genblaze-google 0.3.0

Google provider adapters for genblaze (Veo video, Imagen image)

genblaze-google

Google provider adapters for genblaze — Veo text-to-video and Imagen text-to-image — with SHA-256 provenance manifests on every output.

genblaze-google wraps Google's generative media models (Veo 2, Veo 3, Imagen 3) as genblaze providers via the unified google-genai SDK. Works with both Gemini API keys and Google Cloud Vertex AI authentication. Compose Veo/Imagen calls into multi-step AI pipelines, persist outputs to Backblaze B2 or any S3-compatible store, and emit a tamper-evident provenance manifest for every run.

Why genblaze-google

• Veo 3 with synchronized audio — text-to-video + native audio, wrapped in a provenance manifest.
• Imagen 3 high-fidelity images — photorealistic stills with full parameter tracking.
• Two auth modes — Gemini API (GEMINI_API_KEY) for quick start, Vertex AI for enterprise / GCP orgs.
• Same SDK, any provider — swap to Sora, Runway, Luma, Flux without rewriting pipeline logic.
• Provenance by default — SHA-256 hash + canonical manifest on every generation.
• Durable storage — plug genblaze-s3 in for Backblaze B2 / AWS S3 / Cloudflare R2 / MinIO.

Providers + models

Provider class	Modality	Models
VeoProvider	video	veo-3.0-generate-001 (with audio), veo-3.0-fast-generate-001, veo-2.0-generate-001
ImagenProvider	image	imagen-3.0-generate-002, imagen-3.0-fast-generate-001

Each is registered via entry points (google-veo, google-imagen).

Install

pip install genblaze-google

Quickstart — Veo 3 text-to-video (with audio)

export GEMINI_API_KEY="..."   # or use Vertex AI auth

from genblaze_core import Modality, Pipeline
from genblaze_google import VeoProvider

run, manifest = (
    Pipeline("veo-demo")
    .step(VeoProvider(), model="veo-3.0-generate-001",
          prompt="A time-lapse of a coral reef coming to life, colorful fish "
                 "swimming through vibrant coral, natural ocean lighting",
          modality=Modality.VIDEO,
          aspect_ratio="16:9", duration_seconds="8", resolution="720p",
          enhance_prompt=True)
    .run(timeout=600)
)
print(run.steps[0].assets[0].url, manifest.canonical_hash)

Vertex AI auth instead:

provider = VeoProvider(project="my-gcp-project", location="us-central1")

Quickstart — Imagen 3 text-to-image

from genblaze_google import ImagenProvider

run, manifest = (
    Pipeline("imagen-demo")
    .step(ImagenProvider(output_dir="output/images"),
          model="imagen-3.0-generate-002",
          prompt="A photorealistic aerial view of a coral reef teeming with tropical fish",
          modality=Modality.IMAGE, aspect_ratio="16:9")
    .run(timeout=120)
)

Persist to Backblaze B2

from genblaze_core import KeyStrategy, ObjectStorageSink
from genblaze_s3 import S3StorageBackend

storage = ObjectStorageSink(
    S3StorageBackend.for_backblaze("my-bucket"),
    key_strategy=KeyStrategy.HIERARCHICAL,
)
# pass sink=storage to .run(…) to push assets + manifest to B2

Backblaze B2 is the recommended default sink for genblaze — cost-efficient, S3-compatible, with Object Lock for tamper-evident manifests.

Credentials

Auth mode	Env var / config
Gemini API (quickest)	GEMINI_API_KEY — https://aistudio.google.com/apikey
Vertex AI	VeoProvider(project=..., location=...) + gcloud auth application-default login

Documentation

• Main repo: https://github.com/backblaze-labs/genblaze
• Examples: veo_video_pipeline.py · imagen_pipeline.py

Related packages

• genblaze-core — the pipeline SDK
• genblaze-s3 — durable storage on Backblaze B2 and other S3-compatible backends

License

MIT