hunyuandit

app/multiTurnT2I_app.py

+# -- coding: utf-8 --
+#!/usr/bin/env python
+import gradio as gr
+from PIL import Image
+import sys
+import os
+sys.path.append(os.getcwd())
+import json
+import numpy as np
+from pathlib import Path
+import io
+import hashlib
+import requests
+import base64
+import pandas as pd
+from sample_t2i import inferencer
+from dialoggen.dialoggen_demo import init_dialoggen_model, eval_model
+
+SIZES = {
+    "正方形(square, 1024x1024)": (1024, 1024),
+    "风景(landscape, 1280x768)": (768, 1280),
+    "人像(portrait, 768x1280)": (1280, 768),
+}
+
+global_seed=np.random.randint(0, 10000)
+
+# Helper Functions
+def image_to_base64(image_path):
+    with open(image_path, "rb") as image_file:
+        encoded_image = base64.b64encode(image_file.read()).decode()
+    return encoded_image
+
+def get_strings(lang):
+    lang_file = Path(f"app/lang/{lang}.csv")
+    strings = pd.read_csv(lang_file, header=0)
+    strings = strings.set_index("key")['value'].to_dict()
+    return strings
+
+def get_image_md5(image):
+    image_data = io.BytesIO()
+    image.save(image_data, format="PNG")
+    image_data = image_data.getvalue()
+    md5_hash = hashlib.md5(image_data).hexdigest()
+    return md5_hash
+
+
+# mllm调用
+def request_mllm(server_url='http://0.0.0.0:8080',history_messages=[], question="画一个木制的鸟",image=""):
+    if image != "":
+        image = base64.b64encode(open(image, "rb").read()).decode()
+    print("history_messages before request",history_messages)
+    headers = {
+        'accept': 'application/json',
+        'Content-Type': 'application/json'
+    }
+    data = {
+        "text": question,
+        "image": image, # "image为空字符串，则进行文本对话"
+        "history": history_messages,
+    }
+    response = requests.post(server_url, headers=headers, json=data)
+    print("response",response)
+    response = response.json()
+    print(response)
+    response_text = response["result"]
+    history_messages = response["history"]
+    print("history_messages before request",history_messages)
+    return history_messages, response_text
+
+
+# 画图
+def image_generation(
+    prompt, infer_steps, seed, image_size
+):
+    print(f"prompt sent to T2I model: {prompt}, infer_steps: {infer_steps}, seed: {seed}, size: {image_size}")
+    height, width = SIZES[image_size]
+    results = gen.predict(prompt,
+                          height=height,
+                          width=width,
+                          seed=seed,
+                          infer_steps=infer_steps,
+                          batch_size=1,
+                          )
+    image = results['images'][0]
+    file_name = get_image_md5(image)
+    # Save images
+    save_dir = Path('results')
+    save_dir.mkdir(exist_ok=True)
+    save_path = f'results/multiRound_{file_name}.png'
+    image.save(save_path)
+    encoded_image = image_to_base64(save_path)
+
+    return encoded_image
+
+# 图文对话
+def chat(history_messages, input_text):
+
+    history_messages, response_text = request_mllm(history_messages=history_messages, question=input_text)
+    return history_messages, response_text
+#
+def pipeline(input_text, state, infer_steps, seed, image_size):
+
+    # 忽略空输入
+    if len(input_text) == 0:
+        return state, state[0]
+
+    conversation = state[0]
+    history_messages = state[1]
+
+    system_prompt = '请先判断用户的意图，若为画图则在输出前加入<画图>:'
+    print(f"input history:{history_messages}")
+    if not isinstance(history_messages, list) and len(history_messages.messages) >= 2:
+        response, history_messages = enhancer(input_text, return_history=True, history=history_messages, skip_special=True)
+    else:
+        response, history_messages = enhancer(input_text, return_history=True, history=history_messages, skip_special=False)
+
+    history_messages.messages[-1][-1] = response
+
+    if '<画图>' in response:
+        intention_draw = True
+    else:
+        intention_draw = False
+    
+    print(f"response:{response}")
+    print("-" * 80)
+    print(f"history_messages:{history_messages}")
+    print(f"intention_draw:{intention_draw}")
+    if intention_draw:
+        prompt = response.split('<画图>')[-1]
+        # 画图
+        image_url = image_generation(prompt, infer_steps, seed, image_size)
+        response = f'<img src="data:image/png;base64,{image_url}" style="display: inline-block;"><p style="font-size: 14px; color: #555; margin-top: 0;">{prompt}</p>'
+    conversation += [((input_text, response))]
+    return [conversation, history_messages], conversation
+
+# 页面设计
+def upload_image(state, image_input):
+    conversation = state[0]
+    history_messages = state[1]
+    input_image = Image.open(image_input.name).resize(
+        (224, 224)).convert('RGB')
+    input_image.save(image_input.name)  # Overwrite with smaller image.
+    system_prompt = '请先判断用户的意图，若为画图则在输出前加入<画图>:'
+    history_messages, response = request_mllm(question="这张图描述了什么？",history_messages=history_messages,
+                                              image=image_input.name)
+    conversation += [(f'<img src="./file={image_input.name}"  style="display: inline-block;">', response)]
+    print("conversation" , conversation)
+    print("history_messages after uploading image", history_messages)
+    return [conversation, history_messages], conversation
+
+
+def reset():
+    global global_seed
+    global_seed=np.random.randint(0, 10000)
+    return [[], []], []
+
+
+def reset_last(state):
+    conversation, history = state[0], state[1]
+    conversation = conversation[:-1]
+    history.messages = history.messages[:-2]
