README.md

# svjack/GenshinImpact_XL_Base

This model is derived from [CivitAI](https://civitai.com/models/386505).

## Acknowledgments

Special thanks to [mobeimunan](https://civitai.com/user/mobeimunan) for their contributions to the development of this model.

<!--
from moviepy.editor import ImageClip, VideoFileClip, concatenate_videoclips, clips_array

# 定义图像和视频的路径
image1_path = "https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/3IkZz7uXW9kc-lTnKdQN8.png"
image2_path = "https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/ckrKqytF5MhanjIc_Vn1q.png"
image3_path = "https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/vfffGerUQV9W1MHxc_rN_.png"
video_path = "https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/gBaodBk8z3aI69LiT36w2.mp4"

duration = 10

# 加载图像和视频
image1 = ImageClip(image1_path).set_duration(duration).resize(height = 512, width = 512)  # 设置图像的持续时间为10秒
image2 = ImageClip(image2_path).set_duration(duration).resize(height = 512, width = 1024)
image3 = ImageClip(image3_path).set_duration(duration).resize(height = 512, width = 1024)
video = VideoFileClip(video_path).resize(height = 512, width = 512)

# 调整视频的持续时间以匹配图像
video = video.subclip(0, duration)  # 截取视频的前10秒

# 将图像和视频拼接成一个2x2的网格
final_clip = clips_array([[image1, image2], [video, image3]])

# 调整最终视频的宽高比
final_clip = final_clip.resize(width=1080)  # 调整宽度为1080，高度会自动调整

# 导出最终视频
final_clip.write_videofile("zhongli_merge_im_output.mp4", codec="libx264")

VideoFileClip("zhongli_merge_im_output.mp4").set_duration(2.2).write_videofile("zhongli_merge_output_2_2.mp4", codec="libx264")
-->

<div style="display: flex; flex-direction: column; align-items: center;">
    <div style="margin-bottom: 10px;">
        <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/bsgBrDOOXYN-oBH95q5uK.mp4" style="width: 1024px; height: 768x;"></video>
    </div>
    <div style="margin-bottom: 10px;">
        <img src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/WypE04ag7_4Z1FKhzk475.png" width="1024" height="1024">
    </div>
    <div style="margin-bottom: 10px;">
        <h3>Zhongli Drinking Tea:</h3>
    </div>
    <div style="margin-bottom: 10px;">
        <img src="https://github.com/user-attachments/assets/00451728-f2d5-4009-afa8-23baaabdc223" style="width: 1024px; height: 256px;">
    </div>
    <div style="margin-bottom: 10px;">
        <video controls autoplay src="https://github.com/user-attachments/assets/607e7eb7-d41c-4740-9c8a-8369c31487da" style="width: 1024px; height: 800px;"></video>
    </div>
    <div style="margin-bottom: 10px;">
        <h3>Kamisato Ayato Smiling:</h3>
    </div>
    <div style="margin-bottom: 10px;">
        <img src="https://github.com/user-attachments/assets/7a920f4c-8a3a-4387-98d6-381a798566ef" style="width: 1024px; height: 256px;">
    </div>
    <div style="margin-bottom: 10px;">
        <video controls autoplay src="https://github.com/user-attachments/assets/aaa9849e-0c53-4012-b6c3-9ceb9910f2f8" style="width: 1024px; height: 800px;"></video>
    </div>
    <div style="margin-bottom: 10px;">
        <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/Kmh4NJ1AkfV5X3-kkilAK.mp4" style="width: 1024px; height: 1024px;"></video>
    </div>
</div>


