552 lines
26 KiB
Python
552 lines
26 KiB
Python
# Based on https://huggingface.co/John6666/joy-caption-alpha-two-cli-modand https://github.com/chflame163/ComfyUI_LayerStyle
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import os
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import sys
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import torch
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from torch import nn
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from typing import List, Union
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from PIL import Image
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import torchvision.transforms.functional as TVF
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from torchvision.transforms import ToPILImage
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import numpy as np
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import folder_paths
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import json
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import logging
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from transformers import AutoProcessor, AutoModelForCausalLM
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from huggingface_hub import snapshot_download
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import shutil
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# Define the Joy2_Model class
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class Joy2_Model:
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def __init__(self, clip_processor, clip_model, tokenizer, text_model, image_adapter):
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self.clip_processor = clip_processor
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self.clip_model = clip_model
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self.tokenizer = tokenizer
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self.text_model = text_model
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self.image_adapter = image_adapter
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# Define the ImageAdapter class
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class ImageAdapter(nn.Module):
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def __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int,
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deep_extract: bool):
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super().__init__()
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self.deep_extract = deep_extract
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if self.deep_extract:
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input_features = input_features * 5
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self.linear1 = nn.Linear(input_features, output_features)
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self.activation = nn.GELU()
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self.linear2 = nn.Linear(output_features, output_features)
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self.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)
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self.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))
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# Other tokens (<|image_start|>, <|image_end|>, <|eot_id|>)
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self.other_tokens = nn.Embedding(3, output_features)
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self.other_tokens.weight.data.normal_(mean=0.0, std=0.02) # Matches HF's implementation of LLaMA
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def forward(self, vision_outputs: torch.Tensor):
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if self.deep_extract:
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x = torch.cat((
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vision_outputs[-2],
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vision_outputs[3],
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vision_outputs[7],
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vision_outputs[13],
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vision_outputs[20],
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), dim=-1)
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assert len(x.shape) == 3, f"Expected 3, got {len(x.shape)}" # batch, tokens, features
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assert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}"
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else:
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x = vision_outputs[-2]
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x = self.ln1(x)
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if self.pos_emb is not None:
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assert x.shape[-2:] == self.pos_emb.shape, f"Expected {self.pos_emb.shape}, got {x.shape[-2:]}"
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x = x + self.pos_emb
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x = self.linear1(x)
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x = self.activation(x)
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x = self.linear2(x)
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other_tokens = self.other_tokens(
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torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))
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assert other_tokens.shape == (
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x.shape[0], 2, x.shape[2]), f"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}"
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x = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)
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return x
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def get_eot_embedding(self):
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return self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)
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# Define the model loading function
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def load_models(model_path, dtype, device="cuda"):
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from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, \
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AutoModelForCausalLM
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from peft import PeftModel
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JC_lora = "text_model"
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use_lora = True if JC_lora != "none" else False
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CLIP_PATH = os.path.join(folder_paths.models_dir, "clip_vision", "google--siglip-so400m-patch14-384")
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CHECKPOINT_PATH = os.path.join(folder_paths.models_dir, "Joy_caption", "cgrkzexw-599808")
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LORA_PATH = os.path.join(CHECKPOINT_PATH, "text_model")
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if os.path.exists(CLIP_PATH):
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print("Start to load existing VLM")
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else:
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print("VLM not found locally. Downloading google/siglipso400m-patch14-384...")
