from email.policy import default
from json import encoder
import gradio as gr
import spaces
import numpy as np
import torch
import requests
import random
import os
import sys
import pickle
from PIL import Image
from tqdm.auto import tqdm
from datetime import datetime

import torch.nn as nn
import torch.nn.functional as F

class AttnProcessor(nn.Module):
    r"""
    Default processor for performing attention-related computations.
    """
    def __init__(
        self,
        hidden_size=None,
        cross_attention_dim=None,
    ):
        super().__init__()

    def __call__(
        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, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        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
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

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

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # 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



import diffusers
from diffusers import StableDiffusionXLPipeline
from utils import PhotoMakerStableDiffusionXLPipeline
from diffusers import DDIMScheduler
import torch.nn.functional as F
def cal_attn_mask(total_length,id_length,sa16,sa32,sa64,device="cuda",dtype= torch.float16):
    bool_matrix256 = torch.rand((1, total_length * 256),device = device,dtype = dtype) < sa16
    bool_matrix1024 = torch.rand((1, total_length * 1024),device = device,dtype = dtype) < sa32
    bool_matrix4096 = torch.rand((1, total_length * 4096),device = device,dtype = dtype) < sa64
    bool_matrix256 = bool_matrix256.repeat(total_length,1)
    bool_matrix1024 = bool_matrix1024.repeat(total_length,1)
    bool_matrix4096 = bool_matrix4096.repeat(total_length,1)
    for i in range(total_length):
        bool_matrix256[i:i+1,id_length*256:] = False
        bool_matrix1024[i:i+1,id_length*1024:] = False
        bool_matrix4096[i:i+1,id_length*4096:] = False
        bool_matrix256[i:i+1,i*256:(i+1)*256] = True
        bool_matrix1024[i:i+1,i*1024:(i+1)*1024] = True
        bool_matrix4096[i:i+1,i*4096:(i+1)*4096] = True
    mask256 = bool_matrix256.unsqueeze(1).repeat(1,256,1).reshape(-1,total_length * 256)
    mask1024 = bool_matrix1024.unsqueeze(1).repeat(1,1024,1).reshape(-1,total_length * 1024)
    mask4096 = bool_matrix4096.unsqueeze(1).repeat(1,4096,1).reshape(-1,total_length * 4096)
    return mask256,mask1024,mask4096

def cal_attn_mask_xl(total_length,id_length,sa32,sa64,height,width,device="cuda",dtype= torch.float16):
    nums_1024 = (height // 32) * (width // 32)
    nums_4096 = (height // 16) * (width // 16)
    bool_matrix1024 = torch.rand((1, total_length * nums_1024),device = device,dtype = dtype) < sa32
    bool_matrix4096 = torch.rand((1, total_length * nums_4096),device = device,dtype = dtype) < sa64
    bool_matrix1024 = bool_matrix1024.repeat(total_length,1)
    bool_matrix4096 = bool_matrix4096.repeat(total_length,1)
    for i in range(total_length):
        bool_matrix1024[i:i+1,id_length*nums_1024:] = False
        bool_matrix4096[i:i+1,id_length*nums_4096:] = False
        bool_matrix1024[i:i+1,i*nums_1024:(i+1)*nums_1024] = True
        bool_matrix4096[i:i+1,i*nums_4096:(i+1)*nums_4096] = True
    mask1024 = bool_matrix1024.unsqueeze(1).repeat(1,nums_1024,1).reshape(-1,total_length * nums_1024)
    mask4096 = bool_matrix4096.unsqueeze(1).repeat(1,nums_4096,1).reshape(-1,total_length * nums_4096)
    return mask1024,mask4096
	
import copy
import os
from huggingface_hub import hf_hub_download
from diffusers.utils import load_image
from utils.utils import get_comic # must remove this one