+    return [conversation, history], conversation
+
+if __name__ == '__main__':
+    
+    # Initialize dialoggen and HunyuanDiT model
+    args, gen, enhancer = inferencer()
+    strings = get_strings(args.lang)
+
+    css = """
+        #chatbot { min-height: 800px; }
+        #save-btn {
+            background-image: linear-gradient(to right bottom, rgba(130,217,244, 0.9), rgba(158,231,214, 1.0));
+        }
+        #save-btn:hover {
+            background-image: linear-gradient(to right bottom, rgba(110,197,224, 0.9), rgba(138,211,194, 1.0));
+        }
+        #share-btn {
+            background-image: linear-gradient(to right bottom, rgba(130,217,244, 0.9), rgba(158,231,214, 1.0));
+        }
+        #share-btn:hover {
+            background-image: linear-gradient(to right bottom, rgba(110,197,224, 0.9), rgba(138,211,194, 1.0));
+        }
+        #gallery { z-index: 999999; }
+        #gallery img:hover {transform: scale(2.3); z-index: 999999; position: relative; padding-right: 30%; padding-bottom: 30%;}
+        #gallery button img:hover {transform: none; z-index: 999999; position: relative; padding-right: 0; padding-bottom: 0;}
+        @media (hover: none) {
+            #gallery img:hover {transform: none; z-index: 999999; position: relative; padding-right: 0; 0;}
+        }
+        .html2canvas-container { width: 3000px !important; height: 3000px !important; }
+    """
+
+
+
+    with gr.Blocks(css=css) as demo:
+        DESCRIPTION = '''# <a style="color: black; text-decoration: none;">多轮对话绘图 Multi-turn Text2Image Generation</a>
+            你可以参照[DialogGen](https://arxiv.org/abs/2403.08857)，通过简单的交互式语句来进行历史图片的修改，例如：主体编辑、增加主体、删除主体、背景更换、风格转换、镜头转换、图像合并。
+
+            (You can modify historical images through simple interactive statements referred to [DialogGen](https://arxiv.org/abs/2403.08857), such as: enity edit, add object, remove object, change background, change style, change lens, and combine images. )
+            
+            例如，主体编辑 (For example, enity edit) :
+            ```none
+            Round1: 画一个木制的鸟
+            (Round1: draw a wooden bird)
+            
+            Round2: 变成玻璃的
+            (Round2: turn into glass)
+            ```
+        '''
+
+        gr.Markdown(DESCRIPTION)
+        gr_state = gr.State([[], []])  # conversation, chat_history
+
+        with gr.Row():
+            with gr.Column(scale=1, min_width=1000):
+                with gr.Row():
+                    chatbot = gr.Chatbot(elem_id="chatbot", label="DialogGen&HunyuanDiT")
+                with gr.Row():
+                    infer_steps = gr.Slider(
+                        label='采样步数(sampling steps)', minimum=1, maximum=200, value=100, step=1,
+                    )
+                    seed = gr.Number(
+                        label='种子(seed)', minimum=-1, maximum=1_000_000_000, value=666, step=1, precision=0,
+                    )
+                    size_dropdown = gr.Dropdown(choices=["正方形(square, 1024x1024)", "风景(landscape, 1280x768)", "人像(portrait, 768x1280)"], value="正方形(square, 1024x1024)", label="图片尺寸(Image Size)")
+
+                with gr.Row():
+                    # image_btn = gr.UploadButton("🖼️ Upload Image", file_types=["image"])
+                    text_input = gr.Textbox(label="提示词(prompt)", placeholder="输入提示词(Type a prompt)")
+
+                    with gr.Column():
+                        submit_btn = gr.Button(
+                            "提交(Submit)", interactive=True, variant="primary")
+                        clear_last_btn = gr.Button("回退(Undo)")
+                        clear_btn = gr.Button("全部重置(Reset All)")
+                with gr.Row():
+                    gr.Examples([
+                    ['画一个木制的鸟'],
+                    ['一只小猫'],
+                    ['现实主义风格，画面主要描述一个巴洛克风格的花瓶，带有金色的装饰边框，花瓶上盛开着各种色彩鲜艳的花，白色背景'],
+                    ['一只聪明的狐狸走在阔叶树林里, 旁边是一条小溪, 细节真实, 摄影'],
+                    ['飞流直下三千尺，疑是银河落九天'],
+                    ['一只长靴猫手持亮银色的宝剑，身着铠甲，眼神坚毅，站在一堆金币上，背景是暗色调的洞穴，图像上有金币的光影点缀。'],
+                    ['麻婆豆腐'],
+                    ['苏州园林'],
+                    ['一颗新鲜的草莓特写，红色的外表，表面布满许多种子，背景是淡绿色的叶子'],
+                    ['枯藤老树昏鸦，小桥流水人家'],
+                    ['湖水清澈，天空湛蓝，阳光灿烂。一只优雅的白天鹅在湖边游泳。它周围有几只小鸭子，看起来非常可爱，整个画面给人一种宁静祥和的感觉。'],
+                    ['一朵鲜艳的红色玫瑰花，花瓣撒有一些水珠，晶莹剔透，特写镜头'],
+                    ['臭豆腐'],
+                    ['九寨沟'],
+                    ['俗语“鲤鱼跃龙门”'],
+                    ['风格是写实，画面主要描述一个亚洲戏曲艺术家正在表演，她穿着华丽的戏服，脸上戴着精致的面具，身姿优雅，背景是古色古香的舞台，镜头是近景'],
+                    ], [text_input],
+                    label=strings['examples']
+                    )
+                gr.Markdown('''<p style="font-size: 20px; color: #888;">powered by <a href="https://github.com/Centaurusalpha/DialogGen" target="_blank">DialogGen</a> and <a href="https://github.com/Tencent/HunyuanDiT" target="_blank">HunyuanDiT</a></p>''')
+
+
+        text_input.submit(pipeline, [text_input, gr_state, infer_steps, seed, size_dropdown], [gr_state, chatbot])
+        text_input.submit(lambda: "", None, text_input)  # Reset chatbox.
+        submit_btn.click(pipeline, [text_input, gr_state, infer_steps, seed, size_dropdown], [gr_state, chatbot])
+        submit_btn.click(lambda: "", None, text_input)  # Reset chatbox.
+
+        # image_btn.upload(upload_image, [gr_state, image_btn], [gr_state, chatbot])
+        clear_last_btn.click(reset_last, [gr_state], [gr_state, chatbot])
+        clear_btn.click(reset, [], [gr_state, chatbot])
+
+    interface = demo
+    interface.launch(server_name="0.0.0.0", server_port=443, share=False)

