After installing the models, plugins, and dependencies in the development environment, you can publish the current image generation environment. This process creates a snapshot and deploys it as an online service. After deployment, any changes you make to plugins and dependencies in the development environment do not affect the published online service.
Overview
To ensure the stability and reliability of the online service, the development environment is isolated from the online service environment. You can debug and iterate on your ComfyUI image generation environment in the development environment. Once the environment can reliably run your expected workflow, you publish that specific version to the online service.
ComfyUI image generation environment
Component | Description |
Model | All types of models required by the workflow, such as Checkpoint, LoRA, and ControlNet. Located in the |
Plugin | Community-developed or self-developed custom nodes. Located in the |
Dependency | Python dependency packages for the ComfyUI main program and its plugins. |
Cache | Auxiliary models, datasets, and other assets required to run the workflow. Typically located in the |
The development environment and the online service maintain mutable and immutable image generation environments, respectively. This isolation ensures the stability of online API calls. Each time you publish, the platform creates a snapshot of the current development environment and uses it to update the online service.
Deployment process overview
Phase | Description |
1. Snapshot creation | The plugins, dependencies, and necessary cache from the project development environment are packaged and copied to generate a snapshot, which is stored in the Snapshot creation time increases with the complexity of the image generation environment (more plugins and dependencies). |
2. Service deployment | After the snapshot is created, the platform automatically updates the online service's function configuration and initiates a smooth rotation. This process starts a new instance with the new snapshot. Once the new instance passes its health check, the platform terminates the old instance. A "successful" deployment status indicates that the platform has updated the function configuration and started the instance rotation. |
Procedure
Before you begin
Use a workstation in the development environment to debug your image generation environment. Ensure that your models, plugins, dependencies, and cache allow your workflow to run correctly. For more information, see Quick start for creating a ComfyUI project.
Note: If you have finished debugging, we recommend that you save and shut down the workstation before deploying to avoid unnecessary charges during the deployment process.
Step 1: Deploy the service and view history
Go to the project details page by clicking Projects in the console and then clicking the target ComfyUI project.
On the project details page, click Deploy Online Service.
For your first deployment, the platform guides you through configuring the resource specifications and scaling policies for the online service. Subsequent deployments reuse the existing configuration from .After starting the deployment, you can view its progress in .
Note: Only one deployment task can be in progress at a time. To start a new deployment, wait for the current task to complete.
Step 2: Monitor the instance status
When the deployment history shows a successful status, the smooth rotation of online service instances begins. You can monitor the rotation process in the following locations:
Location | What to check |
Changes in the actual minimum number of instances. | |
The lifecycle of each instance, which helps you determine when new instances have replaced old ones. |
Note: Instance rotation speed depends primarily on the startup time of the image generation environment. If your workstation starts up slowly (for example, due to a slow-loading ComfyUI plugin), the deployment rotation will also take longer.