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try:
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snapshot_download(
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repo_id="google/siglip-so400m-patch14-384",
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local_dir=os.path.join(folder_paths.models_dir, "clip_vision", "cache--google--siglip-so400m-patch14-384"),
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local_dir_use_symlinks=False,
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resume_download=True
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)
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shutil.move(os.path.join(folder_paths.models_dir, "clip_vision", "cache--google--siglip-so400m-patch14-384"), CLIP_PATH)
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print(f"VLM has been downloaded to {CLIP_PATH}")
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except Exception as e:
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print(f"Error downloading CLIP model: {e}")
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raise
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try:
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if dtype == "nf4":
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from transformers import BitsAndBytesConfig
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nf4_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
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print("Loading in NF4")
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print("Loading CLIP")
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clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
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clip_model = AutoModel.from_pretrained(CLIP_PATH).vision_model
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print("Loading VLM's custom vision model")
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checkpoint = torch.load(os.path.join(CHECKPOINT_PATH, "clip_model.pt"), map_location='cpu', weights_only=False)
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checkpoint = {k.replace("_orig_mod.module.", ""): v for k, v in checkpoint.items()}
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clip_model.load_state_dict(checkpoint)
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del checkpoint
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clip_model.eval().requires_grad_(False).to(device)
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print("Loading tokenizer")
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tokenizer = AutoTokenizer.from_pretrained(os.path.join(CHECKPOINT_PATH, "text_model"), use_fast=True)
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assert isinstance(tokenizer,
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(PreTrainedTokenizer, PreTrainedTokenizerFast)), f"Tokenizer is of type {type(tokenizer)}"
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print(f"Loading LLM: {model_path}")
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text_model = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=nf4_config,
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device_map=device, torch_dtype=torch.bfloat16).eval()
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if False and use_lora and os.path.exists(LORA_PATH): # omitted
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print("Loading VLM's custom text model")
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text_model = PeftModel.from_pretrained(model=text_model, model_id=LORA_PATH, device_map=device,
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quantization_config=nf4_config)
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text_model = text_model.merge_and_unload(
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safe_merge=True) # to avoid PEFT bug https://github.com/huggingface/transformers/issues/28515
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else:
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print("VLM's custom text model isn't loaded")
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print("Loading image adapter")
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image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38,
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False).eval().to("cpu")
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image_adapter.load_state_dict(
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torch.load(os.path.join(CHECKPOINT_PATH, "image_adapter.pt"), map_location=device, weights_only=False))
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image_adapter.eval().to(device)
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else: # bf16
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print("Loading in bfloat16")
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print("Loading CLIP")
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clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
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clip_model = AutoModel.from_pretrained(CLIP_PATH).vision_model
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if os.path.exists(os.path.join(CHECKPOINT_PATH, "clip_model.pt")):
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print("Loading VLM's custom vision model")
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checkpoint = torch.load(os.path.join(CHECKPOINT_PATH, "clip_model.pt"), map_location=device, weights_only=False)
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checkpoint = {k.replace("_orig_mod.module.", ""): v for k, v in checkpoint.items()}
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clip_model.load_state_dict(checkpoint)
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del checkpoint
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clip_model.eval().requires_grad_(False).to(device)
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print("Loading tokenizer")
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tokenizer = AutoTokenizer.from_pretrained(os.path.join(CHECKPOINT_PATH, "text_model"), use_fast=True)
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assert isinstance(tokenizer,
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(PreTrainedTokenizer, PreTrainedTokenizerFast)), f"Tokenizer is of type {type(tokenizer)}"
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print(f"Loading LLM: {model_path}")
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text_model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto",
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torch_dtype=torch.bfloat16).eval()
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if use_lora and os.path.exists(LORA_PATH):
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print("Loading VLM's custom text model")
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text_model = PeftModel.from_pretrained(model=text_model, model_id=LORA_PATH, device_map=device)
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text_model = text_model.merge_and_unload(
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safe_merge=True) # to avoid PEFT bug https://github.com/huggingface/transformers/issues/28515
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else:
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print("VLM's custom text model isn't loaded")
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print("Loading image adapter")
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image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38,
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False).eval().to(device)
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image_adapter.load_state_dict(
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torch.load(os.path.join(CHECKPOINT_PATH, "image_adapter.pt"), map_location=device, weights_only=False))
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except Exception as e:
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print(f"Error loading models: {e}")
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finally:
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pass # free_memory() function is not defined in the provided code
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return Joy2_Model(clip_processor, clip_model, tokenizer, text_model, image_adapter)
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# Define the stream_chat function
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@torch.inference_mode()
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def stream_chat(input_images: List[Image.Image], caption_type: str, caption_length: Union[str, int],
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extra_options: list[str], name_input: str, custom_prompt: str,
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max_new_tokens: int, top_p: float, temperature: float, batch_size: int, model: Joy2_Model, device=str):
