app.py

import os
import numpy as np
import datetime
import json
from typing import Optional
import transformers
from dataclasses import dataclass, field
import io
import spaces
import base64
from PIL import Image
import gradio as gr
import time
import hashlib

from utils import build_logger
from conversation import conv_seed_llama2

import hydra
import pyrootutils
import torch
import re
import time
from omegaconf import OmegaConf
from flask import Flask
import json
from typing import Optional
import cv2
from diffusers import AutoencoderKL, UNet2DConditionModel, EulerDiscreteScheduler, StableDiffusionImg2ImgPipeline

pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)

from src.data.any_res import process_anyres_image

BOI_TOKEN = '<img>'
BOP_TOKEN = '<patch>'
EOI_TOKEN = '</img>'
EOP_TOKEN = '</patch>'
IMG_TOKEN = '<img_{:05d}>'

IMG_FLAG = '<image>'
num_img_in_tokens = 64
num_img_out_tokens = 64

resolution_grids = ['1x1', '1x2', '1x3', '1x4', '1x5', '1x6', '1x10', '2x1', '3x1', '4x1', '5x1', '6x1', '10x1', '2x2', '2x3', '3x2', '2x4', '4x2']
base_resolution = 448

app = Flask(__name__)


def decode_image(encoded_image: str) -> Image:
    decoded_bytes = base64.b64decode(encoded_image.encode('utf-8'))
    buffer = io.BytesIO(decoded_bytes)
    image = Image.open(buffer)
    return image


def encode_image(image: Image.Image, format: str = 'PNG') -> str:
    with io.BytesIO() as buffer:
        image.save(buffer, format=format)
        encoded_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
        return encoded_image


@dataclass
class Arguments:
    image_transform: Optional[str] = field(default='configs/processer/qwen_448_transform.yaml', metadata={"help": "config path of image transform"})
    tokenizer: Optional[str] = field(default='configs/tokenizer/clm_llama_tokenizer_224loc_anyres.yaml', metadata={"help": "config path of tokenizer used to initialize tokenizer"})
    llm: Optional[str] = field(default='configs/clm_models/llm_seed_x_i.yaml', metadata={"help": "config path of llm"})
    visual_encoder: Optional[str] = field(default='configs/visual_encoder/qwen_vitg_448.yaml', metadata={"help": "config path of visual encoder"})
    sd_adapter: Optional[str] = field(default='configs/sdxl_adapter/sdxl_qwen_vit_resampler_l4_q64_pretrain_no_normalize.yaml', metadata={"help": "config path of sd adapter"})
    agent: Optional[str] = field(default='configs/clm_models/agent_seed_x_i.yaml', metadata={"help": "config path of agent model"})
    diffusion_path: Optional[str] = field(default='stabilityai/stable-diffusion-xl-base-1.0', metadata={"help": "diffusion model path"})
    has_bbox: Optional[bool] = field(default=True, metadata={"help": "visualize the box"})

    port: Optional[str] = field(default=80, metadata={"help": "network port"})
    llm_device: Optional[str] = field(default='cuda:0', metadata={"help": "llm device"})
    vit_sd_device: Optional[str] = field(default='cuda:0', metadata={"help": "sd and vit device"})
    dtype: Optional[str] = field(default='fp16', metadata={"help": "mix percision"})
    multi_resolution: Optional[bool] = field(default=True, metadata={"help": "multi resolution"})


parser = transformers.HfArgumentParser(Arguments)
args, = parser.parse_args_into_dataclasses()

def extract_box(output_str):
    boxes = re.findall('(.*?)<box_end>', output_str)
    if len(boxes) >0:
        bboxes = [[int(num) for num in re.findall('<loc-(\d+)>', box)] for box in boxes]
    else:
        bboxes = None
    
    return bboxes


def visualize_bbox(image, bboxes):
    img_width, img_height = image.size
    image = np.array(image)
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    for bbox in bboxes:
        x_center, y_center, box_width, box_height = bbox
        
        x_center = x_center / 224 * img_width
        y_center = y_center  / 224 * img_height
        
        box_width = box_width /224 * img_width
        box_height = box_height / 224 * img_height
        
        x1 = int(x_center - box_width / 2)
        y1 = int(y_center - box_height / 2)
        x2 = int(x_center + box_width / 2)
        y2 = int(y_center + box_height / 2)
        
