launch/story_generator.py

import gradio as gr
from examples.story_examples import get_examples
import spaces
import numpy as np
import torch
import random
import os
import torch.nn.functional as F
from diffusers import StableDiffusionXLPipeline, DDIMScheduler
import copy
from huggingface_hub import hf_hub_download
from diffusers.utils import load_image

from storyDiffusion.utils.gradio_utils import AttnProcessor2_0 as AttnProcessor, cal_attn_mask_xl
from storyDiffusion.utils import PhotoMakerStableDiffusionXLPipeline
from storyDiffusion.utils.utils import get_comic
from storyDiffusion.utils.style_template import styles


# Constants
image_encoder_path = "./data/models/ip_adapter/sdxl_models/image_encoder"
ip_ckpt = "./data/models/ip_adapter/sdxl_models/ip-adapter_sdxl_vit-h.bin"
os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "Japanese Anime"
MAX_SEED = np.iinfo(np.int32).max

# Global variables
global models_dict, use_va, photomaker_path, pipe2, pipe4, attn_count, total_count, id_length, total_length, cur_step, cur_model_type, write, sa32, sa64, height, width, attn_procs, unet, num_steps

models_dict = {
    "RealVision": "SG161222/RealVisXL_V4.0",
    "Unstable": "stablediffusionapi/sdxl-unstable-diffusers-y"
}
use_va = True
photomaker_path = hf_hub_download(
    repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model")
device = "cuda"

# Functions


def setup_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.backends.cudnn.deterministic = True


def set_text_unfinished():
    return gr.update(visible=True, value="<h3>(Not Finished) Generating ···  The intermediate results will be shown.</h3>")


def set_text_finished():
    return gr.update(visible=True, value="<h3>Generation Finished</h3>")


class SpatialAttnProcessor2_0(torch.nn.Module):
    r"""
    Attention processor for IP-Adapater for PyTorch 2.0.
    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        text_context_len (`int`, defaults to 77):
            The context length of the text features.
        scale (`float`, defaults to 1.0):
            the weight scale of image prompt.
    """

    def __init__(self, hidden_size=None, cross_attention_dim=None, id_length=4, device="cuda", dtype=torch.float16):
        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
        self.device = device
        self.dtype = dtype
        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.total_length = id_length + 1
        self.id_length = id_length
        self.id_bank = {}

    def __call__(
            self,
            attn,
            hidden_states,
            encoder_hidden_states=None,
            attention_mask=None,
            temb=None):
        # un_cond_hidden_states, cond_hidden_states = hidden_states.chunk(2)
        # un_cond_hidden_states = self.__call2__(attn, un_cond_hidden_states,encoder_hidden_states,attention_mask,temb)
        # 生成一个0到1之间的随机数
        global total_count, attn_count, cur_step, mask1024, mask4096
        global sa32, sa64
        global write
        global height, width
        global num_steps
        if write:
            # print(f"white:{cur_step}")
            self.id_bank[cur_step] = [
                hidden_states[:self.id_length], hidden_states[self.id_length:]]
        else:
            encoder_hidden_states = torch.cat((self.id_bank[cur_step][0].to(
                self.device), hidden_states[:1], self.id_bank[cur_step][1].to(self.device), hidden_states[1:]))
        # 判断随机数是否大于0.5
        if cur_step <= 1:
            hidden_states = self.__call2__(
                attn, hidden_states, None, attention_mask, temb)
        else:   # 256 1024 4096
            random_number = random.random()
            if cur_step < 0.4 * num_steps:
                rand_num = 0.3
            else:
                rand_num = 0.1
            # print(f"hidden state shape {hidden_states.shape[1]}")
            if random_number > rand_num:
                # print("mask shape",mask1024.shape,mask4096.shape)
                if not write:
                    if hidden_states.shape[1] == (height//32) * (width//32):
                        attention_mask = mask1024[mask1024.shape[0] //
                                                  self.total_length * self.id_length:]
                    else:
                        attention_mask = mask4096[mask4096.shape[0] //
                                                  self.total_length * self.id_length:]
                else:
                    # print(self.total_length,self.id_length,hidden_states.shape,(height//32) * (width//32))
                    if hidden_states.shape[1] == (height//32) * (width//32):
                        attention_mask = mask1024[:mask1024.shape[0] // self.total_length *
                                                  self.id_length, :mask1024.shape[0] // self.total_length * self.id_length]
                    else:
                        attention_mask = mask4096[:mask4096.shape[0] // self.total_length *
                                                  self.id_length, :mask4096.shape[0] // self.total_length * self.id_length]
                   # print(attention_mask.shape)
                # print("before attention",hidden_states.shape,attention_mask.shape,encoder_hidden_states.shape if encoder_hidden_states is not None else "None")
                hidden_states = self.__call1__(
                    attn, hidden_states, encoder_hidden_states, attention_mask, temb)
            else:
                hidden_states = self.__call2__(
                    attn, hidden_states, None, attention_mask, temb)
        attn_count += 1
        if attn_count == total_count:
            attn_count = 0
            cur_step += 1
            mask1024, mask4096 = cal_attn_mask_xl(
                self.total_length, self.id_length, sa32, sa64, height, width, device=self.device, dtype=self.dtype)

