| import torch
|
| from transformers import AutoProcessor, LlavaForConditionalGeneration, BitsAndBytesConfig
|
| import folder_paths
|
| from pathlib import Path
|
| from PIL import Image
|
| from torchvision.transforms import ToPILImage
|
| import json
|
| import gc
|
| import os
|
|
|
| class ModelLoadError(Exception):
|
| pass
|
|
|
| def handle_model_error(e, cleanup_func=None):
|
| if cleanup_func:
|
| cleanup_func()
|
| if torch.cuda.is_available():
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
| raise ModelLoadError(f"Error loading model: {str(e)}")
|
|
|
| def cleanup_model_resources(model=None, processor=None):
|
| if model is not None:
|
| del model
|
| if processor is not None:
|
| del processor
|
| if torch.cuda.is_available():
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
|
|
| def validate_model_parameters(quantization, valid_modes):
|
| if quantization not in valid_modes:
|
| raise ValueError(f"Invalid quantization mode: {quantization}. Valid modes: {', '.join(valid_modes)}")
|
|
|
| with open(Path(__file__).parent / "jc_data.json", "r", encoding="utf-8") as f:
|
| config = json.load(f)
|
| CAPTION_TYPE_MAP = config["caption_type_map"]
|
| EXTRA_OPTIONS = config["extra_options"]
|
| MEMORY_EFFICIENT_CONFIGS = config["memory_efficient_configs"]
|
| MODEL_SETTINGS = config["model_settings"]
|
| CAPTION_LENGTH_CHOICES = config["caption_length_choices"]
|
| HF_MODELS = config["hf_models"]
|
|
|
|
|
| custom_path = Path(__file__).parent / "custom_models.json"
|
|
|
| if custom_path.exists():
|
| try:
|
| with open(custom_path, "r", encoding="utf-8") as f:
|
| custom_data = json.load(f) or {}
|
| HF_MODELS.update(custom_data.get("hf_models", {}))
|
| print("[JoyCaption] ✅ Loaded custom HF custom models.")
|
| except Exception as e:
|
| print(f"[JoyCaption] ⚠️ Failed to load custom models → {e}")
|
| else:
|
| print("[JoyCaption] ℹ️ No custom models found, skipping user-defined HF models.")
|
|
|
|
|
| def build_prompt(caption_type: str, caption_length: str | int, extra_options: list[str], name_input: str) -> str:
|
| """Constructs the prompt for the model based on user selections."""
|
| if caption_length == "any":
|
| map_idx = 0
|
| elif isinstance(caption_length, str) and caption_length.isdigit():
|
| map_idx = 1
|
| else:
|
| map_idx = 2
|
|
|
| prompt = CAPTION_TYPE_MAP[caption_type][map_idx]
|
|
|
| if extra_options:
|
| prompt += " " + " ".join(extra_options)
|
|
|
| return prompt.format(
|
| name=name_input or "{NAME}",
|
| length=caption_length,
|
| word_count=caption_length,
|
| )
|
|
|
| _MODEL_CACHE = {}
|
|
|
| class JC_Models:
|
| """Handles loading, caching, and running the LLaVA models."""
|
| def __init__(self, model: str, memory_mode: str):
|
| cache_key = f"{model}_{memory_mode}"
|
|
|
| if cache_key in _MODEL_CACHE:
|
| try:
|
| self.processor = _MODEL_CACHE[cache_key]["processor"]
|
| self.model = _MODEL_CACHE[cache_key]["model"]
|
| self.device = _MODEL_CACHE[cache_key]["device"]
|
| if not next(self.model.parameters()).is_cuda:
|
| raise RuntimeError("Cached model not on GPU")
|
| print(f"Using cached model: {cache_key}")
|
| return
|
| except Exception as e:
|
| print(f"Cache validation failed: {e}, reloading model...")
