wehub-resource-sync
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
from io import BytesIO
|
|
|
|
from ray import serve
|
|
from fastapi import FastAPI
|
|
from fastapi.responses import Response
|
|
import torch
|
|
from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline
|
|
import logging
|
|
|
|
app = FastAPI()
|
|
logger = logging.getLogger("ray.serve")
|
|
|
|
|
|
@serve.deployment(num_replicas=1)
|
|
@serve.ingress(app)
|
|
class APIIngress:
|
|
def __init__(self, diffusion_model_handle) -> None:
|
|
self.handle = diffusion_model_handle
|
|
|
|
@app.get(
|
|
"/imagine",
|
|
responses={200: {"content": {"image/png": {}}}},
|
|
response_class=Response,
|
|
)
|
|
async def generate(self, prompt: str, img_size: int = 512):
|
|
assert len(prompt), "prompt parameter cannot be empty"
|
|
|
|
image = await self.handle.generate.remote(prompt, img_size=img_size)
|
|
file_stream = BytesIO()
|
|
image.save(file_stream, "PNG")
|
|
return Response(content=file_stream.getvalue(), media_type="image/png")
|
|
|
|
|
|
@serve.deployment(
|
|
ray_actor_options={"num_gpus": 1, "num_cpus": 1},
|
|
max_ongoing_requests=2,
|
|
autoscaling_config={
|
|
"min_replicas": 1,
|
|
"max_replicas": 3,
|
|
"target_ongoing_requests": 1,
|
|
},
|
|
)
|
|
class StableDiffusionV2:
|
|
def __init__(self):
|
|
model_id = "stabilityai/stable-diffusion-2"
|
|
|
|
scheduler = EulerDiscreteScheduler.from_pretrained(
|
|
model_id, subfolder="scheduler"
|
|
)
|
|
self.pipe = StableDiffusionPipeline.from_pretrained(
|
|
model_id, scheduler=scheduler, revision="fp16", torch_dtype=torch.float16
|
|
)
|
|
self.pipe = self.pipe.to("cuda")
|
|
|
|
def generate(self, prompt: str, img_size: int = 512):
|
|
assert len(prompt), "prompt parameter cannot be empty"
|
|
logger.info("Prompt: [%s]", prompt)
|
|
image = self.pipe(prompt, height=img_size, width=img_size).images[0]
|
|
return image
|
|
|
|
|
|
entrypoint = APIIngress.bind(StableDiffusionV2.bind())
|