        return hidden_states

    def __call1__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
    ):
        # print("hidden state shape",hidden_states.shape,self.id_length)
        residual = hidden_states
        # if encoder_hidden_states is not None:
        #     raise Exception("not implement")
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)
        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            total_batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(
                total_batch_size, channel, height * width).transpose(1, 2)
        total_batch_size, nums_token, channel = hidden_states.shape
        img_nums = total_batch_size//2
        hidden_states = hidden_states.view(-1, img_nums, nums_token,
                                           channel).reshape(-1, img_nums * nums_token, channel)

        batch_size, sequence_length, _ = hidden_states.shape

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(
                hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states  # B, N, C
        else:
            encoder_hidden_states = encoder_hidden_states.view(
                -1, self.id_length+1, nums_token, channel).reshape(-1, (self.id_length+1) * nums_token, channel)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads,
                           head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads,
                           head_dim).transpose(1, 2)
        # print(key.shape,value.shape,query.shape,attention_mask.shape)
        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        # print(query.shape,key.shape,value.shape,attention_mask.shape)
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(
            total_batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        # if input_ndim == 4:
        #     tile_hidden_states = tile_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        # if attn.residual_connection:
        #     tile_hidden_states = tile_hidden_states + residual

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(
                total_batch_size, channel, height, width)
        if attn.residual_connection:
            hidden_states = hidden_states + residual
        hidden_states = hidden_states / attn.rescale_output_factor
        # print(hidden_states.shape)
        return hidden_states

    def __call2__(
            self,
            attn,
            hidden_states,
            encoder_hidden_states=None,
            attention_mask=None,
            temb=None):
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(
                batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, channel = (
            hidden_states.shape
        )
        # print(hidden_states.shape)
        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(
                attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(
                batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(
                hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states  # B, N, C
        else:
            encoder_hidden_states = encoder_hidden_states.view(
                -1, self.id_length+1, sequence_length, channel).reshape(-1, (self.id_length+1) * sequence_length, channel)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads,
                           head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads,
                           head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(
            batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(
                -1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


def set_attention_processor(unet, id_length, is_ipadapter=False):
    global total_count
    total_count = 0
    attn_procs = {}
    for name in unet.attn_processors.keys():
        cross_attention_dim = None if name.endswith(
            "attn1.processor") else unet.config.cross_attention_dim
        if cross_attention_dim is None:
            if name.startswith("up_blocks"):
                attn_procs[name] = SpatialAttnProcessor2_0(id_length=id_length)
                total_count += 1
            else:
                attn_procs[name] = AttnProcessor()
        else:
            attn_procs[name] = AttnProcessor()

    unet.set_attn_processor(copy.deepcopy(attn_procs))
    print("Successfully loaded paired self-attention")
    print(f"Number of processors: {total_count}")