|
| if cache_key in _MODEL_CACHE:
|
| del _MODEL_CACHE[cache_key]
|
| torch.cuda.empty_cache()
|
|
|
| checkpoint_path = Path(folder_paths.models_dir) / "LLM" / Path(model).stem
|
| if not checkpoint_path.exists():
|
| from huggingface_hub import snapshot_download
|
| snapshot_download(repo_id=model, local_dir=str(checkpoint_path), force_download=False, local_files_only=False)
|
|
|
| self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
|
| if self.device == "cuda":
|
| torch.backends.cudnn.benchmark = True
|
| if hasattr(torch.backends, 'cuda'):
|
| if hasattr(torch.backends.cuda, 'matmul'):
|
| torch.backends.cuda.matmul.allow_tf32 = True
|
| if hasattr(torch.backends.cuda, 'allow_tf32'):
|
| torch.backends.cuda.allow_tf32 = True
|
|
|
| os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
|
|
|
| self.processor = AutoProcessor.from_pretrained(
|
| str(checkpoint_path),
|
| use_fast=True,
|
| image_processor_type="CLIPImageProcessor",
|
| image_size=336
|
| )
|
|
|
|
|
| if hasattr(self.processor, 'image_processor') and hasattr(self.processor.image_processor, 'size'):
|
| size_raw = self.processor.image_processor.size
|
|
|
|
|
| if hasattr(size_raw, 'get') or isinstance(size_raw, dict):
|
| h = size_raw.get('height', size_raw.get('shortest_edge', 336))
|
| w = size_raw.get('width', size_raw.get('shortest_edge', 336))
|
| self.target_size = (int(w), int(h))
|
| elif isinstance(size_raw, (list, tuple)):
|
| self.target_size = (int(size_raw[0]), int(size_raw[1])) if len(size_raw) >= 2 else (int(size_raw[0]), int(size_raw[0]))
|
| else:
|
| self.target_size = (int(size_raw), int(size_raw))
|
| else:
|
| self.target_size = (336, 336)
|
|
|
|
|
| self.target_size = tuple(map(int, self.target_size))
|
|
|
| model_kwargs = {
|
| "device_map": "cuda" if self.device == "cuda" else "cpu",
|
| }
|
|
|
| try:
|
| if "FP8-Dynamic" in model:
|
| print("Loading FP8 model with automatic configuration...")
|
| self.model = LlavaForConditionalGeneration.from_pretrained(
|
| str(checkpoint_path),
|
| torch_dtype="auto",
|
| **model_kwargs
|
| )
|
| elif memory_mode == "Full Precision (bf16)":
|
| self.model = LlavaForConditionalGeneration.from_pretrained(
|
| str(checkpoint_path),
|
| torch_dtype=torch.bfloat16,
|
| **model_kwargs
|
| )
|
| elif memory_mode == "Balanced (8-bit)":
|
| qnt_config = BitsAndBytesConfig(
|
| load_in_8bit=True,
|
| bnb_8bit_compute_dtype=torch.float16,
|
| bnb_8bit_use_double_quant=True,
|
| llm_int8_skip_modules=["vision_tower", "multi_modal_projector"],
|
| llm_int8_enable_fp32_cpu_offload=True
|
| )
|
| self.model = LlavaForConditionalGeneration.from_pretrained(
|
| str(checkpoint_path),
|
| torch_dtype=torch.float16,
|
| quantization_config=qnt_config,
|
| **model_kwargs
|
| )
|
| else:
|
| qnt_config = BitsAndBytesConfig(
|
| load_in_4bit=True,
|
| bnb_4bit_compute_dtype=torch.float16,
|
| bnb_4bit_quant_type="nf4",
|
| bnb_4bit_use_double_quant=True,
|
| llm_int8_skip_modules=["vision_tower", "multi_modal_projector"],
|
| llm_int8_enable_fp32_cpu_offload=True
|
| )
|
| self.model = LlavaForConditionalGeneration.from_pretrained(
|
| str(checkpoint_path),
|
| torch_dtype="auto",
|
| quantization_config=qnt_config,
|
| **model_kwargs
|
| )
|
|
|
| self.model.eval()
|
|
|
| if self.device == "cuda" and not next(self.model.parameters()).is_cuda:
|
| raise RuntimeError("Model failed to load on GPU")
|
|
|
| if memory_mode == "Global Cache":
|
| _MODEL_CACHE[cache_key] = {
|
| "processor": self.processor,
|
| "model": self.model,
|
| "device": self.device
|
| }
|
|
|
| except Exception as e:
|
| cleanup_model_resources(self.model, self.processor)
|
| handle_model_error(e)
|
|
|
| @torch.inference_mode()
|
| def generate(self, image: Image.Image, system: str, prompt: str, max_new_tokens: int, temperature: float, top_p: float, top_k: int) -> str:
|
| """Generates a caption for the given image."""
