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app.py
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app.py
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import base64
import os
from io import BytesIO
import torch
from diffusers import DiffusionPipeline
# Init is ran on server startup
# Load your model to GPU as a global variable here using the variable name "model"
def init():
global pipeline
HF_AUTH_TOKEN = os.getenv("HF_AUTH_TOKEN")
pipeline = DiffusionPipeline.from_pretrained(
"ECRodriguez/ecrodriguez", use_auth_token=HF_AUTH_TOKEN
)
pipeline = pipeline.to("cuda")
# Inference is ran for every server call
# Reference your preloaded global model variable here.
def inference(model_inputs: dict) -> dict:
global pipeline
# Parse out your arguments
prompt = model_inputs.get("prompt", None)
num_inference_steps = model_inputs.get("num_inference_steps", 50)
if prompt == None:
return {"message": "No prompt provided"}
# Run the model
with torch.autocast("cuda"):
image = pipeline(
prompt, num_inference_steps=num_inference_steps
).images[0]
buffered = BytesIO()
image.save(buffered, format="JPEG")
image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
# Return the results as a dictionary
return {"image_base64": image_base64}