torch.Tensor) -> Image:
return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
def gen(
self,
joy_pipeline=JoyPipeline,
image=Image,
prompt="A descriptive caption for this image",
max_new_tokens=300,
temperature=0.5,
cache=False
):
if joy_pipeline.clip_processor == None:
joy_pipeline.parent.loadCheckPoint()
clip_processor = joy_pipeline.clip_processor
tokenizer = joy_pipeline.tokenizer
clip_model = joy_pipeline.clip_model
image_adapter = joy_pipeline.image_adapter
text_model = joy_pipeline.text_model
input_image = image
# Preprocess image
pImge = clip_processor(images=input_image, return_tensors='pt').pixel_values
pImge = pImge.to('cuda')
# Tokenize the prompt
prompt = tokenizer.encode(prompt, return_tensors='pt', padding=False, truncation=False,
add_special_tokens=False)
# Embed image
with torch.amp.autocast_mode.autocast('cuda', enabled=True):
vision_outputs = clip_model(pixel_values=pImge, output_hidden_states=True)
image_features = vision_outputs.hidden_states[-2]
embedded_images = image_adapter(image_features)
embedded_images = embedded_images.to('cuda')
# Embed prompt
prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))
assert prompt_embeds.shape == (1, prompt.shape[1],
text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}"
embedded_bos = text_model.model.embed_tokens(
torch.tensor([[tokenizer.bos_token_id]], device=text_model.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('cuda')
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=max_new_tokens, do_sample=True, top_k=10,
temperature=temperature, 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]
r = caption.strip()
if cache == False:
joy_pipeline.parent.clearCache()
return (r,)
class JoyCaptionHandler:
def __init__(self, config):
self.config = config
self.pipeline, self.joy_caption = self._initialize()
def _initialize(self):
llm_logger.info(_("Loading LLM"))
joy_caption_load = Joy_caption_load()
model_path = self.config.server_settings['llm_caption']['llm']
pipeline, = joy_caption_load.gen(model_path)
joy_caption = Joy_caption()
llm_logger.info(_("LLM loading completed, waiting for command"))
return pipeline, joy_caption
async def get_caption(self, image, ntags=[]):
if image.startswith(b"data:image/png;base64,"):
image = image.replace("data:image/png;base64,", "")
image = Image.open(BytesIO(base64.b64decode(image))).convert(mode="RGB")
extra_ = f"do not describe {','.join(ntags)} if it exist" if ntags else ''
loop = asyncio.get_event_loop()
caption = await loop.run_in_executor(
None,
self.joy_caption.gen,
self.pipeline,
image,
f"A descriptive caption for this image, do not describe a signature or text in the image,{extra_}",
300,
0.5,
True
)
return caption[0]
config = init_instance.config
if config.server_settings['llm_caption']['enable']:
joy_caption_handler = JoyCaptionHandler(config)
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