From 18cbe26efdab691f4b2eccc5f8c4c3951208cc79 Mon Sep 17 00:00:00 2001
From: "yupeng.zhou" <yupeng.zhou@bytedance.com>
Date: Tue, 14 May 2024 00:27:21 +0800
Subject: [PATCH] twoperson

---
 gradio_app_sdxl_specific_id_low_vram.py       | 231 +++---
 ...gradio_app_sdxl_specific_id_old_version.py | 782 ++++++++++++++++++
 storydiffusionpipeline.py                     |   0
 3 files changed, 898 insertions(+), 115 deletions(-)
 create mode 100644 oldversion/gradio_app_sdxl_specific_id_old_version.py
 create mode 100644 storydiffusionpipeline.py

diff --git a/gradio_app_sdxl_specific_id_low_vram.py b/gradio_app_sdxl_specific_id_low_vram.py
index a656027..3239948 100644
--- a/gradio_app_sdxl_specific_id_low_vram.py
+++ b/gradio_app_sdxl_specific_id_low_vram.py
@@ -21,7 +21,7 @@ if is_torch2_available():
         AttnProcessor2_0 as AttnProcessor
 else:
     from utils.gradio_utils  import AttnProcessor
-
+import datetime
 import diffusers
 from diffusers import StableDiffusionXLPipeline
 from utils import PhotoMakerStableDiffusionXLPipeline
@@ -181,83 +181,6 @@ class SpatialAttnProcessor2_0(torch.nn.Module):
             cur_step += 1
             indices1024,indices4096 = cal_attn_indice_xl_effcient_memory(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,
-        attn_indices = 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,
@@ -393,6 +316,60 @@ css = '''
 <style>
 '''
 
+def save_single_character_weights(unet,character,description, filepath):
+    """
+    保存 attention_processor 类中的 id_bank GPU Tensor 列表到指定文件中。
+    参数:
+    - model: 包含 attention_processor 类实例的模型。
+    - filepath: 权重要保存到的文件路径。
+    """
+    weights_to_save = {}
+    weights_to_save["description"] = description
+    weights_to_save["character"]  = character
+    for attn_name, attn_processor in unet.attn_processors.items():
+        if isinstance(attn_processor, SpatialAttnProcessor2_0):
+                # 将每个 Tensor 转到 CPU 并转为列表，以确保它可以被序列化
+                weights_to_save[attn_name] = {}
+                for step_key in attn_processor.id_bank[character].keys():
+                    weights_to_save[attn_name][step_key] = [tensor.cpu() for tensor in attn_processor.id_bank[character][step_key]]
+    # 使用torch.save保存权重
+    torch.save(weights_to_save, filepath)
+
+def load_single_character_weights(unet, filepath):
+    """
+    从指定文件中加载权重到 attention_processor 类的 id_bank 中。
+    参数:
+    - model: 包含 attention_processor 类实例的模型。
+    - filepath: 权重文件的路径。
+    """
+    # 使用torch.load来读取权重
+    weights_to_load = torch.load(filepath, map_location=torch.device('cpu'))
+    character = weights_to_load['character']
+    description = weights_to_load['description']
+    for attn_name, attn_processor in unet.attn_processors.items():
+        if isinstance(attn_processor, SpatialAttnProcessor2_0):
+            # 转移权重到GPU（如果GPU可用的话）并赋值给id_bank
+            attn_processor.id_bank[character] = {}
+            for step_key in weights_to_load[attn_name].keys():
+                attn_processor.id_bank[character][step_key] = [tensor.to(unet.device) for tensor in weights_to_load[attn_name][step_key]]
+
+def save_results(unet,img_list):
+
+    timestamp = datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
+    folder_name = f'results/{timestamp}'
+    weight_folder_name = f'{folder_name}/weights'
+    # 创建文件夹
+    if not os.path.exists(folder_name):
+        os.makedirs(folder_name)
+        os.makedirs(weight_folder_name)
+
+    for idx, img in enumerate(img_list):
+        file_path = os.path.join(folder_name, f'image_{idx}.png')  # 图片文件名
+        img.save(file_path)  
+    global character_dict
+    # for char in character_dict:
+    #     description = character_dict[char]
+    #     save_single_character_weights(unet,char,description,os.path.join(weight_folder_name, f'{char}.pt'))
 
 #################################################
 title = r"""
@@ -426,14 +403,14 @@ If our work is useful for your research, please consider citing:
 ```
 📋 **License**
 <br>
-The Contents you create are under Apache-2.0 LICENSE. The Code are under Attribution-NonCommercial 4.0 International. 