dialoggen/dialoggen_demo.py

+import argparse
+import torch
+import sys
+import os
+# 添加当前命令行运行的目录到 sys.path
+sys.path.append(os.getcwd()+"/dialoggen")
+
+
+from llava.constants import (
+    IMAGE_TOKEN_INDEX,
+    DEFAULT_IMAGE_TOKEN,
+    DEFAULT_IM_START_TOKEN,
+    DEFAULT_IM_END_TOKEN,
+    IMAGE_PLACEHOLDER,
+)
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import (
+    process_images,
+    tokenizer_image_token,
+    get_model_name_from_path,
+)
+
+import requests
+from PIL import Image
+from io import BytesIO
+import re
+
+
+def image_parser(image_file, sep=','):
+    out = image_file.split(sep)
+    return out
+
+
+def load_image(image_file):
+    if image_file.startswith("http") or image_file.startswith("https"):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert("RGB")
+    else:
+        image = Image.open(image_file).convert("RGB")
+    return image
+
+
+def load_images(image_files):
+    out = []
+    for image_file in image_files:
+        image = load_image(image_file)
+        out.append(image)
+    return out
+
+
+def init_dialoggen_model(model_path, model_base=None, load_4bit=False):
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(
+        model_path, model_base, model_name, llava_type_model=True, load_4bit=load_4bit)
+    return {"tokenizer": tokenizer,
+            "model": model,
+            "image_processor": image_processor}
+
+
+def eval_model(models,
+               query='详细描述一下这张图片',
+               image_file=None,
+               sep=',',
+               temperature=0.2,
+               top_p=None,
+               num_beams=1,
+               max_new_tokens=512,
+               return_history=False,
+               history=None,
+               skip_special=False
+               ):
+    # Model
+    disable_torch_init()
+
+    qs = query
+    image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
+    if IMAGE_PLACEHOLDER in qs:
+        if models["model"].config.mm_use_im_start_end:
+            qs = re.sub(IMAGE_PLACEHOLDER, image_token_se, qs)
+        else:
+            qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs)
+    else:
+        if models["model"].config.mm_use_im_start_end:
+            qs = image_token_se + "\n" + qs
+        else:
+            qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
+            
+    if not history:
+        conv = conv_templates['llava_v1'].copy()
+    else:
+        conv = history
+
+    if skip_special:
+        conv.append_message(conv.roles[0], query)
+    else:
+        conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+    if image_file is not None:
+        image_files = image_parser(image_file, sep=sep)
+        images = load_images(image_files)
+        image_sizes = [x.size for x in images]
+        images_tensor = process_images(
+            images,
+            models["image_processor"],
+            models["model"].config
+        ).to(models["model"].device, dtype=torch.float16)
+    else:
+        # fomatted input as training data
+        image_sizes = [(1024, 1024)]
+        images_tensor = torch.zeros(1, 5, 3, models["image_processor"].crop_size["height"], models["image_processor"].crop_size["width"])
+        images_tensor = images_tensor.to(models["model"].device, dtype=torch.float16)
+
+    input_ids = (
+        tokenizer_image_token(prompt, models["tokenizer"], IMAGE_TOKEN_INDEX, return_tensors="pt")
+        .unsqueeze(0)
+        .cuda()
+    )
+    with torch.inference_mode():
+        output_ids = models["model"].generate(
+            input_ids,
+            images=images_tensor,
+            image_sizes=image_sizes,
+            do_sample=True if temperature > 0 else False,
+            temperature=temperature,
+            top_p=top_p,
+            num_beams=num_beams,
+            max_new_tokens=max_new_tokens,
+            use_cache=True,
+        )
+
+    outputs = models["tokenizer"].batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+    if return_history:
+        return outputs, conv
+    return outputs
+
+
+def remove_prefix(text):
+    if text.startswith("<画图>"):
+        return text[len("<画图>"):], True
+    elif text.startswith("对不起"):
+        # 拒绝画图
+        return "", False
+    else:
+        return text, True
+
+
+class DialogGen(object):
+    def __init__(self, model_path, load_4bit=False):
+        self.models = init_dialoggen_model(model_path, load_4bit=load_4bit)
+        self.query_template = "请先判断用户的意图，若为画图则在输出前加入<画图>:{}"
+
+    def __call__(self, prompt, return_history=False, history=None, skip_special=False):
+        enhanced_prompt = eval_model(
+            models=self.models,
+            query=self.query_template.format(prompt),
+            image_file=None,
+            return_history=return_history,
+            history=history,
+            skip_special=skip_special
+        )
+        if return_history:
+            return enhanced_prompt
+
+        enhanced_prompt, compliance = remove_prefix(enhanced_prompt)
+        if not compliance:
+            return False, ""
+        return True, enhanced_prompt
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_path', type=str, default='./ckpts/dialoggen')
+    parser.add_argument('--prompt', type=str, default='画一只小猫')
+    parser.add_argument('--image_file', type=str, default=None) # 'images/demo1.jpeg'
+    args = parser.parse_args()
+
+    query = f"请先判断用户的意图，若为画图则在输出前加入<画图>:{args.prompt}"
+
+    models = init_dialoggen_model(args.model_path)
+
+    res = eval_model(models,
+        query=query,
+        image_file=args.image_file,
+    )
+    print(res)