|
| convo = [
|
| {"role": "system", "content": system.strip()},
|
| {"role": "user", "content": prompt.strip()},
|
| ]
|
|
|
| convo_string = self.processor.apply_chat_template(convo, tokenize=False, add_generation_prompt=True)
|
| assert isinstance(convo_string, str)
|
|
|
| if image.mode != 'RGB':
|
| image = image.convert('RGB')
|
|
|
| image = image.resize(self.target_size, Image.Resampling.LANCZOS)
|
|
|
| inputs = self.processor(text=[convo_string], images=[image], return_tensors="pt").to(self.device)
|
|
|
| if hasattr(inputs, 'pixel_values') and inputs['pixel_values'] is not None:
|
| inputs['pixel_values'] = inputs['pixel_values'].to(self.model.dtype)
|
|
|
| with torch.cuda.amp.autocast(enabled=True):
|
| generate_ids = self.model.generate(
|
| **inputs,
|
| max_new_tokens=max_new_tokens,
|
| do_sample=True if temperature > 0 else False,
|
| suppress_tokens=None,
|
| use_cache=True,
|
| temperature=temperature,
|
| top_k=None if top_k == 0 else top_k,
|
| top_p=top_p,
|
| )[0]
|
|
|
| generate_ids = generate_ids[inputs['input_ids'].shape[1]:]
|
| caption = self.processor.tokenizer.decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
|
| return caption.strip()
|
|
|
| class JC_ExtraOptions:
|
| """A node to collect extra options for captioning."""
|
| @classmethod
|
| def INPUT_TYPES(cls):
|
| inputs = {"required": {}}
|
| for key, value in EXTRA_OPTIONS.items():
|
| inputs["required"][key] = ("BOOLEAN", {"default": value["default"]})
|
| inputs["required"]["character_name"] = ("STRING", {"default": "", "multiline": True, "placeholder": "Character Name"})
|
| return inputs
|
|
|
| RETURN_TYPES = ("JOYCAPTION_EXTRA_OPTIONS",)
|
| RETURN_NAMES = ("extra_options",)
|
| FUNCTION = "get_extra_options"
|
| CATEGORY = "🧪AILab/📝JoyCaption"
|
|
|
| def get_extra_options(self, character_name, **kwargs):
|
| ret_list = []
|
| for key, value in EXTRA_OPTIONS.items():
|
| if kwargs.get(key, False):
|
| ret_list.append(value["description"])
|
| return ([ret_list, character_name],)
|
|
|
| class JC:
|
| """The main, simple JoyCaption node."""