style_list = [
    {
        "name": "(No style)",
        "prompt": "{prompt}",
        "negative_prompt": "",
    },
    {
        "name": "Japanese Anime",
        "prompt": "anime artwork illustrating {prompt}. created by japanese anime studio. highly emotional. best quality, high resolution",
        "negative_prompt": "low quality, low resolution"
    },
    {
        "name": "Cinematic",
        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
    },
    {
        "name": "Disney Charactor",
        "prompt": "A Pixar animation character of {prompt} . pixar-style, studio anime, Disney, high-quality",
        "negative_prompt": "lowres, bad anatomy, bad hands, text, bad eyes, bad arms, bad legs, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, blurry, grayscale, noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo",
    },
    {
        "name": "Photographic",
        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
    },
    {
        "name": "Comic book",
        "prompt": "comic {prompt} . graphic illustration, comic art, graphic novel art, vibrant, highly detailed",
        "negative_prompt": "photograph, deformed, glitch, noisy, realistic, stock photo",
    },
    {
        "name": "Line art",
        "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics",
        "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic",
    }
]

styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}

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"
global models_dict
use_va = True
models_dict = {
#    "Juggernaut": "RunDiffusion/Juggernaut-XL-v8",
   "RealVision": "SG161222/RealVisXL_V4.0" ,
#    "SDXL":"stabilityai/stable-diffusion-xl-base-1.0" ,
   "Unstable": "stablediffusionapi/sdxl-unstable-diffusers-y"
}
photomaker_path =  hf_hub_download(repo_id="TencentARC/PhotoMaker", filename="photomaker-v1.bin", repo_type="model")
MAX_SEED = np.iinfo(np.int32).max
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>")
#################################################
def get_image_path_list(folder_name):
    image_basename_list = os.listdir(folder_name)
    image_path_list = sorted([os.path.join(folder_name, basename) for basename in image_basename_list])
    return image_path_list

#################################################
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)
        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 name.startswith("mid_block"):
            hidden_size = unet.config.block_out_channels[-1]
        elif name.startswith("up_blocks"):
            block_id = int(name[len("up_blocks.")])
            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
        elif name.startswith("down_blocks"):
            block_id = int(name[len("down_blocks.")])
            hidden_size = unet.config.block_out_channels[block_id]
        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:
            if is_ipadapter:
                attn_procs[name] = IPAttnProcessor2_0(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                    scale=1,
                    num_tokens=4,
                ).to(unet.device, dtype=torch.float16)
            else:
                attn_procs[name] = AttnProcessor()

    unet.set_attn_processor(copy.deepcopy(attn_procs))
    print("successsfully load paired self-attention")
    print(f"number of the processor : {total_count}")


canvas_html = "<div id='canvas-root' style='max-width:400px; margin: 0 auto'></div>"
load_js = """
async () => {
const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/sketch-canvas.js"
fetch(url)
  .then(res => res.text())
  .then(text => {
    const script = document.createElement('script');
    script.type = "module"
    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
    document.head.appendChild(script);
  });
}
"""

get_js_colors = """
async (canvasData) => {
  const canvasEl = document.getElementById("canvas-root");
  return [canvasEl._data]
}
"""

css = '''
#color-bg{display:flex;justify-content: center;align-items: center;}
.color-bg-item{width: 100%; height: 32px}
#main_button{width:100%}
<style>
'''


#################################################
title = r"""
<h1 align="center">StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation</h1>
"""

description = r"""
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/HVision-NKU/StoryDiffusion' target='_blank'><b>StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation</b></a>.<br>
❗️❗️❗️[<b>Important</b>] Personalization steps:<br>
1️⃣ Enter a Textual Description for Character, if you add the Ref-Image, making sure to <b>follow the class word</b> you want to customize with the <b>trigger word</b>: `img`, such as: `man img` or `woman img` or `girl img`.<br>
2️⃣ Enter the prompt array, each line corrsponds to one generated image.<br>
3️⃣ Choose your preferred style template.<br>
4️⃣ Click the <b>Submit</b> button to start customizing.
"""

