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README.md 0 → 100644
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# LinFusion
## 论文
LinFusion: 1 GPU, 1 Minute, 16K Image
- https://arxiv.org/abs/2409.02097
## 模型结构
作者将所提出的 Generalized Linear Attention 模块集成到 SD 的架构中，替换原始的 Self-Attention 模块，生成的模型称为 LinFusion。使用知识蒸馏策略，只训练线性注意模块 50K 步，LinFusion 的性能即可与原始 SD 相当甚至更好，同时显著降低了时间和显存占用的复杂度。
<div align=center>
<img src="./assets/linfusin_overview.png"/>
</div>
## 算法原理
为了得到具有线性计算复杂度的 Diffusion Backbone，一个简单的方案是使用 Mamba2 替换所有的 Self-Attention，如图 4 (a) 所示。作者使用双向的 SSM 来确保当前位置可以从后续位置访问信息。SD 中的 Self-Attention 模块不包含 Mamba2 中的门控操作或者 RMS-Norm。作者为了保持一致性，就删除了这些结构，导致性能略有提高。
<div align=center>
<img src="./assets/principle.png"/>
</div>
## 环境配置
### Docker（方法一）
推荐使用docker方式运行， 此处提供[光源](https://www.sourcefind.cn/#/service-details)拉取docker镜像的地址与使用步骤
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.10
docker run -it --shm-size=1024G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name linfusion_pytorch <your IMAGE ID> bash # <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：4555f389bc2a
cd /path/your_code_data/
pip install git+https://github.com/openai/CLIP.git
pip install click clean-fid open_clip_torch
```
Tips:以上dtk驱动、python、torch、vllm等DCU相关工具版本需要严格一一对应。
### Dockerfile（方法二）
此处提供dockerfile的使用方法
```
docker build -t linfusion:latest .
docker run -it --shm-size=1024G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name linfusion_pytorch linfusion bash
cd /path/your_code_data/
pip install git+https://github.com/openai/CLIP.git
pip install click clean-fid open_clip_torch
```
### Anaconda（方法三）
此处提供本地配置、编译的详细步骤，例如：
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```
DTK驱动:dtk24.04.2
python:3.10
torch:2.1.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
其它非深度学习库参照requirement.txt安装：
```
cd /path/your_code_data/
pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
pip install git+https://github.com/openai/CLIP.git
pip install click clean-fid open_clip_torch
```
## 数据集
如果没有，执行训练指令时代码将默认自动将bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images 数据集下载到目录中，其中包含 169k 张图像，需要约 75 GB 的磁盘空间。~/.cache
训练数据集SCNet快速下载链接[bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images](http://113.200.138.88:18080/aidatasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images.git)
训练数据目录结构如下：
```
── bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images
├── train-00000-of-00080-b8c547951c435f2e.parquet
├── train-00001-of-00080-6502db8bd493f966.parquet
├── train-00002-of-00080-73d42259ed4d3c6c.parquet
└── ...
```
验证数据集下载整理如下，也可通过scnet快速下载链接[coco/val2014](http://113.200.138.88:18080/aidatasets/project-dependency/coco2014)下载：
```
wget http://images.cocodataset.org/zips/val2014.zip
unzip val2014.zip -d /path/to/coco
```
## 训练
### 单机单卡
```
cd /path/your_code_data/
bash ./examples/train/train.sh
```
### 单机多卡
```
bash ./examples/training/distill.sh
```
## 推理
### 单机单卡
inference:
```
cd /path/your_code_data/
#注意：可修改pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0"为自己的模型路径
python examples/inference/sdxl_distrifusion_example.py
```
运行examples/eval/eval.sh以生成用于评估的图像。
```
#注意：您可能需要指定outdir、repo_id、resolution等
bash examples/eval/singleDCU_eval.sh
```
### 单机多卡
```
#其中，--nproc_per_node为使用卡数。
bash examples/eval/eval.sh
```
#运行examples/eval/calculate_metrics.sh以计算指标。您可能需要指定/path/to/coco、fake_dir等。
```
#运行时会自动下载clip模型，可离线下载openclip模型laion/CLIP-ViT-g-14-laion2B-s12B-b42K
#同时修改src/eval/calculate_metrics.py中compute_clip_score函数的下述代码行：
#clip, _, clip_preprocessor = open_clip.create_model_and_transforms("ViT-g-14", pretrained="laion2b_s12b_b42k")中pretrained为你的模型地址
#例如：pretrained="/data/luopl/LinFusion/laion/CLIP-ViT-g-14-laion2B-s12B-b42K/open_clip_pytorch_model.bin
