391 lines
17 KiB
Python
391 lines
17 KiB
Python
import os
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import torch
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import folder_paths
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from transformers import AutoTokenizer, AutoModel
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from torchvision.transforms.v2 import ToPILImage
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from comfy.comfy_types import IO
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from comfy_api.input import VideoInput
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import json
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import gc
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import sys
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import io
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import warnings
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from pathlib import Path
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os.environ['TRANSFORMERS_VERBOSITY'] = 'error'
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os.environ['TOKENIZERS_PARALLELISM'] = 'false'
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# Suppress transformers FutureWarnings for better user experience
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warnings.filterwarnings("ignore", category=FutureWarning, module="transformers")
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if torch.cuda.is_available():
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torch.backends.cudnn.benchmark = True
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if hasattr(torch.backends, 'cuda'):
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if hasattr(torch.backends.cuda, 'matmul'):
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torch.backends.cuda.matmul.allow_tf32 = True
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if hasattr(torch.backends.cuda, 'allow_tf32'):
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torch.backends.cuda.allow_tf32 = True
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
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# Load configuration file
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with open(Path(__file__).parent / "minicpm_config.json", "r", encoding="utf-8") as f:
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config = json.load(f)
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MODEL_SETTINGS = config["model_settings"]
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PROMPT_TYPES = config.get("prompt_types", {})
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TRANSFORMERS_MODELS = config.get("transformers_models", {})
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_MODEL_CACHE = {}
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class MiniCPM_Transformers_Models:
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def __init__(self, model: str, processing_mode: str):
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try:
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models_dir = Path(folder_paths.models_dir).resolve()
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prompt_generator_dir = (models_dir / "LLM").resolve()
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prompt_generator_dir.mkdir(parents=True, exist_ok=True)
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model_config = TRANSFORMERS_MODELS[model]
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model_id = model_config["name"]
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self.model_checkpoint = prompt_generator_dir / Path(model_id).name
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if not self.model_checkpoint.exists():
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print(f"Downloading model: {model_id} (this may take several minutes...)")
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from huggingface_hub import snapshot_download
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snapshot_download(
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repo_id=model_id,
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local_dir=str(self.model_checkpoint)
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)
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self.device = torch.device("cuda" if processing_mode == "GPU" and torch.cuda.is_available() else "cpu")
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self.bf16_support = (
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torch.cuda.is_available()
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and torch.cuda.get_device_capability(self.device)[0] >= 8
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)
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old_stdout = sys.stdout
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old_stderr = sys.stderr
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try:
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sys.stdout = io.StringIO()
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sys.stderr = io.StringIO()
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self.tokenizer = AutoTokenizer.from_pretrained(
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str(self.model_checkpoint),
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trust_remote_code=True,
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low_cpu_mem_usage=True,
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)
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self.model = AutoModel.from_pretrained(
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str(self.model_checkpoint),
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trust_remote_code=True,
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low_cpu_mem_usage=True,
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attn_implementation="sdpa",
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torch_dtype=torch.bfloat16 if self.bf16_support else torch.float16,
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)
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if processing_mode == "GPU" and torch.cuda.is_available():
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self.model = self.model.to(self.device)
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self.model.eval()
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finally:
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sys.stdout = old_stdout
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sys.stderr = old_stderr
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except Exception as e:
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raise RuntimeError(f"Model initialization failed: {str(e)}") from e
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def generate(self, images, system: str, prompt: str, max_new_tokens: int,
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temperature: float, top_p: float, top_k: int,
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repetition_penalty: float, video_max_slice_nums: int = 2, seed: int = -1) -> str:
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try:
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if seed != -1:
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torch.manual_seed(seed)
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with torch.no_grad():
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if isinstance(images, list):
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# Video frame processing
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msgs = [{"role": "user", "content": images + [prompt]}]
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else:
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# Single image processing
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msgs = [{"role": "user", "content": [images, prompt]}]
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params = {"use_image_id": False, "max_slice_nums": video_max_slice_nums}
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result = self.model.chat(
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image=None,
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msgs=msgs,
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tokenizer=self.tokenizer,
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sampling=True,
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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repetition_penalty=repetition_penalty,
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max_new_tokens=max_new_tokens,
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**params,
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)
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return result.strip()
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except Exception as e:
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return f"Generation error: {str(e)}"
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finally:
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gc.collect()
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class MiniCPM_Transformers_Base:
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def __init__(self):
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self.predictor = None
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self.current_processing_mode = None
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self.current_model = None
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def _load_model(self, model: str, processing_mode: str, memory_management: str):
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cache_key = f"{model}_{processing_mode}"
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try:
