app.py

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
import spaces

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
).to("cuda")

pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
    # "runwayml/stable-diffusion-v1-5",
    "digiplay/GhostMixV1.2VAE",
    controlnet = controlnet,
    torch_dtype = torch.float16,
    safety_checker =None,
).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),
}

@spaces.GPU()
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(
        """
# Yamamoto QR Code Art Generator

## 🎨 Elevate Your Brand with Creative QR Codes

Welcome to Yamamoto's QR Code Art Generator, a powerful tool designed for our creative team to produce 
stunning, on-brand QR codes that seamlessly blend functionality with artistic expression.

### How it works:
We use cutting-edge AI technology to transform ordinary QR codes into visual masterpieces that align with your campaign's aesthetic. 
The QR code serves as both the initial image and the control image, allowing for natural integration with your provided prompt.

### Tips for optimal results:
- Use a strength value between 0.8 and 0.95
- Choose a conditioning scale between 0.6 and 2.0
- Experiment with prompts that reflect your campaign's theme or brand identity
                """
    )

    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", label="Sampler")
                seed = gr.Slider(
                    minimum=-1,
                    maximum=9999999999,
                    step=1,
                    value=-1,
                    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],
        concurrency_limit=1
    )

    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(max_size=20,api_open=False)
blocks.launch(share=bool(os.environ.get("SHARE", False)), show_api=False)