update

Dockerfile

@@ -0,0 +1,28 @@
+# Use an official Python runtime as a parent image
+FROM python:3.7.4-slim
+
+# Set the working directory to /app
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY ./requirements.txt /app
+
+# # Install any needed packages specified in requirements.txt
+# RUN apt-get install libjpeg-dev
+# RUN apt-get install zlib1g-dev
+
+RUN apt-get install zip unzip cmake libtiff5-dev libjpeg8-dev libopenjp2-7-dev zlib1g-dev libfreetype6-dev liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python3-tk libharfbuzz-dev libfribidi-dev libxcb1-dev
+
+RUN pip install -r /app/requirements.txt
+RUN pip install https://download.pytorch.org/whl/Pillow-9.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+RUN pip install https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl
+
+# Define environment variable
+ENV NAME gradio
+
+# The default gradio port is 7860
+EXPOSE 7860
+
+# Run main.py when the container launches
+CMD ["python", "text-to-image-app.py"]
+~                            

README.md

@@ -1,12 +1,5 @@
 ---
-title: TextDiffuser
-emoji: 🚀
-colorFrom: green
-colorTo: green
 sdk: gradio
-sdk_version: 3.33.1
 app_file: app.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,965 @@
+# ------------------------------------------
+# TextDiffuser: Diffusion Models as Text Painters
+# Paper Link: https://arxiv.org/abs/2305.10855
+# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
+# Copyright (c) Microsoft Corporation.
+# This file provides the inference script.
+# ------------------------------------------
+
+import os
+import re
+import zipfile
+
+if not os.path.exists('textdiffuser-ckpt'):
+    os.system('wget https://layoutlm.blob.core.windows.net/textdiffuser/textdiffuser-ckpt-new.zip')
+    with zipfile.ZipFile('textdiffuser-ckpt-new.zip', 'r') as zip_ref:
+        zip_ref.extractall('.')
+
+if not os.path.exists('images'):
+    os.system('wget https://layoutlm.blob.core.windows.net/textdiffuser/images.zip')
+    with zipfile.ZipFile('images.zip', 'r') as zip_ref:
+        zip_ref.extractall('.')
+
+if not os.path.exists('Arial.ttf'):
+    os.system('wget https://layoutlm.blob.core.windows.net/textdiffuser/Arial.ttf')
+
+    
+os.system('echo finish')
+os.system('ls -a')
+
+import cv2
+import random
+import logging
+import argparse
+import numpy as np
+
+from pathlib import Path
+from tqdm.auto import tqdm
+from typing import Optional
+from packaging import version
+from termcolor import colored
+from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance # import for visualization
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+
+import datasets
+from datasets import load_dataset
+from datasets import disable_caching
+
+import torch
+import torch.utils.checkpoint
+import torch.nn.functional as F
+
+import accelerate
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+
+import diffusers
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel 
+from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
+from diffusers.utils import check_min_version, deprecate
+from diffusers.utils.import_utils import is_xformers_available
+
+import transformers
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from util import segmentation_mask_visualization, make_caption_pil, combine_image, transform_mask, transform_mask_pil, filter_segmentation_mask, inpainting_merge_image
+from model.layout_generator import get_layout_from_prompt
+from model.text_segmenter.unet import UNet
+
+
+disable_caching()
+check_min_version("0.15.0.dev0")
+logger = get_logger(__name__, log_level="INFO")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path", 
+        type=str,
+        default='runwayml/stable-diffusion-v1-5', # no need to modify this  
+        help="Path to pretrained model or model identifier from huggingface.co/models. Please do not modify this.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--mode",
+        type=str,
+        default="text-to-image",
+        # required=True,
+        choices=["text-to-image", "text-to-image-with-template", "text-inpainting"],
+        help="Three modes can be used.",
+    )
+    parser.add_argument(
+        "--prompt", 
+        type=str,
+        default="",
+        # required=True,
+        help="The text prompts provided by users.",
+    )
+    parser.add_argument(
+        "--template_image", 
+        type=str,
+        default="",
+        help="The template image should be given when using 【text-to-image-with-template】 mode.",
+    )
+    parser.add_argument(
+        "--original_image", 
+        type=str,
+        default="",
+        help="The original image should be given when using 【text-inpainting】 mode.",
+    )
+    parser.add_argument(
+        "--text_mask", 
+        type=str,
+        default="",
+        help="The text mask should be given when using 【text-inpainting】 mode.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument(
+        "--seed", 
+        type=int, 
+        default=None, 
+        help="A seed for reproducible training."
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--classifier_free_scale", 
+        type=float,
+        default=7.5, # following stable diffusion (https://github.com/CompVis/stable-diffusion)
+        help="Classifier free scale following https://arxiv.org/abs/2207.12598.",
+    )
+    parser.add_argument(
+        "--drop_caption", 
+        action="store_true", 
+        help="Whether to drop captions during training following https://arxiv.org/abs/2207.12598.."
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0, 
+        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+    )
+    parser.add_argument(
+        "--push_to_hub", 
+        action="store_true", 
+        help="Whether or not to push the model to the Hub."
+    )
+    parser.add_argument(
+        "--hub_token", 
+        type=str, 
+        default=None, 
+        help="The token to use to push to the Model Hub."
+    )
+    parser.add_argument(
+        "--hub_model_id",
+        type=str,
+        default=None,
+        help="The name of the repository to keep in sync with the local `output_dir`.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default='fp16',
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--report_to", 
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument(
+        "--local_rank", 
+        type=int, 
+        default=-1, 
+        help="For distributed training: local_rank"
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500, 
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=5,
+        help=(
+            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
+            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
+            " for more docs"
+        ),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default='textdiffuser-ckpt/diffusion_backbone', # should be specified during inference
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", 
+        action="store_true", 
+        help="Whether or not to use xformers."
+    )
+    parser.add_argument(
+        "--font_path", 
+        type=str, 
+        default='Arial.ttf', 
+        help="The path of font for visualization."
+    )
+    parser.add_argument(
+        "--sample_steps", 
+        type=int, 
+        default=50, # following stable diffusion (https://github.com/CompVis/stable-diffusion)
+        help="Diffusion steps for sampling."
+    )
+    parser.add_argument(
+        "--vis_num", 
+        type=int, 
+        default=4, # please decreases the number if out-of-memory error occurs
+        help="Number of images to be sample. Please decrease it when encountering out of memory error."
+    )
+    parser.add_argument(
+        "--binarization", 
+        action="store_true", 
+        help="Whether to binarize the template image."
+    )
+    parser.add_argument(
+        "--use_pillow_segmentation_mask", 
+        type=bool,
+        default=True, 
+        help="In the 【text-to-image】 mode, please specify whether to use the segmentation masks provided by PILLOW"
+    )
+    parser.add_argument(
+        "--character_segmenter_path", 
+        type=str,
+        default='textdiffuser-ckpt/text_segmenter.pth',
+        help="checkpoint of character-level segmenter"
+    )
+    args = parser.parse_args()
+    
+    print(f'{colored("[√]", "green")} Arguments are loaded.')
+    print(args)
+    
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
+
+
+
+args = parse_args()
+logging_dir = os.path.join(args.output_dir, args.logging_dir)
+
+print(f'{colored("[√]", "green")} Logging dir is set to {logging_dir}.')
+
+accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
+
+accelerator = Accelerator(
+    gradient_accumulation_steps=1,
+    mixed_precision=args.mixed_precision,
+    log_with=args.report_to,
+    logging_dir=logging_dir,
+    project_config=accelerator_project_config,
+)
+
+# Make one log on every process with the configuration for debugging.
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    level=logging.INFO,
+)
+logger.info(accelerator.state, main_process_only=False)
+if accelerator.is_local_main_process:
+    datasets.utils.logging.set_verbosity_warning()
+    transformers.utils.logging.set_verbosity_warning()
+    diffusers.utils.logging.set_verbosity_info()
+else:
+    datasets.utils.logging.set_verbosity_error()
+    transformers.utils.logging.set_verbosity_error()
+    diffusers.utils.logging.set_verbosity_error()
+
+# Handle the repository creation
+if accelerator.is_main_process:
+    if args.push_to_hub:
+        if args.hub_model_id is None:
+            repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+        else:
+            repo_name = args.hub_model_id
+        create_repo(repo_name, exist_ok=True, token=args.hub_token)
+        repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+
+        with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+            if "step_*" not in gitignore:
+                gitignore.write("step_*\n")
+            if "epoch_*" not in gitignore:
+                gitignore.write("epoch_*\n")
+    elif args.output_dir is not None:
+        os.makedirs(args.output_dir, exist_ok=True)
+        print(args.output_dir)
+
+# Load scheduler, tokenizer and models.
+tokenizer = CLIPTokenizer.from_pretrained(
+    args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
+)
+text_encoder = CLIPTextModel.from_pretrained(
+    args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
+)
+vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision).cuda()
+unet = UNet2DConditionModel.from_pretrained(
+    args.resume_from_checkpoint, subfolder="unet", revision=None 
+).cuda() 
+
+# Freeze vae and text_encoder
+vae.requires_grad_(False)
+text_encoder.requires_grad_(False)
+
+if args.enable_xformers_memory_efficient_attention:
+    if is_xformers_available():
+        import xformers
+
+        xformers_version = version.parse(xformers.__version__)
+        if xformers_version == version.parse("0.0.16"):
+            logger.warn(
+                "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+            )
+        unet.enable_xformers_memory_efficient_attention()
+    else:
+        raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+# `accelerate` 0.16.0 will have better support for customized saving
+if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
+    # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
+    def save_model_hook(models, weights, output_dir):
+        
+        for i, model in enumerate(models):
+            model.save_pretrained(os.path.join(output_dir, "unet"))
+
+            # make sure to pop weight so that corresponding model is not saved again
+            weights.pop()
+
+    def load_model_hook(models, input_dir):
+        
+        for i in range(len(models)):
+            # pop models so that they are not loaded again
+            model = models.pop()
+
+            # load diffusers style into model
+            load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
+            model.register_to_config(**load_model.config)
+
+            model.load_state_dict(load_model.state_dict())
+            del load_model
+
+    accelerator.register_save_state_pre_hook(save_model_hook)
+    accelerator.register_load_state_pre_hook(load_model_hook)
+
+
+# setup schedulers                    
+scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") 
+# sample_num = args.vis_num
+
+def to_tensor(image):
+    if isinstance(image, Image.Image):  
+        image = np.array(image)
+    elif not isinstance(image, np.ndarray):  
+        raise TypeError("Error")
+
+    image = image.astype(np.float32) / 255.0
+    image = np.transpose(image, (2, 0, 1))
+    tensor = torch.from_numpy(image)
+
+    return tensor
+
+
+import unicodedata
+
+def full2half(text):
+    half = []
+    for char in text:
+        code = ord(char)
+        if code == 0x3000:  
+            half.append(chr(0x0020))
+        elif 0xFF01 <= code <= 0xFF5E:  
+            half.append(chr(code - 0xFEE0))
+        else:  
+            half.append(char)
+    return ''.join(half)
+
+def text_to_image(prompt,slider_step,slider_guidance,slider_batch):
+
+    prompt = full2half(prompt)
+    prompt = prompt.replace('"', "'")
+    prompt = prompt.replace('‘', "'")
+    prompt = prompt.replace('’', "'")
+    prompt = prompt.replace('“', "'")
+    prompt = prompt.replace('”', "'")
+    prompt = re.sub(r"[^a-zA-Z0-9'\" ]+", "", prompt)
+
+    if slider_step>=50:
+        slider_step = 50
+        
+    args.prompt = prompt 
+    sample_num = slider_batch
+    seed = random.randint(0, 10000000)
+    set_seed(seed)
+    scheduler.set_timesteps(slider_step) 
+
+    noise = torch.randn((sample_num, 4, 64, 64)).to("cuda")  # (b, 4, 64, 64)
+    input = noise # (b, 4, 64, 64)
+
+    captions = [args.prompt] * sample_num
+    captions_nocond = [""] * sample_num
+    print(f'{colored("[√]", "green")} Prompt is loaded: {args.prompt}.')
+    
+    # encode text prompts
+    inputs = tokenizer(
+        captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states = text_encoder(inputs)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states: {encoder_hidden_states.shape}.')
+
+    inputs_nocond = tokenizer(
+        captions_nocond, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states_nocond = text_encoder(inputs_nocond)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states_nocond: {encoder_hidden_states_nocond.shape}.')