        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 4)
    
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = Image.fromarray(image)
    return image

    
class LLMService:

    def __init__(self, args) -> None:

        self.llm_device = args.llm_device
        self.vit_sd_device = args.vit_sd_device

        dtype = args.dtype
        if dtype == 'fp16':
            self.dtype = torch.float16
        elif dtype == 'bf16':
            self.dtype = torch.bfloat16
        else:
            raise ValueError

        image_transform_cfg = OmegaConf.load(args.image_transform)
        self.image_transform = hydra.utils.instantiate(image_transform_cfg)

        tokenizer_cfg = OmegaConf.load(args.tokenizer)
        self.tokenizer = hydra.utils.instantiate(tokenizer_cfg)

        visual_encoder_cfg = OmegaConf.load(args.visual_encoder)
        self.visual_encoder = hydra.utils.instantiate(visual_encoder_cfg)
        self.visual_encoder.eval().to(self.vit_sd_device, dtype=self.dtype)
        print('Init visual encoder done')

        llm_cfg = OmegaConf.load(args.llm)
        llm = hydra.utils.instantiate(llm_cfg, torch_dtype=self.dtype)
        print('Init llm done.')

        agent_cfg = OmegaConf.load(args.agent)
        self.agent = hydra.utils.instantiate(agent_cfg, llm=llm)

        self.agent.eval().to(self.llm_device, dtype=self.dtype)
        print('Init agent mdoel Done')

        noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.diffusion_path, subfolder="scheduler")

        vae = AutoencoderKL.from_pretrained(args.diffusion_path, subfolder="vae").to(self.vit_sd_device, dtype=self.dtype)


        unet = UNet2DConditionModel.from_pretrained(args.diffusion_path, subfolder="unet").to(self.vit_sd_device, dtype=self.dtype)

        sd_adapter_cfg = OmegaConf.load(args.sd_adapter)

        self.sd_adapter = hydra.utils.instantiate(sd_adapter_cfg, unet=unet).eval().to(self.vit_sd_device, dtype=self.dtype)

        # self.sd_adapter.init_pipe(vae=vae,
        #                           scheduler=noise_scheduler,
        #                           visual_encoder=self.visual_encoder.cpu(),
        #                           image_transform=self.image_transform,
        #                           discrete_model=None,
        #                           dtype=self.dtype,
        #                           device="cpu")

        self.sd_adapter.init_pipe(vae=vae,
                                  scheduler=noise_scheduler,
                                  visual_encoder=self.visual_encoder,
                                  image_transform=self.image_transform,
                                  discrete_model=None,
                                  dtype=self.dtype,
                                  device=self.vit_sd_device)

        print('Init sd adapter pipe done.')

        self.visual_encoder.to(self.vit_sd_device, dtype=self.dtype)

        model_id_or_path = "stablediffusionapi/realistic-vision-v51"
        self.vae_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, safety_checker=None, torch_dtype=torch.float16)
        #self.vae_pipe = self.vae_pipe.to(self.vit_sd_device)

        self.boi_token_id = self.tokenizer.encode(BOI_TOKEN, add_special_tokens=False)[0]
        self.eoi_token_id = self.tokenizer.encode(EOI_TOKEN, add_special_tokens=False)[0]

        self.bop_token_id = self.tokenizer.encode(BOP_TOKEN, add_special_tokens=False)[0]
        self.eop_token_id = self.tokenizer.encode(EOP_TOKEN, add_special_tokens=False)[0]

        self.multi_resolution = args.multi_resolution
        if self.multi_resolution:
            self.base_resolution = base_resolution
            grid_pinpoints = []
            for scale in resolution_grids:
                s1, s2 = scale.split('x')
                grid_pinpoints.append([int(s1)*base_resolution, int(s2)*base_resolution])
            self.grid_pinpoints = grid_pinpoints

service = LLMService(args)

