惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

N
Netflix TechBlog - Medium
罗磊的独立博客
云风的 BLOG
云风的 BLOG
Last Week in AI
Last Week in AI
Y
Y Combinator Blog
小众软件
小众软件
Blog — PlanetScale
Blog — PlanetScale
T
The Blog of Author Tim Ferriss
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
博客园 - Franky
F
Fortinet All Blogs
D
Docker
博客园 - 司徒正美
腾讯CDC
Recent Announcements
Recent Announcements
The Cloudflare Blog
B
Blog RSS Feed
GbyAI
GbyAI
T
Tailwind CSS Blog
雷峰网
雷峰网
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园 - 三生石上(FineUI控件)
阮一峰的网络日志
阮一峰的网络日志

Runpod Blog.

New Runpod datacenter now live: AP-IN-1 Track GPU spend across your team with Cost Centers The GPU supply supercycle is here. Here’s what AI builders need to know. Community Spotlight: One-click AI image and video generation on Runpod with SwarmUI | Runpod Blog Community Spotlight: LoRA Pilot Data Prep to Inference Introducing the Runpod Assistant: Manage Your Cloud GPU Resources with Natural Language OpenAI's Parameter Golf: Train the Best Language Model That Fits in 16MB on Runpod LLM inference optimization: techniques that actually reduce latency and cost Pruna P-Video and Vidu Q3 public endpoints now available on Runpod Runpod brand spelling guide Quickstart - Runpod Documentation The AI market looks nothing like the narrative Training StyleGAN3 with Vision-Aided GAN on Runpod KoboldAI – The Other Roleplay Front End, And Why You May Want to Use It How to Connect Cursor to LLM Pods on Runpod for Seamless AI Dev Community Spotlight: How AnonAI Scaled Its Private Chatbot Platform with Runpod Prompt Scheduling with Disco Diffusion on Runpod Runpod's Latest Innovation: Dockerless CLI for Streamlined AI Development Run Your Own AI from Your iPhone Using Runpod Introducing Flash: Run GPU workloads on Runpod Serverless: No Docker required Use Claude Code with your own model on Runpod: No Anthropic account required Avoid Errors by Selecting the Proper Resources for Your Pod What hackers built on Runpod at TreeHacks 2026 Easily Back Up and Restore Your Pod with Cloud Sync + Backblaze B2 The Complete Guide to GPU Requirements for LLM Fine-Tuning AI Guides, Tutorials & GPU Infrastructure Insights | Runpod Your first Claude Code project within Runpod: a complete setup guide 10 billion Serverless requests and counting Building for resilience: Runpod’s response to the AWS us-east-1 outage How to Connect Google Colab to Runpod
Runpod and RandomSeed Bring Stable Diffusion API Access
Brendan McKeag · 2023-09-22 · via Runpod Blog.

Runpod and RandomSeed Bring Stable Diffusion API Access

Runpod is delighted to collaborate with RandomSeed by providing the serverless compute power required to create generative AI art through their API access. The mission of RandomSeed is to help developers build AI image generators by providing a hosted AUTOMATIC1111 API that can create images on-demand through API calls, saving developers the burden of having to host and manage their own infrastructure for Stable Diffusion.

The Benefits of Serverless Pricing

RandomSeed helps leverage the serverless capacity of Runpod by providing a user-friendly method to interact with Stable Diffusion through API calls with none of the technical expertise that would normally be required in building and maintaining such a setup. All you have to do is pass your parameters to RandomSeed in an API request and you'll have your image served wherever it needs to go. The end result is that you only pay for the server time you actually use, which ends up costing you an average of a cent (or even less) per image. This means that your costs scale with your needs, compared to the static, per-hour price of renting a pod.

RandomSeed even has a playground that you can use to test out the service and see if it's a good fit for you:

Stable Diffusion web UI with prompt 'a photo of a cute golden retriever' and the generated puppy output

AUTOMATIC1111 API documentation

RandomSeed has graciously contributed the following documentation on how to pass requests to the Stable Diffusion API.