dialoggen/llava/__init__.py

+from .model import LlavaLlamaForCausalLM

dialoggen/llava/constants.py

+CONTROLLER_HEART_BEAT_EXPIRATION = 30
+WORKER_HEART_BEAT_INTERVAL = 15
+
+LOGDIR = "."
+
+# Model Constants
+IGNORE_INDEX = -100
+IMAGE_TOKEN_INDEX = -200
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_IM_END_TOKEN = "<im_end>"
+IMAGE_PLACEHOLDER = "<image-placeholder>"

dialoggen/llava/conversation.py

+import dataclasses
+from enum import auto, Enum
+from typing import List, Tuple
+import base64
+from io import BytesIO
+from PIL import Image
+
+
+class SeparatorStyle(Enum):
+    """Different separator style."""
+    SINGLE = auto()
+    TWO = auto()
+    MPT = auto()
+    PLAIN = auto()
+    LLAMA_2 = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that keeps all conversation history."""
+    system: str
+    roles: List[str]
+    messages: List[List[str]]
+    offset: int
+    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
+    sep: str = "###"
+    sep2: str = None
+    version: str = "Unknown"
+
+    skip_next: bool = False
+
+    def get_prompt(self):
+        messages = self.messages
+        if len(messages) > 0 and type(messages[0][1]) is tuple:
+            messages = self.messages.copy()
+            init_role, init_msg = messages[0].copy()
+            init_msg = init_msg[0].replace("<image>", "").strip()
+            if 'mmtag' in self.version:
+                messages[0] = (init_role, init_msg)
+                messages.insert(0, (self.roles[0], "<Image><image></Image>"))
+                messages.insert(1, (self.roles[1], "Received."))
+            else:
+                messages[0] = (init_role, "<image>\n" + init_msg)
+
+        if self.sep_style == SeparatorStyle.SINGLE:
+            ret = self.system + self.sep
+            for role, message in messages:
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + ": " + message + self.sep
+                else:
+                    ret += role + ":"
+        elif self.sep_style == SeparatorStyle.TWO:
+            seps = [self.sep, self.sep2]
+            ret = self.system + seps[0]
+            for i, (role, message) in enumerate(messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+        elif self.sep_style == SeparatorStyle.MPT:
+            ret = self.system + self.sep
+            for role, message in messages:
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+        elif self.sep_style == SeparatorStyle.LLAMA_2:
+            wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" if len(msg) > 0 else msg
+            wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
+            ret = ""
+
+            for i, (role, message) in enumerate(messages):
+                if i == 0:
+                    assert message, "first message should not be none"
+                    assert role == self.roles[0], "first message should come from user"
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    if i == 0: message = wrap_sys(self.system) + message
+                    if i % 2 == 0:
+                        message = wrap_inst(message)
+                        ret += self.sep + message
+                    else:
+                        ret += " " + message + " " + self.sep2
+                else:
+                    ret += ""
+            ret = ret.lstrip(self.sep)
+        elif self.sep_style == SeparatorStyle.PLAIN:
+            seps = [self.sep, self.sep2]
+            ret = self.system
+            for i, (role, message) in enumerate(messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += message + seps[i % 2]
+                else:
+                    ret += ""
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+        return ret
+
+    def append_message(self, role, message):
+        self.messages.append([role, message])
+
+    def process_image(self, image, image_process_mode, return_pil=False, image_format='PNG', max_len=1344, min_len=672):
+        if image_process_mode == "Pad":
+            def expand2square(pil_img, background_color=(122, 116, 104)):
+                width, height = pil_img.size
+                if width == height:
+                    return pil_img
+                elif width > height:
+                    result = Image.new(pil_img.mode, (width, width), background_color)
+                    result.paste(pil_img, (0, (width - height) // 2))
+                    return result
+                else:
+                    result = Image.new(pil_img.mode, (height, height), background_color)
+                    result.paste(pil_img, ((height - width) // 2, 0))
+                    return result
+            image = expand2square(image)
+        elif image_process_mode in ["Default", "Crop"]:
+            pass
+        elif image_process_mode == "Resize":
+            image = image.resize((336, 336))
+        else:
+            raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
+        if max(image.size) > max_len:
+            max_hw, min_hw = max(image.size), min(image.size)
+            aspect_ratio = max_hw / min_hw
+            shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
+            longest_edge = int(shortest_edge * aspect_ratio)
+            W, H = image.size
+            if H > W:
+                H, W = longest_edge, shortest_edge
+            else:
+                H, W = shortest_edge, longest_edge
+            image = image.resize((W, H))
+        if return_pil:
+            return image
+        else:
+            buffered = BytesIO()
+            image.save(buffered, format=image_format)
+            img_b64_str = base64.b64encode(buffered.getvalue()).decode()
+            return img_b64_str
+
+    def get_images(self, return_pil=False):
+        images = []
+        for i, (role, msg) in enumerate(self.messages[self.offset:]):
+            if i % 2 == 0:
+                if type(msg) is tuple:
+                    msg, image, image_process_mode = msg
+                    image = self.process_image(image, image_process_mode, return_pil=return_pil)
+                    images.append(image)
+        return images
+
+    def to_gradio_chatbot(self):
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset:]):
+            if i % 2 == 0:
+                if type(msg) is tuple:
+                    msg, image, image_process_mode = msg
+                    img_b64_str = self.process_image(
+                        image, "Default", return_pil=False,