@spaces.GPU
def generate(text_list, image_list, max_new_tokens, force_boi, force_bbox, force_polish):
  with torch.no_grad():
    text_list = text_list.split(IMG_FLAG)
    top_p = 0.5
    assert len(text_list) == len(image_list) + 1

    image_tokens = BOI_TOKEN + ''.join([IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)]) + EOI_TOKEN

    input_images = []
    if len(image_list) > 0:
        image_tensor_list = []
        embeds_cmp_mask = []
        embeds_gen_mask = []

        if service.multi_resolution:
            patch_pos = []
            image_patch_length = []
            image_size_list = []

        for idx, image_item in enumerate(image_list):
            if isinstance(image_item, str):
                image = decode_image(image_item)
                print('after decode image size:', image.size)
                input_images.append(image)

                if service.multi_resolution:
                    image_size_list.append(image.size)
                    print('image size:', image.size)
                    image_tensor, patch_pos_tensor = process_anyres_image(image, service.image_transform, service.grid_pinpoints, service.base_resolution)
                    image_tensor_list.append(image_tensor)
                    patch_pos.append(patch_pos_tensor)
                    image_patch_length.append(image_tensor.shape[0])
                    print('image_patch_length', image_patch_length)
                    embeds_cmp_mask.extend([True]*image_tensor.shape[0])
                    embeds_gen_mask.extend([False]*image_tensor.shape[0])

                else:                    
                    image_tensor = service.image_transform(image)
                    image_tensor_list.append(image_tensor)
                    embeds_cmp_mask.append(True)
                    embeds_gen_mask.append(False)
            else:
                raise ValueError

        if service.multi_resolution:
            pixel_values = torch.cat(image_tensor_list).to(service.vit_sd_device, dtype=service.dtype)
            patch_position = torch.cat(patch_pos, dim=0)
            
            image_tokens_list = []
            for patch_length in image_patch_length:
                image_tokens = ''
                for _ in range(patch_length-1):
                    image_tokens +=  BOP_TOKEN + ''.join(IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)) + EOP_TOKEN
                image_tokens += BOI_TOKEN + ''.join(IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)) + EOI_TOKEN
                image_tokens_list.append(image_tokens)
        else:
            pixel_values = torch.stack(image_tensor_list).to(service.vit_sd_device, dtype=service.dtype)
        
        image_embeds = service.visual_encoder(pixel_values)
        image_embeds = image_embeds.to(service.llm_device)

        embeds_cmp_mask = torch.tensor(embeds_cmp_mask, dtype=torch.bool).to(service.llm_device)
        embeds_gen_mask = torch.tensor(embeds_gen_mask, dtype=torch.bool).to(service.llm_device)

    else:
        image_embeds = None
        patch_position = 0
        embeds_cmp_mask = None
        embeds_gen_mask = None

    if service.multi_resolution:
        input_text = ''
        for i, c in enumerate(text_list[:-1]):
            input_text += c + image_tokens_list[i]
        input_text += text_list[-1]

    else:
        input_text = image_tokens.join(text_list)
    
    if force_boi:
        input_text = input_text + BOI_TOKEN

    if force_bbox:
        input_text = input_text + '[[ <box_start>'
    print('input_text:', input_text)
    input_ids = service.tokenizer.encode(input_text, add_special_tokens=False)
    input_ids = [service.tokenizer.bos_token_id] + input_ids

    input_ids = torch.tensor(input_ids).to(service.llm_device, dtype=torch.long)
    ids_cmp_mask = torch.zeros_like(input_ids, dtype=torch.bool).to(service.llm_device)
    ids_gen_mask = torch.zeros_like(input_ids, dtype=torch.bool).to(service.llm_device)