+
+    # load character-level segmenter
+    segmenter = UNet(3, 96, True).cuda()
+    segmenter = torch.nn.DataParallel(segmenter)
+    segmenter.load_state_dict(torch.load(args.character_segmenter_path))
+    segmenter.eval()
+    print(f'{colored("[√]", "green")} Text segmenter is successfully loaded.')
+
+    #### text-to-image ####
+    render_image, segmentation_mask_from_pillow = get_layout_from_prompt(args)
+    
+    segmentation_mask = torch.Tensor(np.array(segmentation_mask_from_pillow)).cuda() # (512, 512)
+
+    segmentation_mask = filter_segmentation_mask(segmentation_mask)
+    segmentation_mask = torch.nn.functional.interpolate(segmentation_mask.unsqueeze(0).unsqueeze(0).float(), size=(256, 256), mode='nearest')
+    segmentation_mask = segmentation_mask.squeeze(1).repeat(sample_num, 1, 1).long().to('cuda') # (1, 1, 256, 256)
+    print(f'{colored("[√]", "green")} character-level segmentation_mask: {segmentation_mask.shape}.')
+    
+    feature_mask = torch.ones(sample_num, 1, 64, 64).to('cuda') # (b, 1, 64, 64)
+    masked_image = torch.zeros(sample_num, 3, 512, 512).to('cuda') # (b, 3, 512, 512)
+    masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64)
+    masked_feature = masked_feature * vae.config.scaling_factor 
+    print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.')
+    print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.')
+
+    # diffusion process
+    intermediate_images = []
+    for t in tqdm(scheduler.timesteps):
+        with torch.no_grad():
+            noise_pred_cond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noise_pred_uncond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states_nocond, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noisy_residual = noise_pred_uncond + slider_guidance * (noise_pred_cond - noise_pred_uncond) # b, 4, 64, 64     
+            prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample 
+            input = prev_noisy_sample
+            intermediate_images.append(prev_noisy_sample)
+            
+    # decode and visualization
+    input = 1 / vae.config.scaling_factor * input 
+    sample_images = vae.decode(input.float(), return_dict=False)[0] # (b, 3, 512, 512)
+
+    image_pil = render_image.resize((512,512))
+    segmentation_mask = segmentation_mask[0].squeeze().cpu().numpy()
+    character_mask_pil = Image.fromarray(((segmentation_mask!=0)*255).astype('uint8')).resize((512,512))
+    character_mask_highlight_pil = segmentation_mask_visualization(args.font_path,segmentation_mask)
+    caption_pil = make_caption_pil(args.font_path, captions)
+    
+    # save pred_img
+    pred_image_list = []
+    for image in sample_images.float():
+        image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+        image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB')
+        pred_image_list.append(image)
+        
+    blank_pil = combine_image(args, None, pred_image_list, image_pil, character_mask_pil, character_mask_highlight_pil, caption_pil)
+    
+    intermediate_result = Image.new('RGB', (512*3, 512))
+    intermediate_result.paste(image_pil, (0, 0))
+    intermediate_result.paste(character_mask_pil, (512, 0))
+    intermediate_result.paste(character_mask_highlight_pil, (512*2, 0))
+
+    return blank_pil, intermediate_result
+
+
+# load character-level segmenter
+segmenter = UNet(3, 96, True).cuda()
+segmenter = torch.nn.DataParallel(segmenter)
+segmenter.load_state_dict(torch.load(args.character_segmenter_path))
+segmenter.eval()
+print(f'{colored("[√]", "green")} Text segmenter is successfully loaded.')
+
+
+
+
+def text_to_image_with_template(prompt,template_image,slider_step,slider_guidance,slider_batch, binary):
+
+    if slider_step>=50:
+        slider_step = 50
+        
+    orig_template_image = template_image.resize((512,512)).convert('RGB')
+    args.prompt = prompt 
+    sample_num = slider_batch
+    # If passed along, set the training seed now.
+    # seed = slider_seed
+    seed = random.randint(0, 10000000)
+    set_seed(seed)
+    scheduler.set_timesteps(slider_step) 
+
+    noise = torch.randn((sample_num, 4, 64, 64)).to("cuda")  # (b, 4, 64, 64)
+    input = noise # (b, 4, 64, 64)
+
+    captions = [args.prompt] * sample_num
+    captions_nocond = [""] * sample_num
+    print(f'{colored("[√]", "green")} Prompt is loaded: {args.prompt}.')
+    
+    # encode text prompts
+    inputs = tokenizer(
+        captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states = text_encoder(inputs)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states: {encoder_hidden_states.shape}.')
+
+    inputs_nocond = tokenizer(
+        captions_nocond, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states_nocond = text_encoder(inputs_nocond)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states_nocond: {encoder_hidden_states_nocond.shape}.')
+
+    #### text-to-image-with-template ####
+    template_image = template_image.resize((256,256)).convert('RGB')
+    
+    # whether binarization is needed
+    print(f'{colored("[Warning]", "red")} args.binarization is set to {binary}. You may need it when using handwritten images as templates.')
+        
+    if binary:
+        gray = ImageOps.grayscale(template_image)
+        binary = gray.point(lambda x: 255 if x > 96 else 0, '1')
+        template_image = binary.convert('RGB')
+
+    # to_tensor = transforms.ToTensor()
+    image_tensor = to_tensor(template_image).unsqueeze(0).cuda().sub_(0.5).div_(0.5) # (b, 3, 256, 256)
+            
+    with torch.no_grad():
+        segmentation_mask = segmenter(image_tensor) # (b, 96, 256, 256)
+    segmentation_mask = segmentation_mask.max(1)[1].squeeze(0) # (256, 256)
+    segmentation_mask = filter_segmentation_mask(segmentation_mask) # (256, 256)
+    
+    segmentation_mask = torch.nn.functional.interpolate(segmentation_mask.unsqueeze(0).unsqueeze(0).float(), size=(256, 256), mode='nearest') # (b, 1, 256, 256)
+    segmentation_mask = segmentation_mask.squeeze(1).repeat(sample_num, 1, 1).long().to('cuda') # (b, 1, 256, 256)
+    print(f'{colored("[√]", "green")} Character-level segmentation_mask: {segmentation_mask.shape}.')
+    
+    feature_mask = torch.ones(sample_num, 1, 64, 64).to('cuda') # (b, 1, 64, 64)
+    masked_image = torch.zeros(sample_num, 3, 512, 512).to('cuda') # (b, 3, 512, 512)
+    masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64)
+    masked_feature = masked_feature * vae.config.scaling_factor # (b, 4, 64, 64)
+
+    # diffusion process
+    intermediate_images = []
+    for t in tqdm(scheduler.timesteps):
+        with torch.no_grad():
+            noise_pred_cond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noise_pred_uncond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states_nocond, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noisy_residual = noise_pred_uncond + slider_guidance * (noise_pred_cond - noise_pred_uncond) # b, 4, 64, 64     
+            prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample 
+            input = prev_noisy_sample
+            intermediate_images.append(prev_noisy_sample)
+            
+    # decode and visualization
+    input = 1 / vae.config.scaling_factor * input 
+    sample_images = vae.decode(input.float(), return_dict=False)[0] # (b, 3, 512, 512)
+
+    image_pil = None
+    segmentation_mask = segmentation_mask[0].squeeze().cpu().numpy()
+    character_mask_pil = Image.fromarray(((segmentation_mask!=0)*255).astype('uint8')).resize((512,512))
+    character_mask_highlight_pil = segmentation_mask_visualization(args.font_path,segmentation_mask)
+    caption_pil = make_caption_pil(args.font_path, captions)
+    
+    # save pred_img
+    pred_image_list = []
+    for image in sample_images.float():
+        image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+        image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB')
+        pred_image_list.append(image)
+        
+    blank_pil = combine_image(args, None, pred_image_list, image_pil, character_mask_pil, character_mask_highlight_pil, caption_pil)
+    
+    intermediate_result = Image.new('RGB', (512*3, 512))
+    intermediate_result.paste(orig_template_image, (0, 0))
+    intermediate_result.paste(character_mask_pil, (512, 0))
+    intermediate_result.paste(character_mask_highlight_pil, (512*2, 0))
+    
+    return blank_pil, intermediate_result
+
+
+def text_inpainting(prompt,orig_image,mask_image,slider_step,slider_guidance,slider_batch):
+
+    if slider_step>=50:
+        slider_step = 50
+        
+    args.prompt = prompt 
+    sample_num = slider_batch
+    # If passed along, set the training seed now.
+    # seed = slider_seed
+    seed = random.randint(0, 10000000)
+    set_seed(seed)
+    scheduler.set_timesteps(slider_step) 
+
+    noise = torch.randn((sample_num, 4, 64, 64)).to("cuda")  # (b, 4, 64, 64)
+    input = noise # (b, 4, 64, 64)
+
+    captions = [args.prompt] * sample_num
+    captions_nocond = [""] * sample_num
+    print(f'{colored("[√]", "green")} Prompt is loaded: {args.prompt}.')
+    
+    # encode text prompts
+    inputs = tokenizer(
+        captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states = text_encoder(inputs)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states: {encoder_hidden_states.shape}.')
+
+    inputs_nocond = tokenizer(
+        captions_nocond, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states_nocond = text_encoder(inputs_nocond)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states_nocond: {encoder_hidden_states_nocond.shape}.')
+
+    mask_image = cv2.resize(mask_image, (512,512))
+    # mask_image = mask_image.resize((512,512)).convert('RGB')
+    text_mask = np.array(mask_image)
+    threshold = 128  
+    _, text_mask = cv2.threshold(text_mask, threshold, 255, cv2.THRESH_BINARY)
+    text_mask = Image.fromarray(text_mask).convert('RGB').resize((256,256))
+    text_mask.save('text_mask.png') 
+    text_mask_tensor = to_tensor(text_mask).unsqueeze(0).cuda().sub_(0.5).div_(0.5)
+    with torch.no_grad():
+        segmentation_mask = segmenter(text_mask_tensor)
+        
+    segmentation_mask = segmentation_mask.max(1)[1].squeeze(0)
+    segmentation_mask = filter_segmentation_mask(segmentation_mask)
+    segmentation_mask = torch.nn.functional.interpolate(segmentation_mask.unsqueeze(0).unsqueeze(0).float(), size=(256, 256), mode='nearest')
+
+    image_mask = transform_mask_pil(mask_image) 
+    image_mask = torch.from_numpy(image_mask).cuda().unsqueeze(0).unsqueeze(0) 
+
+    orig_image = orig_image.convert('RGB').resize((512,512))
+    image = orig_image
+    image_tensor = to_tensor(image).unsqueeze(0).cuda().sub_(0.5).div_(0.5)   
+    masked_image = image_tensor * (1-image_mask)
+    masked_feature = vae.encode(masked_image).latent_dist.sample().repeat(sample_num, 1, 1, 1)
+    masked_feature = masked_feature * vae.config.scaling_factor
+    
+    image_mask = torch.nn.functional.interpolate(image_mask, size=(256, 256), mode='nearest').repeat(sample_num, 1, 1, 1) 
+    segmentation_mask = segmentation_mask * image_mask 
+    feature_mask = torch.nn.functional.interpolate(image_mask, size=(64, 64), mode='nearest')
+
+    # diffusion process
+    intermediate_images = []
+    for t in tqdm(scheduler.timesteps):
+        with torch.no_grad():
+            noise_pred_cond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noise_pred_uncond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states_nocond, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noisy_residual = noise_pred_uncond + slider_guidance * (noise_pred_cond - noise_pred_uncond) # b, 4, 64, 64     
+            prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample 
+            input = prev_noisy_sample
+            intermediate_images.append(prev_noisy_sample)
+            
+    # decode and visualization
+    input = 1 / vae.config.scaling_factor * input 
+    sample_images = vae.decode(input.float(), return_dict=False)[0] # (b, 3, 512, 512)
+
+    image_pil = None
+    segmentation_mask = segmentation_mask[0].squeeze().cpu().numpy()
+    character_mask_pil = Image.fromarray(((segmentation_mask!=0)*255).astype('uint8')).resize((512,512))
+    character_mask_highlight_pil = segmentation_mask_visualization(args.font_path,segmentation_mask)
+    caption_pil = make_caption_pil(args.font_path, captions)
+    
+    # save pred_img
+    pred_image_list = []
+    for image in sample_images.float():
+        image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+        image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB')
+        
+        # need to merge
+        
+        # image = inpainting_merge_image(orig_image, Image.fromarray(mask_image).convert('L'), image)
+
+        pred_image_list.append(image)
+    
+    character_mask_pil.save('character_mask_pil.png')
+    character_mask_highlight_pil.save('character_mask_highlight_pil.png')
+    
+        
+    blank_pil = combine_image(args, None, pred_image_list, image_pil, character_mask_pil, character_mask_highlight_pil, caption_pil)
+
+
+    background = orig_image.resize((512, 512))
+    alpha = Image.new('L', background.size, int(255 * 0.2))
+    background.putalpha(alpha)
+    # foreground
+    foreground = Image.fromarray(mask_image).convert('L').resize((512, 512))
+    threshold = 200
+    alpha = foreground.point(lambda x: 0 if x > threshold else 255, '1')
+    foreground.putalpha(alpha)
+    merge_image = Image.alpha_composite(foreground.convert('RGBA'), background.convert('RGBA')).convert('RGB')
+
+    intermediate_result = Image.new('RGB', (512*3, 512))
+    intermediate_result.paste(merge_image, (0, 0))
+    intermediate_result.paste(character_mask_pil, (512, 0))
+    intermediate_result.paste(character_mask_highlight_pil, (512*2, 0))
+    
+    return blank_pil, intermediate_result
+    
+import gradio as gr
+    
+with gr.Blocks() as demo:
+
+    gr.HTML(
+        """
+        <div style="text-align: center; max-width: 1200px; margin: 20px auto;">
+        <h1 style="font-weight: 900; font-size: 3rem; margin: 0rem">
+            TextDiffuser: Diffusion Models as Text Painters
+        </h1>        
+        <h3 style="font-weight: 450; font-size: 1rem; margin: 0rem"> 
+        [<a href="https://arxiv.org/abs/2305.10855" style="color:blue;">arXiv</a>] 
