QR-code-AI-art-generator

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QR-code-AI-art-generator / app.py

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Duplicate from huggingface-projects/QR-code-AI-art-generator

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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(
	qr_code_content: str,
	prompt: str,
	negative_prompt: str,
	guidance_scale: float = 10.0,
	controlnet_conditioning_scale: float = 2.0,
	strength: float = 0.8,
	seed: int = -1,
	init_image: Image.Image \| None = None,
	qrcode_image: Image.Image \| None = None,
	use_qr_code_as_init_image = True,
	sampler = "DPM++ Karras SDE",
	):
	if prompt is None or prompt == "":
	raise gr.Error("Prompt is required")

	if qrcode_image is None and qr_code_content == "":
	raise gr.Error("QR Code Image or QR Code 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)

	# hack due to gradio examples
	init_image = qrcode_image

	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


	with gr.Blocks() as blocks:
	gr.Markdown(
	"""
	# QR Code AI Art Generator

	## 💡 How to generate beautiful QR codes

	We use the QR code image as the initial image and the control image, which allows you to generate
	QR Codes that blend in very naturally with your provided prompt.
	The strength parameter defines how much noise is added to your QR code and the noisy QR code is then guided towards both your prompt and the QR code image via Controlnet.
	Use a high strength value between 0.8 and 0.95 and choose a conditioning scale between 0.6 and 2.0.
	This mode arguably achieves the asthetically most appealing QR code images, but also requires more tuning of the controlnet conditioning scale and the strength value. If the generated image
	looks way to much like the original QR code, make sure to gently increase the strength value and reduce the conditioning scale. Also check out the examples below.

	model: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15

	<a href="https://huggingface.co/spaces/huggingface-projects/QR-code-AI-art-generator?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
	<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for no queue on your own hardware.</p>
	"""
	)

	with gr.Row():
	with gr.Column():
	qr_code_content = gr.Textbox(
	label="QR Code Content",
	info="QR Code Content or URL",
	value="",
	)
	with gr.Accordion(label="QR Code Image (Optional)", open=False):
	qr_code_image = gr.Image(
	label="QR Code Image (Optional). Leave blank to automatically generate QR code",
	type="pil",
	)

	prompt = gr.Textbox(
	label="Prompt",
	info="Prompt that guides the generation towards",
	)
	negative_prompt = gr.Textbox(
	label="Negative Prompt",
	value="ugly, disfigured, low quality, blurry, nsfw",
	)
	use_qr_code_as_init_image = gr.Checkbox(label="Use QR code as init image", value=True, interactive=False, info="Whether init image should be QR code. Unclick to pass init image or generate init image with Stable Diffusion 2.1")

	with gr.Accordion(label="Init Images (Optional)", open=False, visible=False) as init_image_acc:
	init_image = gr.Image(label="Init Image (Optional). Leave blank to generate image with SD 2.1", type="pil")


	with gr.Accordion(
	label="Params: The generated QR Code functionality is largely influenced by the parameters detailed below",
	open=True,
	):
	controlnet_conditioning_scale = gr.Slider(
	minimum=0.0,
	maximum=5.0,
	step=0.01,
	value=1.1,
	label="Controlnet Conditioning Scale",
	)
	strength = gr.Slider(
	minimum=0.0, maximum=1.0, step=0.01, value=0.9, label="Strength"
	)
	guidance_scale = gr.Slider(
	minimum=0.0,
	maximum=50.0,
	step=0.25,
	value=7.5,
	label="Guidance Scale",
	)
	sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="DPM++ Karras SDE")
	seed = gr.Slider(
	minimum=-1,
	maximum=9999999999,
	step=1,
	value=2313123,
	label="Seed",
	randomize=True,
	)
	with gr.Row():
	run_btn = gr.Button("Run")
	with gr.Column():
	result_image = gr.Image(label="Result Image")
	run_btn.click(
	inference,
	inputs=[
	qr_code_content,
	prompt,
	negative_prompt,
	guidance_scale,
	controlnet_conditioning_scale,
	strength,
	seed,
	init_image,
	qr_code_image,
	use_qr_code_as_init_image,
	sampler,
	],
	outputs=[result_image],
	)

	gr.Examples(
	examples=[
	[
	"https://huggingface.co/",
	"A sky view of a colorful lakes and rivers flowing through the desert",
	"ugly, disfigured, low quality, blurry, nsfw",
	7.5,
	1.3,
	0.9,
	5392011833,
	None,
	None,
	True,
	"DPM++ Karras SDE",
	],
	[
	"https://huggingface.co/",
	"Bright sunshine coming through the cracks of a wet, cave wall of big rocks",
	"ugly, disfigured, low quality, blurry, nsfw",
	7.5,
	1.11,
	0.9,
	2523992465,
	None,
	None,
	True,
	"DPM++ Karras SDE",
	],
	[
	"https://huggingface.co/",
	"Sky view of highly aesthetic, ancient greek thermal baths in beautiful nature",
	"ugly, disfigured, low quality, blurry, nsfw",
	7.5,
	1.5,
	0.9,
	2523992465,
	None,
	None,
	True,
	"DPM++ Karras SDE",
	],
	],
	fn=inference,
	inputs=[
	qr_code_content,
	prompt,
	negative_prompt,
	guidance_scale,
	controlnet_conditioning_scale,
	strength,
	seed,
	init_image,
	qr_code_image,
	use_qr_code_as_init_image,
	sampler,
	],
	outputs=[result_image],
	cache_examples=True,
	)

	blocks.queue(concurrency_count=1, max_size=20)
	blocks.launch(share=bool(os.environ.get("SHARE", False)))