attn_count = 0
total_count = 0
cur_step = 0
id_length = 4
total_length = 5
cur_model_type = ""
device = "cuda"
attn_procs = {}
write = False
sa32 = 0.5
sa64 = 0.5
height = 768
width = 768


def swap_to_gallery(images):
    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)


def upload_example_to_gallery(images, prompt, style, negative_prompt):
    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)


def remove_back_to_files():
    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)


def remove_tips():
    return gr.update(visible=False)


def apply_style_positive(style_name: str, positive: str):
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    return p.replace("{prompt}", positive)


def apply_style(style_name: str, positives: list, negative: str = ""):
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    return [p.replace("{prompt}", positive) for positive in positives], n + ' ' + negative


def change_visiale_by_model_type(_model_type):
    if _model_type == "Only Using Textual Description":
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
    elif _model_type == "Using Ref Images":
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
    else:
        raise ValueError("Invalid model type", _model_type)


@spaces.GPU(duration=120)
def process_generation(_sd_type, _model_type, _upload_images, _num_steps, style_name, _Ip_Adapter_Strength, _style_strength_ratio, guidance_scale, seed_, sa32_, sa64_, id_length_, general_prompt, negative_prompt, prompt_array, G_height, G_width, _comic_type):
    global sa32, sa64, id_length, total_length, attn_procs, unet, cur_model_type, device, num_steps, write, cur_step, attn_count, height, width, pipe2, pipe4, sd_model_path, models_dict

    _model_type = "Photomaker" if _model_type == "Using Ref Images" else "original"
    if _model_type == "Photomaker" and "img" not in general_prompt:
        raise gr.Error(
            "Please add the trigger word 'img' behind the class word you want to customize, such as: man img or woman img")
    if _upload_images is None and _model_type != "original":
        raise gr.Error("Cannot find any input face image!")
    if len(prompt_array.splitlines()) > 10:
        raise gr.Error(
            f"No more than 10 prompts in Hugging Face demo for speed! But found {len(prompt_array.splitlines())} prompts!")

    height = G_height
    width = G_width
    sd_model_path = models_dict[_sd_type]
    num_steps = _num_steps

    if style_name == "(No style)":
        sd_model_path = models_dict["RealVision"]

    if _model_type == "original":
        pipe = StableDiffusionXLPipeline.from_pretrained(
            sd_model_path, torch_dtype=torch.float16)
        pipe = pipe.to(device)
        pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
        set_attention_processor(pipe.unet, id_length_, is_ipadapter=False)
    elif _model_type == "Photomaker":
        if _sd_type != "RealVision" and style_name != "(No style)":
            pipe = pipe2.to(device)
            pipe.id_encoder.to(device)
            set_attention_processor(pipe.unet, id_length_, is_ipadapter=False)
        else:
            pipe = pipe4.to(device)
            pipe.id_encoder.to(device)
            set_attention_processor(pipe.unet, id_length_, is_ipadapter=False)
    else:
        raise NotImplementedError(
            "You should choose between original and Photomaker!", f"But you chose {_model_type}")

    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
    pipe.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
    cur_model_type = _sd_type + "-" + _model_type + str(id_length_)

    if _model_type != "original":
        input_id_images = [load_image(img) for img in _upload_images]

    prompts = prompt_array.splitlines()
    start_merge_step = int(float(_style_strength_ratio) / 100 * _num_steps)
    if start_merge_step > 30:
        start_merge_step = 30
    print(f"start_merge_step: {start_merge_step}")

    generator = torch.Generator(device="cuda").manual_seed(seed_)
    sa32, sa64 = sa32_, sa64_
    id_length = id_length_
    clipped_prompts = prompts[:]
    prompts = [general_prompt + "," + prompt if "[NC]" not in prompt else prompt.replace(
        "[NC]", "") for prompt in clipped_prompts]
    prompts = [prompt.rpartition(
        '#')[0] if "#" in prompt else prompt for prompt in prompts]
    print(prompts)

    id_prompts = prompts[:id_length]
    real_prompts = prompts[id_length:]
    torch.cuda.empty_cache()
    write = True
    cur_step = 0

