instantid

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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+huggingface/
+checkpoints/
+models/
+
+# Cog
+.cog

Contributors.md

+<div align="center">
+<h1>InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1>
+
+[**Qixun Wang**](https://github.com/wangqixun)<sup>12</sup> · [**Xu Bai**](https://huggingface.co/baymin0220)<sup>12</sup> · [**Haofan Wang**](https://haofanwang.github.io/)<sup>12*</sup> · [**Zekui Qin**](https://github.com/ZekuiQin)<sup>12</sup> · [**Anthony Chen**](https://antonioo-c.github.io/)<sup>123</sup> · Huaxia Li<sup>2</sup> · Xu Tang<sup>2</sup>
+
+<sup>1</sup>InstantX Team · <sup>2</sup>Xiaohongshu Inc · <sup>3</sup>Peking University
+
+<sup>*</sup>corresponding authors
+
+{haofanwang.ai, wangqixun.ai}@gmail.com
+
+<a href='https://instantid.github.io/'><img src='https://img.shields.io/badge/Project-Page-green'></a>
+<a href='https://arxiv.org/abs/2401.07519'><img src='https://img.shields.io/badge/Technique-Report-red'></a>
+<a href='https://huggingface.co/papers/2401.07519'><img src='https://img.shields.io/static/v1?label=Paper&message=Huggingface&color=orange'></a> 
+[![GitHub](https://img.shields.io/github/stars/InstantID/InstantID?style=social)](https://github.com/InstantID/InstantID)
+
+<a href='https://huggingface.co/spaces/InstantX/InstantID'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
+[![ModelScope](https://img.shields.io/badge/ModelScope-Studios-blue)](https://modelscope.cn/studios/instantx/InstantID/summary)
+[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/InstantX/InstantID)
+
+</div>
+
+InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks.
+
+<img src='assets/applications.png'>
+
+## Release
+- [2024/01/23] 🔥 Our pipeline has been merged into [diffusers](https://github.com/huggingface/diffusers/blob/main/examples/community/pipeline_stable_diffusion_xl_instantid.py)!
+- [2024/01/22] 🔥 We release the [pre-trained checkpoints](https://huggingface.co/InstantX/InstantID), [inference code](https://github.com/InstantID/InstantID/blob/main/infer.py) and [gradio demo](https://huggingface.co/spaces/InstantX/InstantID)!
+- [2024/01/15] 🔥 We release the [technical report](https://arxiv.org/abs/2401.07519).
+- [2023/12/11] 🔥 We launch the [project page](https://instantid.github.io/).
+
+## Demos
+
+### Stylized Synthesis
+
+<p align="center">
+  <img src="assets/0.png">
+</p>
+
+### Comparison with Previous Works
+
+<p align="center">
+  <img src="assets/compare-a.png">
+</p>
+
+Comparison with existing tuning-free state-of-the-art techniques. InstantID achieves better fidelity and retain good text editability (faces and styles blend better).
+
+<p align="center">
+  <img src="assets/compare-c.png">
+</p>
+
+Comparison with pre-trained character LoRAs. We don't need multiple images and still can achieve competitive results as LoRAs without any training.
+
+<p align="center">
+  <img src="assets/compare-b.png">
+</p>
+
+Comparison with InsightFace Swapper (also known as ROOP or Refactor). However, in non-realistic style, our work is more flexible on the integration of face and background.
+
+
+## Download
+
+You can directly download the model from [Huggingface](https://huggingface.co/InstantX/InstantID).
+You also can download the model in python script:
+
+```python
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
+hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints")
+hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")
+```
+
+If you cannot access to Huggingface, you can use [hf-mirror](https://hf-mirror.com/) to download models.
+```python
+export HF_ENDPOINT=https://hf-mirror.com
+huggingface-cli download --resume-download InstantX/InstantID --local-dir checkpoints
+```
+
+For face encoder, you need to manually download via this [URL](https://github.com/deepinsight/insightface/issues/1896#issuecomment-1023867304) to `models/antelopev2` as the default link is invalid. Once you have prepared all models, the folder tree should be like:
+
+```
+  .
+  ├── models
+  ├── checkpoints
+  ├── ip_adapter
+  ├── pipeline_stable_diffusion_xl_instantid.py
+  └── README.md
+```
+
+## Usage
+
+```python
+# !pip install opencv-python transformers accelerate insightface
+import diffusers
+from diffusers.utils import load_image
+from diffusers.models import ControlNetModel
+
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+
+from insightface.app import FaceAnalysis
+from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
+
+# prepare 'antelopev2' under ./models
+app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+# prepare models under ./checkpoints
+face_adapter = f'./checkpoints/ip-adapter.bin'
+controlnet_path = f'./checkpoints/ControlNetModel'
+
+# load IdentityNet
+controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+
+base_model = 'wangqixun/YamerMIX_v8'  # from https://civitai.com/models/84040?modelVersionId=196039
+pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+    base_model,
+    controlnet=controlnet,
+    torch_dtype=torch.float16
+)
+pipe.cuda()
+
+# load adapter
+pipe.load_ip_adapter_instantid(face_adapter)
+```
+
+Then, you can customized your own face images