    if service.multi_resolution:
        boi_indices = torch.where(torch.logical_or(input_ids == service.boi_token_id, input_ids == service.bop_token_id))[0].tolist()
        eoi_indices = torch.where(torch.logical_or(input_ids == service.eoi_token_id, input_ids == service.eop_token_id))[0].tolist()
    else:
        boi_indices = torch.where(input_ids == service.boi_token_id)[0].tolist()
        eoi_indices = torch.where(input_ids == service.eoi_token_id)[0].tolist()

    for boi_idx, eoi_idx in zip(boi_indices, eoi_indices):
        ids_cmp_mask[boi_idx + 1:eoi_idx] = True

    input_ids = input_ids.unsqueeze(0)
    ids_cmp_mask = ids_cmp_mask.unsqueeze(0)
    ids_gen_mask = ids_gen_mask.unsqueeze(0)

    error_msg = []

    if service.multi_resolution:
        output = service.agent.generate(
            tokenizer=service.tokenizer,
            input_ids=input_ids,
            image_embeds=image_embeds,
            patch_positions=patch_position,
            embeds_cmp_mask=embeds_cmp_mask,
            ids_cmp_mask=ids_cmp_mask,
            num_img_gen_tokens=num_img_out_tokens,
            max_new_tokens=max_new_tokens,
            dtype=service.dtype,
            device=service.llm_device,
            top_p=top_p,
        )
    else:
        output = service.agent.generate(
            tokenizer=service.tokenizer,
            input_ids=input_ids,
            image_embeds=image_embeds,
            embeds_cmp_mask=embeds_cmp_mask,
            ids_cmp_mask=ids_cmp_mask,
            num_img_gen_tokens=num_img_out_tokens,
            max_new_tokens=max_new_tokens,
            dtype=service.dtype,
            device=service.llm_device,
            top_p=top_p,
        )

    gen_imgs_base64_list = []
    generated_text = output['text']
    generated_text = generated_text.replace(EOI_TOKEN, IMG_FLAG).replace(service.tokenizer.eos_token, '')

    torch.cuda.empty_cache() 
      
    if output['has_img_output']:
        # print('loading visual encoder and llm to CPU, and sd to GPU')
        # a = time.time()
        # service.agent = service.agent.cpu()
        # service.sd_adapter = service.sd_adapter.to(service.vit_sd_device, dtype=service.dtype)
        # print("Loading finished: ", time.time() - a)

        img_gen_feat = output['img_gen_feat'].to(service.vit_sd_device, dtype=service.dtype)

        for img_idx in range(output['num_gen_imgs']):
            img_feat = img_gen_feat[img_idx:img_idx + 1]
            generated_image = service.sd_adapter.generate(image_embeds=img_feat, num_inference_steps=50)[0]

            if force_polish:
                #service.sd_adapter = service.sd_adapter.cpu()
                #service.vae_pipe = service.vae_pipe.to(service.vit_sd_device, dtype=service.dtype)

                torch.cuda.empty_cache()

                service.vae_pipe = service.vae_pipe.to(service.vit_sd_device)
                
                init_image = generated_image.resize((1024, 1024))
                prompt = ""
                images = service.vae_pipe(prompt=prompt, image=init_image, 
                              num_inference_steps=50, guidance_scale=8.0, strength=0.38).images
                generated_image = images[0]
                
                image_base64 = encode_image(generated_image)
                gen_imgs_base64_list.append(image_base64)

                # service.vae_pipe = service.vae_pipe.to("cpu")
                # service.sd_adapter = service.sd_adapter.to(service.vit_sd_device, dtype=service.dtype)
                
                torch.cuda.empty_cache() 