+                        image_format='JPEG')
+                    img_str = f'<img src="data:image/jpeg;base64,{img_b64_str}" alt="user upload image" />'
+                    msg = img_str + msg.replace('<image>', '').strip()
+                    ret.append([msg, None])
+                else:
+                    ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def copy(self):
+        return Conversation(
+            system=self.system,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            version=self.version)
+
+    def dict(self):
+        if len(self.get_images()) > 0:
+            return {
+                "system": self.system,
+                "roles": self.roles,
+                "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
+                "offset": self.offset,
+                "sep": self.sep,
+                "sep2": self.sep2,
+            }
+        return {
+            "system": self.system,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+            "sep": self.sep,
+            "sep2": self.sep2,
+        }
+
+
+conv_vicuna_v0 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    roles=("Human", "Assistant"),
+    messages=(
+        ("Human", "What are the key differences between renewable and non-renewable energy sources?"),
+        ("Assistant",
+            "Renewable energy sources are those that can be replenished naturally in a relatively "
+            "short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
+            "Non-renewable energy sources, on the other hand, are finite and will eventually be "
+            "depleted, such as coal, oil, and natural gas. Here are some key differences between "
+            "renewable and non-renewable energy sources:\n"
+            "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
+            "energy sources are finite and will eventually run out.\n"
+            "2. Environmental impact: Renewable energy sources have a much lower environmental impact "
+            "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
+            "and other negative effects.\n"
+            "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
+            "have lower operational costs than non-renewable sources.\n"
+            "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
+            "locations than non-renewable sources.\n"
+            "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
+            "situations and needs, while non-renewable sources are more rigid and inflexible.\n"
+            "6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
+            "non-renewable sources are not, and their depletion can lead to economic and social instability.\n")
+    ),
+    offset=2,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)
+
+conv_vicuna_v1 = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions.",
+    roles=("USER", "ASSISTANT"),
+    version="v1",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+conv_llama_2 = Conversation(
+    system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""",
+    roles=("USER", "ASSISTANT"),
+    version="llama_v2",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.LLAMA_2,
+    sep="<s>",
+    sep2="</s>",
+)
+
+conv_llava_llama_2 = Conversation(
+    system="You are a helpful language and vision assistant. "
+           "You are able to understand the visual content that the user provides, "
+           "and assist the user with a variety of tasks using natural language.",
+    roles=("USER", "ASSISTANT"),
+    version="llama_v2",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.LLAMA_2,
+    sep="<s>",
+    sep2="</s>",
+)
+
+conv_mpt = Conversation(
+    system="""<|im_start|>system
+A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
+    roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
+    version="mpt",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.MPT,
+    sep="<|im_end|>",
+)
+
+conv_llava_plain = Conversation(
+    system="",
+    roles=("", ""),
+    messages=(
+    ),
+    offset=0,
+    sep_style=SeparatorStyle.PLAIN,
+    sep="\n",
+)
+
+conv_llava_v0 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    roles=("Human", "Assistant"),
+    messages=(
+    ),
+    offset=0,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)
+
+conv_llava_v0_mmtag = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+           "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
+           "The visual content will be provided with the following format: <Image>visual content</Image>.",
+    roles=("Human", "Assistant"),
+    messages=(
+    ),
+    offset=0,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+    version="v0_mmtag",
+)
+
+conv_llava_v1 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    roles=("USER", "ASSISTANT"),
+    version="v1",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+conv_llava_v1_mmtag = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+           "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
+           "The visual content will be provided with the following format: <Image>visual content</Image>.",
+    roles=("USER", "ASSISTANT"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+    version="v1_mmtag",
+)
+
+conv_mistral_instruct = Conversation(
+    system="",
+    roles=("USER", "ASSISTANT"),
+    version="llama_v2",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.LLAMA_2,
+    sep="",
+    sep2="</s>",
+)
+
+conv_chatml_direct = Conversation(
+    system="""<|im_start|>system
+Answer the questions.""",
+    roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
+    version="mpt",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.MPT,
+    sep="<|im_end|>",
+)
+
+default_conversation = conv_vicuna_v1
+conv_templates = {
+    "default": conv_vicuna_v0,
+    "v0": conv_vicuna_v0,
+    "v1": conv_vicuna_v1,
+    "vicuna_v1": conv_vicuna_v1,
+    "llama_2": conv_llama_2,
+    "mistral_instruct": conv_mistral_instruct,
+    "chatml_direct": conv_chatml_direct,
+    "mistral_direct": conv_chatml_direct,
+
+    "plain": conv_llava_plain,
+    "v0_plain": conv_llava_plain,
+    "llava_v0": conv_llava_v0,
+    "v0_mmtag": conv_llava_v0_mmtag,
+    "llava_v1": conv_llava_v1,
+    "v1_mmtag": conv_llava_v1_mmtag,
+    "llava_llama_2": conv_llava_llama_2,
+
+    "mpt": conv_mpt,
+}
+
+
+if __name__ == "__main__":
+    print(default_conversation.get_prompt())

