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Commit b916e230 authored by chenpangpang's avatar chenpangpang
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feat: 新版kolors初始提交

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Dockerfile
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FROM image.sourcefind.cn:5000/gpu/admin/base/jupyterlab-pytorch:2.2.0-python3.10-cuda12.1-ubuntu22.04 as base
RUN cd /root && git clone -b gpu http://developer.hpccube.com/codes/chenpangpang/kolors.git
WORKDIR /root/kolors/Kolors
RUN conda create --name kolors -y python=3.8 && source activate kolors && pip install -r requirements.txt && python3 setup.py install
RUN rm -r build dist kolors.egg-info
ARG IMAGE=kolors
ARG IMAGE_UPPER=Kolors
ARG BRANCH=gpu
RUN cd /root && git clone -b $BRANCH http://developer.hpccube.com/codes/chenpangpang/$IMAGE.git
WORKDIR /root/$IMAGE/$IMAGE_UPPER
RUN pip install -r requirements.txt
#########
# Prod #
#########
FROM image.sourcefind.cn:5000/gpu/admin/base/jupyterlab-pytorch:2.2.0-python3.10-cuda12.1-ubuntu22.04
RUN conda create --name kolors python=3.8
COPY --from=base /opt/conda/envs/kolors/lib/python3.8/site-packages /opt/conda/envs/kolors/lib/python3.8/site-packages
COPY --from=base /root/kolors/Kolors /root/Kolors
COPY --from=base /root/kolors/启动器.ipynb /root/kolors/run.sh /root/
RUN mkdir /root/Kolors/weights
# 模型仓库http://113.200.138.88:18080/aimodels/Kolors.git，放置在/public/home/openaimodels/dockerFileTemp/chenyh
COPY chenyh/Kolors /root/Kolors/weights/Kolors
ARG IMAGE=kolors
ARG IMAGE_UPPER=Kolors
COPY chenyh/$IMAGE/frpc_linux_amd64_v0.2 /opt/conda/lib/python3.10/site-packages/gradio/
RUN chmod +x /opt/conda/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2
COPY chenyh/$IMAGE/Kwai-Kolors/Kolors /root/$IMAGE_UPPER/Kwai-Kolors/Kolors
COPY chenyh/$IMAGE/Kwai-Kolors/Kolors-IP-Adapter-Plus /root/$IMAGE_UPPER/Kwai-Kolors/Kolors-IP-Adapter-Plus
COPY --from=base /opt/conda/lib/python3.10/site-packages /opt/conda/lib/python3.10/site-packages
COPY --from=base /root/$IMAGE/$IMAGE_UPPER /root/$IMAGE_UPPER
COPY --from=base /root/$IMAGE/启动器.ipynb /root/$IMAGE/start.sh /root/$IMAGE/assets /root/
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模型许可协议
模型发布日期：2024/7/6
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英文版
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Kolors/README.md
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English</a>&nbsp ｜ &nbsp<a href="README_CN.md">中文</a>&nbsp
</p>
<br><br>
<p align="center">
<img src="imgs/logo.png" width="400"/>
<p>
<br>
<div align="center">
<a href='https://huggingface.co/Kwai-Kolors/Kolors'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HF-yellow'></a> &ensp;
<a href="https://github.com/Kwai-Kolors/Kolors"><img src="https://img.shields.io/static/v1?label=Kolors Code&message=Github&color=blue&logo=github-pages"></a> &ensp;
<a href="https://kwai-kolors.github.io/"><img src="https://img.shields.io/static/v1?label=Team%20Page&message=Page&color=green"></a> &ensp;
<a href="https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv:Kolors&color=red&logo=arxiv"></a> &ensp;
<a href="https://kolors.kuaishou.com/"><img src="https://img.shields.io/static/v1?label=Official Website&message=Page&color=green"></a> &ensp;
</div>
# Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis
<figure>
<img src="imgs/head_final3.png">
</figure>
<br><br>
## Contents
- [🎉 News](#News)
- [📑 Open-source Plan](#open-source-plan)
- [📖 Introduction](#Introduction)
- [📊 Evaluation 🥇🥇🔥🔥](#Evaluation)
- [🎥 Visualization](#Visualization)
- [🛠️ Usage](#Usage)
- [📜 License & Citation & Acknowledgments](#License)
<br><br>
## <a name="News"></a>🎉 News
* 2024.07.09 💥 Kolors supports [ComfyUI](https://github.com/comfyanonymous/ComfyUI#manual-install-windows-linux). Thanks to [@kijai](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper) with his great work.
