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from typing import Dict, List, Any
from diffusers import AutoPipelineForText2Image
import torch
import numpy as np
# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
raise ValueError("need to run on GPU")
# set mixed precision dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
class EndpointHandler():
def __init__(self, path=""):
# Load StableDiffusionPipeline
self.stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
self.pipe = AutoPipelineForText2Image.from_pretrained(self.stable_diffusion_id,
torch_dtype=dtype,
safety_checker=None)
self.pipe.load_lora_weights("Oysiyl/sd-lora-android-google-toy", weights="pytorch_lora_weights.safetensors")
self.pipe.enable_xformers_memory_efficient_attention()
self.pipe.to(device)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
:param data: A dictionary contains `inputs`.
:return: A dictionary with `image` field contains image in base64.
"""
prompt = data.pop("inputs", None)
seed = data.pop("seed", 42)
# Check if prompt is not provided
if prompt is None:
return {"error": "Please provide a prompt."}
generator = torch.Generator(device=device).manual_seed(seed)
# hyperparamters
num_inference_steps = data.pop("num_inference_steps", 50)
guidance_scale = data.pop("guidance_scale", 7.5)
temperature = data.pop("temperature", 1.0)
# run inference pipeline
out = self.pipe(
prompt=prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
temperature=temperature,
num_images_per_prompt=1,
seed=seed,
generator=generator
)
# return first generate PIL image
return out.images[0]
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