File size: 5,107 Bytes
fdae70a 394463f fdae70a 394463f fdae70a 394463f fdae70a |
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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
from typing import Dict, List, Any
import base64
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
import numpy as np
import cv2
import controlnet_hinter
# 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
# controlnet mapping for controlnet id and control hinter
CONTROLNET_MAPPING = {
"canny_edge": {
"model_id": "lllyasviel/sd-controlnet-canny",
"hinter": controlnet_hinter.hint_canny
},
"pose": {
"model_id": "lllyasviel/sd-controlnet-openpose",
"hinter": controlnet_hinter.hint_openpose
},
"depth": {
"model_id": "lllyasviel/sd-controlnet-depth",
"hinter": controlnet_hinter.hint_depth
},
"scribble": {
"model_id": "lllyasviel/sd-controlnet-scribble",
"hinter": controlnet_hinter.hint_scribble,
},
"segmentation": {
"model_id": "lllyasviel/sd-controlnet-seg",
"hinter": controlnet_hinter.hint_segmentation,
},
"normal": {
"model_id": "lllyasviel/sd-controlnet-normal",
"hinter": controlnet_hinter.hint_normal,
},
"hed": {
"model_id": "lllyasviel/sd-controlnet-hed",
"hinter": controlnet_hinter.hint_hed,
},
"hough": {
"model_id": "lllyasviel/sd-controlnet-mlsd",
"hinter": controlnet_hinter.hint_hough,
}
}
class EndpointHandler():
def __init__(self, path=""):
# define default controlnet id and load controlnet
self.control_type = "normal"
self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],torch_dtype=dtype).to(device)
# Load StableDiffusionControlNetPipeline
self.stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id,
controlnet=self.controlnet,
torch_dtype=dtype,
safety_checker=None).to(device)
# Define Generator with seed
self.generator = torch.Generator(device="cpu").manual_seed(3)
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)
controlnet_type = data.pop("controlnet_type", 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."}
# Check if a new controlnet is provided
if controlnet_type is not None and controlnet_type != self.control_type:
print(f"changing controlnet from {self.control_type} to {controlnet_type} using {CONTROLNET_MAPPING[controlnet_type]['model_id']} model")
self.control_type = controlnet_type
self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],
torch_dtype=dtype).to(device)
self.pipe.controlnet = self.controlnet
# hyperparamters
negatice_prompt = data.pop("negative_prompt", None)
num_inference_steps = data.pop("num_inference_steps", 30)
guidance_scale = data.pop("guidance_scale", 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=guidance_scale,
num_images_per_prompt=1,
height=height,
width=width,
controlnet_conditioning_scale=controlnet_conditioning_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)
return image
|