llava/model/multimodal_encoder/clip_encoder.py

# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.

# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.

# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0

# This file is modified from https://github.com/haotian-liu/LLaVA/
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel, PretrainedConfig

from llava.model.multimodal_encoder.vision_encoder import VisionTower, VisionTowerS2


class CLIPVisionTower(VisionTower):
    def __init__(self, model_name_or_path: str, config: PretrainedConfig):
        super().__init__(model_name_or_path, config)
        self.image_processor = CLIPImageProcessor.from_pretrained(model_name_or_path)
        self.vision_tower = CLIPVisionModel.from_pretrained(model_name_or_path, torch_dtype=eval(config.model_dtype))
        self.is_loaded = True


class CLIPVisionTowerS2(VisionTowerS2):
    def __init__(self, model_name_or_path: str, config: PretrainedConfig):
        super().__init__(model_name_or_path, config)
        self.image_processor = CLIPImageProcessor.from_pretrained(model_name_or_path)
        self.vision_tower = CLIPVisionModel.from_pretrained(model_name_or_path, torch_dtype=eval(config.model_dtype))

        # Make sure it crops/resizes the image to the largest scale in self.scales to maintain high-res information
        self.image_processor.size["shortest_edge"] = self.scales[-1]
        self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.scales[-1]

        self.is_loaded = True