dialoggen/llava/mm_utils.py

+from PIL import Image
+from io import BytesIO
+import base64
+import torch
+import math
+import ast
+
+from transformers import StoppingCriteria
+from llava.constants import IMAGE_TOKEN_INDEX
+
+
+def select_best_resolution(original_size, possible_resolutions):
+    """
+    Selects the best resolution from a list of possible resolutions based on the original size.
+
+    Args:
+        original_size (tuple): The original size of the image in the format (width, height).
+        possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
+
+    Returns:
+        tuple: The best fit resolution in the format (width, height).
+    """
+    original_width, original_height = original_size
+    best_fit = None
+    max_effective_resolution = 0
+    min_wasted_resolution = float('inf')
+
+    for width, height in possible_resolutions:
+        scale = min(width / original_width, height / original_height)
+        downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale)
+        effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height)
+        wasted_resolution = (width * height) - effective_resolution
+
+        if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution):
+            max_effective_resolution = effective_resolution
+            min_wasted_resolution = wasted_resolution
+            best_fit = (width, height)
+
+    return best_fit
+
+
+def resize_and_pad_image(image, target_resolution):
+    """
+    Resize and pad an image to a target resolution while maintaining aspect ratio.
+
+    Args:
+        image (PIL.Image.Image): The input image.
+        target_resolution (tuple): The target resolution (width, height) of the image.
+
+    Returns:
+        PIL.Image.Image: The resized and padded image.
+    """
+    original_width, original_height = image.size
+    target_width, target_height = target_resolution
+
+    scale_w = target_width / original_width
+    scale_h = target_height / original_height
+
+    if scale_w < scale_h:
+        new_width = target_width
+        new_height = min(math.ceil(original_height * scale_w), target_height)
+    else:
+        new_height = target_height
+        new_width = min(math.ceil(original_width * scale_h), target_width)
+
+    # Resize the image
+    resized_image = image.resize((new_width, new_height))
+
+    new_image = Image.new('RGB', (target_width, target_height), (0, 0, 0))
+    paste_x = (target_width - new_width) // 2
+    paste_y = (target_height - new_height) // 2
+    new_image.paste(resized_image, (paste_x, paste_y))
+
+    return new_image
+
+
+def divide_to_patches(image, patch_size):
+    """
+    Divides an image into patches of a specified size.
+
+    Args:
+        image (PIL.Image.Image): The input image.
+        patch_size (int): The size of each patch.
+
+    Returns:
+        list: A list of PIL.Image.Image objects representing the patches.
+    """
+    patches = []
+    width, height = image.size
+    for i in range(0, height, patch_size):
+        for j in range(0, width, patch_size):
+            box = (j, i, j + patch_size, i + patch_size)
+            patch = image.crop(box)
+            patches.append(patch)
+
+    return patches
+
+
+def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
+    """
+    Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
+
+    Args:
+        image_size (tuple): The size of the input image in the format (width, height).
+        grid_pinpoints (str): A string representation of a list of possible resolutions.
+        patch_size (int): The size of each image patch.
+
+    Returns:
+        tuple: The shape of the image patch grid in the format (width, height).
+    """
+    if type(grid_pinpoints) is list:
+        possible_resolutions = grid_pinpoints
+    else:
+        possible_resolutions = ast.literal_eval(grid_pinpoints)
+    width, height = select_best_resolution(image_size, possible_resolutions)
+    return width // patch_size, height // patch_size
+
+
+def process_anyres_image(image, processor, grid_pinpoints):
+    """
+    Process an image with variable resolutions.
+
+    Args:
+        image (PIL.Image.Image): The input image to be processed.
+        processor: The image processor object.
+        grid_pinpoints (str): A string representation of a list of possible resolutions.
+
+    Returns:
+        torch.Tensor: A tensor containing the processed image patches.
+    """
+    if type(grid_pinpoints) is list:
+        possible_resolutions = grid_pinpoints
+    else:
+        possible_resolutions = ast.literal_eval(grid_pinpoints)
+    best_resolution = select_best_resolution(image.size, possible_resolutions)
+    image_padded = resize_and_pad_image(image, best_resolution)
+
+    patches = divide_to_patches(image_padded, processor.crop_size['height'])
+
+    image_original_resize = image.resize((processor.size['shortest_edge'], processor.size['shortest_edge']))
+
+    image_patches = [image_original_resize] + patches
+    image_patches = [processor.preprocess(image_patch, return_tensors='pt')['pixel_values'][0]
+                     for image_patch in image_patches]
+    return torch.stack(image_patches, dim=0)
+
+
+def load_image_from_base64(image):
+    return Image.open(BytesIO(base64.b64decode(image)))
+
+
+def expand2square(pil_img, background_color):
+    width, height = pil_img.size
+    if width == height:
+        return pil_img
+    elif width > height:
+        result = Image.new(pil_img.mode, (width, width), background_color)
+        result.paste(pil_img, (0, (width - height) // 2))