+        [<a href="https://github.com/microsoft/unilm/tree/master/textdiffuser" style="color:blue;">Code</a>]
+        [<a href="https://jingyechen.github.io/textdiffuser/" style="color:blue;">ProjectPage</a>]
+        </h3> 
+        <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem">
+        We propose <b>TextDiffuser</b>, a flexible and controllable framework to generate images with visually appealing text that is coherent with backgrounds. 
+        Main features include: (a) <b><font color="#A52A2A">Text-to-Image</font></b>: The user provides a prompt and encloses the keywords with single quotes (e.g., a text image of ‘hello’). The model first determines the layout of the keywords and then draws the image based on the layout and prompt. (b) <b><font color="#A52A2A">Text-to-Image with Templates</font></b>: The user provides a prompt and a template image containing text, which can be a printed, handwritten, or scene text image. These template images can be used to determine the layout of the characters. (c) <b><font color="#A52A2A">Text Inpainting</font></b>: The user provides an image and specifies the region to be modified along with the desired text content. The model is able to modify the original text or add text to areas without text.
+        </h2>
+        <img src="file/images/huggingface_blank.jpg" alt="textdiffuser">        
+        </div>
+        """)
+
+    with gr.Tab("Text-to-Image"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                prompt = gr.Textbox(label="Input your prompt here. Please enclose keywords with 'single quotes', you may refer to the examples below. The current version only supports input in English characters.", placeholder="Placeholder 'Team' hat")
+                slider_step = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Sampling step", info="The sampling step for TextDiffuser.")
+                slider_guidance = gr.Slider(minimum=1, maximum=9, value=7.5, step=0.5, label="Scale of classifier-free guidance", info="The scale of classifier-free guidance and is set to 7.5 in default.")
+                slider_batch = gr.Slider(minimum=1, maximum=4, value=4, step=1, label="Batch size", info="The number of images to be sampled.")
+                # slider_seed = gr.Slider(minimum=1, maximum=10000, label="Seed", randomize=True)
+                button = gr.Button("Generate")
+                            
+            with gr.Column(scale=1):
+                output = gr.Image(label='Generated image')
+                
+                with gr.Accordion("Intermediate results", open=False):
+                    gr.Markdown("Layout, segmentation mask, and details of segmentation mask from left to right.")
+                    intermediate_results = gr.Image(label='')
+        
+        gr.Markdown("## Prompt Examples")
+        gr.Examples(
+            [
+                ["Distinguished poster of 'SPIDERMAN'. Trending on ArtStation and Pixiv. A vibrant digital oil painting. A highly detailed fantasy character illustration by Wayne Reynolds and Charles Monet and Gustave Dore and Carl Critchlow and Bram Sels"],
+                ["A detailed portrait of a fox guardian with a shield with 'Kung Fu' written on it, by victo ngai and justin gerard, digital art, realistic painting, very detailed, fantasy, high definition, cinematic light, dnd, trending on artstation"],
+                ["portrait of a 'dragon', concept art, sumi - e style, intricate linework, green smoke, artstation, trending, highly detailed, smooth, focus, art by yoji shinkawa,"],
+                ["elderly woman dressed in extremely colorful clothes with many strange patterns posing for a high fashion photoshoot of 'FASHION', haute couture, golden hour, artstation, by J. C. Leyendecker and Peter Paul Rubens"],
+                ["epic digital art of a luxury yacht named 'Time Machine' driving through very dark hard edged city towers from tron movie, faint tall mountains in background, wlop, pixiv"],
+                ["A poster of 'Adventurer'.  A beautiful so tall boy with big eyes and small nose is in the jungle, he wears normal clothes and shows his full length, which we see from the front, unreal engine, cozy indoor lighting, artstation, detailed"],
+                ["A poster of 'AI BABY'. Cute and adorable cartoon it baby, fantasy, dreamlike, surrealism, super cute, trending on artstation"],
+                ["'Team' hat"],
+                ["Thanksgiving 'Fam' Mens T Shirt"],
+                ["A storefront with 'Hello World' written on it."],
+                ["A poster titled 'Quails of North America', showing different kinds of quails."],
+                ["A storefront with 'Deep Learning' written on it."],
+                ["An antique bottle labeled 'Energy Tonic'"],
+                ["A TV show poster titled 'Tango argentino'"],
+                ["A TV show poster with logo 'The Dry' on it"],
+                ["Stupid 'History' eBook Tales of Stupidity Strangeness"],
+                ["Photos of 'Sampa Hostel'"],
+                ["A large recipe book titled 'Recipes from Peru'."],
+                ["New York Skyline with 'Diffusion' written with fireworks on the sky"],
+                ["Books with the word 'Science' printed on them"],
+                ["A globe with the words 'Planet Earth' written in bold letters with continents in bright colors"],
+                ["A logo for the company 'EcoGrow', where the letters look like plants"],
+            ],
+            prompt,
+            examples_per_page=100
+        )
+                    
+        button.click(text_to_image, inputs=[prompt,slider_step,slider_guidance,slider_batch], outputs=[output,intermediate_results])
+        
+    with gr.Tab("Text-to-Image-with-Template"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                prompt = gr.Textbox(label='Input your prompt here.')
+                template_image = gr.Image(label='Template image', type="pil")
+                slider_step = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Sampling step", info="The sampling step for TextDiffuser.")
+                slider_guidance = gr.Slider(minimum=1, maximum=9, value=7.5, step=0.5, label="Scale of classifier-free guidance", info="The scale of classifier-free guidance and is set to 7.5 in default.")
+                slider_batch = gr.Slider(minimum=1, maximum=4, value=4, step=1, label="Batch size", info="The number of images to be sampled.")
+                # binary = gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?")
+                binary = gr.Checkbox(label="Binarization", bool=True, info="Whether to binarize the template image? You may need it when using handwritten images as templates.")
+                button = gr.Button("Generate")
+                    
+            with gr.Column(scale=1):
+                output = gr.Image(label='Generated image')
+                
+                with gr.Accordion("Intermediate results", open=False):
+                    gr.Markdown("Template image, segmentation mask, and details of segmentation mask from left to right.")
+                    intermediate_results = gr.Image(label='')
+
+        gr.Markdown("## Prompt and Template-Image Examples")
+        gr.Examples(
+            [
+                ["summer garden, artwork, highly detailed, sharp focus, realist, digital painting, artstation, concept art, art by jay oh, greg rutkowski, wlop", './images/text-to-image-with-template/6.jpg', False], 
+                ["a hand-drawn blueprint for a time machine with the caption 'Time traveling device'", './images/text-to-image-with-template/5.jpg', False], 
+                ["a book called summer vibe written by diffusion model", './images/text-to-image-with-template/7.jpg', False], 
+                ["a work company", './images/text-to-image-with-template/8.jpg', False], 
+                ["a book of AI in next century written by AI robot ", './images/text-to-image-with-template/9.jpg', False], 
+                ["A board saying having a dog named shark at the beach was a mistake", './images/text-to-image-with-template/1.jpg', False], 
+                ["an elephant holds a newspaper that is written elephant take over the world", './images/text-to-image-with-template/2.jpg', False], 
+                ["a mouse with a flashlight saying i am afraid of the dark", './images/text-to-image-with-template/4.jpg', False], 
+                ["a birthday cake of happy birthday to xyz", './images/text-to-image-with-template/10.jpg', False], 
+                ["a poster of monkey music festival", './images/text-to-image-with-template/11.jpg', False], 
+                ["a meme of are you kidding", './images/text-to-image-with-template/12.jpg', False], 
+                ["a 3d model of a 1980s-style computer with the text my old habit on the screen", './images/text-to-image-with-template/13.jpg', True], 
+                ["a board of hello world", './images/text-to-image-with-template/15.jpg', True], 
+                ["a microsoft bag", './images/text-to-image-with-template/16.jpg', True], 
+                ["a dog holds a paper saying please adopt me", './images/text-to-image-with-template/17.jpg', False], 
+                ["a hello world banner", './images/text-to-image-with-template/18.jpg', False], 
+                ["a stop pizza", './images/text-to-image-with-template/19.jpg', False], 
+                ["a dress with text do not read the next sentence", './images/text-to-image-with-template/20.jpg', False], 
+            ],
+            [prompt,template_image, binary],
+            examples_per_page=100
+        )
+
+        button.click(text_to_image_with_template, inputs=[prompt,template_image,slider_step,slider_guidance,slider_batch,binary], outputs=[output,intermediate_results])
+        
+    with gr.Tab("Text-Inpainting"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                prompt = gr.Textbox(label='Input your prompt here.')
+                with gr.Row():
+                    orig_image = gr.Image(label='Original image', type="pil")
+                    mask_image = gr.Image(label='Mask image', type="numpy")
+                slider_step = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Sampling step", info="The sampling step for TextDiffuser.")
+                slider_guidance = gr.Slider(minimum=1, maximum=9, value=7.5, step=0.5, label="Scale of classifier-free guidance", info="The scale of classifier-free guidance and is set to 7.5 in default.")
+                slider_batch = gr.Slider(minimum=1, maximum=4, value=4, step=1, label="Batch size", info="The number of images to be sampled.")
+                button = gr.Button("Generate")
+            with gr.Column(scale=1):
+                output = gr.Image(label='Generated image')
+                with gr.Accordion("Intermediate results", open=False):
+                    gr.Markdown("Masked image, segmentation mask, and details of segmentation mask from left to right.")
+                    intermediate_results = gr.Image(label='')
+                
+        gr.Markdown("## Prompt, Original Image, and Mask Examples")
+        gr.Examples(
+            [
+                ["eye on security protection", './images/text-inpainting/1.jpg', './images/text-inpainting/1mask.jpg'],
+                ["a logo of poppins", './images/text-inpainting/2.jpg', './images/text-inpainting/2mask.jpg'],
+                ["tips for middle space living ", './images/text-inpainting/3.jpg', './images/text-inpainting/3mask.jpg'],
+                ["george is a proud big sister", './images/text-inpainting/5.jpg', './images/text-inpainting/5mask.jpg'],
+                ["we are the great people", './images/text-inpainting/6.jpg', './images/text-inpainting/6mask.jpg'],
+                ["tech house interesting terrace party", './images/text-inpainting/7.jpg', './images/text-inpainting/7mask.jpg'],
+                ["2023", './images/text-inpainting/8.jpg', './images/text-inpainting/8mask.jpg'],
+                ["wear protective equipment necessary", './images/text-inpainting/9.jpg', './images/text-inpainting/9mask.jpg'],
+                ["a good day in the hometown", './images/text-inpainting/10.jpg', './images/text-inpainting/10mask.jpg'],
+                ["a boy paints good morning on a board", './images/text-inpainting/11.jpg', './images/text-inpainting/11mask.jpg'],
+                ["the word my gift on a basketball", './images/text-inpainting/13.jpg', './images/text-inpainting/13mask.jpg'],
+                ["a logo of mono", './images/text-inpainting/14.jpg', './images/text-inpainting/14mask.jpg'],
+                ["a board saying assyrian on unflagging fry devastates", './images/text-inpainting/15.jpg', './images/text-inpainting/15mask.jpg'],
+                ["a board saying session", './images/text-inpainting/16.jpg', './images/text-inpainting/16mask.jpg'],
+                ["rankin dork", './images/text-inpainting/17mask.jpg', './images/text-inpainting/17.jpg'],
+                ["a coin of mem", './images/text-inpainting/18mask.jpg', './images/text-inpainting/18.jpg'],
+                ["a board without text", './images/text-inpainting/19.jpg', './images/text-inpainting/19mask.jpg'],
+                ["a board without text", './images/text-inpainting/20.jpg', './images/text-inpainting/20mask.jpg'],
+
+            ],
+            [prompt,orig_image,mask_image],
+        )
+                
+                
+        button.click(text_inpainting, inputs=[prompt,orig_image,mask_image,slider_step,slider_guidance,slider_batch], outputs=[output, intermediate_results])
+
+
+
+    gr.HTML(
+        """
+        <div style="text-align: justify; max-width: 1200px; margin: 20px auto;">
+        <h3 style="font-weight: 450; font-size: 0.8rem; margin: 0rem">
+        <b>Version</b>: 1.0
+        </h3>
+        <h3 style="font-weight: 450; font-size: 0.8rem; margin: 0rem">
+        <b>Contact</b>: 
+        For help or issues using TextDiffuser, please email Jingye Chen <a href="mailto:[email protected]">([email protected])</a>, Yupan Huang <a href="mailto:[email protected]">([email protected])</a> or submit a GitHub issue. For other communications related to TextDiffuser, please contact Lei Cui <a href="mailto:[email protected]">([email protected])</a> or Furu Wei <a href="mailto:[email protected]">([email protected])</a>.
+        </h3>
+        </div>
+        """
+    )
+
+demo.launch()