        # print('loading visual encoder and llm to GPU, and sd to CPU')
        # a = time.time()
        # service.sd_adapter = service.sd_adapter.cpu()
        # service.visual_encoder = service.visual_encoder.to(service.vit_sd_device, dtype=service.dtype)
        # service.agent = service.agent.to(service.vit_sd_device, dtype=service.dtype)
        # print("Loading finished: ", time.time() - a)

    if args.has_bbox:
        bboxes = extract_box(generated_text)
        if bboxes is not None and len(input_images) > 0:
            image_viz = visualize_bbox(input_images[-1], bboxes)
            image_base64 = encode_image(image_viz)
            gen_imgs_base64_list.append(image_base64)
            if '<box_start>' in generated_text:
                generated_text = re.sub(r'\[\[ <box_start>.*?<box_end>.*?\]\]', 'the green bounding box', generated_text)
            else:
                generated_text = re.sub(r'<loc-\d+> <loc-\d+> <loc-\d+> <loc-\d+> <box_end>  \]\]', 'the green bounding box', generated_text)
            generated_text += IMG_FLAG
    print(input_text + generated_text)
    return {'text': generated_text, 'images': gen_imgs_base64_list, 'error_msg': error_msg}

def http_bot(dialog_state, input_state, max_new_tokens, max_turns, force_image_gen, force_bbox, force_polish,
             request: gr.Request):
    print('input_state:', input_state)

    if len(dialog_state.messages) == 0 or dialog_state.messages[-1]['role'] != dialog_state.roles[0] or len(
            dialog_state.messages[-1]['message']['text'].strip(' ?.;!/')) == 0:
        return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (no_change_btn,) * 4

    if len(dialog_state.messages) > max_turns * 2:
        output_state = init_input_state()
        output_state['text'] = 'Error: History exceeds maximum rounds, please clear history and restart.'
        dialog_state.messages.append({'role': dialog_state.roles[1], 'message': output_state})
        input_state = init_input_state()
        return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (disable_btn,) * 3 + (enable_btn,)

    prompt = dialog_state.get_prompt()
    text = prompt['text']
    max_new_tokens = int(max_new_tokens)
    images = prompt['images']
    force_boi = force_image_gen
    force_bbox = force_bbox

    results = generate(text, images, max_new_tokens, force_boi, force_bbox, force_polish)
    print('response: ', {'text': results['text'], 'error_msg': results['error_msg']})

    output_state = init_input_state()
    image_dir = get_conv_image_dir()
    output_state['text'] = results['text']

    for image_base64 in results['images']:
        if image_base64 == '':
            image_path = ''
        else:
            image = decode_image(image_base64)
            image = image.convert('RGB')
            image_path = get_image_name(image=image, image_dir=image_dir)
            if not os.path.exists(image_path):
                image.save(image_path)
        output_state['images'].append(image_path)

    dialog_state.messages.append({'role': dialog_state.roles[1], 'message': output_state})

    vote_last_response(dialog_state, 'common', request)
    input_state = init_input_state()
    chatbot = update_error_msg(dialog_state.to_gradio_chatbot(), results['error_msg'])
    return (dialog_state, input_state, chatbot) + (enable_btn,) * 4


IMG_FLAG = '<image>'
LOGDIR = 'log'

logger = build_logger("gradio_seed_x", LOGDIR)
headers = {"User-Agent": "SEED-X Client"}

no_change_btn = gr.Button()
enable_btn = gr.Button(interactive=True)
disable_btn = gr.Button(interactive=False)

conv_seed_llama = conv_seed_llama2


def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
    return name


def get_conv_image_dir():
    name = os.path.join(LOGDIR, 'images')
    os.makedirs(name, exist_ok=True)
    return name


def get_image_name(image, image_dir=None):
    buffer = io.BytesIO()
    image.save(buffer, format='PNG')
    image_bytes = buffer.getvalue()
    md5 = hashlib.md5(image_bytes).hexdigest()

    if image_dir is not None:
        image_name = os.path.join(image_dir, md5 + '.png')
    else:
        image_name = md5 + '.png'

    return image_name


def resize_image_square(image, target_size=448):
    resized_image = image.resize((target_size, target_size))
    return resized_image


def resize_image(image, max_size=512):
    width, height = image.size
    aspect_ratio = float(width) / float(height)

    if width > height:
        new_width = max_size
        new_height = int(new_width / aspect_ratio)
    else:
        new_height = max_size
        new_width = int(new_height * aspect_ratio)

    resized_image = image.resize((new_width, new_height))
    return resized_image


def center_crop_image(image, max_aspect_ratio=1.5):
    width, height = image.size
    aspect_ratio = max(width, height) / min(width, height)