## Supported Characters

The model currently supports the following 73 characters from Genshin Impact:

```python
name_dict = {
    '旅行者女': 'lumine',
    '旅行者男': 'aether',
    '派蒙': 'PAIMON',
    '迪奥娜': 'DIONA',
    '菲米尼': 'FREMINET',
    '甘雨': 'GANYU',
    '凯亚': 'KAEYA',
    '莱依拉': 'LAYLA',
    '罗莎莉亚': 'ROSARIA',
    '七七': 'QIQI',
    '申鹤': 'SHENHE',
    '神里绫华': 'KAMISATO AYAKA',
    '优菈': 'EULA',
    '重云': 'CHONGYUN',
    '夏洛蒂': 'charlotte',
    '莱欧斯利': 'WRIOTHESLEY',
    '艾尔海森': 'ALHAITHAM',
    '柯莱': 'COLLEI',
    '纳西妲': 'NAHIDA',
    '绮良良': 'KIRARA',
    '提纳里': 'TIGHNARI',
    '瑶瑶': 'YAOYAO',
    '珐露珊': 'FARUZAN',
    '枫原万叶': 'KAEDEHARA KAZUHA',
    '琳妮特': 'LYNETTE',
    '流浪者 散兵': 'scaramouche',
    '鹿野院平藏': 'SHIKANOIN HEIZOU',
    '琴': 'JEAN',
    '砂糖': 'SUCROSE',
    '温迪': 'VENTI',
    '魈': 'XIAO',
    '早柚': 'SAYU',
    '安柏': 'AMBER',
    '班尼特': 'BENNETT',
    '迪卢克': 'DILUC',
    '迪西娅': 'DEHYA',
    '胡桃': 'HU TAO',
    '可莉': 'KLEE',
    '林尼': 'LYNEY',
    '托马': 'THOMA',
    '香菱': 'XIANG LING',
    '宵宫': 'YOIMIYA',
    '辛焱': 'XINYAN',
    '烟绯': 'YANFEI',
    '八重神子': 'YAE MIKO',
    '北斗': 'BEIDOU',
    '菲谢尔': 'FISCHL',
    '九条裟罗': 'KUJO SARA',
    '久岐忍': 'KUKI SHINOBU',
    '刻晴': 'KEQING',
    '雷电将军': 'RAIDEN SHOGUN',
    '雷泽': 'RAZOR',
    '丽莎': 'LISA',
    '赛诺': 'CYNO',
    '芙宁娜': 'FURINA',
    '芭芭拉': 'BARBARA',
    '公子 达达利亚': 'TARTAGLIA',
    '坎蒂丝': 'CANDACE',
    '莫娜': 'MONA',
    '妮露': 'NILOU',
    '珊瑚宫心海': 'SANGONOMIYA KOKOMI',
    '神里绫人': 'KAMISATO AYATO',
    '行秋': 'XINGQIU',
    '夜兰': 'YELAN',
    '那维莱特': 'NEUVILLETTE',
    '娜维娅': 'NAVIA',
    '阿贝多': 'ALBEDO',
    '荒泷一斗': 'ARATAKI ITTO',
    '凝光': 'NING GUANG',
    '诺艾尔': 'NOELLE',
    '五郎': 'GOROU',
    '云堇': 'YUN JIN',
    '钟离': 'ZHONGLI'
}
```

## Installation

To use this model, you need to install the following dependencies:

```bash
sudo apt-get update && sudo apt-get install git-lfs ffmpeg cbm
pip install -U diffusers transformers sentencepiece peft controlnet-aux moviepy
```

## Example Usage

### Generating an Image of Zhongli

Here's an example of how to generate an image of Zhongli using this model:

```python
from diffusers import StableDiffusionXLPipeline
import torch

pipeline = StableDiffusionXLPipeline.from_pretrained(
    "svjack/GenshinImpact_XL_Base",
    torch_dtype=torch.float16
).to("cuda")

prompt = "solo,ZHONGLI\(genshin impact\),1boy,portrait,upper_body,highres,"
negative_prompt = "nsfw,lowres,(bad),text,error,fewer,extra,missing,worst quality,jpeg artifacts,low quality,watermark,unfinished,displeasing,oldest,early,chromatic aberration,signature,extra digits,artistic error,username,scan,[abstract],"
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    generator=torch.manual_seed(0),
).images[0]
image
image.save("zhongli_1024x1024.png")
```

<div style="display: flex; flex-direction: column; align-items: center;">
    <div style="margin-bottom: 10px;">
        <img src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/3IkZz7uXW9kc-lTnKdQN8.png" width="768" height="768">
        <p style="text-align: center;">钟离</p>
    </div>
</div>