inference.py

@@ -0,0 +1,609 @@
+# ------------------------------------------
+# TextDiffuser: Diffusion Models as Text Painters
+# Paper Link: https://arxiv.org/abs/2305.10855
+# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
+# Copyright (c) Microsoft Corporation.
+# This file provides the inference script.
+# ------------------------------------------
+
+import os
+import cv2
+import random
+import logging
+import argparse
+import numpy as np
+
+from pathlib import Path
+from tqdm.auto import tqdm
+from typing import Optional
+from packaging import version
+from termcolor import colored
+from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance # import for visualization
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+
+import datasets
+from datasets import load_dataset
+from datasets import disable_caching
+
+import torch
+import torch.utils.checkpoint
+import torch.nn.functional as F
+from torchvision import transforms
+
+import accelerate
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+
+import diffusers
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel 
+from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
+from diffusers.utils import check_min_version, deprecate
+from diffusers.utils.import_utils import is_xformers_available
+
+import transformers
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from util import segmentation_mask_visualization, make_caption_pil, combine_image, transform_mask, filter_segmentation_mask, inpainting_merge_image
+from model.layout_generator import get_layout_from_prompt
+from model.text_segmenter.unet import UNet
+
+import torchsnooper
+
+disable_caching()
+check_min_version("0.15.0.dev0")
+logger = get_logger(__name__, log_level="INFO")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path", 
+        type=str,
+        default='runwayml/stable-diffusion-v1-5', # no need to modify this  
+        help="Path to pretrained model or model identifier from huggingface.co/models. Please do not modify this.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--mode",
+        type=str,
+        default=None,
+        required=True,
+        choices=["text-to-image", "text-to-image-with-template", "text-inpainting"],
+        help="Three modes can be used.",
+    )
+    parser.add_argument(
+        "--prompt", 
+        type=str,
+        default="",
+        required=True,
+        help="The text prompts provided by users.",
+    )
+    parser.add_argument(
+        "--template_image", 
+        type=str,
+        default="",
+        help="The template image should be given when using 【text-to-image-with-template】 mode.",
+    )
+    parser.add_argument(
+        "--original_image", 
+        type=str,
+        default="",
+        help="The original image should be given when using 【text-inpainting】 mode.",
+    )
+    parser.add_argument(
+        "--text_mask", 
+        type=str,
+        default="",
+        help="The text mask should be given when using 【text-inpainting】 mode.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument(
+        "--seed", 
+        type=int, 
+        default=None, 
+        help="A seed for reproducible training."
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--classifier_free_scale", 
+        type=float,
+        default=7.5, # following stable diffusion (https://github.com/CompVis/stable-diffusion)
+        help="Classifier free scale following https://arxiv.org/abs/2207.12598.",
+    )
+    parser.add_argument(
+        "--drop_caption", 
+        action="store_true", 
+        help="Whether to drop captions during training following https://arxiv.org/abs/2207.12598.."
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0, 
+        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+    )
+    parser.add_argument(
+        "--push_to_hub", 
+        action="store_true", 
+        help="Whether or not to push the model to the Hub."
+    )
+    parser.add_argument(
+        "--hub_token", 
+        type=str, 
+        default=None, 
+        help="The token to use to push to the Model Hub."
+    )
+    parser.add_argument(
+        "--hub_model_id",
+        type=str,
+        default=None,
+        help="The name of the repository to keep in sync with the local `output_dir`.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default='fp16',
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--report_to", 
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument(
+        "--local_rank", 
+        type=int, 
+        default=-1, 
+        help="For distributed training: local_rank"
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500, 
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=5,
+        help=(
+            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
+            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
+            " for more docs"
+        ),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None, # should be specified during inference
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", 
+        action="store_true", 
+        help="Whether or not to use xformers."
+    )
+    parser.add_argument(
+        "--font_path", 
+        type=str, 
+        default='assets/font/Arial.ttf', 
+        help="The path of font for visualization."
+    )
+    parser.add_argument(
+        "--sample_steps", 
+        type=int, 
+        default=50, # following stable diffusion (https://github.com/CompVis/stable-diffusion)
+        help="Diffusion steps for sampling."
+    )
+    parser.add_argument(
+        "--vis_num", 
+        type=int, 
+        default=9, # please decreases the number if out-of-memory error occurs
+        help="Number of images to be sample. Please decrease it when encountering out of memory error."
+    )
+    parser.add_argument(
+        "--binarization", 
+        action="store_true", 
+        help="Whether to binarize the template image."
+    )
+    parser.add_argument(
+        "--use_pillow_segmentation_mask", 
+        type=bool,
+        default=True, 
+        help="In the 【text-to-image】 mode, please specify whether to use the segmentation masks provided by PILLOW"
+    )
+    parser.add_argument(
+        "--character_segmenter_path", 
+        type=str,
+        default='textdiffuser-ckpt/text_segmenter.pth',
+        help="checkpoint of character-level segmenter"
+    )
+    args = parser.parse_args()
+    
+    print(f'{colored("[√]", "green")} Arguments are loaded.')
+    print(args)
+    
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
+
+
+# @torchsnooper.snoop()
+def main():
+    args = parse_args()
+    # If passed along, set the training seed now.
+    seed = args.seed if args.seed is not None else random.randint(0, 1000000)
+    set_seed(seed)
+    print(f'{colored("[√]", "green")} Seed is set to {seed}.')
+    
+    logging_dir = os.path.join(args.output_dir, args.logging_dir)
+    sub_output_dir = f"{args.prompt}_[{args.mode.upper()}]_[SEED-{seed}]"
+
+    print(f'{colored("[√]", "green")} Logging dir is set to {logging_dir}.')
+
+    accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=1,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        logging_dir=logging_dir,
+        project_config=accelerator_project_config,
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.push_to_hub:
+            if args.hub_model_id is None:
+                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+
+            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "step_*" not in gitignore:
+                    gitignore.write("step_*\n")
+                if "epoch_*" not in gitignore:
+                    gitignore.write("epoch_*\n")
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+            print(args.output_dir)
+
+    # Load scheduler, tokenizer and models.
+    tokenizer = CLIPTokenizer.from_pretrained(
+        args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
+    )
+    text_encoder = CLIPTextModel.from_pretrained(
+        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
+    )
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision).cuda()
+    unet = UNet2DConditionModel.from_pretrained(
+        args.resume_from_checkpoint, subfolder="unet", revision=None 
+    ).cuda() 
+    
+    # Freeze vae and text_encoder
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = version.parse(xformers.__version__)
+            if xformers_version == version.parse("0.0.16"):
+                logger.warn(
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                )
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # `accelerate` 0.16.0 will have better support for customized saving
+    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
+        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
+        def save_model_hook(models, weights, output_dir):
+           
+            for i, model in enumerate(models):
+                model.save_pretrained(os.path.join(output_dir, "unet"))
+
+                # make sure to pop weight so that corresponding model is not saved again
+                weights.pop()
+
+        def load_model_hook(models, input_dir):
+           
+            for i in range(len(models)):
+                # pop models so that they are not loaded again
+                model = models.pop()
+
+                # load diffusers style into model
+                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
+                model.register_to_config(**load_model.config)
+
+                model.load_state_dict(load_model.state_dict())
+                del load_model
+
+        accelerator.register_save_state_pre_hook(save_model_hook)
+        accelerator.register_load_state_pre_hook(load_model_hook)
+
+
+    # setup schedulers                    
+    scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") 
+    scheduler.set_timesteps(args.sample_steps) 
+    sample_num = args.vis_num
+    noise = torch.randn((sample_num, 4, 64, 64)).to("cuda")  # (b, 4, 64, 64)
+    input = noise # (b, 4, 64, 64)
+
+    captions = [args.prompt] * sample_num
+    captions_nocond = [""] * sample_num
+    print(f'{colored("[√]", "green")} Prompt is loaded: {args.prompt}.')
+    
+    # encode text prompts
+    inputs = tokenizer(
+        captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states = text_encoder(inputs)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states: {encoder_hidden_states.shape}.')
+