bash examples/eval/calculate_metrics.sh
```
## result
使用的加速卡:4张 K100_AI
模型：
- stabilityai/stable-diffusion-xl-base-1.0
- Yuanshi/LinFusion-XL
文生图结果：
inference:
<div align=left>
<img src="./assets/astronaut.png"/>
</div>
### 精度
使用的加速卡:4张 K100_AI
<div align=left>
<img src="./assets/acc.png"/>
</div>
## 应用场景
### 算法类别
`以文生图`
### 热点应用行业
`科研,教育,政府,金融`
## 预训练权重
[stabilityai/stable-diffusion-v1-5模型下载SCNet链接](http://113.200.138.88:18080/aimodels/stable-diffusion-v1-5)
[stabilityai/stable-diffusion-2-1模型下载SCNet链接](http://113.200.138.88:18080/aimodels/stable-diffusion-2-1)
[stabilityai/stable-diffusion-xl-base-1.0模型下载SCNet链接](http://113.200.138.88:18080/aimodels/stable-diffusion-xl-base-1.0)
[Yuanshi/LinFusion-1-5模型下载SCNet链接](http://113.200.138.88:18080/aimodels/yuanshi/LinFusion-1-5.git)
[Yuanshi/LinFusion-2-1模型下载SCNet链接](http://113.200.138.88:18080/aimodels/yuanshi/LinFusion-2-1.git)
[Yuanshi/LinFusion-XL模型下载SCNet链接](http://113.200.138.88:18080/aimodels/yuanshi/LinFusion-XL.git)
[laion/CLIP-ViT-g-14-laion2B-s12B-b42K模型下载SCNet链接](http://113.200.138.88:18080/aimodels/clip-vit-g-14-laion2b-s12b-b42k)
## 源码仓库及问题反馈
- http://developer.hpccube.com/codes/modelzoo/linfusion_pytorch.git
## 参考资料
- https://github.com/Huage001/LinFusion/
README_ori.md 0 → 100644
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<div align="center">
# LinFusion
<a href="https://arxiv.org/abs/2409.02097"><img src="https://img.shields.io/badge/arXiv-2409.02097-A42C25.svg" alt="arXiv"></a>
<a href="https://lv-linfusion.github.io"><img src="https://img.shields.io/badge/ProjectPage-LinFusion-376ED2#376ED2.svg" alt="Home Page"></a>
<a href="https://huggingface.co/spaces/Huage001/LinFusion-SD-v1.5"><img src="https://img.shields.io/static/v1?label=HuggingFace&message=gradio demo&color=yellow"></a>
</div>
> **LinFusion: 1 GPU, 1 Minute, 16K Image**
> <br>
> [Songhua Liu](http://121.37.94.87/),
> [Weuhao Yu](https://whyu.me/),
> [Zhenxiong Tan](https://scholar.google.com/citations?user=HP9Be6UAAAAJ&hl=en),
> and
> [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)
> <br>
> [Learning and Vision Lab](http://lv-nus.org/), National University of Singapore
> <br>
![](./assets/picture.png)
## 🔥News
**[2024/09/28]** We release evaluation codes on the COCO benchmark!
**[2024/09/27]** We successfully integrate LinFusion to [DistriFusion](https://github.com/mit-han-lab/distrifuser), an effective and efficient strategy for generating an image in parallel, and achieve more significant acceleration! Please refer to the example [here](https://github.com/Huage001/LinFusion/blob/main/examples/inference/sdxl_distrifusion_example.py)!
**[2024/09/26]** We enable **16K** image generation with merely **24G** video memory! Please refer to the example [here](https://github.com/Huage001/LinFusion/blob/main/examples/inference/superres_sdxl_low_v_mem.ipynb)!
**[2024/09/20]** We release **a more advanced pipeline for ultra-high-resolution image generation using SD-XL**! It can be used for [text-to-image generation](https://github.com/Huage001/LinFusion/blob/main/examples/inference/ultra_text2image_sdxl.ipynb) and [image super-resolution](https://github.com/Huage001/LinFusion/blob/main/examples/inference/superres_sdxl.ipynb)!
**[2024/09/20]** We release training codes for Stable Diffusion XL [here](https://github.com/Huage001/LinFusion/blob/main/src/train/distill_xl.py)!
**[2024/09/13]** We release LinFusion models for Stable Diffusion v-2.1 and Stable Diffusion XL!
**[2024/09/13]** We release training codes for Stable Diffusion v-1.5, v-2.1, and their variants [here](https://github.com/Huage001/LinFusion/blob/main/src/train/distill.py)!