### Using Canny ControlNet to Restore 2D Images from 3D Toy Photos

Here's an example of how to use Canny ControlNet to restore 2D images from 3D toy photos:

#### Genshin Impact 3D Toys

<div style="display: flex; flex-direction: column; align-items: center;">
    <div style="margin-bottom: 10px;">
        <img src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/YNG9vRqZGvUSxb_UUrLE5.jpeg" width="512" height="768">
        <p style="text-align: center;">钟离</p>
    </div>
    <div style="margin-bottom: 10px;">
        <img src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/1JfhfFi9qogHwB4M2S54m.jpeg" width="512" height="768">
        <p style="text-align: center;">派蒙</p>
    </div>
</div>

```python
from diffusers import AutoPipelineForText2Image, ControlNetModel
from diffusers.utils import load_image
import torch
from PIL import Image
from controlnet_aux import CannyDetector

controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
)

pipeline = AutoPipelineForText2Image.from_pretrained(
    "svjack/GenshinImpact_XL_Base",
    controlnet=controlnet,
    torch_dtype=torch.float16
).to("cuda")
#pipeline.enable_model_cpu_offload()

canny = CannyDetector()
canny(Image.open("zhongli-cb.jpg")).save("zhongli-cb-canny.jpg")
canny_image = load_image(
    "zhongli-cb-canny.jpg"
)

controlnet_conditioning_scale = 0.5
generator = torch.Generator(device="cpu").manual_seed(1)
images = pipeline(
        prompt="solo,ZHONGLI\(genshin impact\),1boy,portrait,highres",
        controlnet_conditioning_scale=controlnet_conditioning_scale,
        image=canny_image,
        num_inference_steps=50,
        guidance_scale=7.0,
        generator=generator,
    ).images
images[0]
images[0].save("zhongli_trans.png")

canny = CannyDetector()
canny(Image.open("paimon-cb-crop.jpg")).save("paimon-cb-canny.jpg")
canny_image = load_image(
    "paimon-cb-canny.jpg"
)

controlnet_conditioning_scale = 0.7
generator = torch.Generator(device="cpu").manual_seed(3)
images = pipeline(
        prompt="solo,PAIMON\(genshin impact\),1girl,portrait,highres, bright, shiny, high detail, anime",
        controlnet_conditioning_scale=controlnet_conditioning_scale,
        image=canny_image,
        num_inference_steps=50,
        guidance_scale=8.0,
        generator=generator,
    ).images
images[0]
images[0].save("paimon_trans.png")
```

### Creating a Grid Image

You can also create a grid image from a list of PIL Image objects:

```python
from PIL import Image

def create_grid_image(image_list, rows, cols, cell_width, cell_height):
    """
    Create a grid image from a list of PIL Image objects.

    :param image_list: A list of PIL Image objects
    :param rows: Number of rows in the grid
    :param cols: Number of columns in the grid
    :param cell_width: Width of each cell in the grid
    :param cell_height: Height of each cell in the grid
    :return: The resulting grid image
    """
    total_width = cols * cell_width
    total_height = rows * cell_height

    grid_image = Image.new('RGB', (total_width, total_height))

    for i, img in enumerate(image_list):
        row = i // cols
        col = i % cols

        img = img.resize((cell_width, cell_height))

        x_offset = col * cell_width
        y_offset = row * cell_height

        grid_image.paste(img, (x_offset, y_offset))

    return grid_image

create_grid_image([Image.open("zhongli-cb.jpg") ,Image.open("zhongli-cb-canny.jpg"), Image.open("zhongli_trans.png")], 1, 3, 512, 768)

create_grid_image([Image.open("paimon-cb-crop.jpg") ,Image.open("paimon-cb-canny.jpg"), Image.open("paimon_trans.png")], 1, 3, 512, 768)
```

This will create a grid image showing the original, Canny edge detection, and transformed images side by side.