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CAPTION_TYPE_MAP = {
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"Descriptive": [
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"Write a descriptive caption for this image in a formal tone.",
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"Write a descriptive caption for this image in a formal tone within {word_count} words.",
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"Write a {length} descriptive caption for this image in a formal tone.",
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],
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"Descriptive (Informal)": [
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"Write a descriptive caption for this image in a casual tone.",
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"Write a descriptive caption for this image in a casual tone within {word_count} words.",
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"Write a {length} descriptive caption for this image in a casual tone.",
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],
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"Training Prompt": [
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"Write a stable diffusion prompt for this image.",
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"Write a stable diffusion prompt for this image within {word_count} words.",
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"Write a {length} stable diffusion prompt for this image.",
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],
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"MidJourney": [
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"Write a MidJourney prompt for this image.",
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"Write a MidJourney prompt for this image within {word_count} words.",
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"Write a {length} MidJourney prompt for this image.",
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],
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"Booru tag list": [
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"Write a list of Booru tags for this image.",
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"Write a list of Booru tags for this image within {word_count} words.",
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"Write a {length} list of Booru tags for this image.",
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],
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"Booru-like tag list": [
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"Write a list of Booru-like tags for this image.",
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"Write a list of Booru-like tags for this image within {word_count} words.",
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"Write a {length} list of Booru-like tags for this image.",
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],
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"Art Critic": [
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"Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc.",
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"Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc. Keep it within {word_count} words.",
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"Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc. Keep it {length}.",
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],
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"Product Listing": [
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"Write a caption for this image as though it were a product listing.",
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"Write a caption for this image as though it were a product listing. Keep it under {word_count} words.",
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"Write a {length} caption for this image as though it were a product listing.",
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],
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"Social Media Post": [
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"Write a caption for this image as if it were being used for a social media post.",
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"Write a caption for this image as if it were being used for a social media post. Limit the caption to {word_count} words.",
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"Write a {length} caption for this image as if it were being used for a social media post.",
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],
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}
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all_captions = []
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# 'any' means no length specified
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length = None if caption_length == "any" else caption_length
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if isinstance(length, str):
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try:
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length = int(length)
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except ValueError:
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pass
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# Build prompt
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if length is None:
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map_idx = 0
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elif isinstance(length, int):
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map_idx = 1
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elif isinstance(length, str):
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map_idx = 2
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else:
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raise ValueError(f"Invalid caption length: {length}")
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prompt_str = CAPTION_TYPE_MAP[caption_type][map_idx]
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# Add extra options
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if len(extra_options) > 0:
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prompt_str += " " + " ".join(extra_options)
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# Add name, length, word_count
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prompt_str = prompt_str.format(name=name_input, length=caption_length, word_count=caption_length)
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if custom_prompt.strip() != "":
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prompt_str = custom_prompt.strip()
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# For debugging
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print(f"Prompt: {prompt_str}")
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for i in range(0, len(input_images), batch_size):
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batch = input_images[i:i + batch_size]
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for input_image in batch:
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try:
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# Preprocess image
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image = input_image.resize((384, 384), Image.LANCZOS)
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pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0
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pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])
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pixel_values = pixel_values.to(device)
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except ValueError as e:
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print(f"Error processing image: {e}")
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print("Skipping this image and continuing...")
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continue
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# Embed image
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with torch.amp.autocast_mode.autocast(device, enabled=True):
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vision_outputs = model.clip_model(pixel_values=pixel_values, output_hidden_states=True)
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image_features = vision_outputs.hidden_states
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embedded_images = model.image_adapter(image_features).to(device)
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# Build the conversation
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convo = [
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{
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"role": "system",
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"content": "You are a helpful image captioner.",
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},
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{
