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import torch
import gradio as gr
from PIL import Image
import qrcode
from pathlib import Path
from multiprocessing import cpu_count
import requests
import io
import os
from PIL import Image
from diffusers import (
StableDiffusionPipeline,
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
DDIMScheduler,
DPMSolverMultistepScheduler,
DEISMultistepScheduler,
HeunDiscreteScheduler,
EulerDiscreteScheduler,
)
qrcode_generator = qrcode.QRCode(
version=1,
error_correction=qrcode.ERROR_CORRECT_H,
box_size=10,
border=4,
)
controlnet = ControlNetModel.from_pretrained(
"DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16,
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
def resize_for_condition_image(input_image: 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
SAMPLER_MAP = {
"DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
"DPM++ Karras": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True),
"Heun": lambda config: HeunDiscreteScheduler.from_config(config),
"Euler": lambda config: EulerDiscreteScheduler.from_config(config),
"DDIM": lambda config: DDIMScheduler.from_config(config),
"DEIS": lambda config: DEISMultistepScheduler.from_config(config),
}
def inference(
first_name: str = "John",
last_name: str = "Doe",
telephone_number: str = "+60123456789",
email_address: str = "[email protected]",
url: str = "https://example.com",
prompt: str = "Sky view of highly aesthetic, ancient greek thermal baths in beautiful nature",
negative_prompt: str = "ugly, disfigured, low quality, blurry, nsfw",
):
guidance_scale = 7.5
controlnet_conditioning_scale = 1.5
strength = 0.9
seed = -1
sampler = "DPM++ Karras SDE"
qrcode_image = None
qr_code_content = f"MECARD:N:{last_name},{first_name};TEL:{telephone_number};EMAIL:{email_address};URL:{url};"
if prompt is None or prompt == "":
raise gr.Error("Prompt is required")
if qr_code_content == "":
raise gr.Error("Content is required")
pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
if qr_code_content != "" or qrcode_image.size == (1, 1):
print("Generating QR Code from content")
qr = qrcode.QRCode(
version=1,
error_correction=qrcode.constants.ERROR_CORRECT_H,
box_size=10,
border=4,
)
qr.add_data(qr_code_content)
qr.make(fit=True)
qrcode_image = qr.make_image(fill_color="black", back_color="white")
qrcode_image = resize_for_condition_image(qrcode_image, 768)
else:
print("Using QR Code Image")
qrcode_image = resize_for_condition_image(qrcode_image, 768)
out = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=qrcode_image,
control_image=qrcode_image, # type: ignore
width=768, # type: ignore
height=768, # type: ignore
guidance_scale=float(guidance_scale),
controlnet_conditioning_scale=float(controlnet_conditioning_scale), # type: ignore
generator=generator,
strength=float(strength),
num_inference_steps=40,
)
return out.images[0] # type: ignore
# MECARD:N:Aqlan Nor Azman;TEL:60173063421;EMAIL:[email protected];
generator = gr.Interface(
fn=inference,
inputs=[
gr.Textbox(
label="First Name",
value="John",
),
gr.Textbox(
label="Last Name",
value="Doe",
),
gr.Textbox(
label="Telephone Number",
value="+60123456789",
),
gr.Textbox(
label="Email Address",
value="[email protected]"
),
gr.Textbox(
label="URL",
value="https://example.com",
),
gr.Textbox(
label="Prompt",
value="Sky view of highly aesthetic, ancient greek thermal baths in beautiful nature",
),
gr.Textbox(
label="Negative Prompt",
value="ugly, disfigured, low quality, blurry, nsfw",
)
],
outputs="image"
)
if __name__ == "__main__":
generator.queue(concurrency_count=1, max_size=20)
generator.launch() |