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if memory_management == "Global Cache":
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if cache_key in _MODEL_CACHE:
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self.predictor = _MODEL_CACHE[cache_key]
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else:
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self.predictor = MiniCPM_Transformers_Models(model, processing_mode)
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_MODEL_CACHE[cache_key] = self.predictor
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elif (self.predictor is None or self.current_processing_mode != processing_mode or self.current_model != model):
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if self.predictor is not None:
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del self.predictor
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self.predictor = None
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torch.cuda.empty_cache()
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gc.collect()
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self.predictor = MiniCPM_Transformers_Models(model, processing_mode)
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self.current_processing_mode = processing_mode
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self.current_model = model
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except Exception as model_error:
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raise RuntimeError(f"Model loading error: {str(model_error)}")
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def _cleanup_memory(self, memory_management: str):
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if memory_management == "Clear After Run":
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try:
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del self.predictor
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self.predictor = None
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torch.cuda.empty_cache()
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gc.collect()
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except:
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pass
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def _process_image(self, image):
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try:
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if isinstance(image, (list, tuple)) and len(image) > 0:
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img_tensor = image[0]
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else:
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img_tensor = image
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if not isinstance(img_tensor, torch.Tensor):
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raise ValueError(f"Expected torch.Tensor, got {type(img_tensor)}")
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if img_tensor.dim() == 4:
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img_tensor = img_tensor[0]
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elif img_tensor.dim() == 2:
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img_tensor = img_tensor.unsqueeze(0).repeat(3, 1, 1)
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if img_tensor.shape[0] == 3:
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pass
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elif img_tensor.shape[-1] == 3:
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img_tensor = img_tensor.permute(2, 0, 1)
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else:
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raise ValueError(f"Unexpected image tensor shape: {img_tensor.shape}")
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pil_image = ToPILImage()(img_tensor).convert("RGB")
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return pil_image
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except Exception as input_error:
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raise RuntimeError(f"Input processing error: {str(input_error)}")
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def _generate_response(self, images, system_prompt, prompt, **gen_params):
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try:
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with torch.inference_mode():
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response = self.predictor.generate(
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images=images,
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system=system_prompt,
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prompt=prompt,
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**gen_params
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)
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return response
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except Exception as gen_error:
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raise RuntimeError(f"Generation error: {str(gen_error)}")
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def encode_video(self, source_video, MAX_NUM_FRAMES):
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def uniform_sample(l, n):
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gap = len(l) / n
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idxs = [int(i * gap + gap / 2) for i in range(n)]
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return [l[i] for i in idxs]
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components = source_video.get_components()
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vr = components.images
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avg_fps = float(components.frame_rate)
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sample_fps = round(avg_fps / 1)
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frame_idx = [i for i in range(0, len(vr), sample_fps)]
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if len(frame_idx) > MAX_NUM_FRAMES:
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frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
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frames = [vr[idx] for idx in frame_idx]
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frames = [ToPILImage()(v.permute([2, 0, 1])).convert("RGB") for v in frames]
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return frames
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class AILab_MiniCPM_4_V(MiniCPM_Transformers_Base):
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@classmethod
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def INPUT_TYPES(cls):
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model_list = list(TRANSFORMERS_MODELS.keys())
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return {
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"required": {},
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"optional": {
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"image": ("IMAGE",),
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"video": ("VIDEO",),
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"model": (model_list, {"default": model_list[0]}),
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"preset_prompt": (list(PROMPT_TYPES.keys()), {"default": "Describe"}),
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"custom_prompt": ("STRING", {"default": "", "multiline": True}),
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"device": (["Auto", "GPU", "CPU"], {"default": "Auto"}),
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"memory_management": (["Keep in Memory", "Clear After Run", "Global Cache"], {"default": "Keep in Memory"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("STRING",)
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FUNCTION = "generate"
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CATEGORY = "🧪AILab/📝MiniCPM"
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def generate(self, image=None, video=None, model=None, preset_prompt="Describe", custom_prompt="", device="Auto", memory_management="Keep in Memory", seed=-1):
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try:
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if image is None and video is None:
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return ("Error: Please provide either an image or video input.",)
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pm = ("GPU" if torch.cuda.is_available() else "CPU") if device == "Auto" else device
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model = model or list(TRANSFORMERS_MODELS.keys())[0]
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self._load_model(model, pm, memory_management)
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if video is not None:
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frames = self.encode_video(video, 64)
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if frames:
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images = frames
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else:
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return ("Error: No frames extracted from video.",)
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elif image is not None:
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pil_image = self._process_image(image)
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images = pil_image
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else:
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return ("Error: No valid input provided.",)
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preset = PROMPT_TYPES.get(preset_prompt, "Describe this image.")