+    inputs_nocond = tokenizer(
+        captions_nocond, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+    ).input_ids # (b, 77)
+    encoder_hidden_states_nocond = text_encoder(inputs_nocond)[0].cuda() # (b, 77, 768)
+    print(f'{colored("[√]", "green")} encoder_hidden_states_nocond: {encoder_hidden_states_nocond.shape}.')
+
+    # load character-level segmenter
+    segmenter = UNet(3, 96, True).cuda()
+    segmenter = torch.nn.DataParallel(segmenter)
+    segmenter.load_state_dict(torch.load(args.character_segmenter_path))
+    segmenter.eval()
+    print(f'{colored("[√]", "green")} Text segmenter is successfully loaded.')
+
+    #### text-to-image ####
+    if args.mode == 'text-to-image':    
+        render_image, segmentation_mask_from_pillow = get_layout_from_prompt(args)
+        
+        if args.use_pillow_segmentation_mask:
+            segmentation_mask = torch.Tensor(np.array(segmentation_mask_from_pillow)).cuda() # (512, 512)
+        else:
+            to_tensor = transforms.ToTensor()
+            image_tensor = to_tensor(render_image).unsqueeze(0).cuda().sub_(0.5).div_(0.5)  
+            with torch.no_grad():
+                segmentation_mask = segmenter(image_tensor)
+            segmentation_mask = segmentation_mask.max(1)[1].squeeze(0)
+            
+        segmentation_mask = filter_segmentation_mask(segmentation_mask)
+        segmentation_mask = torch.nn.functional.interpolate(segmentation_mask.unsqueeze(0).unsqueeze(0).float(), size=(256, 256), mode='nearest')
+        segmentation_mask = segmentation_mask.squeeze(1).repeat(sample_num, 1, 1).long().to('cuda') # (1, 1, 256, 256)
+        print(f'{colored("[√]", "green")} character-level segmentation_mask: {segmentation_mask.shape}.')
+        
+        feature_mask = torch.ones(sample_num, 1, 64, 64).to('cuda') # (b, 1, 64, 64)
+        masked_image = torch.zeros(sample_num, 3, 512, 512).to('cuda') # (b, 3, 512, 512)
+        masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64)
+        masked_feature = masked_feature * vae.config.scaling_factor 
+        print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.')
+        print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.')
+
+
+    #### text-to-image-with-template ####
+    if args.mode == 'text-to-image-with-template':         
+        template_image = Image.open(args.template_image).resize((256,256)).convert('RGB')
+        
+        # whether binarization is needed
+        print(f'{colored("[Warning]", "red")} args.binarization is set to {args.binarization}. You may need it when using handwritten images as templates.')
+        if args.binarization:
+            gray = ImageOps.grayscale(template_image)
+            binary = gray.point(lambda x: 255 if x > 96 else 0, '1')
+            template_image = binary.convert('RGB')
+            
+        to_tensor = transforms.ToTensor()
+        image_tensor = to_tensor(template_image).unsqueeze(0).cuda().sub_(0.5).div_(0.5) # (b, 3, 256, 256)
+                
+        with torch.no_grad():
+            segmentation_mask = segmenter(image_tensor) # (b, 96, 256, 256)
+        segmentation_mask = segmentation_mask.max(1)[1].squeeze(0) # (256, 256)
+        segmentation_mask = filter_segmentation_mask(segmentation_mask) # (256, 256)
+        segmentation_mask_pil = Image.fromarray(segmentation_mask.type(torch.uint8).cpu().numpy()).convert('RGB') 
+        
+        segmentation_mask = torch.nn.functional.interpolate(segmentation_mask.unsqueeze(0).unsqueeze(0).float(), size=(256, 256), mode='nearest') # (b, 1, 256, 256)
+        segmentation_mask = segmentation_mask.squeeze(1).repeat(sample_num, 1, 1).long().to('cuda') # (b, 1, 256, 256)
+        print(f'{colored("[√]", "green")} Character-level segmentation_mask: {segmentation_mask.shape}.')
+        
+        feature_mask = torch.ones(sample_num, 1, 64, 64).to('cuda') # (b, 1, 64, 64)
+        masked_image = torch.zeros(sample_num, 3, 512, 512).to('cuda') # (b, 3, 512, 512)
+        masked_feature = vae.encode(masked_image).latent_dist.sample() # (b, 4, 64, 64)
+        masked_feature = masked_feature * vae.config.scaling_factor # (b, 4, 64, 64)
+        print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.')
+        print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.')
+        
+        render_image = template_image # for visualization
+        
+
+    #### text-inpainting ####
+    if args.mode == 'text-inpainting':         
+        text_mask = cv2.imread(args.text_mask)
+        threshold = 128  
+        _, text_mask = cv2.threshold(text_mask, threshold, 255, cv2.THRESH_BINARY)
+        text_mask = Image.fromarray(text_mask).convert('RGB').resize((256,256))
+        text_mask_tensor = transforms.ToTensor()(text_mask).unsqueeze(0).cuda().sub_(0.5).div_(0.5)
+        with torch.no_grad():
+            segmentation_mask = segmenter(text_mask_tensor)
+            
+        segmentation_mask = segmentation_mask.max(1)[1].squeeze(0)
+        segmentation_mask = filter_segmentation_mask(segmentation_mask)
+        segmentation_mask = torch.nn.functional.interpolate(segmentation_mask.unsqueeze(0).unsqueeze(0).float(), size=(256, 256), mode='nearest')
+
+        image_mask = transform_mask(args.text_mask)
+        image_mask = torch.from_numpy(image_mask).cuda().unsqueeze(0).unsqueeze(0) 
+
+        image = Image.open(args.original_image).convert('RGB').resize((512,512))
+        image_tensor = transforms.ToTensor()(image).unsqueeze(0).cuda().sub_(0.5).div_(0.5)   
+        masked_image = image_tensor * (1-image_mask)
+        masked_feature = vae.encode(masked_image).latent_dist.sample().repeat(sample_num, 1, 1, 1)
+        masked_feature = masked_feature * vae.config.scaling_factor
+        
+        image_mask = torch.nn.functional.interpolate(image_mask, size=(256, 256), mode='nearest').repeat(sample_num, 1, 1, 1) 
+        segmentation_mask = segmentation_mask * image_mask 
+        feature_mask = torch.nn.functional.interpolate(image_mask, size=(64, 64), mode='nearest')
+        print(f'{colored("[√]", "green")} feature_mask: {feature_mask.shape}.')
+        print(f'{colored("[√]", "green")} segmentation_mask: {segmentation_mask.shape}.')
+        print(f'{colored("[√]", "green")} masked_feature: {masked_feature.shape}.')
+        
+        render_image = Image.open(args.original_image) 
+
+
+
+    # diffusion process
+    intermediate_images = []
+    for t in tqdm(scheduler.timesteps):
+        with torch.no_grad():
+            noise_pred_cond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noise_pred_uncond = unet(sample=input, timestep=t, encoder_hidden_states=encoder_hidden_states_nocond, segmentation_mask=segmentation_mask, feature_mask=feature_mask, masked_feature=masked_feature).sample # b, 4, 64, 64
+            noisy_residual = noise_pred_uncond + args.classifier_free_scale * (noise_pred_cond - noise_pred_uncond) # b, 4, 64, 64     
+            prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample 
+            input = prev_noisy_sample
+            intermediate_images.append(prev_noisy_sample)
+            
+    # decode and visualization
+    input = 1 / vae.config.scaling_factor * input 
+    sample_images = vae.decode(input.float(), return_dict=False)[0] # (b, 3, 512, 512)
+
+    image_pil = render_image.resize((512,512))
+    segmentation_mask = segmentation_mask[0].squeeze().cpu().numpy()
+    character_mask_pil = Image.fromarray(((segmentation_mask!=0)*255).astype('uint8')).resize((512,512))
+    character_mask_highlight_pil = segmentation_mask_visualization(args.font_path,segmentation_mask)
+    caption_pil = make_caption_pil(args.font_path, captions)
+    
+    # save pred_img
+    pred_image_list = []
+    for image in sample_images.float():
+        image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
+        image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB')
+        pred_image_list.append(image)
+        
+    os.makedirs(f'{args.output_dir}/{sub_output_dir}', exist_ok=True)
+        
+    # save additional info
+    if args.mode == 'text-to-image':
+        image_pil.save(os.path.join(args.output_dir, sub_output_dir, 'render_text_image.png'))
+        enhancer = ImageEnhance.Brightness(segmentation_mask_from_pillow)
+        im_brightness = enhancer.enhance(5)
+        im_brightness.save(os.path.join(args.output_dir, sub_output_dir, 'segmentation_mask_from_pillow.png'))
+    if args.mode == 'text-to-image-with-template':
+        template_image.save(os.path.join(args.output_dir, sub_output_dir, 'template.png'))
+        enhancer = ImageEnhance.Brightness(segmentation_mask_pil)
+        im_brightness = enhancer.enhance(5)
+        im_brightness.save(os.path.join(args.output_dir, sub_output_dir, 'segmentation_mask_from_template.png'))
+    if args.mode == 'text-inpainting':
+        character_mask_highlight_pil = character_mask_pil
+        # background
+        background = Image.open(args.original_image).resize((512, 512))
+        alpha = Image.new('L', background.size, int(255 * 0.2))
+        background.putalpha(alpha)
+        # foreground
+        foreground = Image.open(args.text_mask).convert('L').resize((512, 512))
+        threshold = 200
+        alpha = foreground.point(lambda x: 0 if x > threshold else 255, '1')
+        foreground.putalpha(alpha)
+        character_mask_pil = Image.alpha_composite(foreground.convert('RGBA'), background.convert('RGBA')).convert('RGB')
+        # merge
+        pred_image_list_new = []
+        for pred_image in pred_image_list:
+            pred_image = inpainting_merge_image(Image.open(args.original_image), Image.open(args.text_mask).convert('L'), pred_image)
+            pred_image_list_new.append(pred_image)
+        pred_image_list = pred_image_list_new
+
+    
+    combine_image(args, sub_output_dir, pred_image_list, image_pil, character_mask_pil, character_mask_highlight_pil, caption_pil)
+
+
+    # create a soft link
+    if os.path.exists(os.path.join(args.output_dir, 'latest')):
+        os.unlink(os.path.join(args.output_dir, 'latest'))
+    os.symlink(os.path.abspath(os.path.join(args.output_dir, sub_output_dir)), os.path.abspath(os.path.join(args.output_dir, 'latest/')))
+
+
+    color_sub_output_dir = colored(sub_output_dir, 'green')
+    print(f'{colored("[√]", "green")} Save successfully. Please check the output at {color_sub_output_dir} OR the latest folder')
+
+if __name__ == "__main__":
+    main()