    if aspect_ratio >= max_aspect_ratio:
        if width > height:
            new_width = int(height * max_aspect_ratio)
            left = (width - new_width) // 2
            right = (width + new_width) // 2
            top = 0
            bottom = height
        else:
            new_height = int(width * max_aspect_ratio)
            left = 0
            right = width
            top = (height - new_height) // 2
            bottom = (height + new_height) // 2

        cropped_image = image.crop((left, top, right, bottom))
        return cropped_image
    else:
        return image


def vote_last_response(state, vote_type, request: gr.Request):
    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(time.time(), 4),
            "type": vote_type,
            "state": state.dict(),
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


def upvote_last_response(state, request: gr.Request):
    logger.info(f"upvote. ip: {request.client.host}")
    vote_last_response(state, "upvote", request)
    return (disable_btn,) * 2


def downvote_last_response(state, request: gr.Request):
    logger.info(f"downvote. ip: {request.client.host}")
    vote_last_response(state, "downvote", request)
    return (disable_btn,) * 2


def regenerate(dialog_state, request: gr.Request):
    logger.info(f"regenerate. ip: {request.client.host}")
    if dialog_state.messages[-1]['role'] == dialog_state.roles[1]:
        dialog_state.messages.pop()
    return (
               dialog_state,
               dialog_state.to_gradio_chatbot(),
           ) + (disable_btn,) * 4


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    dialog_state = conv_seed_llama.copy()
    input_state = init_input_state()
    return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (disable_btn,) * 4


def init_input_state():
    return {'images': [], 'text': ''}


def add_text(dialog_state, input_state, text, request: gr.Request):
    logger.info(f"add_text. ip: {request.client.host}.")
    if text is None or len(text) == 0:
        return (dialog_state, input_state, "", dialog_state.to_gradio_chatbot()) + (no_change_btn,) * 4
    input_state['text'] += text


    if len(dialog_state.messages) > 0 and dialog_state.messages[-1]['role'] == dialog_state.roles[0]:
        dialog_state.messages[-1]['message'] = input_state
    else:
        dialog_state.messages.append({'role': dialog_state.roles[0], 'message': input_state})
    print('add_text: ', dialog_state.to_gradio_chatbot())

    return (dialog_state, input_state, "", dialog_state.to_gradio_chatbot()) + (disable_btn,) * 4


def is_blank(image):
    image_array = np.array(image)
    unique_colors = np.unique(image_array)
    print('unique_colors', len(unique_colors))
    return len(unique_colors) == 1


def add_image(dialog_state, input_state, image, request: gr.Request):
    logger.info(f"add_image. ip: {request.client.host}.")
    if image is None:
        return (dialog_state, input_state, None, dialog_state.to_gradio_chatbot()) + (no_change_btn,) * 4

    image = image.convert('RGB')

    print('image size:', image.size)

    image = center_crop_image(image, max_aspect_ratio=10)

    image_dir = get_conv_image_dir()
    image_path = get_image_name(image=image, image_dir=image_dir)
    if not os.path.exists(image_path):
        image.save(image_path)
    input_state['images'].append(image_path)
    input_state['text'] += IMG_FLAG

    if len(dialog_state.messages) > 0 and dialog_state.messages[-1]['role'] == dialog_state.roles[0]:
        dialog_state.messages[-1]['message'] = input_state
    else:
        dialog_state.messages.append({'role': dialog_state.roles[0], 'message': input_state})

    print('add_image:', dialog_state)

    return (dialog_state, input_state, None, dialog_state.to_gradio_chatbot()) + (disable_btn,) * 4


def update_error_msg(chatbot, error_msg):
    if len(error_msg) > 0:
        info = '\n-------------\nSome errors occurred during response, please clear history and restart.\n' + '\n'.join(
            error_msg)
        chatbot[-1][-1] = chatbot[-1][-1] + info

    return chatbot


def load_demo(request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}")
    dialog_state = conv_seed_llama.copy()
    input_state = init_input_state()
    return dialog_state, input_state


title = ("""
# SEED-X-I
[[Paper]](https://arxiv.org/abs/2404.14396) [[Code]](https://github.com/AILab-CVC/SEED-X) [[Faster Demo]](https://arc.tencent.com/en/ai-demos/multimodal) 
         
Demo of a general instruction-tuned model SEED-X-I (17B) from the foundation model SEED-X. 