* 2024.07.06 🔥🔥🔥 We release **Kolors**, a large text-to-image model trained on billions of text-image pairs. This model is bilingual in both Chinese and English, and supports a context length of 256 tokens. For more technical details, please refer to [technical report](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf).
* 2024.07.03 📊 Kolors won the second place on [FlagEval Multimodal Text-to-Image Leaderboard](https://flageval.baai.ac.cn/#/leaderboard/multimodal?kind=t2i), excelling particularly in the Chinese and English subjective quality assessment where Kolors took the first place.
* 2024.07.02 🎉 Congratulations! Our paper on controllable video generation, [DragAnything: Motion Control for Anything using Entity Representation](https://arxiv.org/abs/2403.07420), have been accepted by ECCV 2024.
* 2024.02.08 🎉 Congratulations! Our paper on generative model evaluation, [Learning Multi-dimensional Human Preference for Text-to-Image Generation](https://wangbohan97.github.io/MPS/), have been accepted by CVPR 2024.
<br><br>
## <a name="open-source-plan"></a>📑 Open-source Plan
- Kolors (Text-to-Image Model)
- [x] Inference
- [x] Checkpoints
- [ ] LoRA
- [ ] ControlNet (Pose, Canny, Depth)
- [ ] IP-Adapter
- [x] ComfyUI
- [x] Gradio
- [ ] Diffusers
<br><br>
##
## <a name="Introduction"></a>📖 Introduction
Kolors is a large-scale text-to-image generation model based on latent diffusion, developed by the Kuaishou Kolors team. Trained on billions of text-image pairs, Kolors exhibits significant advantages over both open-source and closed-source models in visual quality, complex semantic accuracy, and text rendering for both Chinese and English characters. Furthermore, Kolors supports both Chinese and English inputs, demonstrating strong performance in understanding and generating Chinese-specific content. For more details, please refer to this <a href="https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf">technical report</a></b>.
<br><br>
## <a name="Evaluation"></a>📊 Evaluation
We have collected a comprehensive text-to-image evaluation dataset named KolorsPrompts to compare Kolors with other state-of-the-art open models and closed-source models. KolorsPrompts includes over 1,000 prompts across 14 catagories and 12 evaluation dimensions. The evaluation process incorporates both human and machine assessments. In relevant benchmark evaluations, Kolors demonstrated highly competitive performance, achieving industry-leading standards.
<br><br>
### Human Assessment
For the human evaluation, we invited 50 imagery experts to conduct comparative evaluations of the results generated by different models. The experts rated the generated images based on three criteria: visual appeal, text faithfulness, and overall satisfaction. In the evaluation, Kolors achieved the highest overall satisfaction score and significantly led in visual appeal compared to other models.
| Model | Average Overall Satisfaction | Average Visual Appeal | Average Text Faithfulness |
| :--------------: | :--------: | :--------: | :--------: |
| Adobe-Firefly | 3.03 | 3.46 | 3.84 |
| Stable Diffusion 3 | 3.26 | 3.50 | 4.20 |
| DALL-E 3 | 3.32 | 3.54 | 4.22 |
| Midjourney-v5 | 3.32 | 3.68 | 4.02 |
| Playground-v2.5 | 3.37 | 3.73 | 4.04 |
| Midjourney-v6 | 3.58 | 3.92 | 4.18 |
| **Kolors** | **3.59** | **3.99** | **4.17** |
------
<div style="color: gray; font-size: small;">
**All model results are tested with the April 2024 product versions**
</div>
<br>
### Machine Assessment
We used [MPS](https://arxiv.org/abs/2405.14705) (Multi-dimensional Human Preference Score) on KolorsPrompts as the evaluation metric for machine assessment. Kolors achieved the highest MPS score, which is consistent with the results of the human evaluations.
<div style="text-align:center">
| Models | Overall MPS |
|:-------------------:|:-------------:|
| Adobe-Firefly | 8.5 |
| Stable Diffusion 3 | 8.9 |
| DALL-E 3 | 9.0 |
| Midjourney-v5 | 9.4 |
| Playground-v2.5 | 9.8 |
| Midjourney-v6 | 10.2 |
| **Kolors** | **10.3** |
</div>
<br>
For more experimental results and details, please refer to our [technical report](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf).