+Apache-2.0 LICENSE. 
 
 📧 **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>
+<h3 align="center">StoryDiffusion Version 0.02 (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>
@@ -528,9 +505,29 @@ def change_visiale_by_model_type(_model_type):
     else:
         raise ValueError("Invalid model type",_model_type)
 
+def load_character_files(character_files:str):
+    if character_files == "":
+        raise gr.Error("Please set a character file!")
+    character_files_arr = character_files.splitlines()
+    primarytext = []
+    for character_file_name in character_files_arr:
+        character_file = torch.load(character_file_name, map_location=torch.device('cpu'))
+        primarytext.append(character_file["character"] + character_file["description"])
+    return array2string(primarytext)
+
+
+def load_character_files_on_running(unet,character_files:str):
+    if character_files == "":
+        return False
+    character_files_arr = character_files.splitlines()
+    for character_file in character_files_arr:
+        load_single_character_weights(unet, character_file)
+    return True
 
 ######### Image Generation ##############
-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, font_choice): # Corrected font_choice usage
+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, font_choice,_char_files): # Corrected font_choice usage
+    if len(general_prompt.splitlines()) >= 3:
+        raise gr.Error("Support for more than three characters is temporarily unavailable due to VRAM limitations, but this issue will be resolved soon.")
     _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")
@@ -574,17 +571,13 @@ def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_nam
         unet = pipe.unet
         # unet.set_attn_processor(copy.deepcopy(attn_procs))
 
-
+    load_chars = load_character_files_on_running(unet,character_files=_char_files)
 
     prompts = prompt_array.splitlines()
     global character_dict,character_index_dict,invert_character_index_dict,ref_indexs_dict,ref_totals
     character_dict,character_list  = character_to_dict(general_prompt)
 
 
-
-
-
-
     start_merge_step = int(float(_style_strength_ratio) / 100 * _num_steps)
     if start_merge_step > 30:
         start_merge_step = 30
@@ -627,33 +620,37 @@ def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_nam
     id_images = []
     results_dict = {}
     global cur_character
-    for character_key in character_dict.keys():
-        cur_character  = [character_key]
-        ref_indexs = ref_indexs_dict[character_key]
-        print(character_key,ref_indexs)
-        current_prompts = [replace_prompts[ref_ind] for ref_ind in  ref_indexs]
-        print(current_prompts)
-        setup_seed(seed_)
-        generator = torch.Generator(device="cuda").manual_seed(seed_)
-        cur_step = 0
-        cur_positive_prompts, negative_prompt = apply_style(style_name, current_prompts, negative_prompt)
-        if _model_type == "original":
-            id_images = pipe(cur_positive_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(cur_positive_prompts,input_id_images=input_id_images_dict[character_key], 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
-        print(id_images)
-        for ind,img in enumerate(id_images):
-            print(ref_indexs[ind])
-            results_dict[ref_indexs[ind]] = img
-        # real_images = []
+    if not load_chars:
+        for character_key in character_dict.keys():
+            cur_character  = [character_key]
+            ref_indexs = ref_indexs_dict[character_key]
+            print(character_key,ref_indexs)
+            current_prompts = [replace_prompts[ref_ind] for ref_ind in  ref_indexs]
+            print(current_prompts)
+            setup_seed(seed_)
+            generator = torch.Generator(device="cuda").manual_seed(seed_)
+            cur_step = 0
+            cur_positive_prompts, negative_prompt = apply_style(style_name, current_prompts, negative_prompt)
+            if _model_type == "original":
+                id_images = pipe(cur_positive_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(cur_positive_prompts,input_id_images=input_id_images_dict[character_key], 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
+            print(id_images)
+            for ind,img in enumerate(id_images):
+                print(ref_indexs[ind])
+                results_dict[ref_indexs[ind]] = img
+            # real_images = []
+            yield [results_dict[ind] for ind in results_dict.keys()]
     write = False
-
-    real_prompts_inds =  [ind for ind in range(len(prompts)) if ind not in ref_totals]
+    if not load_chars:
+        real_prompts_inds =  [ind for ind in range(len(prompts)) if ind not in ref_totals]
+    else:
+        real_prompts_inds =  [ind for ind in range(len(prompts))]
     print(real_prompts_inds)
 
     for real_prompts_ind in real_prompts_inds:
@@ -672,7 +669,7 @@ def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_nam
             results_dict[real_prompts_ind] = (pipe(real_prompt, input_id_images=input_id_images_dict[cur_character[0]] if real_prompts_ind not in nc_indexs else input_id_images_dict[character_list[0]], 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,nc_flag = True if real_prompts_ind in nc_indexs else False).images[0])