SEED-X-I can follow multimodal instruction (including images with **dynamic resolutions**) and make responses with **images, texts and bounding boxes** in multi-turn conversation.
         
SEED-X-I **does not support image manipulation**. If you want to experience **SEED-X-Edit** for high-precision image editing, please refer to [[Inference Code]](https://github.com/AILab-CVC/SEED-X).
        
If you want to experience the normal model inference speed, you can use [[Faster Demo]](https://arc.tencent.com/en/ai-demos/multimodal) or run [[Inference Code]](https://github.com/AILab-CVC/SEED-X) locally.
         

## Tips:
* Check out the conversation examples (at the bottom) for inspiration.

* You can adjust "Max History Rounds" to try a conversation with up to **three rounds due to insufficient GPU memory**. For more turns, you can download our checkpoints from GitHub and deploy them locally for inference.

* Our demo supports a mix of images and texts as input. You can freely upload an image or enter text, and then click on "Add Image/Text". You can repeat the former step multiple times, and click on "Submit" for model inference at last.

* You can click "Force Image Generation" to compel the model to produce images when necessary. For example, our model might struggle to generate images when there is an excessive amount of text-only context.

* You can click "Force Bounding Box" to compel the model to produce bounding box for object detection.

* You can click "Force Polishing Generated Image" to compel the model to polish the generated image with image post-processing.
         
* SEED-X was trained with English-only data. It may process with other languages due to the inherent capabilities from LLaMA, but might not stable.

""")

css = """
img {
  font-family: 'Helvetica';
  font-weight: 300;
  line-height: 2;  
  text-align: center;
  
  width: auto;
  height: auto;
  display: block;
  position: relative;
}

img:before { 
  content: " ";
  display: block;

  position: absolute;
  top: -10px;
  left: 0;
  height: calc(100% + 10px);
  width: 100%;
  background-color: rgb(230, 230, 230);
  border: 2px dotted rgb(200, 200, 200);
  border-radius: 5px;
}

img:after { 
  content: " ";
  display: block;
  font-size: 16px;
  font-style: normal;
  font-family: FontAwesome;
  color: rgb(100, 100, 100);
  
  position: absolute;
  top: 5px;
  left: 0;
  width: 100%;
  text-align: center;
}

"""

if __name__ == '__main__':

    examples_mix = [
        ['https://github.com/AILab-CVC/SEED-X/blob/main/demos/bank.png?raw=true', 'Can I conntect with an advisor on Sunday?'],
        ['https://github.com/AILab-CVC/SEED-X/blob/main/demos/ground.png?raw=true',
         'Is there anything in the image that can protect me from catching the flu virus when I go out? Show me the location.'],
        ['https://github.com/AILab-CVC/SEED-X/blob/main/demos/arrow.jpg?raw=true', 'What is the object pointed by the red arrow?'],
        ['https://github.com/AILab-CVC/SEED-X/blob/main/demos/shanghai.png?raw=true', 'Where was this image taken? Explain your answer.'],
        ['https://github.com/AILab-CVC/SEED-X/blob/main/demos/GPT4.png?raw=true', 'How long does it take to make GPT-4 safer?'],
        ['https://github.com/AILab-CVC/SEED-X/blob/main/demos/twitter.png?raw=true',
         'Please provide a comprehensive description of this image.'],
    ]
    examples_text = [
        ['I want to build a two story cabin in the woods, with many commanding windows. Can you show me a picture?'],
        ['Use your imagination to design a concept image for Artificial General Intelligence (AGI). Show me an image.'],
        [
            'Can you design an illustration for “The Three-Body Problem” to depict a scene from the novel? Show me a picture.'],
        [
            'My four year old son loves toy trains. Can you design a fancy birthday cake for him? Please generate a picture.'],
        [
            'Generate an image of a portrait of young nordic girl, age 25, freckled skin, neck tatoo, blue eyes 35mm lens, photography, ultra details.'],
        ['Generate an impressionist painting of an astronaut in a jungle.']
    ]
    with gr.Blocks(css=css) as demo:
        gr.Markdown(title)
        dialog_state = gr.State()
        input_state = gr.State()
        with gr.Row():
            with gr.Column(scale=3):
                with gr.Row():
                    image = gr.Image(type='pil', label='input_image')
                with gr.Row():
                    text = gr.Textbox(lines=5,
                                      show_label=False,
                                      label='input_text',
                                      elem_id='textbox',
                                      placeholder="Enter text or add image, and press submit,", container=False)
                with gr.Row():
                    add_image_btn = gr.Button("Add Image")
                    add_text_btn = gr.Button("Add Text")