model/layout_generator.py

@@ -0,0 +1,223 @@
+# ------------------------------------------
+# TextDiffuser: Diffusion Models as Text Painters
+# Paper Link: https://arxiv.org/abs/2305.10855
+# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
+# Copyright (c) Microsoft Corporation.
+# This file aims to predict the layout of keywords in user prompts.
+# ------------------------------------------
+
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import re
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import CLIPTokenizer
+from PIL import Image, ImageDraw, ImageFont
+from util import get_width, get_key_words, adjust_overlap_box, shrink_box, adjust_font_size, alphabet_dic
+from model.layout_transformer import LayoutTransformer, TextConditioner
+from termcolor import colored
+
+# import layout transformer
+model = LayoutTransformer().cuda().eval()
+model.load_state_dict(torch.load('textdiffuser-ckpt/layout_transformer.pth'))
+
+# import text encoder and tokenizer
+text_encoder = TextConditioner().cuda().eval()
+tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+
+
+def process_caption(font_path, caption, keywords):
+    # remove punctuations. please remove this statement if you want to paint punctuations
+    caption = re.sub(u"([^\u0041-\u005a\u0061-\u007a\u0030-\u0039])", " ", caption) 
+    
+    # tokenize it into ids and get length
+    caption_words = tokenizer([caption], truncation=True, max_length=77, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+    caption_words_ids = caption_words['input_ids'] # (1, 77)
+    length = caption_words['length'] # (1, )
+    
+    # convert id to words
+    words = tokenizer.convert_ids_to_tokens(caption_words_ids.view(-1).tolist())
+    words = [i.replace('</w>', '') for i in words]
+    words_valid = words[:int(length)]
+
+    # store the box coordinates and state of each token
+    info_array = np.zeros((77,5)) # (77, 5)
+
+    # split the caption into words and convert them into lower case
+    caption_split = caption.split() 
+    caption_split = [i.lower() for i in caption_split]
+
+    start_dic = {} # get the start index of each word
+    state_list = [] # 0: start, 1: middle, 2: special token
+    word_match_list = [] # the index of the word in the caption
+    current_caption_index = 0 
+    current_match = ''
+    for i in range(length): 
+
+        # the first and last token are special tokens
+        if i == 0 or i == length-1:
+            state_list.append(2) 
+            word_match_list.append(127)
+            continue
+
+        if current_match == '':
+            state_list.append(0)
+            start_dic[current_caption_index] = i
+        else:
+            state_list.append(1)
+
+        current_match += words_valid[i]
+        word_match_list.append(current_caption_index)
+        if current_match == caption_split[current_caption_index]:
+            current_match = ''
+            current_caption_index += 1
+
+    while len(state_list) < 77:
+        state_list.append(127)
+    while len(word_match_list) < 77:
+        word_match_list.append(127)
+
+    length_list = []
+    width_list =[]
+    for i in range(len(word_match_list)):
+        if word_match_list[i] == 127:
+            length_list.append(0)
+            width_list.append(0)
+        else:
+            length_list.append(len(caption.split()[word_match_list[i]]))
+            width_list.append(get_width(font_path, caption.split()[word_match_list[i]]))
+
+    while len(length_list) < 77:
+        length_list.append(127)
+        width_list.append(0)
+
+    length_list = torch.Tensor(length_list).long() # (77, )
+    width_list = torch.Tensor(width_list).long() # (77, )
+
+    boxes = []
+    duplicate_dict = {} # some words may appear more than once
+    for keyword in keywords: 
+        keyword = keyword.lower()
+        if keyword in caption_split:
+            if keyword not in duplicate_dict:
+                duplicate_dict[keyword] = caption_split.index(keyword) 
+                index = caption_split.index(keyword)
+            else:
+                if duplicate_dict[keyword]+1 < len(caption_split) and keyword in caption_split[duplicate_dict[keyword]+1:]:
+                    index = duplicate_dict[keyword] + caption_split[duplicate_dict[keyword]+1:].index(keyword)
+                    duplicate_dict[keyword] = index
+                else:
+                    continue
+                
+            index = caption_split.index(keyword) 
+            index = start_dic[index] 
+            info_array[index][0] = 1 
+
+            box = [0,0,0,0] 
+            boxes.append(list(box))
+            info_array[index][1:] = box
+    
+    boxes_length = len(boxes)
+    if boxes_length > 8:
+        boxes = boxes[:8]
+    while len(boxes) < 8:
+        boxes.append([0,0,0,0])
+
+    return caption, length_list, width_list, torch.from_numpy(info_array), words, torch.Tensor(state_list).long(), torch.Tensor(word_match_list).long(), torch.Tensor(boxes), boxes_length
+
+
+def get_layout_from_prompt(args):
+
+    # prompt = args.prompt
+    font_path = args.font_path
+    keywords = get_key_words(args.prompt)
+    
+    print(f'{colored("[!]", "red")} Detected keywords: {keywords} from prompt {args.prompt}')
+    
+    text_embedding, mask = text_encoder(args.prompt) # (1, 77 768) / (1, 77)
+
+    # process all relevant info
+    caption, length_list, width_list, target, words, state_list, word_match_list, boxes, boxes_length = process_caption(font_path, args.prompt, keywords)
+    target = target.cuda().unsqueeze(0) # (77, 5)
+    width_list = width_list.cuda().unsqueeze(0) # (77, )
+    length_list = length_list.cuda().unsqueeze(0) # (77, )
+    state_list = state_list.cuda().unsqueeze(0) # (77, )
+    word_match_list = word_match_list.cuda().unsqueeze(0) # (77, )
+
+    padding = torch.zeros(1, 1, 4).cuda()
+    boxes = boxes.unsqueeze(0).cuda()
+    right_shifted_boxes = torch.cat([padding, boxes[:,0:-1,:]],1) # (1, 8, 4)
+   
+    # inference
+    return_boxes= []
+    with torch.no_grad():
+        for box_index in range(boxes_length):
+            
+            if box_index == 0:
+                encoder_embedding = None
+                
+            output, encoder_embedding = model(text_embedding, length_list, width_list, mask, state_list, word_match_list, target, right_shifted_boxes, train=False, encoder_embedding=encoder_embedding) 
+            output = torch.clamp(output, min=0, max=1) # (1, 8, 4)
+            
+            # add overlap detection
+            output = adjust_overlap_box(output, box_index) # (1, 8, 4)
+            
+            right_shifted_boxes[:,box_index+1,:] = output[:,box_index,:]
+            xmin, ymin, xmax, ymax = output[0, box_index, :].tolist()
+            return_boxes.append([xmin, ymin, xmax, ymax])
+            
+            
+    # print the location of keywords
+    print(f'index\tkeyword\tx_min\ty_min\tx_max\ty_max')
+    for index, keyword in enumerate(keywords):
+        x_min = int(return_boxes[index][0] * 512)
+        y_min = int(return_boxes[index][1] * 512)
+        x_max = int(return_boxes[index][2] * 512)
+        y_max = int(return_boxes[index][3] * 512)
+        print(f'{index}\t{keyword}\t{x_min}\t{y_min}\t{x_max}\t{y_max}')
+    
+    
+    # paint the layout
+    render_image = Image.new('RGB', (512, 512), (255, 255, 255))
+    draw = ImageDraw.Draw(render_image)
+    segmentation_mask = Image.new("L", (512,512), 0)
+    segmentation_mask_draw = ImageDraw.Draw(segmentation_mask)
+
+    for index, box in enumerate(return_boxes):
+        box = [int(i*512) for i in box]
+        xmin, ymin, xmax, ymax = box
+        
+        width = xmax - xmin
+        height = ymax - ymin
+        text = keywords[index]
+
+        font_size = adjust_font_size(args, width, height, draw, text)
+        font = ImageFont.truetype(args.font_path, font_size)
+
+        # draw.rectangle([xmin, ymin, xmax,ymax], outline=(255,0,0))
+        draw.text((xmin, ymin), text, font=font, fill=(0, 0, 0))
+            
+        boxes = []
+        for i, char in enumerate(text):
+            
+            # paint character-level segmentation masks
+            # https://github.com/python-pillow/Pillow/issues/3921
+            bottom_1 = font.getsize(text[i])[1]
+            right, bottom_2 = font.getsize(text[:i+1])
+            bottom = bottom_1 if bottom_1 < bottom_2 else bottom_2
+            width, height = font.getmask(char).size
+            right += xmin
+            bottom += ymin
+            top = bottom - height
+            left = right - width
+            
+            char_box = (left, top, right, bottom)
+            boxes.append(char_box)
+            
+            char_index = alphabet_dic[char]
+            segmentation_mask_draw.rectangle(shrink_box(char_box, scale_factor = 0.9), fill=char_index)
+    
+    print(f'{colored("[√]", "green")} Layout is successfully generated')
+    return render_image, segmentation_mask