|
| @classmethod
|
| def INPUT_TYPES(cls):
|
| model_list = list(HF_MODELS.keys())
|
| return {
|
| "required": {
|
| "image": ("IMAGE",),
|
| "model": (model_list, {"default": model_list[1], "tooltip": "Select the AI model to use for caption generation"}),
|
| "quantization": (list(MEMORY_EFFICIENT_CONFIGS.keys()), {"default": "Balanced (8-bit)", "tooltip": "Choose between speed and quality. 8-bit is recommended for most users"}),
|
| "prompt_style": (list(CAPTION_TYPE_MAP.keys()), {"default": "Descriptive", "tooltip": "Select the style of caption you want to generate"}),
|
| "caption_length": (CAPTION_LENGTH_CHOICES, {"default": "any", "tooltip": "Control the length of the generated caption"}),
|
| "memory_management": (["Keep in Memory", "Clear After Run", "Global Cache"], {"default": "Keep in Memory", "tooltip": "Choose how to manage model memory. 'Keep in Memory' for faster processing, 'Clear After Run' for limited VRAM, 'Global Cache' for fastest processing if you have enough VRAM"}),
|
| },
|
| "optional": {
|
| "extra_options": ("JOYCAPTION_EXTRA_OPTIONS", {"tooltip": "Additional options to customize the caption generation"}),
|
| }
|
| }
|
|
|
| RETURN_TYPES = ("STRING",)
|
| RETURN_NAMES = ("STRING",)
|
| FUNCTION = "generate"
|
| CATEGORY = "🧪AILab/📝JoyCaption"
|
|
|
| def __init__(self):
|
| self.predictor = None
|
| self.current_memory_mode = None
|
| self.current_model = None
|
|
|
| def generate(self, image, model, quantization, prompt_style, caption_length, memory_management, extra_options=None):
|
| try:
|
| validate_model_parameters(quantization, list(MEMORY_EFFICIENT_CONFIGS.keys()))
|
|
|
| if memory_management == "Global Cache":
|
| try:
|
| model_name = HF_MODELS[model]["name"]
|
| self.predictor = JC_Models(model_name, quantization)
|
| except Exception as e:
|
| return (f"Error loading model: {e}",)
|
| elif self.predictor is None or self.current_memory_mode != quantization or self.current_model != model:
|
| if self.predictor is not None:
|
| del self.predictor
|
| self.predictor = None
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
| try:
|
| model_name = HF_MODELS[model]["name"]
|
| self.predictor = JC_Models(model_name, quantization)
|
| self.current_memory_mode = quantization
|
| self.current_model = model
|
| except Exception as e:
|
| return (f"Error loading model: {e}",)
|
|
|
| prompt = build_prompt(prompt_style, caption_length, extra_options[0] if extra_options else [], extra_options[1] if extra_options else "{NAME}")
|
| system_prompt = MODEL_SETTINGS["default_system_prompt"]
|
| pil_image = ToPILImage()(image[0].permute(2, 0, 1))
|
|
|
| response = self.predictor.generate(
|
| image=pil_image,
|
| system=system_prompt,
|
| prompt=prompt,
|
| max_new_tokens=MODEL_SETTINGS["default_max_tokens"],
|
| temperature=MODEL_SETTINGS["default_temperature"],
|
| top_p=MODEL_SETTINGS["default_top_p"],
|
| top_k=MODEL_SETTINGS["default_top_k"],
|
| )
|
|
|
| if memory_management == "Clear After Run":
|
| del self.predictor
|
| self.predictor = None
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
|
|
| return (response,)
|
| except Exception as e:
|
| if memory_management == "Clear After Run":
|
| del self.predictor
|
| self.predictor = None
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
| raise e
|
|
|
| class JC_adv:
|
| """The advanced JoyCaption node with more settings."""
|
| @classmethod
|
| def INPUT_TYPES(cls):
|
| model_list = list(HF_MODELS.keys())
|
| return {
|
| "required": {
|
| "image": ("IMAGE",),
|
| "model": (model_list, {"default": model_list[1], "tooltip": "Select the AI model to use for caption generation"}),
|
| "quantization": (list(MEMORY_EFFICIENT_CONFIGS.keys()), {"default": "Balanced (8-bit)", "tooltip": "Choose between speed and quality. 8-bit is recommended for most users"}),
|
| "prompt_style": (list(CAPTION_TYPE_MAP.keys()), {"default": "Descriptive", "tooltip": "Select the style of caption you want to generate"}),
|