                    submit_btn = gr.Button("Submit")

                with gr.Row():
                    max_new_tokens = gr.Slider(minimum=64,
                                               maximum=1024,
                                               value=768,
                                               step=64,
                                               interactive=True,
                                               label="Max Output Tokens")
                    max_turns = gr.Slider(minimum=1, maximum=3, value=3, step=1, interactive=True,
                                          label="Max History Rounds")
                    force_img_gen = gr.Radio(choices=[True, False], value=False, label='Force Image Generation')
                    force_bbox = gr.Radio(choices=[True, False], value=False, label='Force Bounding Box')
                    force_polish = gr.Radio(choices=[True, False], value=True, label='Force Polishing Generated Image')

            with gr.Column(scale=7):
                chatbot = gr.Chatbot(elem_id='chatbot', label="SEED-X-I", height=700)
                with gr.Row():
                    upvote_btn = gr.Button(value="👍  Upvote", interactive=False)
                    downvote_btn = gr.Button(value="👎  Downvote", interactive=False)
                    regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
                    clear_btn = gr.Button(value="🗑️  Clear history", interactive=False)

        with gr.Row():
            with gr.Column(scale=0.7):
                gr.Examples(examples=examples_mix, label='Input examples', inputs=[image, text], cache_examples=False)
            with gr.Column(scale=0.3):
                gr.Examples(examples=examples_text, label='Input examples', inputs=[text], cache_examples=False)

        # Register listeners
        btn_list = [upvote_btn, downvote_btn, regenerate_btn, clear_btn]
        upvote_btn.click(upvote_last_response, [dialog_state], [upvote_btn, downvote_btn])
        downvote_btn.click(downvote_last_response, [dialog_state], [upvote_btn, downvote_btn])

        regenerate_btn.click(regenerate, [dialog_state], [dialog_state, chatbot] + btn_list).then(
            http_bot, [dialog_state, input_state, max_new_tokens, max_turns, force_img_gen, force_bbox, force_polish],
            [dialog_state, input_state, chatbot] + btn_list)
        add_image_btn.click(add_image, [dialog_state, input_state, image],
                            [dialog_state, input_state, image, chatbot] + btn_list)

        add_text_btn.click(add_text, [dialog_state, input_state, text],
                           [dialog_state, input_state, text, chatbot] + btn_list)

        submit_btn.click(
            add_image, [dialog_state, input_state, image], [dialog_state, input_state, image, chatbot] + btn_list).then(
            add_text, [dialog_state, input_state, text],
            [dialog_state, input_state, text, chatbot, upvote_btn, downvote_btn, regenerate_btn, clear_btn]).then(
            http_bot,
            [dialog_state, input_state, max_new_tokens, max_turns, force_img_gen, force_bbox, force_polish],
            [dialog_state, input_state, chatbot] + btn_list)
        clear_btn.click(clear_history, None, [dialog_state, input_state, chatbot] + btn_list)

        demo.load(load_demo, None, [dialog_state, input_state])

    demo.launch(debug=True)