AUTOMATIC1111 has emerged as a leading tool for image generation through stable diffusion, boasting a comprehensive API that enables a multitude of functions. However, it’s near impossible to find in-depth documentation explaining what each parameter does. In this article, we attempt to share our findings from using the API while running our own cloud-based AUTOMATIC1111 API at RandomSeed.

txt2img

Model Module
control_v11p_sd15_cannycanny
control_v11p_sd15_mlsdMLSD
control_v11p_sd15_depthdepth, depth_leres, depth_leres_boost
control_v11p_sd15_normalbaenormal-bae
control_v11p_sd15_segsegmentation
control_v11p_sd15_inpaintinpaint, inpaint_only, inpaint_only+lama
control_v11p_sd15_lineartlineart, lineart_coarse, lineart_standard
control_v11p_sd15s2_lineart_animelineart_anime
control_v11p_sd15_openposeopenpose, openpose_face, openpose_faceonly, openpose_full, openpose_hand
control_v11p_sd15_scribblescribble_pidinet, scribble_hed, scribble_xdog, fake_scribble
control_v11p_sdd15_softedgesoftedge_hed
control_v11p_sd15_tiletile_resample, tile_colorfix, tile_colorfix+sharp
control_v11e_sd15_shuffleshuffle
control_v11e_sd15_ip2p aka instruct Pix2Pixnull
control_v1p_sd15_grcode_monsternull
Model Module
control_v11p_sd15_cannycanny
control_v11p_sd15_mlsdMLSD
control_v11p_sd15_depthdepth, depth_leres, depth_leres_boost
control_v11p_sd15_normalbaenormal-bae
control_v11p_sd15_segsegmentation
control_v11p_sd15_inpaintinpaint, inpaint_only, inpaint_only+lama
control_v11p_sd15_lineartlineart, lineart_coarse, lineart_standard
control_v11p_sd15s2_lineart_animelineart_anime
control_v11p_sd15_openposeopenpose, openpose_face, openpose_faceonly, openpose_full, openpose_hand
control_v11p_sd15_scribblescribble_pidinet, scribble_hed, scribble_xdog, fake_scribble
control_v11p_sdd15_softedgesoftedge_hed
control_v11p_sd15_tiletile_resample, tile_colorfix, tile_colorfix+sharp
control_v11e_sd15_shuffleshuffle
control_v11e_sd15_ip2p aka instruct Pix2Pixnull
control_v1p_sd15_grcode_monsternull
Parameter Description
maskBase64 image specifying area to inpaint
inpainting_mask_invert0 = mask area, 1 = area outside mask
inpainting_fillFill behavior (0: fill, 1: original, 2: latent noise, 3: latent nothing)
resize_modeResize behavior (0–3: just resize, crop+resize, resize, fill)
inpaint_full_res_paddingPadding in pixels (default: 0)
inpaint_full_restrue = keep same res as source, false = stretch area
image_cfg_scaleDegree of resemblance to input image (lower = more different)

Example Calls

We’re going to show you how you can use the API to do some basic image generations. Make sure that you have cloned the auto1111 repo from the AUTOMATIC1111 Stable Diffusion WebUI repo, and have it running locally on your PC.

txt2img generation:

AI-generated close-up of a tabby cat with green eyes among yellow flowers

img2img generation:

AI-generated puppy sniffing yellow wildflowers in a sunlit field

Inpainting example:

We’re going to inpaint over the dog’s ears for this example.

AI-generated puppy sniffing yellow wildflowers in a sunlit field

QR Monster ControlNet (Illusion Diffusion)

QR Monster Controlnet is taking the internet by storm. If you want to generate images like Pinsky QR Monster example, or DeepFates QR Monster example, you can make the request like below:

AI-generated puppy surrounded by spiraling rings of yellow flowers

Conclusion

We hope this documentation gives you a good starting point for generating images with AUTO1111 API. As you work with the API, don't be afraid to tweak the settings and observe its impact. Refer to the documentation and examples as needed. With some practice, you'll be leveraging AUTOMATIC1111 to create amazing AI artworks.1 Let us know if you make something cool!

Author profile: Brendan McKeag

The Chips Got Faster. The Stack Didn't.

The Chips Got Faster. The Stack Didn't.

Explore why faster chips have shifted the bottleneck to AI infrastructure, and what that means for teams running production workloads.

All

Multi-Instance GPUs on Runpod: Stop Paying for Compute You Don't Need

Multi-Instance GPUs on Runpod: Stop Paying for Compute You Don't Need

With MIG, we can partition RTX 6000 Pro cards into isolated 24 GB instances. Here's when it makes sense for your workloads.

All

OpenAI Parameter Golf: what 1,100 researchers built in six weeks

OpenAI Parameter Golf: what 1,100 researchers built in six weeks

How 1,100 researchers beat OpenAI's own baseline with 16 megabytes and 10 minutes.

All

Build what’s next.

Build, train, and scale AI workloads on Runpod with cloud GPUs, Serverless, and Clusters.