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import gradio as gr
import numpy as np
from models import make_inpainting
import utils
from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests
from transformers import pipeline
import torch
import random
import io
import base64
import json
from diffusers import DiffusionPipeline
from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
from diffusers import StableDiffusionUpscalePipeline
def removeFurniture(input_img1,
input_img2,
positive_prompt,
negative_prompt,
num_of_images,
resolution
):
print("removeFurniture")
HEIGHT = resolution
WIDTH = resolution
input_img1 = input_img1.resize((resolution, resolution))
input_img2 = input_img2.resize((resolution, resolution))
canvas_mask = np.array(input_img2)
mask = utils.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
def imageToString(img):
output = io.BytesIO()
img.save(output, format="png")
return output.getvalue()
def segmentation(img):
print("segmentation")
# semantic_segmentation = pipeline("image-segmentation", "nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
pipe = pipeline("image-segmentation", "facebook/maskformer-swin-large-ade")
results = pipe(img)
for p in results:
p['mask'] = utils.image_to_byte_array(p['mask'])
p['mask'] = base64.b64encode(p['mask']).decode("utf-8")
#print(results)
return json.dumps(results)
def upscale(image, prompt):
print("upscale",image,prompt)
image.thumbnail((512, 512))
print("resize",image)
pipeline = StableDiffusionUpscalePipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")
#up
#pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler")
# pipeline = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16).to('cuda')
ret = pipeline(prompt=prompt,
image=image,
num_inference_steps=20,
guidance_scale=0)
print("ret",ret)
upscaled_image = ret.images[0]
print("up",upscaled_image)
return upscaled_image
with gr.Blocks() as app:
with gr.Row():
with gr.Column():
gr.Button("FurnituRemove").click(removeFurniture,
inputs=[gr.Image(label="img", type="pil"),
gr.Image(label="mask", 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(),
gr.Image(),
gr.Image(),
gr.Image(),
gr.Image(),
gr.Image(),
gr.Image(),
gr.Image(),
gr.Image(),
gr.Image()])
with gr.Column():
gr.Button("Segmentation").click(segmentation, inputs=gr.Image(type="pil"), outputs=gr.JSON())
with gr.Column():
gr.Button("Upscale").click(upscale, inputs=[gr.Image(type="pil"),gr.Textbox(label="prompt",value="empty room")], outputs=gr.Image())
app.launch(debug=True,share=True)
# UP 1 |