Play with Stable Diffusion on a low-configuration computer
If you are looking for a no need to worry gpu There are no restrictions, no need to download the model yourself, and a method that can quickly generate high-quality images, then run it on the cloud Stable Diffusion way would be a good choice.
Today I will take stock of three companies that provide Stable Diffusion Web UI We conduct a comparative analysis of the platforms from billing methods, prices, number of models, generation speed, security and other dimensions so that everyone can have a clear understanding.
Recommendation index: ★★★☆☆
- Recommended reason: RunDiffusion is a platform focusing on the Stable Diffusion model, according to the rentalserverCharges are based on time and number of models.
- Advantages: It supports the use of ControlNet to control the style and detail of generated images and allow developers to add their own models. The maximum ratio supported is 1024*1024.
- Disadvantages: It's relatively expensive and supports a limited number of 59 models. The generation speed of RunDiffusion takes an average of 5 seconds per picture.
Recommendation index: ★★☆☆☆
URL:https://colab.google/notebooks
- Reasons for recommendation:Google Colab is a cloud-based interactive Python environment, which allows users to run any Stable Diffusion model using Google's computing resources.
- Pros: It offers free and paid options, can support multiple users simultaneously, and can leverage Google cloud resources to expand. The maximum ratio supported is 2048 * 2048.
- Disadvantages: Paid users are billed by the hour, and users need to install and configure related libraries and codes themselves, and users need to download and run related models by themselves.Google Colab The generation speed takes an average of 10 seconds per picture.
Recommendation index: ★★★★☆
- Reasons for recommendation:Omniinfer It is a platform that provides Stable Diffusion model API, which allows users to generate high-quality images with simple calls.
- Advantages of Omniinfer: Each account has an initial quota, you can experience the service for free, supports more than 10,000 Stable Diffusion models, and allows developers to add their own models. Omniinfer also supports the use of ControlNet to control the style and details of the generated images. The maximum ratio supports 2048*2048.
- Another advantage of Omniinfer is that its price is relatively low, and it is charged according to the number of uses, allowing users to flexibly control their own costs. Each 512 * 512 map is $0.0015. . Omniinfer's generation speed is also very fast, taking only 3 seconds per picture on average.
- Disadvantages: Inpainting is still in beta and currently unavailable
To sum up, if you consider the number of Stable Diffusion models, generation speed, price and ease of use, I suggest you use Omniinfer; if you consider the customization and control of servers and models, I recommend you use RunDiffusion; If you consider free and scalable, I recommend you use Google Colab. Of course, these are my personal opinions, and you can make the choice that suits you best based on your actual situation and needs.
I hope this article was helpful to you. If you have any further questions, please let me know.