| "caption_length": (CAPTION_LENGTH_CHOICES, {"default": "any", "tooltip": "Control the length of the generated caption"}),
|
| "max_new_tokens": ("INT", {"default": MODEL_SETTINGS["default_max_tokens"], "min": 1, "max": 2048, "tooltip": "Maximum number of tokens to generate. Higher values allow longer captions"}),
|
| "temperature": ("FLOAT", {"default": MODEL_SETTINGS["default_temperature"], "min": 0.0, "max": 2.0, "step": 0.05, "tooltip": "Control the randomness of the output. Higher values make the output more creative but less predictable"}),
|
| "top_p": ("FLOAT", {"default": MODEL_SETTINGS["default_top_p"], "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Control the diversity of the output. Higher values allow more diverse word choices"}),
|
| "top_k": ("INT", {"default": MODEL_SETTINGS["default_top_k"], "min": 0, "max": 100, "tooltip": "Limit the number of possible next tokens. Lower values make the output more focused"}),
|
| "custom_prompt": ("STRING", {"default": "", "multiline": True, "tooltip": "Custom prompt template. If empty, will use the selected prompt style"}),
|
| "memory_management": (["Keep in Memory", "Clear After Run", "Global Cache"], {"default": "Keep in Memory", "tooltip": "Choose how to manage model memory. 'Keep in Memory' for faster processing, 'Clear After Run' for limited VRAM, 'Global Cache' for fastest processing if you have enough VRAM"}),
|
| },
|
| "optional": {
|
| "extra_options": ("JOYCAPTION_EXTRA_OPTIONS", {"tooltip": "Additional options to customize the caption generation"}),
|
| }
|
| }
|
|
|
| RETURN_TYPES = ("STRING", "STRING")
|
| RETURN_NAMES = ("PROMPT", "STRING")
|
| FUNCTION = "generate"
|
| CATEGORY = "🧪AILab/📝JoyCaption"
|
|
|
| def __init__(self):
|
| self.predictor = None
|
| self.current_memory_mode = None
|
| self.current_model = None
|
|
|
| def generate(self, image, model, quantization, prompt_style, caption_length, max_new_tokens, temperature, top_p, top_k, custom_prompt, memory_management, extra_options=None):
|
| try:
|
| validate_model_parameters(quantization, list(MEMORY_EFFICIENT_CONFIGS.keys()))
|
|
|
| if memory_management == "Global Cache":
|
| try:
|
| model_name = HF_MODELS[model]["name"]
|
| self.predictor = JC_Models(model_name, quantization)
|
| except Exception as e:
|
| return (f"Error loading model: {e}", "")
|
| elif self.predictor is None or self.current_memory_mode != quantization or self.current_model != model:
|
| if self.predictor is not None:
|
| del self.predictor
|
| self.predictor = None
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
| try:
|
| model_name = HF_MODELS[model]["name"]
|
| self.predictor = JC_Models(model_name, quantization)
|
| self.current_memory_mode = quantization
|
| self.current_model = model
|
| except Exception as e:
|
| return (f"Error loading model: {e}", "")
|
|
|
| if custom_prompt and custom_prompt.strip():
|
| prompt = custom_prompt.strip()
|
| else:
|
| prompt = build_prompt(prompt_style, caption_length, extra_options[0] if extra_options else [], extra_options[1] if extra_options else "{NAME}")
|
|
|
| system_prompt = MODEL_SETTINGS["default_system_prompt"]
|
| pil_image = ToPILImage()(image[0].permute(2, 0, 1))
|
|
|
| response = self.predictor.generate(
|
| image=pil_image,
|
| system=system_prompt,
|
| prompt=prompt,
|
| max_new_tokens=max_new_tokens,
|
| temperature=temperature,
|
| top_p=top_p,
|
| top_k=top_k,
|
| )
|
|
|
| if memory_management == "Clear After Run":
|
| del self.predictor
|
| self.predictor = None
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
|
|
| return (prompt, response)
|
| except Exception as e:
|
| if memory_management == "Clear After Run":
|
| del self.predictor
|
| self.predictor = None
|
| torch.cuda.empty_cache()
|
| gc.collect()
|
| raise e
|
|
|
| NODE_CLASS_MAPPINGS = {
|
| "JC": JC,
|
| "JC_adv": JC_adv,
|
| "JC_ExtraOptions": JC_ExtraOptions,
|
| }
|
|
|
| NODE_DISPLAY_NAME_MAPPINGS = {
|
| "JC": "JoyCaption",
|
| "JC_adv": "JoyCaption (Advanced)",
|
| "JC_ExtraOptions": "JoyCaption Extra Options",
|
| }
|
|
|
|
|