    attn_count = 0
    id_prompts, negative_prompt = apply_style(
        style_name, id_prompts, negative_prompt)
    setup_seed(seed_)
    total_results = []

    if _model_type == "original":
        id_images = pipe(id_prompts, num_inference_steps=_num_steps, guidance_scale=guidance_scale,
                         height=height, width=width, negative_prompt=negative_prompt, generator=generator).images
    elif _model_type == "Photomaker":
        id_images = pipe(id_prompts, input_id_images=input_id_images, num_inference_steps=_num_steps, guidance_scale=guidance_scale,
                         start_merge_step=start_merge_step, height=height, width=width, negative_prompt=negative_prompt, generator=generator).images
    else:
        raise NotImplementedError(
            "You should choose between original and Photomaker!", f"But you chose {_model_type}")

    total_results = id_images + total_results
    yield total_results

    real_images = []
    write = False
    for real_prompt in real_prompts:
        setup_seed(seed_)
        cur_step = 0
        real_prompt = apply_style_positive(style_name, real_prompt)
        if _model_type == "original":
            real_images.append(pipe(real_prompt, num_inference_steps=_num_steps, guidance_scale=guidance_scale,
                               height=height, width=width, negative_prompt=negative_prompt, generator=generator).images[0])
        elif _model_type == "Photomaker":
            real_images.append(pipe(real_prompt, input_id_images=input_id_images, num_inference_steps=_num_steps, guidance_scale=guidance_scale,
                               start_merge_step=start_merge_step, height=height, width=width, negative_prompt=negative_prompt, generator=generator).images[0])
        else:
            raise NotImplementedError(
                "You should choose between original and Photomaker!", f"But you chose {_model_type}")
        total_results = [real_images[-1]] + total_results
        yield total_results

    if _comic_type != "No typesetting (default)":
        from PIL import ImageFont
        captions = prompt_array.splitlines()
        captions = [caption.replace("[NC]", "") for caption in captions]
        captions = [caption.split(
            '#')[-1] if "#" in caption else caption for caption in captions]
        total_results = get_comic(id_images + real_images, _comic_type, captions=captions,
                                  font=ImageFont.truetype("./storyDiffusion/fonts/Inkfree.ttf", int(45))) + total_results

    if _model_type == "Photomaker":
        pipe = pipe2.to("cpu")
        pipe.id_encoder.to("cpu")
        set_attention_processor(pipe.unet, id_length_, is_ipadapter=False)

    yield total_results


# Initialize pipelines
pipe2 = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
    models_dict["Unstable"], torch_dtype=torch.float16, use_safetensors=False)
pipe2 = pipe2.to("cpu")
pipe2.load_photomaker_adapter(
    os.path.dirname(photomaker_path),
    subfolder="",
    weight_name=os.path.basename(photomaker_path),
    trigger_word="img"
)
pipe2 = pipe2.to("cpu")
pipe2.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
pipe2.fuse_lora()

pipe4 = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
    models_dict["RealVision"], torch_dtype=torch.float16, use_safetensors=True)
pipe4 = pipe4.to("cpu")
pipe4.load_photomaker_adapter(
    os.path.dirname(photomaker_path),
    subfolder="",
    weight_name=os.path.basename(photomaker_path),
    trigger_word="img"
)
pipe4 = pipe4.to("cpu")
pipe4.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
pipe4.fuse_lora()


