File size: 3,262 Bytes
6b1429b f00ee27 6b1429b f00ee27 9c37207 f00ee27 6b1429b d2d3231 6b1429b 9c37207 3c3f339 6b1429b 1dad009 d78d925 6b1429b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
from typing import Dict, List, Any
import base64
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionImg2ImgPipeline
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
model_id = "nitrosocke/Ghibli-Diffusion"
class EndpointHandler():
def __init__(self, path=""):
# define default controlnet id and load controlnet
# Load StableDiffusionControlNetPipeline
self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained("nitrosocke/Ghibli-Diffusion", torch_dtype=torch.float16).to(
device
)
# Define Generator with seed
# self.generator = torch.Generator(device="cpu").manual_seed(3)
self.generator = torch.Generator(device=device).manual_seed(1024)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
:param data: A dictionary contains `inputs` and optional `image` field.
:return: A dictionary with `image` field contains image in base64.
"""
prompt = data.pop("inputs", None)
image = data.pop("image", None)
# strength = data.pop("strength", None)
# steps = data.pop("steps", None)
# Check if neither prompt nor image is provided
if prompt is None and image is None:
return {"error": "Please provide a prompt and base64 encoded image."}
# hyperparamters
# num_inference_steps = data.pop("num_inference_steps", 30)
guidance_scale = data.pop("guidance_scale", 7.5)
strength = data.pop("strength", 7.5)
negative_prompt = data.pop("negative_prompt", None)
# height = data.pop("height", None)
# width = data.pop("width", None)
# controlnet_conditioning_scale = data.pop(
# "controlnet_conditioning_scale", 1.0)
# process image
image = self.decode_base64_image(image)
# control_image = CONTROLNET_MAPPING[self.control_type]["hinter"](image)
# run inference pipeline
# out = self.pipe(
# prompt=prompt,
# negative_prompt=negative_prompt,
# image=control_image,
# num_inference_steps=num_inference_steps,
# guidance_scale=strength,
# num_images_per_prompt=1,
# height=height,
# width=width,
# controlnet_conditioning_scale=controlnet_conditioning_scale,
# generator=self.generator
# )
out = self.pipe(
prompt=prompt,
image=image,
negative_prompt=negative_prompt,
strength=strength,
guidance_scale=guidance_scale,
generator=self.generator
)
# return first generate PIL image
return out.images[0]
# helper to decode input image
def decode_base64_image(self, image_string):
base64_image = base64.b64decode(image_string)
buffer = BytesIO(base64_image)
image = Image.open(buffer).convert("RGB")
return image
|