sd-for-designers

一个完全自动化的流程，用于触发、运行和管理使用 Vertex AI 对自定义稳定扩散模型进行微调、训练和部署

描述

Sd-aa-S 是一个全自动的 MLOps 流水线，用于在 GCP 上通过 Google Cloud Storage、Cloud Build、Cloud PubSub、Firestore、Cloud Run、Cloud Functions 和 Vertex AI 等 GCP 组件触发、管理和跟踪 Stable diffusion 微调任务。它旨在简化使用不同技术（从 Dreambooth 开始）对 Stable diffusion 进行调优的 ML 工作流。即将支持 Lora、ControlNet 等。该项目面向 ML/数据工程师、数据科学家以及任何有兴趣或正在构建大规模微调稳定扩散平台的人。

三部分

1. 应用部分

1. Set up your Cloud Environment
2. Create a backend service for handling uploads to a GCS bucket
    - Receive images from clients and store them under a predefined GCS bucket path
    - Track the status of individual uploads in a Firestore collection
    - Track the status of the overall upload job in a separate Firestore collection 
    - Once the job is compelted, publish the jobID as the message on a predefined PubSub topic
3. Deploy this backend service as a Cloud Run endpoint using Cloud build
4. Create a frontend portal to upload images using ReactJs
5. Deploy the frontend service on Cloud Run

2. Vertex AI 部分

1. Set up your Cloud Environment
2. Create a new custom container artifact for running the pipeline components
3. Create a new custom container artifact for running the training job itself 
4. Create a Jupyter notebook outlining the Pipeline flow & components
5. Compile a YAML file from a Vertex AI workbench and store the precompiled YAML file under a GCS bucket path

3. 基础设施部分

1. Set up your Cloud Environment
2. Create a cloud function that gets triggered every time the jobID is published on a predefinied topic (from 1st part)
3. Within the cloud function, the python code subscribes to the topic and triggers a Vertex AI pipeline job using the precomiled YAML file (from 2nd part)
4. The pipeline jobs finetunes the stable diffusion model using Dreambooth, uploads the new custom model to Model registy & deploys an endpoint
5. The job also updates Firestore with the status of the pipeline job from start to end