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prompt = custom_prompt.strip() if (isinstance(custom_prompt, str) and custom_prompt.strip()) else preset
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if isinstance(seed, int) and seed != -1:
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torch.manual_seed(seed)
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response = self._generate_response(
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images=images,
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system_prompt=MODEL_SETTINGS.get("default_system_prompt", ""),
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prompt=prompt,
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max_new_tokens=MODEL_SETTINGS["default_max_tokens"],
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temperature=MODEL_SETTINGS["default_temperature"],
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top_p=MODEL_SETTINGS["default_top_p"],
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top_k=MODEL_SETTINGS["default_top_k"],
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repetition_penalty=MODEL_SETTINGS["default_repetition_penalty"],
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video_max_slice_nums=2,
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seed=seed,
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)
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self._cleanup_memory(memory_management)
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return (response,)
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except Exception as e:
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self._cleanup_memory(memory_management)
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return (f"Error: {str(e)}",)
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class AILab_MiniCPM_4_V_Advanced(MiniCPM_Transformers_Base):
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@classmethod
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def INPUT_TYPES(cls):
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model_list = list(TRANSFORMERS_MODELS.keys())
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return {
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"required": {},
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"optional": {
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"image": ("IMAGE",),
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"video": ("VIDEO",),
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"model": (model_list, {"default": model_list[0]}),
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"preset_prompt": (list(PROMPT_TYPES.keys()), {"default": "Describe"}),
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"custom_prompt": ("STRING", {"default": "", "multiline": True}),
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"system_prompt": ("STRING", {"default": MODEL_SETTINGS.get("default_system_prompt", ""), "multiline": True}),
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"max_new_tokens": ("INT", {"default": MODEL_SETTINGS["default_max_tokens"], "min": 1, "max": 4096}),
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"temperature": ("FLOAT", {"default": MODEL_SETTINGS["default_temperature"], "min": 0, "max": 2.0, "step": 0.05}),
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"top_p": ("FLOAT", {"default": MODEL_SETTINGS["default_top_p"], "min": 0.0, "max": 1.0, "step": 0.01}),
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"top_k": ("INT", {"default": MODEL_SETTINGS["default_top_k"], "min": 0, "max": 200}),
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"repetition_penalty": ("FLOAT", {"default": MODEL_SETTINGS["default_repetition_penalty"], "min": 0.8, "max": 1.5, "step": 0.01}),
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"video_max_num_frames": ("INT", {"default": 64, "min": 1, "max": 128}),
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"video_max_slice_nums": ("INT", {"default": 2, "min": 1, "max": 4}),
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"device": (["Auto", "GPU", "CPU"], {"default": "Auto"}),
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"memory_management": (["Keep in Memory", "Clear After Run", "Global Cache"], {"default": "Keep in Memory"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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}
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}
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RETURN_TYPES = ("STRING", "STRING")
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RETURN_NAMES = ("PROMPT", "STRING")
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FUNCTION = "generate"
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CATEGORY = "🧪AILab/📝MiniCPM"
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def generate(self, image=None, video=None, model=None, preset_prompt="Describe", custom_prompt="", system_prompt="", max_new_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None, video_max_num_frames=64, video_max_slice_nums=2, device="Auto", memory_management="Keep in Memory", seed=-1):
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try:
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if image is None and video is None:
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return ("", "Error: Please provide either an image or video input.")
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pm = ("GPU" if torch.cuda.is_available() else "CPU") if device == "Auto" else device
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model = model or list(TRANSFORMERS_MODELS.keys())[0]
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self._load_model(model, pm, memory_management)
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if video is not None:
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frames = self.encode_video(video, video_max_num_frames)
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if frames:
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images = frames
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else:
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return ("", "Error: No frames extracted from video.")
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elif image is not None:
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pil_image = self._process_image(image)
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images = pil_image
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else:
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return ("", "Error: No valid input provided.")
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preset = PROMPT_TYPES.get(preset_prompt, "Describe this image.")
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prompt = custom_prompt.strip() if (isinstance(custom_prompt, str) and custom_prompt.strip()) else preset
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if isinstance(seed, int) and seed != -1:
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torch.manual_seed(seed)
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max_new_tokens = max_new_tokens if max_new_tokens is not None else MODEL_SETTINGS["default_max_tokens"]
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temperature = temperature if temperature is not None else MODEL_SETTINGS["default_temperature"]
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top_p = top_p if top_p is not None else MODEL_SETTINGS["default_top_p"]
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top_k = top_k if top_k is not None else MODEL_SETTINGS["default_top_k"]
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repetition_penalty = repetition_penalty if repetition_penalty is not None else MODEL_SETTINGS["default_repetition_penalty"]
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response = self._generate_response(
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images=images,
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system_prompt=system_prompt or MODEL_SETTINGS.get("default_system_prompt", ""),
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prompt=prompt,
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max_new_tokens=max_new_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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repetition_penalty=repetition_penalty,
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video_max_slice_nums=video_max_slice_nums,
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seed=seed,
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)
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self._cleanup_memory(memory_management)
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return (prompt, response)
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except Exception as e:
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self._cleanup_memory(memory_management)
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return ("", f"Error: {str(e)}")
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NODE_CLASS_MAPPINGS = {
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"AILab_MiniCPM_4_V": AILab_MiniCPM_4_V,
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"AILab_MiniCPM_4_V_Advanced": AILab_MiniCPM_4_V_Advanced,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AILab_MiniCPM_4_V": "MiniCPM-4-V",
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"AILab_MiniCPM_4_V_Advanced": "MiniCPM-4-V Advanced",
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} |