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使用 Diffusers 运行推理流程

从预训练模型生成图像

第 4 / 4 课13 个步骤

使用 Diffusers 运行推理流程 是 CoddyKit 上的免费 Deep Learning Academy 课时。这是第 4 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Meet Diffusers

Hugging Face Diffusers is the go-to library for running diffusion models. It wraps all the hard parts behind a friendly interface. 🤗

Install It

Getting started takes one pip install. You will also want transformers and accelerate alongside it for full support.

pip install diffusers transformers accelerate

The Pipeline Idea

A pipeline bundles the U-Net, scheduler, and text encoder into one object, so you call a single line to generate.

Load a Pretrained Model

You download weights straight from the Hub by name. No training needed; someone already did the heavy lifting for you.

pipe = DiffusionPipeline.from_pretrained(model_id)

Move It to the GPU

Send the pipeline to your device so generation runs fast. On a CPU it works, but you will wait a while.

pipe = pipe.to("cuda")

Generate from a Prompt

Call the pipeline with a text prompt and it returns an image. This is the whole magic in one friendly line.

image = pipe("a cozy cabin at sunset").images[0]

Save Your Image

The result is a standard image object, so you can save it like any picture and open it anywhere. 💾

image.save("cabin.png")

Tune the Knobs

Pass the parameters you learned, like inference steps and guidance scale, right into the call to shape the output.

pipe(prompt, num_inference_steps=30, guidance_scale=7.5)

Reproducible Results

Set a seed with a generator and the same prompt yields the same image every time, which is great for comparing settings.

g = torch.Generator("cuda").manual_seed(42)

Swap the Scheduler

You can switch the scheduler on a loaded pipeline to trade speed for quality without retraining anything.

From Theory to Pictures

With a few lines you turned all this theory into real images. The library handles the loop while you focus on prompts. 🎨

Quick Check

What does a Diffusers pipeline bundle together?

在交互式课程中回答

Recap

Load a pipeline, move it to your device, and call it with a prompt to generate. Tune steps, guidance, and seeds to control the result. 🎉

免费开始

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常见问题解答

「使用 Diffusers 运行推理流程」课时是免费的吗？

是的 — 「使用 Diffusers 运行推理流程」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。

「使用 Diffusers 运行推理流程」这节课中我会学到什么？

从预训练模型生成图像你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Deep Learning Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 4 节课，共 4 节。

「使用 Diffusers 运行推理流程」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Deep Learning Academy 课中编写并运行代码吗？

能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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