+
+```python
+# load an image
+face_image = load_image("./examples/yann-lecun_resize.jpg")
+
+# prepare face emb
+face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
+face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1]  # only use the maximum face
+face_emb = face_info['embedding']
+face_kps = draw_kps(face_image, face_info['kps'])
+
+# prompt
+prompt = "film noir style, ink sketch|vector, male man, highly detailed, sharp focus, ultra sharpness, monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic"
+negative_prompt = "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful"
+
+# generate image
+image = pipe(
+    prompt,
+    image_embeds=face_emb,
+    image=face_kps,
+    controlnet_conditioning_scale=0.8,
+    ip_adapter_scale=0.8,
+).images[0]
+```
+
+## Speed Up with LCM-LoRA
+
+Our work is compatible with [LCM-LoRA](https://github.com/luosiallen/latent-consistency-model). First, download the model.
+
+```python
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id="latent-consistency/lcm-lora-sdxl", filename="pytorch_lora_weights.safetensors", local_dir="./checkpoints")
+```
+
+To use it, you just need to load it and infer with a small num_inference_steps. Note that it is recommendated to set guidance_scale between [0, 1].
+```python
+from diffusers import LCMScheduler
+
+lcm_lora_path = "./checkpoints/pytorch_lora_weights.safetensors"
+
+pipe.load_lora_weights(lcm_lora_path)
+pipe.fuse_lora()
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+num_inference_steps = 10
+guidance_scale = 0
+```
+
+## Start a local gradio demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>
+Run the following command:
+
+```python
+python gradio_demo/app.py
+```
+
+## Usage Tips
+- For higher similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter).
+- For over-saturation, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale.
+- For higher text control ability, decrease ip_adapter_scale.
+- For specific styles, choose corresponding base model makes differences.
+- We have not supported multi-person yet, will only use the largest face as reference pose.
+- We provide a [style template](https://github.com/ahgsql/StyleSelectorXL/blob/main/sdxl_styles.json) for reference.
+
+## Community Resources
+
+### Replicate Demo
+- [zsxkib/instant-id](https://replicate.com/zsxkib/instant-id)
+
+### WebUI
+- [Mikubill/sd-webui-controlnet](https://github.com/Mikubill/sd-webui-controlnet/discussions/2589)
+
+### ComfyUI
+- [ZHO-ZHO-ZHO/ComfyUI-InstantID](https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID)
+- [huxiuhan/ComfyUI-InstantID](https://github.com/huxiuhan/ComfyUI-InstantID)
+
+### Windows
+- [sdbds/InstantID-for-windows](https://github.com/sdbds/InstantID-for-windows)
+
+## Acknowledgements
+- InstantID is developed by InstantX Team at Xiaohongshu Inc, all copyright reserved.
+- Our work is highly inspired by [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter) and [ControlNet](https://github.com/lllyasviel/ControlNet). Thanks for their great works!
+- Thanks [Yamer](https://civitai.com/user/Yamer) for developing [YamerMIX](https://civitai.com/models/84040?modelVersionId=196039), we use it as base model in our demo.
+- Thanks [ZHO-ZHO-ZHO](https://github.com/ZHO-ZHO-ZHO), [huxiuhan](https://github.com/huxiuhan), [sdbds](https://github.com/sdbds), [zsxkib](https://replicate.com/zsxkib) for their generous contributions.
+- Thanks to the [HuggingFace](https://github.com/huggingface) gradio team for their free GPU support!
+- Thanks to the [ModelScope](https://github.com/modelscope/modelscope) team for their free GPU support!
+- Thanks to the [OpenXLab](https://openxlab.org.cn/apps/detail/InstantX/InstantID) team for their free GPU support!
+
+## Disclaimer
+The code of InstantID is released under [Apache License](https://github.com/InstantID/InstantID?tab=Apache-2.0-1-ov-file#readme) for both academic and commercial usage. **However, both manual-downloading and auto-downloading face models from insightface are for non-commercial research purposes only** accoreding to their [license](https://github.com/deepinsight/insightface?tab=readme-ov-file#license). Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users.
+
+## Declaration
+🚨🚨🚨 We solemnly clarify that [FAKE][FAKE][FAKE] http://instantid.org [FAKE][FAKE][FAKE] is not authorized and has no relationship with us. It is infringing and quite misleading, and has never contacted us for official cooperation. Please be aware of your personal privacy and subscription fraud. We reserve all legal rights.
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=InstantID/InstantID&type=Date)](https://star-history.com/#InstantID/InstantID&Date)
+
+
+## Cite
+If you find InstantID useful for your research and applications, please cite us using this BibTeX:
+
+```bibtex
+@article{wang2024instantid,
+  title={InstantID: Zero-shot Identity-Preserving Generation in Seconds},
+  author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony},
+  journal={arXiv preprint arXiv:2401.07519},
+  year={2024}
+}
+```
+
+For any question, please feel free to contact us via haofanwang.ai@gmail.com or wangqixun.ai@gmail.com.