+        return result
+    else:
+        result = Image.new(pil_img.mode, (height, height), background_color)
+        result.paste(pil_img, ((height - width) // 2, 0))
+        return result
+
+
+def process_images(images, image_processor, model_cfg):
+    image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
+    new_images = []
+    if image_aspect_ratio == 'pad':
+        for image in images:
+            image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
+            image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+            new_images.append(image)
+    elif image_aspect_ratio == "anyres":
+        for image in images:
+            image = process_anyres_image(image, image_processor, model_cfg.image_grid_pinpoints)
+            new_images.append(image)
+    else:
+        return image_processor(images, return_tensors='pt')['pixel_values']
+    if all(x.shape == new_images[0].shape for x in new_images):
+        new_images = torch.stack(new_images, dim=0)
+    return new_images
+
+
+def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
+    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
+
+    def insert_separator(X, sep):
+        return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
+
+    input_ids = []
+    offset = 0
+    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
+        offset = 1
+        input_ids.append(prompt_chunks[0][0])
+
+    for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
+        input_ids.extend(x[offset:])
+
+    if return_tensors is not None:
+        if return_tensors == 'pt':
+            return torch.tensor(input_ids, dtype=torch.long)
+        raise ValueError(f'Unsupported tensor type: {return_tensors}')
+    return input_ids
+
+
+def get_model_name_from_path(model_path):
+    model_path = model_path.strip("/")
+    model_paths = model_path.split("/")
+    if model_paths[-1].startswith('checkpoint-'):
+        return model_paths[-2] + "_" + model_paths[-1]
+    else:
+        return model_paths[-1]
+
+class KeywordsStoppingCriteria(StoppingCriteria):
+    def __init__(self, keywords, tokenizer, input_ids):
+        self.keywords = keywords
+        self.keyword_ids = []
+        self.max_keyword_len = 0
+        for keyword in keywords:
+            cur_keyword_ids = tokenizer(keyword).input_ids
+            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
+                cur_keyword_ids = cur_keyword_ids[1:]
+            if len(cur_keyword_ids) > self.max_keyword_len:
+                self.max_keyword_len = len(cur_keyword_ids)
+            self.keyword_ids.append(torch.tensor(cur_keyword_ids))
+        self.tokenizer = tokenizer
+        self.start_len = input_ids.shape[1]
+
+    def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
+        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
+        for keyword_id in self.keyword_ids:
+            truncated_output_ids = output_ids[0, -keyword_id.shape[0]:]
+            if torch.equal(truncated_output_ids, keyword_id):
+                return True
+        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
+        for keyword in self.keywords:
+            if keyword in outputs:
+                return True
+        return False
+
+    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        outputs = []
+        for i in range(output_ids.shape[0]):
+            outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores))
+        return all(outputs)

dialoggen/llava/model/__init__.py

+try:
+    from .language_model.llava_llama import LlavaLlamaForCausalLM, LlavaConfig
+    from .language_model.llava_mpt import LlavaMptForCausalLM, LlavaMptConfig
+    from .language_model.llava_mistral import LlavaMistralForCausalLM, LlavaMistralConfig
+except:
+    pass

dialoggen/llava/model/apply_delta.py

+"""
+Usage:
+python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta
+"""
+import argparse
+
+import torch
+from tqdm import tqdm
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from llava import LlavaLlamaForCausalLM
+
+
+def apply_delta(base_model_path, target_model_path, delta_path):
+    print("Loading base model")
+    base = AutoModelForCausalLM.from_pretrained(
+        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+
+    print("Loading delta")
+    delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+    delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
+
+    print("Applying delta")
+    for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"):
+        if name not in base.state_dict():
+            assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
+            continue
+        if param.data.shape == base.state_dict()[name].shape:
+            param.data += base.state_dict()[name]
+        else:
+            assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \
+                f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
+            bparam = base.state_dict()[name]
+            param.data[:bparam.shape[0], :bparam.shape[1]] += bparam
+
+    print("Saving target model")
+    delta.save_pretrained(target_model_path)
+    delta_tokenizer.save_pretrained(target_model_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base-model-path", type=str, required=True)
+    parser.add_argument("--target-model-path", type=str, required=True)
+    parser.add_argument("--delta-path", type=str, required=True)
+
+    args = parser.parse_args()
+
+    apply_delta(args.base_model_path, args.target_model_path, args.delta_path)

