1580158
/
stable-diffusion-aws-extension
mirror of https://github.com/awslabs/stable-diffusion-aws-extension.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

feat: update dreambooth en doc.

update-dreambooth-doc
Tang Jie 2023-11-01 09:08:33 +00:00
parent 2483ec7a2f
commit c43c8bfdda
2 changed files with 3 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
docs/en/images/dreambooth-lora-save.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 142 KiB

7
docs/en/user-guide/dreambooth-guide.md
View File

@ -26,11 +26,10 @@ You can open **Dreambooth** tab, by combining the use with native Dreambooth, th
- Set the concepts that need to be trained. A total of four concepts can be set, and we will use the first concept as an example.
- In the *Dataset Directory* field, enter the path to the images required for training. It can be a path on a web server or an S3 path. For S3 paths, you can obtain them by uploading the data through AWS Dataset Management or by uploading them to S3 on your own. The path should start with “s3://".
- In the *Instance Prompt* section under *Training Prompts*, enter the keywords for the concept. These keywords will be used to generate the concept during the training process in txt2img. Therefore, avoid using common English words (as they might get confused with other concepts in the base model).
![Input concepts](../images/dreambooth-input-concepts.png)
- You need to check **Save Checkpoint to Subdirectory** to save the model to a subdirectory. In addition, it is not supported to save the Lora model separately. Please don't check **Generate lora weights for extra networks**.
- You need to check **Save Checkpoint to Subdirectory** to save the model to a subdirectory.
- If you need to save the lora model separately (the model file will be smaller, but it needs to be used with the SD basic model), please check **Generate lora weights for extra networks**.
![Input saving](../images/dreambooth-lora-save.png)
4. Click **SageMaker Train** to start model training task. The **Training Job Details** section will be updated in real-time with the status of the model training job. When the status changes to *Complete*, an email notification will be sent to the email address provided during the initial deployment of the solution, indicating that the model training is complete.
5. Future steps. For example: Navigate to **txt2img** tab **Amazon SageMaker Inference** panel, check trained model by refreshing **Stable Diffusion Checkpoint** dropdown list.