395 lines
14 KiB
Python
395 lines
14 KiB
Python
import os
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import folder_paths
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import comfy.model_management as mm
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import io
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import base64
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import torch
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import requests
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import numpy as np
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import torchvision.transforms as T
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from torchvision.transforms.functional import InterpolationMode
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from PIL import Image
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from typing import Union, List
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# from lmdeploy import TurbomindEngineConfig, pipeline
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from huggingface_hub import snapshot_download
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from transformers import AutoTokenizer, AutoModel
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class InternVLModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": (
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[
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"OpenGVLab/InternVL2-1B",
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"OpenGVLab/InternVL2-2B",
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"OpenGVLab/InternVL2-4B",
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"OpenGVLab/InternVL2-8B",
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"OpenGVLab/InternVL2-26B",
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"OpenGVLab/InternVL2-40B",
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],
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{
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"default": "OpenGVLab/InternVL2-2B"
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}),
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}
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}
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RETURN_TYPES = ("InternVLModel",)
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RETURN_NAMES = ("intervl_model",)
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FUNCTION = "load_model"
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CATEGORY = "internvl"
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def load_model(self, model):
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device = mm.get_torch_device()
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model_name = model.rsplit('/', 1)[-1]
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model_dir = (os.path.join(folder_paths.models_dir, "LLM", model_name))
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if not os.path.exists(model_dir):
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print(f"Downloading {model}")
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snapshot_download(repo_id=model, cache_dir=model_dir, local_dir_use_symlinks=False)
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# huggingface-cli download --resume-download --local-dir-use-symlinks False OpenGVLab/InternVL2-2B --local-dir InternVL2-2B
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tokenizer = AutoTokenizer.from_pretrained(model_dir,trust_remote_code=True)
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model = AutoModel.from_pretrained(model_dir,
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torch_dtype=torch.float16,
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trust_remote_code=True,
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low_cpu_mem_usage=True).eval().to(device)
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model = {
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"model": model,
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"tokenizer": tokenizer
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}
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return (model,)
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class DynamicPreprocess:
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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},
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"optional": {
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"min_num": ("INT", {"default": 1, "min": 1, "max": 40}),
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"max_num": ("INT", {"default": 6, "min": 1, "max": 40}),
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"image_size": ("INT", {"default": 448, }),
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"use_thumbnail": ("BOOLEAN", {"default": True, }),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "load_image"
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CATEGORY = "internvl"
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def load_image(self, image, min_num=1, max_num=6, image_size=448, use_thumbnail=True):
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pil_image = self.convert_to_pil_image(image)
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transform = self.build_transform(input_size=image_size)
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images = self.preprocess(pil_image, min_num, max_num, image_size, use_thumbnail)
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# import pdb;pdb.set_trace()
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pixel_values = [transform(image) for image in images]
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pixel_values = torch.stack(pixel_values)
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return (pixel_values,)
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def preprocess(self, image, min_num=1, max_num=6, image_size=448, use_thumbnail=True):
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orig_width, orig_height = image.size
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aspect_ratio = orig_width / orig_height
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# 该代码功能是生成并排序一个集合，其中包含所有在指定范围内（min_num和max_num）的两个数的乘积
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target_ratios = set(
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(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
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i * j <= max_num and i * j >= min_num)
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target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
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# 寻找与给定aspect_ratio最接近的宽高比
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target_aspect_ratio = self.find_closest_aspect_ratio(
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aspect_ratio, target_ratios, orig_width, orig_height, image_size)
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size
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)
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images
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def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
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best_ratio_diff = float('inf')
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def convert_to_pil_image(self, image: Union[
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np.ndarray, List[np.ndarray], bytes, str, Image.Image, torch.Tensor]) -> Image.Image:
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try:
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if isinstance(image, np.ndarray):
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return Image.fromarray(self._ensure_rgb(image))
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elif isinstance(image, list):
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return self._handle_list_input(image)
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elif isinstance(image, bytes):
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return Image.open(io.BytesIO(image)).convert('RGB')
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elif isinstance(image, str):
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return self._handle_string_input(image)
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elif isinstance(image, Image.Image):
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return image.convert('RGB')
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elif isinstance(image, torch.Tensor):
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return self._convert_tensor_to_pil(image)
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else:
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raise ValueError(f"Unsupported image type: {type(image)}")
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except Exception as e:
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raise ValueError(f"Failed to convert image: {str(e)}")
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def _handle_list_input(self, image_list: List) -> Image.Image:
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if len(image_list) == 0:
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raise ValueError("Empty list provided as image")
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if isinstance(image_list[0], np.ndarray):
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return Image.fromarray(self._ensure_rgb(image_list[0]))
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elif all(isinstance(x, (int, float)) for x in image_list):
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arr = np.array(image_list).astype('uint8')
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if arr.size in [1024 * 1024, 1024 * 1024 * 3]:
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arr = arr.reshape((1024, 1024, -1))
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elif arr.size in [512 * 512, 512 * 512 * 3]:
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arr = arr.reshape((512, 512, -1))
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else:
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arr = arr.reshape((arr.shape[0], -1))
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return Image.fromarray(self._ensure_rgb(arr))
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else:
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raise ValueError(f"Unsupported list content type: {type(image_list[0])}")
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def _handle_string_input(self, image_string: str) -> Image.Image:
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if image_string.startswith(('http://', 'https://')):
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response = requests.get(image_string)
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response.raise_for_status()
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return Image.open(io.BytesIO(response.content)).convert('RGB')
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elif image_string.startswith('data:image'):
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image_data = base64.b64decode(image_string.split(',')[1])
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return Image.open(io.BytesIO(image_data)).convert('RGB')