<div>
    <b><h3 style="text-align: center;">Below image list in : (Genshin Impact Toy/ Canny Image / Gemshin Impact Restore 2D Image)</h3></b>
    <div style="display: flex; flex-direction: column; align-items: center;">
        <div style="margin-bottom: 10px;">
            <img src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/ckrKqytF5MhanjIc_Vn1q.png" width="1536" height="768">
            <p style="text-align: center;">钟离</p>
        </div>
        <div style="margin-bottom: 10px;">
            <img src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/vfffGerUQV9W1MHxc_rN_.png" width="1536" height="768">
            <p style="text-align: center;">派蒙</p>
        </div>
    </div>
</div>

### Generating an Animation of Zhongli
Here's an example of how to generate an animation of Zhongli using the `AnimateDiffSDXLPipeline`:

```python
import torch
from diffusers.models import MotionAdapter
from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained(
    "a-r-r-o-w/animatediff-motion-adapter-sdxl-beta", torch_dtype=torch.float16
)

model_id = "svjack/GenshinImpact_XL_Base"
scheduler = DDIMScheduler.from_pretrained(
    model_id,
    subfolder="scheduler",
    clip_sample=False,
    timestep_spacing="linspace",
    beta_schedule="linear",
    steps_offset=1,
)

pipe = AnimateDiffSDXLPipeline.from_pretrained(
    model_id,
    motion_adapter=adapter,
    scheduler=scheduler,
    torch_dtype=torch.float16,
).to("cuda")

# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()

output = pipe(
    prompt="solo,ZHONGLI\(genshin impact\),1boy,portrait,upper_body,highres, keep eyes forward.",
    negative_prompt="low quality, worst quality",
    num_inference_steps=20,
    guidance_scale=8,
    width=1024,
    height=1024,
    num_frames=16,
    generator=torch.manual_seed(4),
)
frames = output.frames[0]
export_to_gif(frames, "zhongli_animation.gif")

from diffusers.utils import export_to_video
export_to_video(frames, "zhongli_animation.mp4")
from IPython import display
display.Video("zhongli_animation.mp4", width=512, height=512)
```

Use `AutoPipelineForImage2Image` to enhance output:

```python
from moviepy.editor import VideoFileClip
from PIL import Image
clip = VideoFileClip("zhongli_animation.mp4")
frames = list(map(Image.fromarray ,clip.iter_frames()))

from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
from diffusers.utils import load_image, make_image_grid
import torch

pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
    "svjack/GenshinImpact_XL_Base",
    torch_dtype=torch.float16
)

# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")

from tqdm import tqdm
req = []
for init_image in tqdm(frames):
    prompt = "solo,ZHONGLI\(genshin impact\),1boy,portrait,upper_body,highres, keep eyes forward."
    image = pipeline(prompt, image=init_image, strength=0.8, guidance_scale=10.5).images[0]
    req.append(image)

from diffusers.utils import export_to_video
export_to_video(req, "zhongli_animation_im2im.mp4")
from IPython import display
display.Video("zhongli_animation_im2im.mp4", width=512, height=512)
```

##### Enhancing Animation with RIFE
To enhance the animation using RIFE (Real-Time Intermediate Flow Estimation):

```bash
git clone https://github.com/svjack/Practical-RIFE && cd Practical-RIFE && pip install -r requirements.txt
python inference_video.py --multi=128 --video=../zhongli_animation_im2im.mp4
```

```python
from moviepy.editor import VideoFileClip
clip = VideoFileClip("zhongli_animation_im2im_128X_1280fps.mp4")

def speed_change_video(video_clip, speed_factor, output_path):
    if speed_factor == 1:
        # 如果变速因子为1，直接复制原视频
        video_clip.write_videofile(output_path, codec="libx264")
    else:
        # 否则，按变速因子调整视频速度
        new_duration = video_clip.duration / speed_factor
        sped_up_clip = video_clip.speedx(speed_factor)
        sped_up_clip.write_videofile(output_path, codec="libx264")

speed_change_video(clip, 0.05, "zhongli_animation_im2im_128X_1280fps_wrt.mp4")

VideoFileClip("zhongli_animation_im2im_128X_1280fps_wrt.mp4").set_duration(10).write_videofile("zhongli_animation_im2im_128X_1280fps_wrt_10s.mp4", codec="libx264")
from IPython import display
display.Video("zhongli_animation_im2im_128X_1280fps_wrt_10s.mp4", width=512, height=512)
```