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+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file

README.md

+# InstantID
+
+## 论文
+
+**InstantID:Zero-shot Identity-Preserving Generation in Seconds**
+
+* https://arxiv.org/pdf/2401.07519.pdf
+
+## 模型结构
+
+该模型主要结构为Stable Diffusion，并使用`IdentityNet`（提取面部特征），`IPA`（`Porjection + FaceEmbedding + Cross Attention`，获取面部prompt）以及`Text Encoder + Text Embedding + Cross Attention`（获取文字prompt）的输出作为`Unet`的控制条件。
+
+![Alt text](readme_imgs/image-1.png)
+
+## 算法原理
+
+该算法在`ControlNet`思想的基础上，通过增加`IdentityNet`，`IPA`等获取面部特征及Prompt，并将此作为`Unet`的控制条件，具体如下
+
+1、IdentityNet
+
+仅使用五个面部关键点（两个用于眼睛，一个用于鼻子，两个用于嘴巴）作为条件输入；取消了文本提示，并将ID嵌入作为ControlNet中交叉注意力层的条件。
+
+![Alt text](readme_imgs/image-2.png)
+
+2、IPA
+
+引入了一种轻量级的自适应模块，其中包含独立的交叉注意力，以支持图像作为提示。
+
+## 环境配置
+
+### Docker（方法一）
+
+    docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.13.1-centos7.6-dtk-23.04.1-py39-latest
+    docker run --shm-size 10g --network=host --name=instantid --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -it <your IMAGE ID> bash
+
+    pip install -r requirements.txt
+    pip install .
+
+### Dockerfile（方法二）
+
+    # 需要在对应的目录下
+    docker build -t <IMAGE_NAME>:<TAG> .
+    # <your IMAGE ID>用以上拉取的docker的镜像ID替换
+    docker run --shm-size 10g --network=host --name=instantid --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -it <your IMAGE ID> bash
+
+    pip install -r requirements.txt
+    pip install .
+
+
+## 数据集
+
+无
+
+## 推理
+
+### 模型下载
+
+|url|名称|是否必要|
+|:---|:-----|:----|
+|https://huggingface.co/InstantX/InstantID||是|
+|https://huggingface.co/wangqixun/YamerMIX_v8/tree/main||是|
+|https://drive.google.com/file/d/18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8/view|antelopev2|是|
+|https://huggingface.co/latent-consistency/lcm-lora-sdxl/tree/main|pytorch_lora_weights|是|
+|https://huggingface.co/FMNing/sd-control-facenet/tree/main|facenet|否|
+|https://hf-mirror.com/diffusers/controlnet-canny-sdxl-1.0/tree/main||否|
+|https://hf-mirror.com/diffusers/controlnet-depth-sdxl-1.0-small/tree/main||否|
+|https://hf-mirror.com/Intel/dpt-hybrid-midas/tree/main||否|
+|https://huggingface.co/lllyasviel/ControlNet/tree/main/annotator/ckpts||否|
+|https://huggingface.co/thibaud/controlnet-openpose-sdxl-1.0/tree/main||否|
+
+
+注意：如果huggingface无法访问，可访问（https://hf-mirror.com/），`是否必须`中`否`涉及的模型仅在`app-multicontrolnet.py`需要。
+
+
+    models/
+    └── antelopev2
+        ├── 1k3d68.onnx
+        ├── 2d106det.onnx
+        ├── genderage.onnx
+        ├── glintr100.onnx
+        └── scrfd_10g_bnkps.onnx
+    
+    checkpoints/
+    ├── ControlNetModel
+    │   ├── config.json
+    │   └── diffusion_pytorch_model.safetensors
+    ├── diffusers
+    │   ├── controlnet-canny-sdxl-1.0
+    │   │   ├── config.json
+    │   │   └── diffusion_pytorch_model.bin
+    │   └── controlnet-depth-sdxl-1.0-small