dialoggen/llava/model/builder.py

+#    Copyright 2023 Haotian Liu
+#
+#    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.
+
+
+import os
+import warnings
+import shutil
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
+import torch
+from llava.model import *
+from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+
+
+def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda", use_flash_attn=False, llava_type_model=True, **kwargs):
+    kwargs = {"device_map": device_map, **kwargs}
+
+    if device != "cuda":
+        kwargs['device_map'] = {"": device}
+    if load_8bit:
+        kwargs['load_in_8bit'] = True
+    elif load_4bit:
+        kwargs['load_in_4bit'] = True
+        kwargs['quantization_config'] = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4'
+        )
+    else:
+        kwargs['torch_dtype'] = torch.float16
+
+    if use_flash_attn:
+        kwargs['attn_implementation'] = 'flash_attention_2'
+
+    if 'llava' in model_name.lower():
+        # Load LLaVA model
+        if 'lora' in model_name.lower() and model_base is None:
+            warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
+        if 'lora' in model_name.lower() and model_base is not None:
+            from llava.model.language_model.llava_llama import LlavaConfig
+            lora_cfg_pretrained = LlavaConfig.from_pretrained(model_path)
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+            print('Loading LLaVA from base model...')
+            model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
+            token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
+            if model.lm_head.weight.shape[0] != token_num:
+                model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
+                model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
+
+            print('Loading additional LLaVA weights...')
+            if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
+                non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
+            else:
+                # this is probably from HF Hub
+                from huggingface_hub import hf_hub_download
+                def load_from_hf(repo_id, filename, subfolder=None):
+                    cache_file = hf_hub_download(
+                        repo_id=repo_id,
+                        filename=filename,
+                        subfolder=subfolder)
+                    return torch.load(cache_file, map_location='cpu')
+                non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
+            non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
+            if any(k.startswith('model.model.') for k in non_lora_trainables):
+                non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
+            model.load_state_dict(non_lora_trainables, strict=False)
+
+            from peft import PeftModel
+            print('Loading LoRA weights...')
+            model = PeftModel.from_pretrained(model, model_path)
+            print('Merging LoRA weights...')
+            model = model.merge_and_unload()
+            print('Model is loaded...')
+        elif model_base is not None:
+            # this may be mm projector only
+            print('Loading LLaVA from base model...')
+            if 'mpt' in model_name.lower():
+                if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')):
+                    shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py'))
+                tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
+                cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+                model = LlavaMptForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+                cfg_pretrained = AutoConfig.from_pretrained(model_path)
+                model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
+
+            mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
+            mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
+            model.load_state_dict(mm_projector_weights, strict=False)
+        else:
+            if 'mpt' in model_name.lower():
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+                model = LlavaMptForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+            elif 'mistral' in model_name.lower():
+                tokenizer = AutoTokenizer.from_pretrained(model_path)
+                model = LlavaMistralForCausalLM.from_pretrained(
+                    model_path,
+                    low_cpu_mem_usage=True,
+                    **kwargs
+                )
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+                model = LlavaLlamaForCausalLM.from_pretrained(
+                    model_path,
+                    low_cpu_mem_usage=True,
+                    **kwargs
+                )
+    else:
+        # Load language model
+        if model_base is not None:
+            # PEFT model
+            from peft import PeftModel
+            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
+            model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs)
+            print(f"Loading LoRA weights from {model_path}")
+            model = PeftModel.from_pretrained(model, model_path)
+            print(f"Merging weights")
+            model = model.merge_and_unload()
+            print('Convert to FP16...')
+            model.to(torch.float16)
+        else:
+            use_fast = False
+            if 'mpt' in model_name.lower():
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
+                model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+                model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
+
+    image_processor = None
+
+    if llava_type_model:
+        mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
+        mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
+        if mm_use_im_patch_token:
+            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+        if mm_use_im_start_end:
+            tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+        model.resize_token_embeddings(len(tokenizer))
+
+        vision_tower = model.get_vision_tower()
+        if not vision_tower.is_loaded:
+            vision_tower.load_model(device_map=device_map)
+        if device_map != 'auto':
+            vision_tower.to(device=device_map, dtype=torch.float16)
+        image_processor = vision_tower.image_processor
+
+    if hasattr(model.config, "max_sequence_length"):
+        context_len = model.config.max_sequence_length
+    else:
+        context_len = 2048
+
+    return tokenizer, model, image_processor, context_len

dialoggen/llava/model/consolidate.py

+"""
+Usage:
+python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate
+"""
+import argparse
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from llava.model import *
+from llava.model.utils import auto_upgrade
+
+
+def consolidate_ckpt(src_path, dst_path):
+    print("Loading model")
+    auto_upgrade(src_path)
+    src_model = AutoModelForCausalLM.from_pretrained(src_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+    src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False)
+    src_model.save_pretrained(dst_path)
+    src_tokenizer.save_pretrained(dst_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--src", type=str, required=True)
+    parser.add_argument("--dst", type=str, required=True)
+
+    args = parser.parse_args()
+
+    consolidate_ckpt(args.src, args.dst)