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"role": "user",
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"content": prompt_str,
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},
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]
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# Format the conversation
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if hasattr(model.tokenizer, 'apply_chat_template'):
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convo_string = model.tokenizer.apply_chat_template(convo, tokenize=False, add_generation_prompt=True)
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else:
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# Fallback if apply_chat_template is not available
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convo_string = "<|eot_id|>\n"
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for message in convo:
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if message['role'] == 'system':
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convo_string += f"<|system|>{message['content']}<|endoftext|>\n"
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elif message['role'] == 'user':
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convo_string += f"<|user|>{message['content']}<|endoftext|>\n"
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else:
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convo_string += f"{message['content']}<|endoftext|>\n"
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convo_string += "<|eot_id|>"
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assert isinstance(convo_string, str)
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# Tokenize the conversation
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convo_tokens = model.tokenizer.encode(convo_string, return_tensors="pt", add_special_tokens=False,
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truncation=False)
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prompt_tokens = model.tokenizer.encode(prompt_str, return_tensors="pt", add_special_tokens=False,
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truncation=False)
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assert isinstance(convo_tokens, torch.Tensor) and isinstance(prompt_tokens, torch.Tensor)
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convo_tokens = convo_tokens.squeeze(0)
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prompt_tokens = prompt_tokens.squeeze(0)
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# Calculate where to inject the image
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eot_id_indices = (convo_tokens == model.tokenizer.convert_tokens_to_ids("<|eot_id|>")).nonzero(as_tuple=True)[
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0].tolist()
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assert len(eot_id_indices) == 2, f"Expected 2 <|eot_id|> tokens, got {len(eot_id_indices)}"
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preamble_len = eot_id_indices[1] - prompt_tokens.shape[0]
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# Embed the tokens
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convo_embeds = model.text_model.model.embed_tokens(convo_tokens.unsqueeze(0).to(device))
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# Construct the input
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input_embeds = torch.cat([
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convo_embeds[:, :preamble_len],
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embedded_images.to(dtype=convo_embeds.dtype),
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convo_embeds[:, preamble_len:],
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], dim=1).to(device)
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input_ids = torch.cat([
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convo_tokens[:preamble_len].unsqueeze(0),
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torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),
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convo_tokens[preamble_len:].unsqueeze(0),
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], dim=1).to(device)
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attention_mask = torch.ones_like(input_ids)
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generate_ids = model.text_model.generate(input_ids=input_ids, inputs_embeds=input_embeds,
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attention_mask=attention_mask, do_sample=True,
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suppress_tokens=None, max_new_tokens=max_new_tokens, top_p=top_p,
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temperature=temperature)
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# Trim off the prompt
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generate_ids = generate_ids[:, input_ids.shape[1]:]
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if generate_ids[0][-1] == model.tokenizer.eos_token_id or generate_ids[0][-1] == model.tokenizer.convert_tokens_to_ids(
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"<|eot_id|>"):
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generate_ids = generate_ids[:, :-1]
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caption = model.tokenizer.batch_decode(generate_ids, skip_special_tokens=False,
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clean_up_tokenization_spaces=False)[0]
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all_captions.append(caption.strip())
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return all_captions
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def free_memory():
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import gc
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def analyze_tensor(tensor: object) -> str:
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result = ''
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if isinstance(tensor, torch.Tensor):
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result += f"\n Dimensions: {tensor.dim()}, First dimension size: {tensor.shape[0]} \n"
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for idx, t in enumerate(tensor):
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image = Image.fromarray(np.clip(255.0 * t.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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result += f'\n Index {idx}: Image dimensions = {image.size}, Mode = {image.mode}, Tensor dims = {t.dim()}, '
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result += ', '.join([f'Dimension {j} size = {t.shape[j]}' for j in range(t.dim())])
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else:
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result = f"analyze_tensor: Input is not a tensor, found {type(tensor)} instead"
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return result
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class JoyCaption2:
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CATEGORY = 'TTP_Toolset'
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FUNCTION = "joycaption2"
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("text",)
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OUTPUT_IS_LIST = (True,)
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def __init__(self):
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self.NODE_NAME = 'JoyCaption2'
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self.previous_model = None
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@classmethod
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def INPUT_TYPES(cls):
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llm_model_list = ["unsloth/Meta-Llama-3.1-8B-Instruct", "Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2"]
|
|
dtype_list = ['nf4', 'bf16']
|
|
caption_type_list = [
|
|
"Descriptive", "Descriptive (Informal)", "Training Prompt", "MidJourney",
|
|
"Booru tag list", "Booru-like tag list", "Art Critic", "Product Listing",
|
|
"Social Media Post"
|
|
]
|
|
caption_length_list = ["any", "very short", "short", "medium-length", "long", "very long"] + [str(i) for i in range(20, 261, 5)]
|
|
|
|
# get extra_option.json path
|
|
base_dir = os.path.dirname(os.path.abspath(__file__))
|
|
extra_option_file = os.path.join(base_dir, "extra_option.json")
|
|
|
|
# load extra_options_list
|
|
extra_options_list = {}
|
|
if os.path.isfile(extra_option_file):
|
|
try:
|
|
with open(extra_option_file, "r", encoding='utf-8') as f:
|
|
json_content = json.load(f)
|
|
for item in json_content:
|
|
option_name = item.get("name")
|
|
if option_name:
|
|
extra_options_list[option_name] = ("BOOLEAN", {"default": False})
|
|
# logger.info(f"Loaded extra option: {option_name}")
|
|
except Exception as e:
|
|
logger.error(f"Error loading extra_option.json: {e}")
|
|
else:
|
|
logger.info(f"extra_option.json not found at {extra_option_file}. No extra options will be available.")