         else:
             raise NotImplementedError("You should choice between original and Photomaker!",f"But you choice {_model_type}")
-        
+        yield [results_dict[ind] for ind in results_dict.keys()]
     total_results = [results_dict[ind] for ind in range(len(prompts))]
     if _comic_type != "No typesetting (default)":
         captions= prompt_array.splitlines()
@@ -684,6 +681,8 @@ def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_nam
         print(f"Attempting to load font from path: {font_path}")
         font = ImageFont.truetype(font_path, int(45))
     total_results = get_comic(total_results, _comic_type, captions=captions, font=font) + total_results
+    save_results(pipe.unet,total_results)
+
     yield total_results
 
 
@@ -731,6 +730,8 @@ with gr.Blocks(css=css) as demo:
                 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)
+                char_path = gr.Textbox(lines = 2,value='', visible = False,label="(Optional) Character files", interactive=True)
+                char_btn = gr.Button("Load Character files",visible = False)
                 with gr.Accordion("(4) Tune the hyperparameters", open=True):
                     font_choice = gr.Dropdown(label="Select Font", choices=[f for f in os.listdir("./fonts") if f.endswith('.ttf')], value="Inkfree.ttf", info="Select font for the final slide.", interactive=True)
                     sa32_ = gr.Slider(label=" (The degree of Paired Attention at 32 x 32 self-attention layers) ", minimum=0, maximum=1., value=0.5, step=0.1)
@@ -792,9 +793,9 @@ with gr.Blocks(css=css) as demo:
     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])
-
+    char_btn.click(fn=load_character_files,inputs=char_path,outputs=[general_prompt])
     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, font_choice], outputs=out_image
+    ).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, font_choice,char_path], outputs=out_image
     ).then(fn=set_text_finished,outputs=generated_information)
 
 
diff --git a/oldversion/gradio_app_sdxl_specific_id_old_version.py b/oldversion/gradio_app_sdxl_specific_id_old_version.py
new file mode 100644
index 0000000..82fa171
--- /dev/null
+++ b/oldversion/gradio_app_sdxl_specific_id_old_version.py
@@ -0,0 +1,782 @@
+from email.policy import default
+import gradio as gr
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+import requests
+import random
+import os
+import sys
+import pickle
+from PIL import Image
+from tqdm.auto import tqdm
+from datetime import datetime
+from utils.gradio_utils import is_torch2_available
+if is_torch2_available():
+    from utils.gradio_utils import \
+        AttnProcessor2_0 as AttnProcessor
+else:
+    from utils.gradio_utils  import AttnProcessor
+
+import diffusers
+from diffusers import StableDiffusionXLPipeline
+from utils import PhotoMakerStableDiffusionXLPipeline
+from diffusers import DDIMScheduler
+import torch.nn.functional as F
+from utils.gradio_utils import cal_attn_mask_xl
+import copy
+import os
+from diffusers.utils import load_image
+from utils.utils import get_comic
+from utils.style_template import styles
+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
+models_dict = {
+#    "Juggernaut": "RunDiffusion/Juggernaut-XL-v9",
+   "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)
+        # 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
+        if write:
+            # print(f"white:{cur_step}")
+            self.id_bank[cur_step] = [hidden_states[:self.id_length].clone(), hidden_states[self.id_length:].clone()]
+        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 <20:
+                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 attn_procs
+    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)
+            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))
+#################################################
+#################################################
+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.")</h5>
+<h5 align="center">Tips: </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 pipe
+global sd_model_path
+pipe = None
+sd_model_path = models_dict["RealVision"]#"SG161222/RealVisXL_V4.0"
+### LOAD Stable Diffusion Pipeline
+pipe = StableDiffusionXLPipeline.from_pretrained(sd_model_path, torch_dtype=torch.float16, use_safetensors = True)
+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)
+unet = pipe.unet
+### Insert PairedAttention
+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 and (name.startswith("up_blocks") ) :
+        attn_procs[name] =  SpatialAttnProcessor2_0(id_length = id_length)
+        total_count +=1
+    else:
+        attn_procs[name] = AttnProcessor()
+print("successsfully load paired self-attention")
+print(f"number of the processor : {total_count}")
+unet.set_attn_processor(copy.deepcopy(attn_procs))
+global mask1024,mask4096
+mask1024, mask4096 = cal_attn_mask_xl(total_length,id_length,sa32,sa64,height,width,device=device,dtype= torch.float16)
+
+######### 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)
+
+
+######### Image Generation ##############
+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!")
