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+assets/cir_candi_2.png filter=lfs diff=lfs merge=lfs -text
+assets/cir_query.png filter=lfs diff=lfs merge=lfs -text
+assets/res-ft-mmeb.png filter=lfs diff=lfs merge=lfs -text
+assets/res-scaling.png filter=lfs diff=lfs merge=lfs -text
+assets/res-zs-cir.png filter=lfs diff=lfs merge=lfs -text
+assets/res-zs-mmeb.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,167 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- llava-hf/llava-v1.6-mistral-7b-hf
+tags:
+- multimodal-retrieval
+- embedding-model
+---
+<h1 align="center">MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval</h1>
+
+<p align="center">
+    <a href="https://arxiv.org/abs/2412.14475">
+        <img alt="Build" src="http://img.shields.io/badge/cs.CV-arXiv%3A2412.14475-B31B1B.svg">
+    </a>
+    <a href="https://github.com/VectorSpaceLab/MegaPairs">
+        <img alt="Build" src="https://img.shields.io/badge/Github-Code-blue">
+    </a>
+    <a href="https://huggingface.co/datasets/JUNJIE99/MegaPairs">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Datasets-MegaPairs-yellow">
+</p>
+
+<p align="center">
+</a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-base">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_base-yellow">
+    </a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-large">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_large-yellow">
+    </a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-MLLM-S1">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_MLLM_S1-yellow">
+    </a>
+    <a href="https://huggingface.co/JUNJIE99/MMRet-MLLM-S2">
+        <img alt="Build" src="https://img.shields.io/badge/🤗 Model-MMRet_MLLM_S2-yellow">
+    </a>
+</p>
+
+## News
+```2024-3-4``` 🚀🚀 We have released the MMRet-MLLM models on Hugging Face: [MMRet-MLLM-S1](https://huggingface.co/JUNJIE99/MMRet-MLLM-S1) and [MMRet-MLLM-S2](https://huggingface.co/JUNJIE99/MMRet-MLLM-S2). **MMRet-MLLM-S1** is trained exclusively on our MegaPairs dataset, achieving outstanding performance in composed image retrieval, with an 8.1% improvement on the CIRCO benchmark (mAP@5) over the previous state-of-the-art. **MMRet-MLLM-S2** builds on MMRet-MLLM-S1 with an additional epoch of fine-tuning on the MMEB benchmark training set, delivering enhanced performance across a broader range of multimodal embedding tasks.
+
+```2024-12-27``` 🚀🚀 MMRet-CLIP models are released in Huggingface: [MMRet-base](https://huggingface.co/JUNJIE99/MMRet-base) and [MMRet-large](https://huggingface.co/JUNJIE99/MMRet-large).
+
+```2024-12-19``` 🎉🎉 Release our paper: [MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval](https://arxiv.org/pdf/2412.14475).
+
+## Release Plan
+- [x] Paper
+- [x] MMRet-base and MMRet-large models
+- [x] MMRet-MLLM model
+- [ ] MegaPairs Dataset
+- [ ] Evaluation code
+- [ ] Fine-tuning code
+
+
+## Introduction
+In this project, we introduce **MegaPairs**, a novel data synthesis method that leverages open-domain images to create *heterogeneous KNN triplets* for universal multimodal retrieval. Our MegaPairs dataset contains over 26 million triplets, and we have trained a series of multimodal retrieval models, **MMRets**, including MMRet-CLIP (base and large) and MMRet-MLLM.
+
+MMRets achieve state-of-the-art performance on four popular zero-shot composed image retrieval benchmarks and the massive multimodal embedding benchmark (MMEB). Extensive experiments demonstrate the ***efficiency, scalability, and generalization*** features of MegaPairs. Please refer to our [paper](https://arxiv.org/abs/2412.14475) for more details.
+
+## Model Usage
+
+### 1. MMRet-CLIP Models
+You can easily use MMRet-CLIP models based on ```transformers```
+```python
+import torch
+from transformers import AutoModel
+
+MODEL_NAME = "JUNJIE99/MMRet-base" # or "JUNJIE99/MMRet-large"
+
+model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True) # You must set trust_remote_code=True