<br><br>
## <a name="Visualization"></a>🎥 Visualization
* **High-quality Portrait**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/zl8.png" />
</div>
<br>
* **Chinese Elements Generation**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/cn_all.png"/>
</div>
<br>
* **Complex Semantic Understanding**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/fz_all.png"/>
</div>
<br>
* **Text Rendering**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/wz_all.png" />
</div>
<br>
</div>
The visualized case prompts mentioned above can be accessed [here](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/prompt_vis.txt).
<br><br>
## <a name="Usage"></a>🛠️ Usage
### Requirements
* Python 3.8 or later
* PyTorch 1.13.1 or later
* Transformers 4.26.1 or later
* Recommended: CUDA 11.7 or later
<br>
1. Repository Cloning and Dependency Installation
```bash
apt-get install git-lfs
git clone https://github.com/Kwai-Kolors/Kolors
cd Kolors
conda create --name kolors python=3.8
conda activate kolors
pip install -r requirements.txt
python3 setup.py install
```
2. Weights download（[link](https://huggingface.co/Kwai-Kolors/Kolors)）：
```bash
huggingface-cli download --resume-download Kwai-Kolors/Kolors --local-dir weights/Kolors
```
or
```bash
git lfs clone https://huggingface.co/Kwai-Kolors/Kolors weights/Kolors
```
3. Inference：
```bash
python3 scripts/sample.py "一张瓢虫的照片，微距，变焦，高质量，电影，拿着一个牌子，写着“可图”"
# The image will be saved to "scripts/outputs/sample_text.jpg"
```
4. Web demo：
```bash
python3 scripts/sampleui.py
```
<br><br>
## <a name="License"></a>📜 License & Citation & Acknowledgments
### License
Kolors are fully open-sourced for academic research. For commercial use, please fill out this [questionnaire](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/可图KOLORS模型商业授权申请书.docx) and sent it to kwai-kolors@kuaishou.com to obtain the agreement for free use.
We open-source Kolors to promote the development of large text-to-image models in collaboration with the open-source community. The code of this project is open-sourced under the Apache-2.0 license. We sincerely urge all developers and users to strictly adhere to the [open-source license](MODEL_LICENSE), avoiding the use of the open-source model, code, and its derivatives for any purposes that may harm the country and society or for any services not evaluated and registered for safety. Note that despite our best efforts to ensure the compliance, accuracy, and safety of the data during training, due to the diversity and combinability of generated content and the probabilistic randomness affecting the model, we cannot guarantee the accuracy and safety of the output content, and the model is susceptible to misleading. This project does not assume any legal responsibility for any data security issues, public opinion risks, or risks and liabilities arising from the model being misled, abused, misused, or improperly utilized due to the use of the open-source model and code.
### Citation
If you find our work helpful, please cite it!
```
@article{kolors,
title={Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis},
author={Kolors Team},
journal={arXiv preprint},
year={2024}
}
```
### Acknowledgments
- Thanks to [Diffusers](https://github.com/huggingface/diffusers) for providing the codebase.
- Thanks to [ChatGLM3](https://github.com/THUDM/ChatGLM3) for providing the powerful Chinese language model.
<br>
### Contact Us
If you want to leave a message for our R&D team and product team, feel free to join our [WeChat group](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/wechat.png). You can also contact us via email (kwai-kolors@kuaishou.com).
