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from flask import Flask, request, jsonify
from PIL import Image
import torch
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
from diffusers.utils import load_image
app = Flask(__name__)
controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15",
torch_dtype=torch.float16, local_files_only=False)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
def resize_for_condition_image(input_image: Image, resolution: int):
input_image = input_image.convert("RGB")
W, H = input_image.size
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS)
return img
@app.route('/generate_image', methods=['POST'])
def generate_image():
# Get input parameters from the request
prompt = request.json.get('prompt')
negative_prompt = request.json.get('negative_prompt')
image_url = request.json.get('image_url')
control_image_url = request.json.get('control_image_url')
# Load the images from URLs
source_image = load_image(image_url)
init_image = load_image(control_image_url)
# Resize images for conditioning
condition_image = resize_for_condition_image(source_image, 768)
init_image = resize_for_condition_image(init_image, 768)
# Generate the image using the pipeline
generator = torch.manual_seed(123121231)
image = pipe(prompt=prompt,
negative_prompt=negative_prompt,
image=init_image,
control_image=condition_image,
width=768,
height=768,
guidance_scale=20,
controlnet_conditioning_scale=1.5,
generator=generator,
strength=0.9,
num_inference_steps=150)
# Return the generated image
return jsonify({'image': image.images[0]})
if __name__ == '__main__':
app.run()
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