+    │       ├── config.json
+    │       └── diffusion_pytorch_model.bin
+    ├── Intel
+    │   └── dpt-hybrid-midas
+    │       ├── config.json
+    │       ├── preprocessor_config.json
+    │       └── pytorch_model.bin
+    ├── ip-adapter.bin
+    ├── lllyasviel
+    │   └── ControlNet
+    │       └── annotator
+    │           └── ckpts
+    │               ├── body_pose_model.pth
+    │               ├── facenet.pth
+    │               └── hand_pose_model.pth
+    ├── pytorch_lora_weights.safetensors
+    ├── thibaud
+    │   └── controlnet-openpose-sdxl-1.0
+    │       ├── config.json
+    │       └── diffusion_pytorch_model.bin
+    └── wangqixun
+        └── YamerMIX_v8
+            ├── model_index.json
+            ├── scheduler
+            │   └── scheduler_config.json
+            ├── text_encoder
+            │   ├── config.json
+            │   └── model.safetensors
+            ├── text_encoder_2
+            │   ├── config.json
+            │   └── model.safetensors
+            ├── tokenizer
+            │   ├── merges.txt
+            │   ├── special_tokens_map.json
+            │   ├── tokenizer_config.json
+            │   └── vocab.json
+            ├── tokenizer_2
+            │   ├── merges.txt
+            │   ├── special_tokens_map.json
+            │   ├── tokenizer_config.json
+            │   └── vocab.json
+            ├── unet
+            │   ├── config.json
+            │   └── diffusion_pytorch_model.safetensors
+            └── vae
+                ├── config.json
+                └── diffusion_pytorch_model.safetensors
+
+
+### 命令
+
+    python gradio_demo/app.py
+
+    # 多controlnet
+    python gradio_demo/app-multicontrolnet.py 
+
+## result
+
+![Alt text](readme_imgs/image-3.png)
+
+![Alt text](readme_imgs/image-4.png)
+
+### 精度
+
+无
+
+## 应用场景
+
+### 算法类别
+
+`AIGC`
+
+### 热点行业
+
+`零售,广媒,设计`
+
+## 源码仓库及问题反馈
+
+* https://developer.hpccube.com/codes/modelzoo/instantid_pytorch
+
+## 参考资料
+
+* https://github.com/InstantID/InstantID

cog.yaml

+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  # set to true if your model requires a GPU
+  gpu: true
+  # cuda: "12.1"
+
+  # a list of ubuntu apt packages to install
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+
+  # python version in the form '3.11' or '3.11.4'
+  python_version: "3.11"
+
+  # a list of packages in the format <package-name>==<version>
+  python_packages:
+    - "opencv-python==4.9.0.80"
+    - "transformers==4.37.0"
+    - "accelerate==0.26.1"
+    - "insightface==0.7.3"
+    - "diffusers==0.25.1"
+    - "onnxruntime==1.16.3"
+
+  # commands run after the environment is setup
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.6.0/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+
+# predict.py defines how predictions are run on your model
+predict: "cog/predict.py:Predictor"