[![Star History Chart](https://api.star-history.com/svg?repos=Kwai-Kolors/Kolors&type=Date)](https://star-history.com/#Kwai-Kolors/Kolors&Date)
---
title: Kolors
emoji: 🖼
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 4.38.1
app_file: app.py
pinned: false
license: apache-2.0
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Kolors/README_CN.md deleted 100644 → 0
View file @ b3fae090
<p align="left">
中文</a>&nbsp ｜ &nbsp<a href="README.md">English</a>&nbsp
</p>
<!-- <br><br> -->
<p align="center">
<img src="imgs/logo.png" width="400"/>
<p>
<br>
<!-- <div align="center">
<a href='https://kwai-kolors.github.io/'><img src='https://img.shields.io/badge/Team-Page-green'></a> <a href=''><img src='https://img.shields.io/badge/Technique-Report-red'></a> [![Teampage](https://img.shields.io/badge/Website-Page-blue)](https://kolors.kuaishou.com/)
</div> -->
<div align="center">
<a href='https://huggingface.co/Kwai-Kolors/Kolors'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HF-yellow'></a> &ensp;
<a href="https://github.com/Kwai-Kolors/Kolors"><img src="https://img.shields.io/static/v1?label=Kolors Code&message=Github&color=blue&logo=github-pages"></a> &ensp;
<a href="https://kwai-kolors.github.io/"><img src="https://img.shields.io/static/v1?label=Team%20Page&message=Page&color=green"></a> &ensp;
<a href="https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv:Kolors&color=red&logo=arxiv"></a> &ensp;
<a href="https://kolors.kuaishou.com/"><img src="https://img.shields.io/static/v1?label=Official Website&message=Page&color=green"></a> &ensp;
</div>
</p>
# Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis
<figure>
<img src="imgs/head_final3.png">
</figure>
<br><br>
## 目录
- [🎉 新闻](#新闻)
- [📑 开源计划](#开源计划)
- [📖 模型介绍](#模型介绍)
- [📊 评测表现 🥇🥇🔥🔥](#评测表现)
- [🎥 可视化](#可视化)
- [🛠️ 快速使用](#快速使用)
- [📜 协议、引用、致谢](#协议引用)
<br><br>
## <a name="新闻"></a>🎉 新闻
* 2024.07.09 💥 Kolors 支持了 [ComfyUI](https://github.com/comfyanonymous/ComfyUI#manual-install-windows-linux)，感谢 [@kijai](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper) 的工作。
* 2024.07.06 🔥🔥🔥 我们开源了基于隐空间扩散的文生图大模型 **Kolors** ，该模型基于数十亿图文对进行训练，支持256的上下文token数，支持中英双语，技术细节参考[技术报告](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf)。
* 2024.07.03 📊 Kolors 在智源研究院 [FlagEval 多模态文生图](https://flageval.baai.ac.cn/#/leaderboard/multimodal?kind=t2i)评测中取得第二名，其中中文主观质量、英文主观质量两个单项排名第一。
* 2024.07.02 🎉 祝贺，可图项目组提出的可控视频生成方法 [DragAnything: Motion Control for Anything using Entity Representation](https://arxiv.org/abs/2403.07420) 被 ECCV 2024 接收。
* 2024.02.08 🎉 祝贺，可图项目组提出的生成模型评估方法 [Learning Multi-dimensional Human Preference for Text-to-Image Generation](https://wangbohan97.github.io/MPS/) 被 CVPR 2024 接收。
<br><br>
## <a name="开源计划"></a>📑 开源计划
- Kolors (Text-to-Image Model)
- [x] Inference
- [x] Checkpoints
- [ ] LoRA
- [ ] ControlNet (Pose, Canny, Depth)
- [ ] IP-Adapter
- [x] ComfyUI
- [x] Gradio
- [ ] Diffusers
<br><br>
## <a name="模型介绍"></a>📖 模型介绍
可图大模型是由快手可图团队开发的基于潜在扩散的大规模文本到图像生成模型。Kolors 在数十亿图文对下进行训练，在视觉质量、复杂语义理解、文字生成（中英文字符）等方面，相比于开源/闭源模型，都展示出了巨大的优势。同时，Kolors 支持中英双语，在中文特色内容理解方面更具竞争力。更多的实验结果和细节请查看我们的<a href="https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf">技术报告</a></b>。
<br><br>
## <a name="评测表现"></a>📊 评测表现
为了全面比较 Kolors 与其他模型的生成能力，我们构建了包含人工评估、机器评估的全面评测内容。
在相关基准评测中，Kolors 具有非常有竞争力的表现，达到业界领先水平。我们构建了一个包含14种垂类，12个挑战项，总数量为一千多个 prompt 的文生图评估集 KolorsPrompts。在 KolorsPrompts 上，我们收集了 Kolors 与市面上常见的 SOTA 级别的开源/闭源系统的文生图结果，并进行了人工评测和机器评测。
