74 lines
6.1 KiB
Markdown
74 lines
6.1 KiB
Markdown
# Main Tab
|
||
This chapter will provide a detailed overview of the convenient cloud-based resource management approach offered by this solution.
|
||
|
||
## Upload Model
|
||
To use extra models for inference, you could upload model through steps below in two ways, and follow steps in [txt2img](txt2img-guide.md) or [img2img](img2img-guide.md)to inference with extra models as need.
|
||
|
||
Method One:
|
||
1. Within Stable Diffusion WebUI, navigate to solution main tab **Amazon SageMaker**, find session **Cloud Assets Management**.
|
||
|
||
2. Select the Upload Model to S3 from WebUI drop-down box, which means to upload the model from the models file path where the WebUI service is deployed.
|
||
3. Enter the model path where the WebUI service is deployed under corresponding model text box.
|
||
> **Note**: You can upload multiple kinds of models by entering multiple local model paths in text box.
|
||
4. Click **Upload Models to Cloud** to start uploading process.
|
||
5.Message will appear on left right once uploading completes.
|
||
|
||
Method Two:
|
||
1. Within Stable Diffusion WebUI, navigate to solution main tab **Amazon SageMaker**, find session **Cloud Assets Management**.
|
||
|
||
2. Select the Upload Model to S3 from Laptop drop-down box, which means to upload the model from the local path to access the WebUI.
|
||
3. Select the type of model to upload, currently supports six types: SD Checkpoints, Textual Inversion, LoRA model, ControlNet model, Hypernetwork, VAE
|
||
3. Select the model file to be uploaded locally.
|
||
> **Note**: You can select models multiple, but subject to browser restrictions, it is best to select no more than 10 files, and the total size should not exceed 8g.
|
||
4. Click **Upload Models to Cloud** to start uploading process.
|
||
5. The upload will be uploaded in pieces asynchronously based on the file size and quantity. After each piece is uploaded, you will see a prompt under the **Choose File** button
|
||
|
||
|
||
## Amazon SageMaker Endpoint Management
|
||
### Deploy new endpoint
|
||
1. Navigate to the **Amazon SageMaker** main tab of the solution. In the **Cloud Assets Management** module, locate the **Deploy New SageMaker Endpoint** section.
|
||
2. The default deployment type for the solution is ml.g5.2xlarge, with 1 instance. The endpoint autoscaling feature is enabled by default. Simply click the **Deploy** button to initiate the deployment of the Sagemaker endpoint.
|
||
3. If users wish to specify the endpoint name, instance type, and maximum instance count for the endpoint's instances, they can check the **Advanced Endpoint Configuration** checkbox. This will display additional parameters for user input. The following table lists the names and descriptions of these parameters:
|
||
|
||
| Parameter Name | Description |
|
||
|-------------------|--------------------------------------------------------------------------------------------------------------|
|
||
| Endpoint Name (Optional) | If you need to specify a name for the Sagemaker endpoint, enter it in this input box. If not modified, the default endpoint name will be infer-endpoint-XXXXX. |
|
||
| Instance Type | Select the instance type for the deployed endpoint from the dropdown list. |
|
||
| Max Instance Count | Choose the maximum number of instances for the deployed endpoint from the dropdown list. If Autoscaling is selected, Sagemaker will elastically scale between 0 and the Max Instance Count based on average CPU usage. |
|
||
| Enable Autoscaling | If this checkbox is selected, Sagemaker will elastically scale between 0 and the Max Instance Count based on average CPU usage. Otherwise, the instance count for the endpoint will remain fixed at the Max Instance Count. |
|
||
|
||
4. After selecting the default endpoint configuration or setting up the advanced endpoint configuration, click **Deploy**. You'll see a message indicating **Endpoint deployment started** on the left side under **Label**.
|
||
|
||
|
||
5. You can navigate to tab **txt2img**, session **Amazon SageMaker Inference**, refresh and select drop down list **Select Cloud SageMaker Endpoint** to check all the deployment status of endpoints.
|
||
|
||
> **Note:** The format of the drop down list is：endpoint name+ deployment status (including Creating/Failed/InService)+deployment completing time.
|
||
|
||
6. It will take around 10 mins for endpoint deployment status changing to *InService*, which indicates that the endpoint has been successfully deployed.
|
||
|
||
|
||
### Delete deployed endpoints
|
||
1. Refresh and select endpoint(s) under dropdown list of **Select Cloud SageMaker Endpoint**.
|
||
2. Click **Delete**, message *Endpoint delete completed* will appear on left side, which indicates that the selected endpoint(s) havs been successfully deleted.
|
||
|
||
|
||
|
||
# AWS Dataset Management
|
||
|
||
## Create Dataset
|
||
In functions such as model fine-tuning, it is necessary to provide a file of images for fine-tuning work. This functional module helps users quickly upload images to the cloud.
|
||
|
||
1. Navigate to main tab **Amazon SageMaker**, section **AWS Dataset Management**，sub-tab **Create**.
|
||
|
||
|
||
2. Click **Click to Upload a File**, in the local file browser that pops up, confirm to select all the images required for one model fine-tuning.
|
||
3. Enter file name in **Dataset Name**, enter file description in **Dataset Description**, click **Create Dataset**.
|
||
4. The message **Complete Dataset XXXX creation** will be displayed on the right side once the process completes.
|
||
|
||
## Explore Dataset
|
||
After the dataset has been successfully uploaded, this feature module allows users to quickly obtain the corresponding cloud-based address of the dataset. Users can copy this address and paste it into the location where the collection of images needs to be uploaded.
|
||
|
||
1. Navigate to **Amazon SageMaker**，**AWS Dataset Management** session，**Browse** tab, refresh the list **Dataset From Cloud** and select desired dataset.
|
||
2. Field **dataset s3 location** will display the corresponding S3 path on the cloud. User can copy to use as need.
|
||
|