##### Merging Videos Horizontally
You can merge two videos horizontally using the following function:

```python
from moviepy.editor import VideoFileClip, CompositeVideoClip

def merge_videos_horizontally(video_path1, video_path2, output_video_path):
    clip1 = VideoFileClip(video_path1)
    clip2 = VideoFileClip(video_path2)

    max_duration = max(clip1.duration, clip2.duration)

    if clip1.duration < max_duration:
        clip1 = clip1.loop(duration=max_duration)
    if clip2.duration < max_duration:
        clip2 = clip2.loop(duration=max_duration)

    total_width = clip1.w + clip2.w
    total_height = max(clip1.h, clip2.h)

    final_clip = CompositeVideoClip([
        clip1.set_position(("left", "center")),
        clip2.set_position(("right", "center"))
    ], size=(total_width, total_height))

    final_clip.write_videofile(output_video_path, codec='libx264')

    print(f"Merged video saved to {output_video_path}")

# Example usage
video_path1 = "zhongli_animation.mp4"  # 第一个视频文件路径
video_path2 = "zhongli_animation_im2im_128X_1280fps_wrt_10s.mp4"  # 第二个视频文件路径
output_video_path = "zhongli_inter_video_im2im_compare.mp4"  # 输出视频的路径
merge_videos_horizontally(video_path1, video_path2, output_video_path)
```



<div>
    <b><h3 style="text-align: center;">Left is zhongli_animation.mp4 (By AnimateDiffSDXLPipeline), Right is zhongli_animation_im2im_128X_1280fps_wrt_10s.mp4 (By AutoPipelineForImage2Image + Practical-RIFE)</h3></b>
    <div style="display: flex; flex-direction: column; align-items: center;">
        <div style="margin-bottom: 10px;">
            <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/gBaodBk8z3aI69LiT36w2.mp4"></video>
            <p style="text-align: center;">钟离</p>
        </div>
    </div>
</div>

# Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs - ICML 2024

This repository contains the implementation of a cutting-edge text-to-image diffusion model that leverages multimodal large language models (LLMs) for advanced image generation. The project focuses on recaptioning, planning, and generating high-quality images from textual descriptions, showcasing the capabilities of modern AI in creative content production.

## Installation

To get started with the project, follow these steps to set up the environment and install the necessary dependencies:

1. **Clone the Repository:**
   ```bash
   git clone https://github.com/svjack/RPG-DiffusionMaster
   cd RPG-DiffusionMaster
   ```

2. **Create and Activate Conda Environment:**
   ```bash
   conda create -n RPG python==3.9
   conda activate RPG
   ```

3. **Install Jupyter Kernel:**
   ```bash
   pip install ipykernel
   python -m ipykernel install --user --name RPG --display-name "RPG"
   ```

4. **Install Required Packages:**
   ```bash
   pip install -r requirements.txt
   ```

5. **Clone Diffusers Repository:**
   ```bash
   git clone https://github.com/huggingface/diffusers
   ```

## Demo

This section provides a quick demonstration of how to use the `RegionalDiffusionXLPipeline` to generate images based on textual prompts. The example below demonstrates the process of generating an image using a multimodal LLM to split and refine the prompt.

### Import Required Modules

```python
from RegionalDiffusion_base import RegionalDiffusionPipeline
from RegionalDiffusion_xl import RegionalDiffusionXLPipeline
from diffusers.schedulers import KarrasDiffusionSchedulers, DPMSolverMultistepScheduler
from mllm import local_llm, GPT4, DeepSeek
import torch
```

### Load the Model and Configure Scheduler

```python
pipe = RegionalDiffusionXLPipeline.from_single_file(
    "https://huggingface.co/svjack/GenshinImpact_XL_Base/blob/main/sdxlBase_v10.safetensors",
    torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
)
pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True)
pipe.enable_xformers_memory_efficient_attention()
```

### User Input and MLLM Processing

```python
# User input prompt
prompt = 'ZHONGLI(genshin impact) with NING GUANG(genshin impact) in red cheongsam in the bar.'

# Process the prompt using DeepSeek MLLM
para_dict = DeepSeek(prompt)

# Extract parameters for image generation
split_ratio = para_dict['Final split ratio']
regional_prompt = para_dict['Regional Prompt']
negative_prompt = ""  # Optional negative prompt
```