**[2024/09/08]** We release codes for **16K** image generation [here](https://github.com/Huage001/LinFusion/blob/main/examples/inference/ultra_text2image_w_sdedit.ipynb)!
**[2024/09/05]** [Gradio demo](https://huggingface.co/spaces/Huage001/LinFusion-SD-v1.5) for SD-v1.5 is released! Text-to-image, image-to-image, and IP-Adapter are supported currently.
## Supported Models
1. `Yuanshi/LinFusion-1-5`: For Stable Diffusion v-1.5 and its variants. <a href="https://huggingface.co/Yuanshi/LinFusion-1-5"><img src="https://img.shields.io/badge/%F0%9F%A4%97-LinFusion for SD v1.5-yellow"></a>
1. `Yuanshi/LinFusion-2-1`: For Stable Diffusion v-2.1 and its variants. <a href="https://huggingface.co/Yuanshi/LinFusion-2-1"><img src="https://img.shields.io/badge/%F0%9F%A4%97-LinFusion for SD v2.1-yellow"></a>
1. `Yuanshi/LinFusion-XL`: For Stable Diffusion XL and its variants. <a href="https://huggingface.co/Yuanshi/LinFusion-XL"><img src="https://img.shields.io/badge/%F0%9F%A4%97-LinFusion for SD XL-yellow"></a>
## Quick Start
* If you have not, install [PyTorch](https://pytorch.org/get-started/locally/) and [diffusers](https://huggingface.co/docs/diffusers/index).
* Clone this repo to your project directory:
``` bash
git clone https://github.com/Huage001/LinFusion.git
```
* **You only need two lines!**
```diff
from diffusers import AutoPipelineForText2Image
import torch
+ from src.linfusion import LinFusion
sd_repo = "Lykon/dreamshaper-8"
pipeline = AutoPipelineForText2Image.from_pretrained(
sd_repo, torch_dtype=torch.float16, variant="fp16"
).to(torch.device("cuda"))
+ linfusion = LinFusion.construct_for(pipeline)
image = pipeline(
"An astronaut floating in space. Beautiful view of the stars and the universe in the background.",
generator=torch.manual_seed(123)
).images[0]
```
`LinFusion.construct_for(pipeline)` will return a LinFusion model that matches the pipeline's structure. And this LinFusion model will **automatically mount to** the pipeline's forward function.
* `examples/inference/basic_usage.ipynb` shows a basic text-to-image example.
## Gradio Demo
* Currently, you can try LinFusion for SD-v1.5 online [here](https://huggingface.co/spaces/Huage001/LinFusion-SD-v1.5). Text-to-image, image-to-image, and IP-Adapter are supported currently.
* We are building Gradio local demos for more base models and applications, so that everyone can deploy the demos locally.
## Ultrahigh-Resolution Generation
From the perspective of efficiency, our method supports high-resolution generation such as 16K images. Nevertheless, directly applying diffusion models trained on low resolutions for higher-resolution generation can result in content distortion and duplication. To tackle this challenge, we apply following techniques:
* [SDEdit](https://huggingface.co/docs/diffusers/v0.30.2/en/api/pipelines/stable_diffusion/img2img#image-to-image). **The basic idea is to generate a low-resolution result at first, based on which we gradually upscale the image.**
**Please refer to `examples/inference/ultra_text2image_w_sdedit.ipynb` for an example.**
* [DemoFusion](https://github.com/PRIS-CV/DemoFusion). It also generates high-resolution images from low-resolution results. Latents of the low-resolution generation are reused for high-resolution generation. Dilated convolutions are involved. Compared with the original version:
1. We can generate high-resolution directly with the help of LinFusion instead of using patch-wise strategies.
2. Insights in SDEdit are also applied here, so that the high-resolution branch does not need to go through full denoising steps.
3. Image are upscaled to 2x, 4x, 8x, ... resolutions instead of 1x, 2x, 3x, ...
**Please refer to `examples/inference/ultra_text2image_sdxl.ipynb` for an example of high-resolution text-to-image generation** (first generate 1024 resolution, then generate 2048, 4096, 8192, etc) **and `examples/inference/superres_sdxl.ipynb` for an example of image super resolution** (directly upscale to the target resolution, generally 2x is recommended and using it multiple times if you want higher scales).