cog/README.md

+# InstantID Cog Model
+
+[![Replicate](https://replicate.com/zsxkib/instant-id/badge)](https://replicate.com/zsxkib/instant-id)
+
+## Overview
+This repository contains the implementation of [InstantID](https://github.com/InstantID/InstantID) as a [Cog](https://github.com/replicate/cog) model. 
+
+Using [Cog](https://github.com/replicate/cog) allows any users with a GPU to run the model locally easily, without the hassle of downloading weights, installing libraries, or managing CUDA versions. Everything just works.
+
+## Development
+To push your own fork of InstantID to [Replicate](https://replicate.com), follow the [Model Pushing Guide](https://replicate.com/docs/guides/push-a-model).
+
+## Basic Usage
+To make predictions using the model, execute the following command from the root of this project:
+
+```bash
+cog predict \
+-i image=@examples/sam_resize.png \
+-i prompt="analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality" \
+-i negative_prompt="nsfw" \
+-i width=680 \
+-i height=680 \
+-i ip_adapter_scale=0.8 \
+-i controlnet_conditioning_scale=0.8 \
+-i num_inference_steps=30 \
+-i guidance_scale=5
+```
+
+<table>
+  <tr>
+    <td>
+      <p align="center">Input</p>
+      <img src="https://replicate.delivery/pbxt/KGy0R72cMwriR9EnCLu6hgVkQNd60mY01mDZAQqcUic9rVw4/musk_resize.jpeg" alt="Sample Input Image" width="90%"/>
+    </td>
+    <td>
+      <p align="center">Output</p>
+      <img src="https://replicate.delivery/pbxt/oGOxXELcLcpaMBeIeffwdxKZAkuzwOzzoxKadjhV8YgQWk8IB/result.jpg" alt="Sample Output Image" width="100%"/>
+    </td>
+  </tr>
+</table>
+
+## Input Parameters
+
+The following table provides details about each input parameter for the `predict` function:
+
+| Parameter                       | Description                        | Default Value                                                                                                  | Range       |
+| ------------------------------- | ---------------------------------- | -------------------------------------------------------------------------------------------------------------- | ----------- |
+| `image`                         | Input image                        | A path to the input image file                                                                                 | Path string |
+| `prompt`                        | Input prompt                       | "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, ... " | String      |
+| `negative_prompt`               | Input Negative Prompt              | (empty string)                                                                                                 | String      |
+| `width`                         | Width of output image              | 640                                                                                                            | 512 - 2048  |
+| `height`                        | Height of output image             | 640                                                                                                            | 512 - 2048  |
+| `ip_adapter_scale`              | Scale for IP adapter               | 0.8                                                                                                            | 0.0 - 1.0   |
+| `controlnet_conditioning_scale` | Scale for ControlNet conditioning  | 0.8                                                                                                            | 0.0 - 1.0   |
+| `num_inference_steps`           | Number of denoising steps          | 30                                                                                                             | 1 - 500     |
+| `guidance_scale`                | Scale for classifier-free guidance | 5                                                                                                              | 1 - 50      |
+
+This table provides a quick reference to understand and modify the inputs for generating predictions using the model.
+
+