|
|
|
|
# 定义额外的输入字段
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"llm_model": (llm_model_list,),
|
|
"dtype": (dtype_list,),
|
|
"caption_type": (caption_type_list,),
|
|
"caption_length": (caption_length_list,),
|
|
"user_prompt": ("STRING", {"default": "", "multiline": True}),
|
|
"max_new_tokens": ("INT", {"default": 260, "min": 8, "max": 4096, "step": 1}),
|
|
"top_p": ("FLOAT", {"default": 0.8, "min": 0, "max": 1, "step": 0.01}),
|
|
"temperature": ("FLOAT", {"default": 0.6, "min": 0, "max": 1, "step": 0.01}),
|
|
"cache_model": ("BOOLEAN", {"default": False}),
|
|
"enable_extra_options": ("BOOLEAN", {"default": True, "label": "启用额外选项"}), # 新增开关
|
|
**extra_options_list,
|
|
"character_name": ("STRING", {"default": "", "multiline": False}),
|
|
},
|
|
}
|
|
|
|
|
|
def joycaption2(
|
|
self, image, llm_model, dtype, caption_type, caption_length,
|
|
user_prompt, max_new_tokens, top_p, temperature, cache_model,
|
|
enable_extra_options, character_name, **extra_options
|
|
):
|
|
ret_text = []
|
|
comfy_model_dir = os.path.join(folder_paths.models_dir, "LLM")
|
|
print(f"comfy_model_dir:{comfy_model_dir}")
|
|
if not os.path.exists(comfy_model_dir):
|
|
os.mkdir(comfy_model_dir)
|
|
|
|
sanitized_model_name = llm_model.replace('/', '--')
|
|
llm_model_path = os.path.join(comfy_model_dir, sanitized_model_name)
|
|
llm_model_path_cache = os.path.join(comfy_model_dir, "cache--" + sanitized_model_name)
|
|
|
|
if os.path.exists(llm_model_path):
|
|
print(f"Start to load existing model")
|
|
else:
|
|
print(f"Model not found locally. Downloading {llm_model}...")
|
|
snapshot_download(repo_id=llm_model, local_dir=llm_model_path_cache, local_dir_use_symlinks=False, resume_download=True)
|
|
shutil.move(llm_model_path_cache, llm_model_path)
|
|
print(f"Model downloaded to {llm_model_path}...")
|
|
device = 'cuda'
|
|
JC_lora = 'text_model'
|
|
|
|
if self.previous_model is None:
|
|
model = load_models(llm_model_path, dtype, device=device)
|
|
else:
|
|
model = self.previous_model
|
|
|
|
base_dir = os.path.dirname(os.path.abspath(__file__))
|
|
extra_option_file = os.path.join(base_dir, "extra_option.json") # 调整为 JSON 文件的正确路径
|
|
extra_prompts = {}
|
|
|
|
if enable_extra_options and os.path.isfile(extra_option_file):
|
|
try:
|
|
with open(extra_option_file, "r", encoding='utf-8') as f:
|
|
json_content = json.load(f)
|
|
for item in json_content:
|
|
name = item.get("name")
|
|
prompt = item.get("prompt")
|
|
if name and prompt:
|
|
extra_prompts[name] = prompt
|
|
except Exception as e:
|
|
logger.error(f"Error reading extra_option.json: {e}")
|
|
elif not os.path.isfile(extra_option_file):
|
|
logger.info(f"extra_option.json not found at {extra_option_file} during processing.")
|
|
|
|
extra = []
|
|
if enable_extra_options:
|
|
for option_name, is_enabled in extra_options.items():
|
|
if is_enabled and option_name in extra_prompts:
|
|
extra.append(extra_prompts[option_name])
|
|
|
|
for img in image:
|
|
img = Image.fromarray(
|
|
np.clip(255.0 * img.unsqueeze(0).cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
|
|
).convert('RGB')
|
|
|
|
caption = stream_chat(
|
|
[img], caption_type, caption_length,
|
|
extra, character_name, user_prompt,
|
|
max_new_tokens, top_p, temperature, 1,
|
|
model, device
|
|
)
|
|
ret_text.append(caption[0])
|
|
|
|
if cache_model:
|
|
self.previous_model = model
|
|
else:
|
|
self.previous_model = None
|
|
del model
|
|
free_memory()
|
|
|
|
return (ret_text,)
|
|
|
|
# Register the node
|
|
NODE_CLASS_MAPPINGS = {
|
|
"JoyCaption2": JoyCaption2,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"JoyCaption2": "TTP_JoyCaption2",
|
|
}
|