article = r"""
If StoryDiffusion is helpful, please help to ⭐ the <a href='https://github.com/HVision-NKU/StoryDiffusion' target='_blank'>Github Repo</a>. Thanks! 
[![GitHub Stars](https://img.shields.io/github/stars/HVision-NKU/StoryDiffusion?style=social)](https://github.com/HVision-NKU/StoryDiffusion)
---
📝 **Citation**
<br>
If our work is useful for your research, please consider citing:
```bibtex
@article{Zhou2024storydiffusion,
  title={StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation},
  author={Zhou, Yupeng and Zhou, Daquan and Cheng, Ming-Ming and Feng, Jiashi and Hou, Qibin},
  year={2024}
}
```
📋 **License**
<br>
The Contents you create are under Apache-2.0 LICENSE. The Code are under Attribution-NonCommercial 4.0 International. 
📧 **Contact**
<br>
If you have any questions, please feel free to reach me out at <b>ypzhousdu@gmail.com</b>.
"""
version = r"""
<h3 align="center">StoryDiffusion Version 0.01 (test version)</h3>
<h5 >1. Support image ref image. (Cartoon Ref image is not support now)</h5>
<h5 >2. Support Typesetting Style and Captioning.(By default, the prompt is used as the caption for each image. If you need to change the caption, add a # at the end of each line. Only the part after the # will be added as a caption to the image.)</h5>
<h5 >3. [NC]symbol (The [NC] symbol is used as a flag to indicate that no characters should be present in the generated scene images. If you want do that, prepend the "[NC]" at the beginning of the line. For example, to generate a scene of falling leaves without any character, write: "[NC] The leaves are falling."),Currently, support is only using Textual Description</h5>
<h5>Tips: Not Ready Now! Just Test! It's better to use prompts to assist in controlling the character's attire. Depending on the limited code integration time, there might be some undiscovered bugs. If you find that a particular generation result is significantly poor, please email me (ypzhousdu@gmail.com)  Thank you very much.</h4>
"""

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

global sd_model_path
sd_model_path = models_dict["Unstable"]#"SG161222/RealVisXL_V4.0"
use_safetensors= False

### LOAD Stable Diffusion Pipeline
# pipe1 = StableDiffusionXLPipeline.from_pretrained(sd_model_path, torch_dtype=torch.float16, use_safetensors= use_safetensors)
# pipe1 = pipe1.to("cpu")
# pipe1.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
# # pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
# pipe1.scheduler.set_timesteps(50)
### 
pipe2 = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
    models_dict["Unstable"], torch_dtype=torch.float16, use_safetensors=use_safetensors)
pipe2 = pipe2.to("cpu")
pipe2.load_photomaker_adapter(
    os.path.dirname(photomaker_path),
    subfolder="",
    weight_name=os.path.basename(photomaker_path),
    trigger_word="img"  # define the trigger word
)
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"  # define the trigger word
)
pipe4 = pipe4.to("cpu")
pipe4.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
pipe4.fuse_lora()

# pipe3 = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V4.0", torch_dtype=torch.float16)
# pipe3 = pipe3.to("cpu")
# pipe3.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
# # pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
# pipe3.scheduler.set_timesteps(50)
######### Gradio Fuction

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):
    _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 triger 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(f"Cannot find any input face image!")
    if len(prompt_array.splitlines()) > 10:
        raise gr.Error(f"No more than 10 prompts in huggface demo for Speed! But found {len(prompt_array.splitlines())} prompts!")
    global sa32, sa64,id_length,total_length,attn_procs,unet,cur_model_type,device
    global num_steps
    global write
    global cur_step,attn_count
    global height,width
    height = G_height
    width = G_width
    global pipe2,pipe4
    global sd_model_path,models_dict
    sd_model_path = models_dict[_sd_type]
    num_steps =_num_steps
    use_safe_tensor = True
    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)
        # pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        # pipe.scheduler.set_timesteps(50)
        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 choice between original and Photomaker!",f"But you choice {_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 = []
        for img in _upload_images:
            print(img)
            input_id_images.append(load_image(img))
    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 choice between original and Photomaker!",f"But you choice {_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 choice between original and Photomaker!",f"But you choice {_model_type}")
        total_results = [real_images[-1]] + total_results
        yield total_results
    if _comic_type != "No typesetting (default)":
        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]
        from PIL import ImageFont
        total_results = get_comic(id_images + real_images, _comic_type,captions= captions,font=ImageFont.truetype("./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