* Above codes for 16K image generation require a GPU with 80G video memory. **If you encounter OOM issues, you may consider `examples/inference/superres_sdxl_low_w_mem.ipynb`, which requires only 24G video memory.** We achieve this by 1) chunked forward of classifier-free guidance inference, 2) chunked forward of feed-forward network in Transformer blocks, 3) in-placed activation functions in ResNets, and 4) caching UNet residuals on CPU.
* [DistriFusion](https://github.com/mit-han-lab/distrifuser). Alternatively, if you have multiple GPU cards, you can try integrating LinFusion to DistriFusion, which achieves **more significant acceleration due to its linearity and thus almost constant communication cost**. You can run an minimal example with:
```bash
torchrun --nproc_per_node=$N_GPUS -m examples.inference.sdxl_distrifusion_example
```
* We are working on integrating LinFusion with more advanced approaches that are dedicated on high-resolution extension! **Feel free to create pull requests if you come up with better solutions!**
## Training
* Before training, make sure you have the packages shown in `requirements.txt` installed:
```bash
pip install -r requirements.txt
```
* Training codes for Stable Diffusion v-1.5, v-2.1, and their variants are released in `src/train/distill.py`. We present an exampler running script in `examples/train/distill.sh`. You can run it on a 8-GPU machine via:
```bash
bash ./examples/training/distill.sh
```
The codes will download `bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images` [dataset](https://huggingface.co/datasets/bhargavsdesai/laion_improved_aesthetics_6.5plus_with_images) automatically to `~/.cache` directory by default if there is not, which contains 169k images and requires ~75 GB disk space.
We use fp16 precision and 512 resolution for Stable Diffusion v-1.5 and bf16 precision and 768 resolution for Stable Diffusion v-2.1.
* Training codes for Stable Diffusion XL are released in `src/train/distill_xl.py`. We present an exampler running script in `examples/train/distill_xl.sh`. You can run it on a 8-GPU machine via:
```bash
bash ./examples/training/distill_xl.sh
```
## Evaluation
Following [GigaGAN](https://github.com/mingukkang/GigaGAN/tree/main/evaluation), we use 30,000 COCO captions to generate 30,000 images for evaluation. FID against COCO val2014 is reported as a metric, and CLIP text cosine similarity is used to reflect the text-image alignment.
* To evaluate LinFusion, first install the required packages:
```bash
pip install git+https://github.com/openai/CLIP.git
pip install click clean-fid open_clip_torch
```
* Download and unzip COCO val2014 to `/path/to/coco`:
```bash
wget http://images.cocodataset.org/zips/val2014.zip
unzip val2014.zip -d /path/to/coco
```
* Run `examples/eval/eval.sh` to generate images for evaluation. You may need to specify `outdir`, `repo_id`, `resolution`, etc.
```bash
bash examples/eval/eval.sh
```
* Run `examples/eval/calculate_metrics.sh` to calculate the metrics. You may need to specify `/path/to/coco`, `fake_dir`, etc.
```bash
bash examples/eval/calculate_metrics.sh
```
## ToDo
- [x] Stable Diffusion 1.5 support.
- [x] Stable Diffusion 2.1 support.
- [x] Stable Diffusion XL support.
- [x] Release training code for LinFusion.
- [x] Release evaluation code for LinFusion.
- [ ] Gradio local interface.
## Acknowledgement
* We extend our gratitude to the authors of [SDEdit](https://huggingface.co/docs/diffusers/v0.30.2/en/api/pipelines/stable_diffusion/img2img#image-to-image), [DemoFusion](https://github.com/PRIS-CV/DemoFusion), and [DistriFusion](https://github.com/mit-han-lab/distrifuser) for their contributions, which inspire us a lot on applying LinFusion for high-resolution generation.
* Our evaluation codes are adapted from [SiD-LSG](https://github.com/mingyuanzhou/SiD-LSG) and [GigaGAN](https://github.com/mingukkang/GigaGAN/tree/main/evaluation).
* We thank [@Adamdad](https://github.com/Adamdad), [@yu-rp](https://github.com/yu-rp), and [@czg1225](https://github.com/czg1225) for valuable discussions.