cog/predict.py

+# Prediction interface for Cog ⚙️
+# https://github.com/replicate/cog/blob/main/docs/python.md
+
+import os
+import sys
+
+import time
+import subprocess
+from cog import BasePredictor, Input, Path
+
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+
+from diffusers.utils import load_image
+from diffusers.models import ControlNetModel
+
+from insightface.app import FaceAnalysis
+
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+from pipeline_stable_diffusion_xl_instantid import (
+    StableDiffusionXLInstantIDPipeline,
+    draw_kps,
+)
+
+# for `ip-adaper`, `ControlNetModel`, and `stable-diffusion-xl-base-1.0`
+CHECKPOINTS_CACHE = "./checkpoints"
+CHECKPOINTS_URL = (
+    "https://weights.replicate.delivery/default/InstantID/checkpoints.tar"
+)
+
+# for `models/antelopev2`
+MODELS_CACHE = "./models"
+MODELS_URL = "https://weights.replicate.delivery/default/InstantID/models.tar"
+
+
+def resize_img(
+    input_image,
+    max_side=1280,
+    min_side=1024,
+    size=None,
+    pad_to_max_side=False,
+    mode=Image.BILINEAR,
+    base_pixel_number=64,
+):
+    w, h = input_image.size
+    if size is not None:
+        w_resize_new, h_resize_new = size
+    else:
+        ratio = min_side / min(h, w)
+        w, h = round(ratio * w), round(ratio * h)
+        ratio = max_side / max(h, w)
+        input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
+        w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
+        h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
+    input_image = input_image.resize([w_resize_new, h_resize_new], mode)
+
+    if pad_to_max_side:
+        res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
+        offset_x = (max_side - w_resize_new) // 2
+        offset_y = (max_side - h_resize_new) // 2
+        res[
+            offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new
+        ] = np.array(input_image)
+        input_image = Image.fromarray(res)
+    return input_image
+
+
+def download_weights(url, dest):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+        if not os.path.exists(CHECKPOINTS_CACHE):
+            download_weights(CHECKPOINTS_URL, CHECKPOINTS_CACHE)
+
+        if not os.path.exists(MODELS_CACHE):
+            download_weights(MODELS_URL, MODELS_CACHE)
+
+        self.width, self.height = 640, 640
+        self.app = FaceAnalysis(
+            name="antelopev2",
+            root="./",
+            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+        )
+        self.app.prepare(ctx_id=0, det_size=(self.width, self.height))
+
+        # Path to InstantID models
+        face_adapter = f"./checkpoints/ip-adapter.bin"
+        controlnet_path = f"./checkpoints/ControlNetModel"
+
+        # Load pipeline
+        self.controlnet = ControlNetModel.from_pretrained(
+            controlnet_path,
+            torch_dtype=torch.float16,
+            cache_dir=CHECKPOINTS_CACHE,
+            local_files_only=True,
+        )
+
+        base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
+        self.pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+            base_model_path,
+            controlnet=self.controlnet,
+            torch_dtype=torch.float16,
+            cache_dir=CHECKPOINTS_CACHE,
+            local_files_only=True,
+        )
+        self.pipe.cuda()
+        self.pipe.load_ip_adapter_instantid(face_adapter)
+
+    def predict(
+        self,
+        image: Path = Input(description="Input image"),
+        prompt: str = Input(
+            description="Input prompt",
+            default="analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality",
+        ),
+        negative_prompt: str = Input(
+            description="Input Negative Prompt",
+            default="",
+        ),
+        width: int = Input(
+            description="Width of output image",
+            default=640,
+            ge=512,
+            le=2048,
+        ),
+        height: int = Input(
+            description="Height of output image",
+            default=640,
+            ge=512,
+            le=2048,
+        ),
+        ip_adapter_scale: float = Input(
+            description="Scale for IP adapter",
+            default=0.8,
+            ge=0,
+            le=1,
+        ),
+        controlnet_conditioning_scale: float = Input(
+            description="Scale for ControlNet conditioning",
+            default=0.8,
+            ge=0,
+            le=1,
+        ),
+        num_inference_steps: int = Input(
+            description="Number of denoising steps",
+            default=30,
+            ge=1,
+            le=500,
+        ),
+        guidance_scale: float = Input(
+            description="Scale for classifier-free guidance",
+            default=5,
+            ge=1,
+            le=50,
+        ),
+    ) -> Path:
+        """Run a single prediction on the model"""
+        if self.width != width or self.height != height:
+            print(f"[!] Resizing output to {width}x{height}")
+            self.width = width
+            self.height = height
+            self.app.prepare(ctx_id=0, det_size=(self.width, self.height))
+
+        face_image = load_image(str(image))
+        face_image = resize_img(face_image)
+
+        face_info = self.app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
+        face_info = sorted(
+            face_info,
+            key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]),
+            reverse=True,
+        )[
+            0
+        ]  # only use the maximum face
+        face_emb = face_info["embedding"]
+        face_kps = draw_kps(face_image, face_info["kps"])
+
+        self.pipe.set_ip_adapter_scale(ip_adapter_scale)
+        image = self.pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image_embeds=face_emb,
+            image=face_kps,
+            controlnet_conditioning_scale=controlnet_conditioning_scale,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+        ).images[0]
+
+        output_path = "result.jpg"
+        image.save(output_path)
+        return Path(output_path)