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import os
from flask import Flask, request
import requests
from gradio_client import Client
import base64
from PIL import Image
from io import BytesIO
import base64
import os
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch
import gradio as gr
controlnet = ControlNetModel.from_pretrained("rgres/sd-controlnet-aerialdreams", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
# CPU offloading for faster inference times
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
app = Flask(__name__, static_url_path='/static')
@app.route('/')
def index():
return app.send_static_file('index.html')
def save_base64_image(base64Image):
image_data = base64.b64decode(base64Image)
path = "input_image.jpg"
with open(path, 'wb') as f:
f.write(image_data)
return path
def encode_image_to_base64(filepath):
with open(filepath, "rb") as image_file:
encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
return encoded_image
def generate_map(image, prompt, steps, seed):
#image = Image.open(BytesIO(base64.b64decode(image_base64)))
generator = torch.manual_seed(seed)
image = pipe(
prompt=prompt,
num_inference_steps=steps,
image=image
).images[0]
return image
@app.route('/predict', methods=['POST'])
def predict():
data = request.get_json()
base64Image = data['data'][0]
prompt = data['data'][1]
steps = data['data'][2]
seed = data['data'][3]
b64meta, b64_data = base64Image.split(',')
image = Image.open(BytesIO(base64.b64decode(b64_data)))
return generate_map(image, prompt, steps, seed)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=int(
os.environ.get('D2M_PORT', 8000)), debug=True)
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