<br><br>
### 人工评测
我们邀请了50个具有图像领域知识的专业评估人员对不同模型的生成结果进行对比评估，为生成图像打分，衡量维度为：画面质量、图文相关性、整体满意度三个方面。
Kolors 在整体满意度方面处于最优水平，其中画面质量显著领先其他模型。
<div style="text-align: center;">
| 模型 | 整体满意度平均分 | 画面质量平均分 | 图文相关性平均分 |
| :--------------: | :--------: | :--------: | :--------: |
| Adobe-Firefly | 3.03 | 3.46 | 3.84 |
| Stable Diffusion 3 | 3.26 | 3.50 | 4.20 |
| DALL-E 3 | 3.32 | 3.54 | 4.22 |
| Midjourney-v5 | 3.32 | 3.68 | 4.02 |
| Playground-v2.5 | 3.37 | 3.73 | 4.04 |
| Midjourney-v6 | 3.58 | 3.92 | 4.18 |
| **Kolors** | **3.59** | **3.99** | **4.17** |
</div>
<div style="color: gray; font-size: small;">
**所有模型结果取自 2024.04 的产品版本**
</div>
<br>
### 机器评测
我们采用 [MPS](https://arxiv.org/abs/2405.14705) (Multi-dimensional Human preference Score) 来评估上述模型。
我们以 KolorsPrompts 作为基础评估数据集，计算多个模型的 MPS 指标。Kolors 实现了最高的MPS 指标，这与人工评估的指标一致。
<div style="text-align:center">
| 模型 | MPS综合得分 |
|-------------------|-------------|
| Adobe-Firefly | 8.5 |
| Stable Diffusion 3 | 8.9 |
| DALL-E 3 | 9.0 |
| Midjourney-v5 | 9.4 |
| Playground-v2.5 | 9.8 |
| Midjourney-v6 | 10.2 |
| **Kolors** | **10.3** |
</div>
<br>
更多的实验结果和细节请查看我们的技术报告。点击[技术报告](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf)。
<br><br>
## <a name="可视化"></a>🎥 可视化
* **高质量人像**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/zl8.png" />
</div>
<br>
* **中国元素**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/cn_all.png"/>
</div>
<br>
* **复杂语义理解**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/fz_all.png"/>
</div>
<br>
* **文字绘制**
<div style="display: flex; justify-content: space-between;">
<img src="imgs/wz_all.png" />
</div>
<br>
</div>
上述可视化 case，可以点击[可视化prompts](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/prompt_vis.txt) 获取
<br><br>
## <a name="快速使用"></a>🛠️ 快速使用
### 要求
* python 3.8及以上版本
* pytorch 1.13.1及以上版本
* transformers 4.26.1及以上版本
* 建议使用CUDA 11.7及以上
<br>
1、仓库克隆及依赖安装
```bash
apt-get install git-lfs
git clone https://github.com/Kwai-Kolors/Kolors
cd Kolors
conda create --name kolors python=3.8
conda activate kolors
pip install -r requirements.txt
python3 setup.py install
```
2、模型权重下载（[链接](https://huggingface.co/Kwai-Kolors/Kolors)）：
```bash
huggingface-cli download --resume-download Kwai-Kolors/Kolors --local-dir weights/Kolors
```
或者
```bash
git lfs clone https://huggingface.co/Kwai-Kolors/Kolors weights/Kolors
```
3、模型推理：
```bash
python3 scripts/sample.py "一张瓢虫的照片，微距，变焦，高质量，电影，拿着一个牌子，写着“可图”"
# The image will be saved to "scripts/outputs/sample_text.jpg"
```
4、 Web demo：
```bash
python3 scripts/sampleui.py
```
<br><br>
## <a name="协议引用"></a>📜协议、引用、致谢
### 协议
**Kolors**（可图) 权重对学术研究完全开放，如需商用请填写[问卷](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/可图KOLORS模型商业授权申请书.docx)，发送问卷至 kwai-kolors@kuaishou.com 进行登记获取许可后免费使用。
本开源模型旨在与开源社区共同推进文生图大模型技术的发展。本项目代码依照 Apache-2.0 协议开源，模型权重需要遵循本《模型许可协议》，我们恳请所有开发者和用户严格遵守[开源协议](MODEL_LICENSE)，避免将开源模型、代码及其衍生物用于任何可能对国家和社会造成危害的用途，或用于任何未经安全评估和备案的服务。需要注意，尽管模型在训练中我们尽力确保数据的合规性、准确性和安全性，但由于视觉生成模型存在生成多样性和可组合性等特点，以及生成模型受概率随机性因素的影响，模型无法保证输出内容的准确性和安全性，且模型易被误导。本项目不对因使用开源模型和代码而导致的任何数据安全问题、舆情风险或因模型被误导、滥用、传播、不当利用而产生的风险和责任承担任何法律责任。
<br>
### 引用
如果你觉得我们的工作对你有帮助，欢迎引用！
```
@article{kolors,
title={Kolors: Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis},
author={Kolors Team},
journal={arXiv preprint},
year={2024}
}
```
<br>
### 致谢
- 感谢 [Diffusers](https://github.com/huggingface/diffusers) 提供的codebase
