Spaces:
Sleeping
Sleeping
File size: 2,720 Bytes
372f297 13cb3ce eff32bf 1cd414e ff7142e 26987c4 13cb3ce eff32bf 405f281 8a5a456 26987c4 405f281 f59e95f 1cd414e 28471f6 af3627e c1dd8ce 13cb3ce 1cd414e 26987c4 1cd414e f59e95f bc435a1 8a5a456 28471f6 1cd414e 920ece4 e9f68f0 13cb3ce 405f281 c1dd8ce 13cb3ce c1dd8ce 8a5a456 7bff278 13cb3ce e9f68f0 13cb3ce c1dd8ce 405f281 8a5a456 c1dd8ce 405f281 13cb3ce 372f297 |
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 |
import gradio as gr
import io
from PIL import Image
import numpy as np
# from config import setResoluton
from models import make_image_controlnet, make_inpainting
from preprocessing import get_mask
def image_to_byte_array(image: Image) -> bytes:
# BytesIO is a fake file stored in memory
imgByteArr = io.BytesIO()
# image.save expects a file as a argument, passing a bytes io ins
image.save(imgByteArr, format='png') # image.format
# Turn the BytesIO object back into a bytes object
imgByteArr = imgByteArr.getvalue()
return imgByteArr
def predict(input_img1,
input_img2,
positive_prompt,
negative_prompt,
num_of_images,
resolution
):
print("predict")
# bla bla
# input_img1 = Image.fromarray(input_img1)
# input_img2 = Image.fromarray(input_img2)
# setResoluton(resolution)
HEIGHT = resolution
WIDTH = resolution
# WIDTH = resolution
# HEIGHT = resolution
input_img1 = input_img1.resize((resolution, resolution))
input_img2 = input_img2.resize((resolution, resolution))
canvas_mask = np.array(input_img2)
mask = get_mask(canvas_mask)
print(input_img1, mask, positive_prompt, negative_prompt)
retList= make_inpainting(positive_prompt=positive_prompt,
image=input_img1,
mask_image=mask,
negative_prompt=negative_prompt,
num_of_images=num_of_images,
resolution=resolution
)
# add the rest up to 10
while (len(retList)<10):
retList.append(None)
return retList
app = gr.Interface(
predict,
inputs=[gr.Image(label="img", sources=['upload'], type="pil"),
gr.Image(label="mask", sources=['upload'], type="pil"),
gr.Textbox(label="positive_prompt",value="empty room"),
gr.Textbox(label="negative_prompt",value=""),
gr.Number(label="num_of_images",value=2),
gr.Number(label="resolution",value=512)
],
outputs= [
gr.Image(label="resp0"),
gr.Image(label="resp1"),
gr.Image(label="resp2"),
gr.Image(label="resp3"),
gr.Image(label="resp4"),
gr.Image(label="resp5"),
gr.Image(label="resp6"),
gr.Image(label="resp7"),
gr.Image(label="resp8"),
gr.Image(label="resp9")],
title="rem fur 1",
)
app.launch(share=True)
#
# gr.Interface(
# test1,
# inputs=[gr.Textbox(label="param1")],
# outputs= gr.Textbox(label="result"),
# title="rem fur 1",
# ).launch(share=True)
|