+model.set_processor(MODEL_NAME)
+model.eval()
+
+with torch.no_grad():
+    query = model.encode(
+        images = "./assets/cir_query.png", 
+        text = "Make the background dark, as if the camera has taken the photo at night"
+    )
+
+    candidates = model.encode(
+        images = ["./assets/cir_candi_1.png", "./assets/cir_candi_2.png"]
+    )
+    
+    scores = query @ candidates.T
+print(scores)
+```
+
+### 2. MMRet-MLLM Models
+```python
+import torch
+from transformers import AutoModel
+from PIL import Image
+
+MODEL_NAME= "JUNJIE99/MMRet-MLLM-S1"
+
+model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)
+model.eval()
+model.cuda()
+
+with torch.no_grad():
+    model.set_processor(MODEL_NAME)
+
+    query_inputs = model.data_process(
+        text="Make the background dark, as if the camera has taken the photo at night", 
+        images="./assets/cir_query.png",
+        q_or_c="q",
+        task_instruction="Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: "
+    )
+
+    candidate_inputs = model.data_process(
+        images=["./assets/cir_candi_1.png", "./assets/cir_candi_2.png"],
+        q_or_c="c",
+    )
+
+    query_embs = model(**query_inputs, output_hidden_states=True)[:, -1, :]
+    candi_embs = model(**candidate_inputs, output_hidden_states=True)[:, -1, :]
+    
+    query_embs = torch.nn.functional.normalize(query_embs, dim=-1)
+    candi_embs = torch.nn.functional.normalize(candi_embs, dim=-1)
+
+    scores = torch.matmul(query_embs, candi_embs.T)
+print(scores)
+```
+
+
+## Model Performance
+### Zero-Shot Composed Image Retrieval
+
+MMRet sets a new performance benchmark in zero-shot composed image retrieval tasks. On the CIRCO benchmark, our MMRet-base model, with only 149 million parameters, surpasses all previous models, including those with 50 times more parameters. Additionally, MMRet-MLLM achieves an 8.1% improvement over the previous state-of-the-art model.
+
+<img src="./assets/res-zs-cir.png" width="800">
+
+### Zero-Shot Performance on MMEB
+
+MMRet-MLLM achieves state-of-the-art zero-shot performance on the Massive Multimodal Embedding Benchmark (MMEB), despite being trained only on the ImageText-to-Image paradigm. This demonstrates the excellent generalization capability of MegaPairs for multimodal embedding.
+
+<img src="./assets/res-zs-mmeb.png" width="800">
+
+### Fine-Tuning Performance on MMEB
+
+After fine-tuning on downstream tasks, MMRet-MLLM maintains its leading performance. Notably, it surpasses the previous state-of-the-art by 7.1% on the MMEB out-of-distribution (OOD) set. These results demonstrate the robust generalization capability of MMRet-MLLM and highlight the potential of MegaPairs as foundational training data for universal multimodal embedding.
+
+<img src="./assets/res-ft-mmeb.png" width="800">
+
+### Performance Scaling
+MegaPairs showcases **scalability**: MMRet-base improves as training data increases. It also demonstrates **efficiency**: with just 0.5M training samples, MMRet-base significantly outperforms MagicLens, which uses the same CLIP-base backbone and was trained on 36.7M samples.
+
+<img src="./assets/res-scaling.png" width="800">
+
+
+## License
+The annotations for MegaPairs and the MMRet models are released under the [MIT License](LICENSE). The images in MegaPairs originate from the [Recap-Datacomp](https://huggingface.co/datasets/UCSC-VLAA/Recap-DataComp-1B), which is released under the CC BY 4.0 license.
+
+
+
+## Citation
+If you find this repository useful, please consider giving a star ⭐ and citation
+
+```
+@article{zhou2024megapairs,
+  title={MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval},
+  author={Zhou, Junjie and Liu, Zheng and Liu, Ze and Xiao, Shitao and Wang, Yueze and Zhao, Bo and Zhang, Chen Jason and Lian, Defu and Xiong, Yongping},
+  journal={arXiv preprint arXiv:2412.14475},
+  year={2024}
+}
+```