- 感谢 [ChatGLM3](https://github.com/THUDM/ChatGLM3) 提供的强大中文语言模型
<br>
### 联系我们
如果你想给我们的研发团队和产品团队留言，欢迎加入我们的[微信群](https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/wechat.png)。当然也可以通过邮件（kwai-kolors@kuaishou.com）联系我们。
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=Kwai-Kolors/Kolors&type=Date)](https://star-history.com/#Kwai-Kolors/Kolors&Date)
Kolors/app.py 0 → 100644
View file @ b916e230
import random
import torch
from huggingface_hub import snapshot_download
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
from kolors.pipelines import pipeline_stable_diffusion_xl_chatglm_256_ipadapter, \
pipeline_stable_diffusion_xl_chatglm_256
from kolors.models.modeling_chatglm import ChatGLMModel
from kolors.models.tokenization_chatglm import ChatGLMTokenizer
from kolors.models import unet_2d_condition
from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel
import gradio as gr
import numpy as np
device = "cuda"
ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors")
ckpt_IPA_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors-IP-Adapter-Plus")
text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device)
tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device)
scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
unet_t2i = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
unet_i2i = unet_2d_condition.UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_IPA_dir}/image_encoder',
ignore_mismatched_sizes=True).to(dtype=torch.float16,
device=device)
ip_img_size = 336
clip_image_processor = CLIPImageProcessor(size=ip_img_size, crop_size=ip_img_size)
pipe_t2i = pipeline_stable_diffusion_xl_chatglm_256.StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet_t2i,
scheduler=scheduler,
force_zeros_for_empty_prompt=False
).to(device)
pipe_i2i = pipeline_stable_diffusion_xl_chatglm_256_ipadapter.StableDiffusionXLPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet_i2i,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=clip_image_processor,
force_zeros_for_empty_prompt=False
).to(device)
if hasattr(pipe_i2i.unet, 'encoder_hid_proj'):
pipe_i2i.unet.text_encoder_hid_proj = pipe_i2i.unet.encoder_hid_proj
pipe_i2i.load_ip_adapter(f'{ckpt_IPA_dir}', subfolder="", weight_name=["ip_adapter_plus_general.bin"])
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(prompt,
ip_adapter_image=None,
ip_adapter_scale=0.5,
negative_prompt="",
seed=0,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=5.0,
num_inference_steps=25
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if ip_adapter_image is None:
pipe_t2i.to(device)
image = pipe_t2i(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator
).images[0]
return image
else:
pipe_i2i.to(device)
image_encoder.to(device)
pipe_i2i.image_encoder = image_encoder
pipe_i2i.set_ip_adapter_scale([ip_adapter_scale])
image = pipe_i2i(
prompt=prompt,
ip_adapter_image=[ip_adapter_image],
negative_prompt=negative_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=1,
generator=generator
).images[0]
return image
examples = [
["一张瓢虫的照片，微距，变焦，高质量，电影，拿着一个牌子，写着“可图”", None, None],
["3D anime style, hyperrealistic oil painting, dolphin leaping out of the water", None, None],
["穿着黑色T恤衫，上面中文绿色大字写着“可图”", "image/test_ip.jpg", 0.5],
["A cute dog is running", "image/test_ip2.png", 0.5]
]
css = """
#col-left {
margin: 0 auto;
max-width: 600px;