+    global sa32, sa64,id_length,total_length,attn_procs,unet,cur_model_type
+    global write
+    global cur_step,attn_count
+    global height,width
+    height = G_height
+    width = G_width
+    global pipe
+    global sd_model_path,models_dict
+    sd_model_path = models_dict[_sd_type]
+    use_safe_tensor = True
+    if cur_model_type != _sd_type+"-"+_model_type+""+str(id_length_):
+        if _sd_type == "Unstable":
+            use_safe_tensor = False
+        # apply the style template
+        ##### load pipe
+
+        if _model_type == "original":
+            pipe = StableDiffusionXLPipeline.from_pretrained(sd_model_path, torch_dtype=torch.float16, use_safetensors=use_safe_tensor)
+            pipe = pipe.to(device)
+            set_attention_processor(pipe.unet,id_length_,is_ipadapter = False)
+        elif _model_type == "Photomaker":
+            pipe = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
+                sd_model_path, torch_dtype=torch.float16, use_safetensors=use_safe_tensor)
+            pipe = pipe.to(device)
+            pipe.load_photomaker_adapter(
+                os.path.dirname(photomaker_path),
+                subfolder="",
+                weight_name=os.path.basename(photomaker_path),
+                trigger_word="img"  # define the trigger word
+            )
+            pipe.fuse_lora()
+            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_)
+    else:
+        unet = pipe.unet
+        unet.set_attn_processor(copy.deepcopy(attn_procs))
+    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[:]
+    nc_indexs = []
+    for ind,prompt in enumerate(clipped_prompts):
+        if "[NC]"  in prompt:
+            nc_indexs.append(ind)
+            if ind < id_length:
+                raise gr.Error(f"The first {id_length} row is id prompts, cannot use [NC]!")
+    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 ind,real_prompt in enumerate(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,nc_flag = True if ind+id_length in nc_indexs else False ).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
+    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
+
+
+#################################################
+#################################################
+### define the interface
+with gr.Blocks(css=css) as demo:
+    binary_matrixes = gr.State([])
+    color_layout = gr.State([])
+
+    # gr.Markdown(logo)
+    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):
+                    sa32_ = gr.Slider(label=" (The degree of Paired Attention at 32 x 32 self-attention layers) ", minimum=0, maximum=1., value=0.5, step=0.1)
+                    sa64_ = gr.Slider(label=" (The degree of Paired Attention at 64 x 64 self-attention layers) ", minimum=0, maximum=1., value=0.5, step=0.1)
+                    id_length_ = gr.Slider(label= "Number of id images in total images" , minimum=2, maximum=4, value=2, 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=20,
+                        maximum=100,
+                        step=1,
+                        value=50,
+                    )
+                    G_height = gr.Slider( 
+                        label="height",
+                        minimum=256,
+                        maximum=1024,
+                        step=32,
+                        value=768,
+                    )
+                    G_width = gr.Slider( 
+                        label="width",
+                        minimum=256,
+                        maximum=1024,
+                        step=32,
+                        value=768,
+                    )
+                    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 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/Robert'),1024,1024
+            ],
+            [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],
+        # outputs=[post_sketch, binary_matrixes, *color_row, *colors, *prompts, gen_prompt_vis, general_prompt, seed_],
+        # run_on_click=True,
+        label='😺 Examples 😺',
+    )
+    gr.Markdown(article)
+
+
+demo.launch(server_name="0.0.0.0", share = False)
\ No newline at end of file
diff --git a/storydiffusionpipeline.py b/storydiffusionpipeline.py
new file mode 100644
index 0000000..e69de29