huggingface/diffusers 源码下载与部署教程

项目概述

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.

项目地址

https://github.com/huggingface/diffusers

项目页面预览

关键指标

• Stars：32465
• 主要语言：Python
• License：Apache License 2.0
• 最近更新：2026-01-15T12:21:48Z
• 默认分支：main

本站高速下载（国内可用）

点击下载（本站镜像）
– SHA256：d2182bd06fa668206773ab730015eb3bc614ed6ab7c8d7afc3352243ff4b5090

安装部署要点（README 精选）

Installation

We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing PyTorch, please refer to their official documentation.

Quickstart

Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the from_pretrained method to load any pretrained diffusion model (browse the Hub for 30,000+ checkpoints):

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipeline.to("cuda")
pipeline("An image of a squirrel in Picasso style").images[0]

You can also dig into the models and schedulers toolbox to build your own diffusion system:

from diffusers import DDPMScheduler, UNet2DModel
from PIL import Image
import torch

scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
scheduler.set_timesteps(50)

sample_size = model.config.sample_size
noise = torch.randn((1, 3, sample_size, sample_size), device="cuda")
input = noise

for t in scheduler.timesteps:
    with torch.no_grad():
        noisy_residual = model(input, t).sample
        prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
        input = prev_noisy_sample

image = (input / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = Image.fromarray((image * 255).round().astype("uint8"))
image

Check out the Quickstart to launch your diffusion journey today!

常用命令（从 README 提取）

pip install --upgrade diffusers[torch]

conda install -c conda-forge diffusers

通用部署说明

1. 下载源码并阅读 README
2. 安装依赖（pip/npm/yarn 等）
3. 配置环境变量（API Key、模型路径、数据库等）
4. 启动服务并测试访问
5. 上线建议：Nginx 反代 + HTTPS + 进程守护（systemd / pm2）

免责声明与版权说明

本文仅做开源项目整理与教程索引，源码版权归原作者所有，请遵循对应 License 合规使用。