def story_generation_ui():
    with gr.Row():
        with gr.Group(elem_id="main-image"):
            prompts = []
            colors = []
            with gr.Column(visible=True) as gen_prompt_vis:
                sd_type = gr.Dropdown(choices=list(models_dict.keys(
                )), value="Unstable", label="sd_type", info="Select pretrained model")
                model_type = gr.Radio(["Only Using Textual Description", "Using Ref Images"], label="model_type",
                                      value="Only Using Textual Description", info="Control type of the Character")
                with gr.Group(visible=False) as control_image_input:
                    files = gr.Files(
                        label="Drag (Select) 1 or more photos of your face",
                        file_types=["image"],
                    )
                    uploaded_files = gr.Gallery(
                        label="Your images", visible=False, columns=5, rows=1, height=200)
                    with gr.Column(visible=False) as clear_button:
                        remove_and_reupload = gr.ClearButton(
                            value="Remove and upload new ones", components=files, size="sm")
                general_prompt = gr.Textbox(
                    value='', label="(1) Textual Description for Character", interactive=True)
                negative_prompt = gr.Textbox(
                    value='', label="(2) Negative_prompt", interactive=True)
                style = gr.Dropdown(
                    label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
                prompt_array = gr.Textbox(
                    lines=3, value='', label="(3) Comic Description (each line corresponds to a frame).", interactive=True)
                with gr.Accordion("(4) Tune the hyperparameters", open=True):
                    sa32_ = gr.Slider(label="(The degree of Paired Attention at 32 x 32 self-attention layers)",
                                      minimum=0, maximum=1., value=0.7, step=0.1)
                    sa64_ = gr.Slider(label="(The degree of Paired Attention at 64 x 64 self-attention layers)",
                                      minimum=0, maximum=1., value=0.7, step=0.1)
                    id_length_ = gr.Slider(
                        label="Number of id images in total images", minimum=2, maximum=4, value=3, step=1)
                    seed_ = gr.Slider(label="Seed", minimum=-1,
                                      maximum=MAX_SEED, value=0, step=1)
                    num_steps = gr.Slider(
                        label="Number of sample steps",
                        minimum=25,
                        maximum=50,
                        step=1,
                        value=50,
                    )
                    G_height = gr.Slider(
                        label="height",
                        minimum=256,
                        maximum=1024,
                        step=32,
                        value=1024,
                    )
                    G_width = gr.Slider(
                        label="width",
                        minimum=256,
                        maximum=1024,
                        step=32,
                        value=1024,
                    )
                    comic_type = gr.Radio(["No typesetting (default)", "Four Pannel", "Classic Comic Style"],
                                          value="Classic Comic Style", label="Typesetting Style", info="Select the typesetting style ")
                    guidance_scale = gr.Slider(
                        label="Guidance scale",
                        minimum=0.1,
                        maximum=10.0,
                        step=0.1,
                        value=5,
                    )
                    style_strength_ratio = gr.Slider(
                        label="Style strength of Ref Image (%)",
                        minimum=15,
                        maximum=50,
                        step=1,
                        value=20,
                        visible=False
                    )
                    Ip_Adapter_Strength = gr.Slider(
                        label="Ip_Adapter_Strength",
                        minimum=0,
                        maximum=1,
                        step=0.1,
                        value=0.5,
                        visible=False
                    )
                final_run_btn = gr.Button("Generate ! 😺")

        with gr.Column():
            out_image = gr.Gallery(label="Result", columns=2, height='auto')
            generated_information = gr.Markdown(
                label="Generation Details", value="", visible=False)

    model_type.change(fn=change_visiale_by_model_type, inputs=model_type, outputs=[
                      control_image_input, style_strength_ratio, Ip_Adapter_Strength])
    files.upload(fn=swap_to_gallery, inputs=files, outputs=[
                 uploaded_files, clear_button, files])
    remove_and_reupload.click(fn=remove_back_to_files, outputs=[
                              uploaded_files, clear_button, files])

    final_run_btn.click(fn=set_text_unfinished, outputs=generated_information
                        ).then(process_generation, inputs=[sd_type, model_type, files, num_steps, style, Ip_Adapter_Strength, style_strength_ratio, guidance_scale, seed_, sa32_, sa64_, id_length_, general_prompt, negative_prompt, prompt_array, G_height, G_width, comic_type], outputs=out_image
                               ).then(fn=set_text_finished, outputs=generated_information)

    gr.Examples(
        examples=get_examples(),
        inputs=[seed_, sa32_, sa64_, id_length_, general_prompt, negative_prompt,
                prompt_array, style, model_type, files, G_height, G_width],
        label='😺 Examples 😺',
    )