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enesjakozh

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by couchrishi
GitHub
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Integrations

  • Enables an automated MLOps pipeline for Stable Diffusion model fine-tuning using multiple Google Cloud services, including Vertex AI, Cloud Storage, Cloud Build, PubSub, Firestore, Cloud Run, and Cloud Functions.

  • Handles image storage for training data, maintains predefined bucket paths for uploads, and stores compiled pipeline artifacts for Stable Diffusion fine-tuning jobs.

  • Used to create notebooks that outline pipeline workflows and components for Stable Diffusion model fine-tuning on Vertex AI.

디자이너를 위한 SD

Vertex AI를 사용하여 사용자 정의 안정 확산 모델을 트리거, 실행 및 관리하고, 미세 조정, 훈련 및 배포하기 위한 완전 자동화된 워크플로

설명

Sd-aa-S는 Google Cloud Storage, Cloud Build, Cloud PubSub, Firestore, Cloud Run, Cloud Functions, Vertex AI 등의 GCP 구성 요소를 사용하여 GCP에서 안정적 확산 미세 조정 작업을 트리거, 관리 및 추적하는 완전 자동화된 MLOps 파이프라인입니다. Dreambooth를 시작으로 다양한 기법을 사용하여 안정적 확산을 조정하는 ML 워크플로를 간소화하는 것을 목표로 합니다. Lora, ControlNet 등에 대한 지원도 곧 제공될 예정입니다. 이 프로젝트는 ML/데이터 엔지니어, 데이터 과학자, 그리고 대규모 안정적 확산 미세 조정 플랫폼 구축에 관심이 있거나 이를 구축하려는 모든 분들을 대상으로 합니다.

세 부분

1. 앱 부분

지엑스피1

2. Vertex AI 부분

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. 배관 부분

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

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remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

키라흐스디카쉬드

  1. 설명
    1. 세 부분
      1. 앱 부분
        1. Vertex AI 부분
          1. 배관 부분

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