Instructions to use deepsafe/deepsafe-services with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use deepsafe/deepsafe-services with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("deepsafe/deepsafe-services", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 553 Bytes
4b0b144 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | from typing import Any, Dict
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI(title="Ensemble Core Service")
class EnsembleRequest(BaseModel):
media_type: str
model_results: Dict[str, Any]
method: str = "stacking"
@app.post("/calculate")
async def calculate_ensemble(request: EnsembleRequest):
# TODO: Migrate proprietary ensemble logic from gateway
return {"verdict": "fake", "confidence": 0.95, "method_used": request.method}
@app.get("/health")
async def health():
return {"status": "healthy"}
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