added_tokens.json

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+  "<instruct>": 32002,
+  "<pad>": 32001,
+  "<query_txt_img>": 32003,
+  "<target_img>": 32004
+}

config.json

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+{
+  "architectures": [
+    "LlavaNextForConditionalGeneration"
+  ],
+  "ignore_index": -100,
+  "auto_map": {
+    "AutoModel": "modeling_llavanext_for_embedding.LLaVANextForEmbedding"
+  },
+  "image_grid_pinpoints": [
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+      336
+    ],
+    [
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+    ]
+  ],
+  "image_token_index": 32000,
+  "model_type": "llava_next",
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
+    "architectures": [
+      "MistralForCausalLM"
+    ],
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "model_type": "mistral",
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 32005
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.0.dev0",
+  "use_image_newline_parameter": true,
+  "vision_config": {
+    "hidden_size": 1024,
+    "image_size": 336,
+    "intermediate_size": 4096,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768,
+    "vocab_size": 32000
+  },
+  "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 32005
+}

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.2"
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+  }
+}

modeling_llavanext_for_embedding.py

@@ -0,0 +1,329 @@
+import logging
+import transformers
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, AutoModel
+import torch
+from PIL import Image
+import requests
+from typing import List, Optional, Tuple, Union
+from transformers.cache_utils import Cache
+from transformers.models.llava_next.modeling_llava_next import image_size_to_num_patches
+
+logger = logging.getLogger(__name__)
+
+
+def my_mistral_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    num_logits_to_keep: int = 0,
+):
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        num_logits_to_keep (`int`, *optional*):
+            Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
+            `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
+            token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
+
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+    >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+    >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = self.model(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+    )
+
+    hidden_states = outputs[0]
+    
+    return hidden_states
+
+
+def transfer_mistral_forward():
+    transformers.models.mistral.MistralForCausalLM.forward = my_mistral_forward
+
+class LLaVANextForEmbedding(LlavaNextForConditionalGeneration):
+    def __init__(self, config):
+        super().__init__(config)
+        
+        transfer_mistral_forward()
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        image_sizes: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        vision_feature_layer: Optional[int] = None,
+        vision_feature_select_strategy: Optional[str] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+    ):
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if pixel_values is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+            )
+
+        legacy_processing = False
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+            # if the number of image tokens is more than image embeddings seq length, then prob we expanded it in processing
+            # not very reliable, but we don't expect one to actually pass 500+ images for one prompt
+            # In case we're in decoding stage, legacy behavior is checked by presence of pixel values even if use_cache=True
+            legacy_processing = (
+                (input_ids == self.config.image_token_index).sum(1).max() < self.config.image_seq_length
+            ) or (input_ids.shape[-1] == 1 and pixel_values is not None)
+
+        if pixel_values is not None and pixel_values.size(0) > 0:
+            # ! infer image_num_patches from image_sizes
+            image_num_patches = [
+                image_size_to_num_patches(
+                    image_size=imsize,
+                    grid_pinpoints=self.config.image_grid_pinpoints,
+                    patch_size=self.config.vision_config.image_size,
+                )
+                for imsize in image_sizes
+            ]
+            # figure out if pixel_values is concatenated or stacked
+            if pixel_values.dim() == 5:
+                # stacking when input is (batch_size, num_patches, num_channels, height, width)
+                _pixel_values_list = [
+                    pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)
+                ]
+                pixel_values = torch.cat(_pixel_values_list, dim=0)
+            elif pixel_values.dim() != 4:
+                # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
+                raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+            image_features = self.vision_tower(pixel_values, output_hidden_states=True)
+            selected_image_feature = image_features.hidden_states[vision_feature_layer]
+            if vision_feature_select_strategy == "default":
+                selected_image_feature = selected_image_feature[:, 1:]
+            elif vision_feature_select_strategy == "full":
+                selected_image_feature = selected_image_feature
+            image_features = self.multi_modal_projector(selected_image_feature)
+            image_features = torch.split(image_features, image_num_patches, dim=0)
+
+            # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
+            image_features, feature_lens = self.pack_image_features(
+                image_features,
+                image_sizes,
+                vision_feature_select_strategy=vision_feature_select_strategy,
+                image_newline=self.image_newline,
+            )
+            if legacy_processing:
+                logger.warning_once(
+                    "Expanding inputs for image tokens in LLaVa-NeXT should be done in processing. "
+                    "Please add `patch_size` and `vision_feature_select_strategy` to the model's processing config or set directly "
+                    "with `processor.patch_size = {{patch_size}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. "
+                    "Using processors without these attributes in the config is deprecated and will throw an error in v4.47."
+                )
+                if input_ids.shape[1] != 1:
+                    inputs_embeds = inputs_embeds.to(image_features.dtype)
+                    inputs_embeds, attention_mask, position_ids, labels, _ = self._merge_input_ids_with_image_features(
+                        image_features,
+                        feature_lens,
+                        inputs_embeds,
+                        input_ids,
+                        attention_mask,
+                        position_ids,
+                        labels=labels,
+                    )
+                    cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)
+                else:
+                    # Retrieve the first layer to inspect the logits and mask out the hidden states
+                    # that are set to 0
+                    first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
+
+                    # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
+                    batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
+
+                    # Get the target length
+                    target_length = input_ids.shape[1]
+                    past_length = first_layer_past_key_value.shape[-1]
+
+                    extended_attention_mask = torch.ones(
+                        (attention_mask.shape[0], past_length),
+                        dtype=attention_mask.dtype,
+                        device=attention_mask.device,
+                    )
+
+                    # Filter out only the tokens that can be un-attended, this can happen
+                    # if one uses Llava + Fused modules where the cache on the
+                    # first iteration is already big enough, or if one passes custom cache
+                    valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
+                    new_batch_index = batch_index[valid_indices]
+                    new_non_attended_tokens = non_attended_tokens[valid_indices]
+
+                    # Zero-out the places where we don't need to attend
+                    extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
+                    attention_mask = torch.cat((extended_attention_mask, attention_mask[:, -target_length:]), dim=1)
+                    position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
+                    cache_position = torch.arange(attention_mask.shape[1], device=attention_mask.device)[
+                        -target_length:
+                    ]
+
+            # TODO: @raushan retain only the new behavior after v4.47
+            else:
+                special_image_mask = (
+                    (input_ids == self.config.image_token_index).unsqueeze(-1).expand_as(inputs_embeds)
+                )
+                image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+                inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+        outputs = self.language_model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            num_logits_to_keep=num_logits_to_keep,
+        )
+
+        return outputs
+
+    def set_processor(self, model_name):
+        self.processor = LlavaNextProcessor.from_pretrained(model_name)
+    def prepare_text_input(self, image=None, text=None, q_or_c=None, task_instruction=None):
+        task_instruction_example_cir = "Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: "
+        
+        assert q_or_c in ["query", "candidate", "q", "c"]
+        
+        if "q" in q_or_c:
+            if task_instruction is None:
+                text_input = "[INST] \n <instruct>  <query>"
+                print(f"""
+                        Warning: For optimal performance, MMRet-MLLM requires the task instruction to be specified in the query.
+                        For example, for the composed image retrieval task, you might use a specific instruction like: {task_instruction_example_cir}.
+                        Instructions for other tasks can be referenced in the MMEB benchmark.
+                    """)
+            elif task_instruction is not None:
+                text_input = f"[INST] \n <instruct> {task_instruction} <query> "
+            
+            if text is not None:
+                text_input = f"{text_input} {text} \n"
+            if image is not None:
+                text_input = f"{text_input} <image>"
+
+            text_input = f"{text_input} [/INST]"
+        else:
+            text_input = "[INST] "
+            if text is not None:
+                text_input = f"{text_input} {text} \n"
+            if image is not None:
+                text_input = f"{text_input} <image>"
+            text_input = f"{text_input} [/INST]"
+        
+        return text_input
+
+    def data_process(self, images=None, text=None, q_or_c=None, task_instruction=None):
+        if images is not None:
+            _is_list = isinstance(images, list)
+        elif text is not None:
+            _is_list = isinstance(text, list)
+        else:
+            raise ValueError("images and text cannot be both None.")
+        
+        assert q_or_c in ["query", "candidate", "q", "c"]
+
+        if not _is_list :
+            text_input = self.prepare_text_input(images, text, q_or_c, task_instruction)
+            text_input = [text_input]
+            
+
+            if images is not None:
+                images = Image.open(images).resize((512,512)).convert("RGB")
+                images = [images]
+                inputs = self.processor(images=images, text=text_input, return_tensors="pt", padding=True)
+            else:
+                inputs = self.processor(text=text_input, return_tensors="pt", padding=True)
+
+        else:
+            if text is None:
+                text = [None] * len(images)
+            text_input = [self.prepare_text_input(_image, _text, q_or_c, task_instruction) for _image, _text in zip(images, text)]
+            
+            if images is not None:
+                images = [Image.open(_image).resize((512,512)).convert("RGB") for _image in images]
+                inputs = self.processor(images=images, text=text_input, return_tensors="pt", padding=True)
+            else:
+                inputs = self.processor(text=text_input, return_tensors="pt", padding=True)
+        
+        inputs = inputs.to(self.device)
+
+        return inputs

preprocessor_config.json

@@ -0,0 +1,52 @@
+{
+  "aspect_ratio_setting": "anyres",
+  "crop_size": {
+    "height": 336,
+    "width": 336
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_grid_pinpoints": [
+    [
+      336,
+      672
+    ],
+    [
+      672,
+      336
+    ],
+    [
+      672,
+      672
+    ],
+    [
+      1008,
+      336
+    ],
+    [
+      336,
+      1008
+    ]
+  ],
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "LlavaNextImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "processor_class": "LlavaNextProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 336
+  }
+}

special_tokens_map.json

@@ -0,0 +1,53 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<instruct>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<query_txt_img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<target_img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

tokenizer_config.json

@@ -0,0 +1,93 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": true,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<instruct>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<query_txt_img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<target_img>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<instruct>",
+    "<query_txt_img>",
+    "<target_img>"
+  ],
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "max_length": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "left",
+  "processor_class": "LlavaNextProcessor",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}