### Generate and Save the Image

```python
images = pipe(
    prompt=regional_prompt,
    split_ratio=split_ratio,  # The ratio of the regional prompt
    batch_size=1,  # Batch size
    base_ratio=0.5,  # The ratio of the base prompt
    base_prompt=prompt,
    num_inference_steps=20,  # Sampling steps
    height=1024,
    negative_prompt=negative_prompt,  # Negative prompt
    width=1024,
    seed=0,  # Random seed
    guidance_scale=7.0
).images[0]

# Save the generated image
images.save("test_zhong_ning.png")
```

This demo showcases the power of combining text-to-image diffusion with multimodal LLMs to generate high-quality images from complex textual descriptions. The generated image is saved as `test_zhong_ning.png`.

---

Feel free to explore the repository and experiment with different prompts and configurations to see the full potential of this advanced text-to-image generation model.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/ZJZkSaMOGRI7QM0uegeqS.png)

## MotionCtrl and MasaCtrl: Genshin Impact Character Synthesis
Check https://github.com/svjack/MasaCtrl to view example about Genshin Impact Character Synthesis video by MasaCtrl 

- **Zhongli Drinking Tea:**
  ```
  "solo,ZHONGLI(genshin impact),1boy,highres," -> "solo,ZHONGLI drink tea use chinese cup (genshin impact),1boy,highres,"
  ```
![Screenshot 2024-11-17 132742](https://github.com/user-attachments/assets/00451728-f2d5-4009-afa8-23baaabdc223)

- **Kamisato Ayato Smiling:**
  ```
  "solo,KAMISATO AYATO(genshin impact),1boy,highres," -> "solo,KAMISATO AYATO smiling (genshin impact),1boy,highres,"
  ```

![Screenshot 2024-11-17 133421](https://github.com/user-attachments/assets/7a920f4c-8a3a-4387-98d6-381a798566ef)

<div style="display: flex; flex-direction: column; align-items: center;">
    <div style="margin-bottom: 10px;">
        <h3>Zhongli Drinking Tea:</h3>
    </div>
    <div style="margin-bottom: 10px;">
        <video controls autoplay src="https://github.com/user-attachments/assets/607e7eb7-d41c-4740-9c8a-8369c31487da" style="width: 1024px; height: 800px;"></video>
    </div>
    <div style="margin-bottom: 10px;">
        <h3>Kamisato Ayato Smiling:</h3>
    </div>
    <div style="margin-bottom: 10px;">
        <video controls autoplay src="https://github.com/user-attachments/assets/aaa9849e-0c53-4012-b6c3-9ceb9910f2f8" style="width: 1024px; height: 800px;"></video>
    </div>
</div>


## Perturbed-Attention-Guidance with Genshin Impact XL
Here's an example of how to enhance Genshin Impact XL by [https://github.com/svjack/Perturbed-Attention-Guidance](https://github.com/svjack/Perturbed-Attention-Guidance):

### Clone the Repository

Next, clone the repository from Hugging Face:

```bash
git clone https://huggingface.co/spaces/svjack/perturbed-attention-guidance-genshin_impact_xl
```

### Navigate to the Repository Directory

Change into the cloned repository directory:

```bash
cd perturbed-attention-guidance-genshin_impact_xl
```

### Install Python Requirements

Install the required Python packages using `pip`:

```bash
pip install -r requirements.txt
```

### Run the Application

Finally, run the application:

```bash
python app.py
```

<div>
    <b><h3 style="text-align: center;">Left Use BreadcrumbsPerturbed-Attention-Guidance
, Right Original Genshin Impact XL</h3></b>
    <b><h4 style="text-align: center;">Left Seems more pretty </h4></b>
    <div style="display: flex; flex-direction: column; align-items: center;">
        <div style="margin-bottom: 10px;">
            <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/Kmh4NJ1AkfV5X3-kkilAK.mp4"></video>
        </div>
    </div>
</div>