model/layout_transformer.py

@@ -0,0 +1,107 @@
+# ------------------------------------------
+# TextDiffuser: Diffusion Models as Text Painters
+# Paper Link: https://arxiv.org/abs/2305.10855
+# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
+# Copyright (c) Microsoft Corporation.
+# This file define the Layout Transformer for predicting the layout of keywords.
+# ------------------------------------------
+
+import torch
+import torch.nn as nn 
+from transformers import CLIPTokenizer, CLIPTextModel
+
+class TextConditioner(nn.Module):
+    
+    def __init__(self):
+        super(TextConditioner, self).__init__()
+        self.transformer = CLIPTextModel.from_pretrained('openai/clip-vit-large-patch14')
+        self.tokenizer = CLIPTokenizer.from_pretrained('openai/clip-vit-large-patch14')
+        
+        # fix
+        self.transformer.eval()
+        for param in self.transformer.parameters():
+            param.requires_grad = False
+        
+    def forward(self, prompt_list):
+        batch_encoding = self.tokenizer(prompt_list, truncation=True, max_length=77, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+        text_embedding = self.transformer(batch_encoding["input_ids"].cuda())
+        return text_embedding.last_hidden_state.cuda(), batch_encoding["attention_mask"].cuda() # 1, 77, 768  /  1, 768
+
+
+class LayoutTransformer(nn.Module): 
+    
+    def __init__(self, layer_number=2):
+        super(LayoutTransformer, self).__init__()
+        
+        self.encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
+        self.transformer = torch.nn.TransformerEncoder(
+            self.encoder_layer, num_layers=layer_number 
+        )
+        
+        self.decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
+        self.decoder_transformer = torch.nn.TransformerDecoder(
+            self.decoder_layer, num_layers=layer_number 
+        )
+
+        self.mask_embedding = nn.Embedding(2,512)
+        self.length_embedding = nn.Embedding(256,512)
+        self.width_embedding = nn.Embedding(256,512)
+        self.position_embedding = nn.Embedding(256,512)
+        self.state_embedding = nn.Embedding(256,512)
+        self.match_embedding = nn.Embedding(256,512)
+        
+        self.x_embedding = nn.Embedding(512,512)
+        self.y_embedding = nn.Embedding(512,512)
+        self.w_embedding = nn.Embedding(512,512)
+        self.h_embedding = nn.Embedding(512,512)
+        
+        self.encoder_target_embedding = nn.Embedding(256,512)
+
+        self.input_layer = nn.Sequential(
+            nn.Linear(768, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+        )
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(512, 128),
+            nn.ReLU(),
+            nn.Linear(128, 4),
+        )
+
+    def forward(self, x, length, width, mask, state, match, target, right_shifted_boxes, train=False, encoder_embedding=None):
+        
+        # detect whether the encoder_embedding is cached
+        if encoder_embedding is None:
+            # augmentation
+            if train:
+                width = width + torch.randint(-3, 3, (width.shape[0], width.shape[1])).cuda()
+
+            x = self.input_layer(x) # (1, 77, 512)    
+            width_embedding = self.width_embedding(torch.clamp(width, 0, 255).long()) # (1, 77, 512)    
+            encoder_target_embedding = self.encoder_target_embedding(target[:,:,0].long()) # (1, 77, 512)    
+            pe_embedding = self.position_embedding(torch.arange(77).cuda()).unsqueeze(0) # (1, 77, 512)    
+            total_embedding = x + width_embedding + pe_embedding + encoder_target_embedding # combine all the embeddings (1, 77, 512)    
+            total_embedding = total_embedding.permute(1,0,2) # (77, 1, 512)     
+            encoder_embedding = self.transformer(total_embedding) # (77, 1, 512)    
+      
+        right_shifted_boxes_resize = (right_shifted_boxes * 512).long() # (1, 8, 4)
+        right_shifted_boxes_resize = torch.clamp(right_shifted_boxes_resize, 0, 511) # (1, 8, 4)
+        
+        # decoder pe
+        pe_decoder = torch.arange(8).cuda() # (8, )
+        pe_embedding_decoder = self.position_embedding(pe_decoder).unsqueeze(0) # (1, 8, 512)
+        decoder_input = pe_embedding_decoder + self.x_embedding(right_shifted_boxes_resize[:,:,0]) + self.y_embedding(right_shifted_boxes_resize[:,:,1]) + self.w_embedding(right_shifted_boxes_resize[:,:,2]) + self.h_embedding(right_shifted_boxes_resize[:,:,3]) # (1, 8, 512)
+        decoder_input = decoder_input.permute(1,0,2) # (8, 1, 512)
+        
+        # generate triangular mask
+        mask = nn.Transformer.generate_square_subsequent_mask(8) # (8, 8)
+        mask = mask.cuda() # (8, 8)
+        decoder_result = self.decoder_transformer(decoder_input, encoder_embedding, tgt_mask=mask) # (8, 1, 512)
+        decoder_result = decoder_result.permute(1,0,2) # (1, 8, 512)
+        
+        box_prediction = self.output_layer(decoder_result) # (1, 8, 4)
+        return box_prediction, encoder_embedding
+
+
+    

model/text_segmenter/unet.py

@@ -0,0 +1,53 @@
+# ------------------------------------------
+# TextDiffuser: Diffusion Models as Text Painters
+# Paper Link: https://arxiv.org/abs/2305.10855
+# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
+# Copyright (c) Microsoft Corporation.
+# This file define the architecture of unet.
+# ------------------------------------------
+
+import torch.nn.functional as F
+from model.text_segmenter.unet_parts import *
+
+
+class UNet(nn.Module):
+    def __init__(self, n_channels, n_classes, bilinear=True):
+        super(UNet, self).__init__()
+        self.n_channels = n_channels
+        self.n_classes = n_classes
+        self.bilinear = bilinear
+
+        self.inc = DoubleConv(n_channels, 64)
+        self.down1 = Down(64, 128)
+        self.down2 = Down(128, 256)
+        self.down3 = Down(256, 512)
+        factor = 2 if bilinear else 1
+        self.down4 = Down(512, 1024 // factor)
+        self.up1 = Up(1024, 512 // factor, bilinear)
+        self.up2 = Up(512, 256 // factor, bilinear)
+        self.up3 = Up(256, 128 // factor, bilinear)
+        self.up4 = Up(128, 64, bilinear)
+        self.outc = OutConv(64, n_classes)
+
+    def forward(self, x):
+        x1 = self.inc(x)
+        x2 = self.down1(x1)
+        x3 = self.down2(x2)
+        x4 = self.down3(x3)
+        x5 = self.down4(x4)
+        x = self.up1(x5, x4)
+        x = self.up2(x, x3)
+        x = self.up3(x, x2)
+        x = self.up4(x, x1)
+        logits = self.outc(x)
+        # logits = torch.sigmoid(logits)
+        return logits
+
+if __name__ == '__main__':
+    net = UNet(39,39,True)
+
+    net = net.cuda()
+
+    image = torch.Tensor(32,39,64,64).cuda()
+    result = net(image)
+    print(result.shape)

model/text_segmenter/unet_parts.py

@@ -0,0 +1,82 @@
+# ------------------------------------------
+# TextDiffuser: Diffusion Models as Text Painters
+# Paper Link: https://arxiv.org/abs/2305.10855
+# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
+# Copyright (c) Microsoft Corporation.
+# This file define the architecture of unet.
+# ------------------------------------------
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DoubleConv(nn.Module):
+    """(convolution => [BN] => ReLU) * 2"""
+
+    def __init__(self, in_channels, out_channels, mid_channels=None):
+        super().__init__()
+        if not mid_channels:
+            mid_channels = out_channels
+        self.double_conv = nn.Sequential(
+            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(mid_channels),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        return self.double_conv(x)
+
+
+class Down(nn.Module):
+    """Downscaling with maxpool then double conv"""
+
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.maxpool_conv = nn.Sequential(
+            nn.MaxPool2d(2),
+            DoubleConv(in_channels, out_channels)
+        )
+
+    def forward(self, x):
+        return self.maxpool_conv(x)
+
+
+class Up(nn.Module):
+    """Upscaling then double conv"""
+
+    def __init__(self, in_channels, out_channels, bilinear=True):
+        super().__init__()
+
+        # if bilinear, use the normal convolutions to reduce the number of channels
+        if bilinear:
+            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
+            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
+        else:
+            self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
+            self.conv = DoubleConv(in_channels, out_channels)
+
+
+    def forward(self, x1, x2):
+        x1 = self.up(x1)
+        # input is CHW
+        diffY = x2.size()[2] - x1.size()[2]
+        diffX = x2.size()[3] - x1.size()[3]
+
+        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
+                        diffY // 2, diffY - diffY // 2])
+
+        x = torch.cat([x2, x1], dim=1)
+        return self.conv(x)
+
+
+class OutConv(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(OutConv, self).__init__()
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
+
+    def forward(self, x):
+        return self.conv(x)

requirements.txt

@@ -0,0 +1,13 @@
+setuptools==66.0.0
+datasets==2.11.0
+numpy==1.24.2
+tokenizers==0.13.3
+transformers==4.27.4
+xformers==0.0.16
+accelerate==0.18.0
+triton==2.0.0.post1
+termcolor==2.3.0
+tinydb
+flask
+-e git+https://github.com/JingyeChen/diffusers.git#egg=diffusers
+opencv-python

test.py

@@ -0,0 +1,5 @@
+import os
+
+os.system('wget https://layoutlm.blob.core.windows.net/textdiffuser/textdiffuser-ckpt.zip')
+os.system('unzip textdiffuser-ckpt.zip')
+os.system('finish')

test/app.py

@@ -0,0 +1,55 @@
+import numpy as np
+import gradio as gr
+
+
+def flip_text(x):
+    return x[::-1]
+
+
+def flip_image(x):
+    return np.fliplr(x)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("TextDiffuser.")
+    with gr.Tab("Text-to-Image"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                text_input = gr.Textbox(label='Input your prompt here. Please enclose keywords with single quotes.')
+                slider_step = gr.Slider(minimum=1, maximum=1000, value=50, label="Sample Step", info="The sampling step for TextDiffuser ranging from [1,1000].")
+                slider_batch = gr.Slider(minimum=1, maximum=4, value=2, label="Sample Size", info="Number of samples generated from TextDiffuser.")
+                slider_seed = gr.Slider(minimum=1, maximum=10000, label="Seed", info="The random seed for sampling.", randomize=True)
+                button = gr.Button("Generate")
+            with gr.Column(scale=1):
+                output = gr.Image()
+        button.click(flip_text, inputs=text_input, outputs=output)
+        
+    with gr.Tab("Text-to-Image-with-Template"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                text_input = gr.Textbox(label='Input your prompt here.')
+                template_image = gr.Image(label='Template image')
+                slider_step = gr.Slider(minimum=1, maximum=1000, value=50, label="Sample Step", info="The sampling step for TextDiffuser ranging from [1,1000].")
+                slider_batch = gr.Slider(minimum=1, maximum=4, value=2, label="Sample Size", info="Number of samples generated from TextDiffuser.")
+                slider_seed = gr.Slider(minimum=1, maximum=10000, label="Seed", info="The random seed for sampling.", randomize=True)
+                button = gr.Button("Generate")
+            with gr.Column(scale=1):
+                output = gr.Image()
+        button.click(flip_text, inputs=text_input, outputs=output)
+        
+    with gr.Tab("Text-Inpainting"):
+        with gr.Row():
+            with gr.Column(scale=1):
+                text_input = gr.Textbox(label='Input your prompt here.')
+                with gr.Row():
+                    orig_image = gr.Image(label='Original image')
+                    mask_image = gr.Image(label='Mask image')
+                slider_step = gr.Slider(minimum=1, maximum=1000, value=50, label="Sample Step", info="The sampling step for TextDiffuser ranging from [1,1000].")
+                slider_batch = gr.Slider(minimum=1, maximum=4, value=2, label="Sample Size", info="Number of samples generated from TextDiffuser.")
+                slider_seed = gr.Slider(minimum=1, maximum=10000, label="Seed", info="The random seed for sampling.", randomize=True)
+                button = gr.Button("Generate")
+            with gr.Column(scale=1):
+                output = gr.Image()
+        button.click(flip_text, inputs=text_input, outputs=output)
+
+demo.launch()

text-inpainting.sh

@@ -0,0 +1,8 @@
+CUDA_VISIBLE_DEVICES=0 python inference.py \
+  --mode="text-inpainting" \
+  --resume_from_checkpoint="textdiffuser-ckpt/diffusion_backbone" \
+  --prompt="a boy draws good morning on a board" \
+  --original_image="assets/examples/text-inpainting/case2.jpg" \
+  --text_mask="assets/examples/text-inpainting/case2_mask.jpg" \
+  --output_dir="./output" \
+  --vis_num=4