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else:
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return Image.open(image_string).convert('RGB')
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def _ensure_rgb(self, arr: np.ndarray) -> np.ndarray:
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if arr.ndim == 2:
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return np.stack([arr] * 3, axis=-1)
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elif arr.ndim == 3 and arr.shape[2] == 1:
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return np.repeat(arr, 3, axis=2)
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elif arr.ndim == 3 and arr.shape[2] == 3:
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return arr
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elif arr.ndim == 3 and arr.shape[2] == 4:
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return arr[:, :, :3]
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else:
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raise ValueError(f"Unsupported array shape: {arr.shape}")
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def _convert_tensor_to_pil(self, tensor: torch.Tensor) -> Image.Image:
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if tensor.ndim == 4:
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tensor = tensor[0]
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if tensor.ndim == 3:
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if tensor.shape[0] in [1, 3, 4]:
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tensor = tensor.permute(1, 2, 0)
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elif tensor.ndim == 2:
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tensor = tensor.unsqueeze(-1).repeat(1, 1, 3)
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np_array = tensor.cpu().numpy()
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if np_array.dtype != np.uint8:
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if np_array.max() <= 1.0:
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np_array = (np_array * 255).astype(np.uint8)
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else:
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np_array = np_array.astype(np.uint8)
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return Image.fromarray(self._ensure_rgb(np_array))
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def build_transform(self, input_size):
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MEAN, STD = self.IMAGENET_MEAN, self.IMAGENET_STD
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transform = T.Compose([
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T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
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T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD)
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])
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return transform
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class InternVLHFInference:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"model": ("InternVLModel",),
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"system_prompt": ("STRING", {
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"multiline": False,
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"default": "You are a helpful assistant."
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}),
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"prompt": ("STRING", {
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"multiline": False,
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"default": "What is this?"
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}),
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},
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"optional": {
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"keep_model_loaded": ("BOOLEAN", {"default": False}),
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"max_new_tokens": ("INT", {"default": 1024, "min": 1, "max": 4096}),
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"do_sample": ("BOOLEAN", {"default": False}),
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"num_beams": ("INT", {"default": 1})
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("output",)
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FUNCTION = "process"
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CATEGORY = "internvl"
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def process(self,
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image,
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model,
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system_prompt,
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prompt,
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keep_model_loaded=False,
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max_new_tokens=1024,
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do_sample=False,
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num_beams=1):
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mm.soft_empty_cache()
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device = mm.get_torch_device()
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offload_device = mm.unet_offload_device()
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image = image.to(torch.float16).to(device)
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generation_config = dict(num_beams=num_beams, max_new_tokens=max_new_tokens, do_sample=do_sample)
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internvl_model = model['model']
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tokenizer = model['tokenizer']
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question = f'<image>\n{system_prompt}\n{prompt}'
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response, _ = internvl_model.chat(tokenizer, image, question, generation_config, history=None,
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return_history=True)
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if not keep_model_loaded:
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print("Offloading model...")
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internvl_model.to(offload_device)
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mm.soft_empty_cache()
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return (response,)
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class InternVLLMDEPLOYInference:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"model": (
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[
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"OpenGVLab/InternVL2-1B",
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"OpenGVLab/InternVL2-2B",
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"OpenGVLab/InternVL2-4B",
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"OpenGVLab/InternVL2-8B",
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"OpenGVLab/InternVL2-26B",
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"OpenGVLab/InternVL2-40B",
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],
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{
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"default": "OpenGVLab/InternVL2-2B"
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}),
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"system_prompt": ("STRING", {
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"multiline": False,
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"default": "You are a helpful assistant."
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}),
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"prompt": ("STRING", {
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"multiline": False,
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"default": "What is this?"
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}),
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},
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"optional": {
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"tp": ("INT", {"default": 1, "min": 1, "max": 8}),
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"cache_max_entry_count": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("output",)
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FUNCTION = "process"
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CATEGORY = "internvl"
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def process(self,
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image,
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model,
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system_prompt,
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prompt,
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tp=1,
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cache_max_entry_count=0.5):
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mm.soft_empty_cache()
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# load_model
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model_name = model.rsplit('/', 1)[-1]
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model_dir = (os.path.join(folder_paths.models_dir, "LLM", model_name))
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if not os.path.exists(model_dir):
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print(f"Downloading {model}")
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snapshot_download(repo_id=model, cache_dir=model_dir, local_dir_use_symlinks=False)
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pipe = pipeline(model_dir,
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backend_config=TurbomindEngineConfig(tp=tp, cache_max_entry_count=cache_max_entry_count))
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image_pil = self.tensor2pil(image).convert("RGB")
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question = f'<image>\n{system_prompt}\n{prompt}'
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response = pipe((question, image_pil))
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return (response,)
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def tensor2pil(self, image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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NODE_CLASS_MAPPINGS = {
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"InternVLModelLoader": InternVLModelLoader,
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"DynamicPreprocess": DynamicPreprocess,
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"InternVLHFInference": InternVLHFInference,
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# "InternVLSWIFTInference",
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# "InternVLLMDEPLOYInference": InternVLLMDEPLOYInference
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"InternVLModelLoader": "InternVL Model Loader",
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"DynamicPreprocess": "Dynamic Preprocess",
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"InternVLHFInference": "InternVL HF Inference",
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# "InternVLLMDEPLOYInference": "InternVL LMDEPLOY Inference"
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}
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