## Citation
If you finds this repo is helpful, please consider citing:
```bib
@article{liu2024linfusion,
title = {LinFusion: 1 GPU, 1 Minute, 16K Image},
author = {Liu, Songhua and Yu, Weihao and Tan, Zhenxiong and Wang, Xinchao},
year = {2024},
eprint = {2409.02097},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
assets/captions.txt 0 → 100644
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examples/eval/calculate_metrics.sh 0 → 100644
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how_many=30000
ref_data="coco2014"
ref_dir="/path/to/coco/"
ref_type="val2014"
eval_res=256
batch_size=128
clip_model="ViT-G/14"
caption_file='assets/captions.txt'
fake_dir='eval_results/sdxl'
python -m src.eval.calculate_metrics --how_many $how_many --ref_data $ref_data --ref_dir $ref_dir --ref_type $ref_type --fake_dir $fake_dir --eval_res $eval_res --batch_size $batch_size --clip_model $clip_model --caption_file $caption_file
examples/eval/eval.sh 0 → 100644
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torchrun --standalone --nproc_per_node=4 -m src.eval.eval \
--outdir='eval_results/sd21' \
--seeds=0-29999 \
--batch=4 \
--repo_id='stabilityai/stable-diffusion-2-1' \
--resolution=512 \
--guidance_scale=7.5
\ No newline at end of file
examples/eval/singleDCU_eval.sh 0 → 100644
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python -m src.eval.eval \
--outdir='eval_results/sd21' \
--seeds=0-29999 \
--batch=4 \
--repo_id='stabilityai/stable-diffusion-2-1' \
--resolution=512 \
--guidance_scale=7.5
\ No newline at end of file
examples/inference/basic_usage.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from diffusers import AutoPipelineForText2Image\n",
"import torch\n",
"\n",
"from src.linfusion import LinFusion\n",
"from src.tools import seed_everything"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sd_repo = \"Lykon/dreamshaper-8\"\n",
"\n",
"pipeline = AutoPipelineForText2Image.from_pretrained(\n",
" sd_repo, torch_dtype=torch.float16, variant=\"fp16\"\n",
").to(torch.device(\"cuda\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"linfusion = LinFusion.construct_for(pipeline, pretrained_model_name_or_path=\"Yuanshi/LinFusion-1-5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"seed_everything(123)\n",
"image = pipeline(\n",
"\t\"An astronaut floating in space. Beautiful view of the stars and the universe in the background.\"\n",
").images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "new_vc",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
examples/inference/basic_usage_sd2.ipynb 0 → 100644
View file @ 346d2571
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from diffusers import AutoPipelineForText2Image\n",
"import torch\n",
"\n",
"from src.linfusion import LinFusion\n",
"from src.tools import seed_everything"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sd_repo = \"stabilityai/stable-diffusion-2-1\"\n",
"\n",
"pipeline = AutoPipelineForText2Image.from_pretrained(\n",
" sd_repo, torch_dtype=torch.bfloat16, variant=\"fp16\"\n",
").to(torch.device(\"cuda\"))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"linfusion = LinFusion.construct_for(pipeline, pretrained_model_name_or_path=\"Yuanshi/LinFusion-2-1\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"seed_everything(123)\n",
"image = pipeline(\n",
"\t\"An astronaut floating in space. Beautiful view of the stars and the universe in the background.\"\n",
").images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "new_vc",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
examples/inference/basic_usage_sdxl.ipynb 0 → 100644
View file @ 346d2571
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from diffusers import AutoPipelineForText2Image\n",
"import torch\n",
"\n",
"from src.linfusion import LinFusion\n",
"from src.tools import seed_everything"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sd_repo = \"stabilityai/stable-diffusion-xl-base-1.0\"\n",
"\n",
"pipeline = AutoPipelineForText2Image.from_pretrained(\n",
" sd_repo, torch_dtype=torch.float16, variant=\"fp16\"\n",
").to(torch.device(\"cuda\"))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"linfusion = LinFusion.construct_for(pipeline, pretrained_model_name_or_path=\"Yuanshi/LinFusion-XL\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"seed_everything(123)\n",
"image = pipeline(\n",
"\t\"An astronaut floating in space. Beautiful view of the stars and the universe in the background.\"\n",
").images[0]\n",
"image"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "new_vc",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
examples/inference/sdxl_distrifusion_example.py 0 → 100644
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import torch
from src.pipelines.pipelines_distrifusion_sdxl import DistriSDXLPipeline
from src.distrifuser.utils import DistriConfig
distri_config = DistriConfig(height=1024, width=1024, warmup_steps=4)
pipeline = DistriSDXLPipeline.from_pretrained(
distri_config=distri_config,
pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0",
variant="fp16",
use_safetensors=True,
)
pipeline.set_progress_bar_config(disable=distri_config.rank != 0)
image = pipeline(
prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
generator=torch.Generator(device="cuda").manual_seed(233)
).images[0]
if distri_config.rank == 0:
image.save("astronaut.png")
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