text-to-image-with-template.sh

@@ -0,0 +1,7 @@
+CUDA_VISIBLE_DEVICES=0 python inference.py \
+  --mode="text-to-image-with-template" \
+  --resume_from_checkpoint="textdiffuser-ckpt/diffusion_backbone" \
+  --prompt="a poster of monkey music festival" \
+  --template_image="assets/examples/text-to-image-with-template/case2.jpg" \
+  --output_dir="./output" \
+  --vis_num=4

text-to-image.sh

@@ -0,0 +1,6 @@
+CUDA_VISIBLE_DEVICES=0 python inference.py \
+  --mode="text-to-image" \
+  --resume_from_checkpoint="textdiffuser-ckpt/diffusion_backbone" \
+  --prompt="A sign that says 'Hello'" \
+  --output_dir="./output" \
+  --vis_num=4

util.py

@@ -0,0 +1,306 @@
+# ------------------------------------------
+# TextDiffuser: Diffusion Models as Text Painters
+# Paper Link: https://arxiv.org/abs/2305.10855
+# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser
+# Copyright (c) Microsoft Corporation.
+# This file defines a set of commonly used utility functions.
+# ------------------------------------------
+
+import os
+import re
+import cv2
+import math
+import shutil
+import string
+import textwrap
+import numpy as np
+from PIL import Image, ImageFont, ImageDraw, ImageOps
+
+from typing import *
+
+# define alphabet and alphabet_dic
+alphabet = string.digits + string.ascii_lowercase + string.ascii_uppercase + string.punctuation + ' ' # len(aphabet) = 95
+alphabet_dic = {}
+for index, c in enumerate(alphabet):
+    alphabet_dic[c] = index + 1 # the index 0 stands for non-character
+    
+
+
+def transform_mask_pil(mask_root):
+    """
+    This function extracts the mask area and text area from the images.
+    
+    Args:
+        mask_root (str): The path of mask image.
+            * The white area is the unmasked area
+            * The gray area is the masked area
+            * The white area is the text area
+    """
+    img = np.array(mask_root)
+    img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_NEAREST)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    ret, binary = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY) # pixel value is set to 0 or 255 according to the threshold
+    return 1 - (binary.astype(np.float32) / 255) 
+
+
+def transform_mask(mask_root: str):
+    """
+    This function extracts the mask area and text area from the images.
+    
+    Args:
+        mask_root (str): The path of mask image.
+            * The white area is the unmasked area
+            * The gray area is the masked area
+            * The white area is the text area
+    """
+    img = cv2.imread(mask_root)
+    img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_NEAREST)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    ret, binary = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY) # pixel value is set to 0 or 255 according to the threshold
+    return 1 - (binary.astype(np.float32) / 255) 
+
+
+def segmentation_mask_visualization(font_path: str, segmentation_mask: np.array):
+    """
+    This function visualizes the segmentaiton masks with characters.
+    
+    Args:
+        font_path (str): The path of font. We recommand to use Arial.ttf
+        segmentation_mask (np.array): The character-level segmentation mask.
+    """
+    segmentation_mask = cv2.resize(segmentation_mask, (64, 64), interpolation=cv2.INTER_NEAREST)
+    font = ImageFont.truetype(font_path, 8)
+    blank = Image.new('RGB', (512,512), (0,0,0))
+    d = ImageDraw.Draw(blank)
+    for i in range(64):
+        for j in range(64):
+            if int(segmentation_mask[i][j]) == 0 or int(segmentation_mask[i][j])-1 >= len(alphabet): 
+                continue
+            else:
+                d.text((j*8, i*8), alphabet[int(segmentation_mask[i][j])-1], font=font, fill=(0, 255, 0))
+    return blank
+
+
+def make_caption_pil(font_path: str, captions: List[str]):
+    """
+    This function converts captions into pil images.
+    
+    Args:
+        font_path (str): The path of font. We recommand to use Arial.ttf
+        captions (List[str]): List of captions.
+    """
+    caption_pil_list = []
+    font = ImageFont.truetype(font_path, 18)
+
+    for caption in captions:
+        border_size = 2
+        img = Image.new('RGB', (512-4,48-4), (255,255,255)) 
+        img = ImageOps.expand(img, border=(border_size, border_size, border_size, border_size), fill=(127, 127, 127))
+        draw = ImageDraw.Draw(img)
+        border_size = 2
+        text = caption
+        lines = textwrap.wrap(text, width=40)
+        x, y = 4, 4
+        line_height = font.getsize('A')[1] + 4 
+
+        start = 0
+        for line in lines:
+            draw.text((x, y+start), line, font=font, fill=(200, 127, 0))
+            y += line_height
+
+        caption_pil_list.append(img)
+    return caption_pil_list
+
+
+def filter_segmentation_mask(segmentation_mask: np.array):
+    """
+    This function removes some noisy predictions of segmentation masks.
+    
+    Args:
+        segmentation_mask (np.array): The character-level segmentation mask.
+    """
+    segmentation_mask[segmentation_mask==alphabet_dic['-']] = 0
+    segmentation_mask[segmentation_mask==alphabet_dic[' ']] = 0
+    return segmentation_mask
+    
+    
+
+def combine_image(args, sub_output_dir: str, pred_image_list: List, image_pil: Image, character_mask_pil: Image, character_mask_highlight_pil: Image, caption_pil_list: List):
+    """
+    This function combines all the outputs and useful inputs together.
+    
+    Args:
+        args (argparse.ArgumentParser): The arguments.
+        pred_image_list (List): List of predicted images.
+        image_pil (Image): The original image.
+        character_mask_pil (Image): The character-level segmentation mask.
+        character_mask_highlight_pil (Image): The character-level segmentation mask highlighting character regions with green color.
+        caption_pil_list (List): List of captions.
+    """
+    
+    size = len(pred_image_list)
+    
+    if size == 1:
+        return pred_image_list[0]
+    elif size == 2:
+        blank = Image.new('RGB', (512*2, 512), (0,0,0))
+        blank.paste(pred_image_list[0],(0,0))
+        blank.paste(pred_image_list[1],(512,0))
+    elif size == 3:
+        blank = Image.new('RGB', (512*3, 512), (0,0,0))
+        blank.paste(pred_image_list[0],(0,0))
+        blank.paste(pred_image_list[1],(512,0))
+        blank.paste(pred_image_list[2],(1024,0))
+    elif size == 4:
+        blank = Image.new('RGB', (512*2, 512*2), (0,0,0))
+        blank.paste(pred_image_list[0],(0,0))
+        blank.paste(pred_image_list[1],(512,0))
+        blank.paste(pred_image_list[2],(0,512))
+        blank.paste(pred_image_list[3],(512,512))
+
+    
+    return blank
+    
+def get_width(font_path, text):
+    """
+    This function calculates the width of the text.
+    
+    Args:
+        font_path (str): user prompt.
+        text (str): user prompt.
+    """
+    font = ImageFont.truetype(font_path, 24)
+    width, _ = font.getsize(text)
+    return width
+
+
+
+def get_key_words(text: str):
+    """
+    This function detect keywords (enclosed by quotes) from user prompts. The keywords are used to guide the layout generation.
+    
+    Args:
+        text (str): user prompt.
+    """
+
+    words = []
+    text = text
+    matches = re.findall(r"'(.*?)'", text) # find the keywords enclosed by ''
+    if matches:
+        for match in matches:
+            words.extend(match.split())
+            
+    if len(words) >= 8:
+        return []
+   
+    return words
+
+
+def adjust_overlap_box(box_output, current_index):
+    """
+    This function adjust the overlapping boxes.
+    
+    Args:
+        box_output (List): List of predicted boxes.
+        current_index (int): the index of current box.
+    """
+    
+    if current_index == 0:
+        return box_output
+    else:
+        # judge whether it contains overlap with the last output
+        last_box = box_output[0, current_index-1, :]
+        xmin_last, ymin_last, xmax_last, ymax_last = last_box
+        
+        current_box = box_output[0, current_index, :]
+        xmin, ymin, xmax, ymax = current_box
+        
+        if xmin_last <= xmin <= xmax_last and ymin_last <= ymin <= ymax_last:
+            print('adjust overlapping')
+            distance_x = xmax_last - xmin
+            distance_y = ymax_last - ymin
+            if distance_x <= distance_y:
+                # avoid overlap
+                new_x_min = xmax_last + 0.025
+                new_x_max = xmax - xmin + xmax_last + 0.025
+                box_output[0,current_index,0] = new_x_min
+                box_output[0,current_index,2] = new_x_max
+            else:
+                new_y_min = ymax_last + 0.025
+                new_y_max = ymax - ymin + ymax_last + 0.025
+                box_output[0,current_index,1] = new_y_min
+                box_output[0,current_index,3] = new_y_max  
+                
+        elif xmin_last <= xmin <= xmax_last and ymin_last <= ymax <= ymax_last:
+            print('adjust overlapping')
+            new_x_min = xmax_last + 0.05
+            new_x_max = xmax - xmin + xmax_last + 0.05
+            box_output[0,current_index,0] = new_x_min
+            box_output[0,current_index,2] = new_x_max
+                    
+        return box_output
+    
+    
+def shrink_box(box, scale_factor = 0.9):
+    """
+    This function shrinks the box.
+    
+    Args:
+        box (List): List of predicted boxes.
+        scale_factor (float): The scale factor of shrinking.
+    """
+    
+    x1, y1, x2, y2 = box
+    x1_new = x1 + (x2 - x1) * (1 - scale_factor) / 2
+    y1_new = y1 + (y2 - y1) * (1 - scale_factor) / 2
+    x2_new = x2 - (x2 - x1) * (1 - scale_factor) / 2
+    y2_new = y2 - (y2 - y1) * (1 - scale_factor) / 2
+    return (x1_new, y1_new, x2_new, y2_new)
+
+
+def adjust_font_size(args, width, height, draw, text):
+    """
+    This function adjusts the font size.
+    
+    Args:
+        args (argparse.ArgumentParser): The arguments.
+        width (int): The width of the text.
+        height (int): The height of the text.
+        draw (ImageDraw): The ImageDraw object.
+        text (str): The text.
+    """
+    
+    size_start = height
+    while True:
+        font = ImageFont.truetype(args.font_path, size_start)
+        text_width, _ = draw.textsize(text, font=font)
+        if text_width >= width:
+            size_start = size_start - 1
+        else:
+            return size_start
+    
+    
+def inpainting_merge_image(original_image, mask_image, inpainting_image):
+    """
+    This function merges the original image, mask image and inpainting image.
+        
+    Args:
+        original_image (PIL.Image): The original image.
+        mask_image (PIL.Image): The mask images.
+        inpainting_image (PIL.Image): The inpainting images.
+    """
+    
+    original_image = original_image.resize((512, 512))
+    mask_image = mask_image.resize((512, 512))
+    inpainting_image = inpainting_image.resize((512, 512))
+    mask_image.convert('L')
+    threshold = 250 
+    table = []
+    for i in range(256):
+        if i < threshold:
+            table.append(1)
+        else:
+            table.append(0)
+    mask_image = mask_image.point(table, "1")
+    merged_image = Image.composite(inpainting_image, original_image, mask_image)
+    return merged_image