def array2string(arr):
    stringtmp = ""
    for i,part in enumerate(arr):
        if i != len(arr)-1:
            stringtmp += part +"\n"
        else:
            stringtmp += part

    return stringtmp


with gr.Blocks(css=css) as demo:
    binary_matrixes = gr.State([])
    color_layout = gr.State([])

    gr.Markdown(title)
    gr.Markdown(description)

    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):
                    #sa16_ = gr.Slider(label=" (The degree of Paired Attention at 16 x 16 self-attention layers) ", minimum=0, maximum=1., value=0.3, step=0.1)
                    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)
                    # total_length_ = gr.Slider(label= "Number of total images", minimum=1, maximum=20, value=1, 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)
            gr.Markdown(version)
    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=[
            [0,0.5,0.5,2,"a man, wearing black suit",
                "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
                array2string(["at home, read new paper #at home, The newspaper says there is a treasure house in the forest.",
                            "on the road, near the forest",
                            "[NC] The car on the road, near the forest #He drives to the forest in search of treasure.",
                            "[NC]A tiger appeared in the forest, at night ",
                            "very frightened, open mouth, in the forest, at night",
                            "running very fast, in the forest, at night",
                            "[NC] A house in the forest, at night #Suddenly, he discovers the treasure house!",
                            "in the house filled with  treasure, laughing, at night #He is overjoyed inside the house."
                            ]),
                            "Comic book","Only Using Textual Description",get_image_path_list('./examples/taylor'),768,768
            ],
            [0,0.5,0.5,2,"a policeman img, wearing a white shirt",
                "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
                array2string(["Directing traffic on the road. ",
                            "walking on the streets.",
                            "Chasing a man on the street.",
                            "At the police station.",
                            ]),
                            "Japanese Anime","Using Ref Images",get_image_path_list('./examples/lecun'),768,768
            ],
            [1,0.5,0.5,3,"a woman img, wearing a white T-shirt, blue loose hair",
                   "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
                   array2string(["wake up in the bed",
                                "have breakfast",
                                "is on the road, go to company",
                                "work in the company",
                                "Take a walk next to the company at noon",
                                "lying in bed at night"]),
                                "Japanese Anime",  "Using Ref Images",get_image_path_list('./examples/taylor'),768,768
                ],
                [0,0.5,0.5,3,"a man, wearing black jacket",
                   "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
                   array2string(["wake up in the bed",
                                "have breakfast",
                                "is on the road, go to the company,  close look",
                                "work in the company",
                                "laughing happily",
                                "lying in bed at night"
                                ]),
                                "Japanese Anime","Only Using Textual Description",get_image_path_list('./examples/taylor'),768,768
                ],
                [0,0.3,0.5,3,"a girl, wearing white shirt, black skirt, black tie, yellow hair",
                   "bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs",
                    array2string([
                            "at home #at home, began to go to drawing",
                            "sitting alone on a park bench.",
                            "reading a book on a park bench.",
                            "[NC]A squirrel approaches, peeking over the bench. ",
                            "look around in the park. # She looks around and enjoys the beauty of nature.",
                            "[NC]leaf falls from the tree, landing on the sketchbook.",
                            "picks up the leaf, examining its details closely.",
                            "[NC]The brown squirrel appear.",
                            "is very happy # She is very happy to see the squirrel again",
                            "[NC]The brown squirrel takes the cracker and scampers up a tree. # She gives the squirrel cracker"]),
                    "Japanese Anime","Only Using Textual Description",get_image_path_list('./examples/taylor'),768,768
                ]
                ],
        inputs=[seed_, sa32_, sa64_, id_length_,  general_prompt, negative_prompt, prompt_array,style,model_type,files,G_height,G_width],
        label='😺 Examples 😺',
    )
    gr.Markdown(article)


demo.launch()