}
#col-right {
margin: 0 auto;
max-width: 750px;
}
"""
def load_description(fp):
with open(fp, 'r', encoding='utf-8') as f:
content = f.read()
return content
with gr.Blocks(css=css) as Kolors:
gr.HTML(load_description("assets/title.md"))
with gr.Row():
with gr.Column(elem_id="col-left"):
with gr.Row():
prompt = gr.Textbox(
label="Prompt",
placeholder="Enter your prompt",
lines=2
)
with gr.Row():
ip_adapter_image = gr.Image(label="Image Prompt (optional)", type="pil")
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Textbox(
label="Negative prompt",
placeholder="Enter a negative prompt",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=5.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=10,
maximum=50,
step=1,
value=25,
)
with gr.Row():
ip_adapter_scale = gr.Slider(
label="Image influence scale",
info="Use 1 for creating variations",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.5,
)
with gr.Row():
run_button = gr.Button("Run")
with gr.Column(elem_id="col-right"):
result = gr.Image(label="Result", show_label=False)
with gr.Row():
gr.Examples(
fn=infer,
examples=examples,
inputs=[prompt, ip_adapter_image, ip_adapter_scale],
outputs=[result]
)
run_button.click(
fn=infer,
inputs=[prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height,
guidance_scale, num_inference_steps],
outputs=[result]
)
Kolors.queue().launch(server_name="0.0.0.0", share=True)
Kolors/assets/title.md 0 → 100644
View file @ b916e230
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
<div>
<h1>Kolors</h1>
<span>Effective Training of Diffusion Model for Photorealistic Text-to-Image Synthesis</span>
<br>
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
<a href="https://github.com/Kwai-Kolors/Kolors"><img src="https://img.shields.io/static/v1?label=Kolors Code&message=Github&color=blue&logo=github-pages"></a> &ensp;
<a href="https://kwai-kolors.github.io/"><img src="https://img.shields.io/static/v1?label=Team%20Page&message=Page&color=green"></a> &ensp;
<a href="https://github.com/Kwai-Kolors/Kolors/blob/master/imgs/Kolors_paper.pdf"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Kolors&color=red"></a> &ensp;
<a href="https://klingai.kuaishou.com/"><img src="https://img.shields.io/static/v1?label=Official Website&message=Page&color=green"></a>
</div>
</div>
</div>
\ No newline at end of file
Kolors/imgs/head_final3.png deleted 100644 → 0
View file @ b3fae090
This image diff could not be displayed because it is too large. You can view the blob instead.
Kolors/imgs/prompt_vis.txt deleted 100644 → 0
View file @ b3fae090
1、视觉质量：
- 一对年轻的中国情侣，皮肤白皙，穿着时尚的运动装，背景是现代的北京城市天际线。面部细节，清晰的毛孔，使用最新款的相机拍摄，特写镜头，超高画质，8K，视觉盛宴
2、中国元素
- 万里长城，蜿蜒
- 一张北京国家体育场（俗称“鸟巢”）的高度细节图片。图片应展示体育场复杂的钢结构，强调其独特的建筑设计。场景设定在白天，天空晴朗，突显出体育场的宏伟规模和现代感。包括周围的奥林匹克公园和一些游客，以增加场景的背景和生气。
- 上海外滩
3、复杂语义理解
- 满月下的街道，熙熙攘攘的行人正在享受繁华夜生活。街角摊位上，一位有着火红头发、穿着标志性天鹅绒斗篷的年轻女子，正在和脾气暴躁的老小贩讨价还价。这个脾气暴躁的小贩身材高大、老道，身着一套整洁西装，留着小胡子，用他那部蒸汽朋克式的电话兴致勃勃地交谈
- 画面有四只神兽：朱雀、玄武、青龙、白虎。朱雀位于画面上方，羽毛鲜红如火，尾羽如凤凰般绚丽，翅膀展开时似燃烧的火焰。玄武居于下方，是龟蛇相缠的形象，巨龟背上盘绕着一条黑色巨蛇，龟甲上有古老的符文，蛇眼冰冷锐利。青龙位于右方，长身盘旋在天际，龙鳞碧绿如翡翠，龙须飘逸，龙角如鹿，口吐云雾。白虎居于左方，体态威猛，白色的皮毛上有黑色斑纹，双眼炯炯有神，尖牙利爪，周围是苍茫的群山和草原。
- 一张高对比度的照片，熊猫骑在马上，戴着巫师帽，正在看书，马站在土墙旁的街道上，有绿草从街道的裂缝中长出来。
4、文字绘制
- 一张瓢虫的照片，微距，变焦，高质量，电影，瓢虫拿着一个木牌，上面写着“我爱世界” 的文字
- 一只小橘猫在弹钢琴，钢琴是黑色的牌子是“KOLORS”，猫的身影清晰的映照在钢琴上
- 街边的路牌，上面写着“天道酬勤”
\ No newline at end of file
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