app.py

import spaces
import torch
import gradio as gr
from PIL import Image
import random

from diffusers import (
    AutoencoderKL,
    StableDiffusionControlNetPipeline,
    ControlNetModel,
    StableDiffusionControlNetImg2ImgPipeline,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler,
)
import time
from share_btn import community_icon_html, loading_icon_html, share_js
from illusion_style import css
import os
from transformers import CLIPImageProcessor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker

BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"

# Initialize both pipelines
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained(
    "monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16
)

# Initialize the safety checker conditionally
SAFETY_CHECKER_ENABLED = os.environ.get("SAFETY_CHECKER", "0") == "1"
safety_checker = None
feature_extractor = None
if SAFETY_CHECKER_ENABLED:
    safety_checker = StableDiffusionSafetyChecker.from_pretrained(
        "CompVis/stable-diffusion-safety-checker"
    ).to("cuda")
    feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32")

main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
    BASE_MODEL,
    controlnet=controlnet,
    vae=vae,
    safety_checker=safety_checker,
    feature_extractor=feature_extractor,
    torch_dtype=torch.float16,
).to("cuda")

image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)

# Sampler map
SAMPLER_MAP = {
    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(
        config, use_karras=True, algorithm_type="sde-dpmsolver++"
    ),
    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
}


def center_crop_resize(img, output_size=(512, 512)):
    width, height = img.size
    new_dimension = min(width, height)
    left = (width - new_dimension) / 2
    top = (height - new_dimension) / 2
    right = (width + new_dimension) / 2
    bottom = (height + new_dimension) / 2
    img = img.crop((left, top, right, bottom))
    img = img.resize(output_size)
    return img


def common_upscale(samples, width, height, upscale_method, crop=False):
    if crop == "center":
        old_width = samples.shape[3]
        old_height = samples.shape[2]
        old_aspect = old_width / old_height
        new_aspect = width / height
        x = 0
        y = 0
        if old_aspect > new_aspect:
            x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
        elif old_aspect < new_aspect:
            y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
        s = samples[:, :, y:old_height - y, x:old_width - x]
    else:
        s = samples
    return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)


def upscale(samples, upscale_method, scale_by):
    width = round(samples["images"].shape[3] * scale_by)
    height = round(samples["images"].shape[2] * scale_by)
    s = common_upscale(samples["images"], width, height, upscale_method, "disabled")
    return s


def check_inputs(prompt: str, control_image: Image.Image):
    if control_image is None:
        raise gr.Error("Please select or upload an Input Illusion")
    if prompt is None or prompt == "":
        raise gr.Error("Prompt is required")


# Inference function
@spaces.GPU
def inference(
    control_image: Image.Image,
    prompt: str,
    negative_prompt: str,
    guidance_scale: float = 8.0,
    controlnet_conditioning_scale: float = 1,
    control_guidance_start: float = 1,
    control_guidance_end: float = 1,
    upscaler_strength: float = 0.5,
    seed: int = -1,
    sampler="DPM++ Karras SDE",
    progress=gr.Progress(track_tqdm=True),
):
    start_time = time.time()
    start_time_struct = time.localtime(start_time)
    start_time_formatted = time.strftime("%H:%M:%S", start_time_struct)
    print(f"Inference started at {start_time_formatted}")

    control_image_small = center_crop_resize(control_image)
    control_image_large = center_crop_resize(control_image, (1024, 1024))

    main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
    my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
    generator = torch.Generator(device="cuda").manual_seed(my_seed)

    out = main_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=control_image_small,
        guidance_scale=float(guidance_scale),
        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
        generator=generator,
        control_guidance_start=float(control_guidance_start),
        control_guidance_end=float(control_guidance_end),
        num_inference_steps=15,
        output_type="latent",
    )
    upscaled_latents = upscale(out, "nearest-exact", 2)
    out_image = image_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        control_image=control_image_large,
        image=upscaled_latents,
        guidance_scale=float(guidance_scale),
        generator=generator,
        num_inference_steps=20,
        strength=upscaler_strength,
        control_guidance_start=float(control_guidance_start),
        control_guidance_end=float(control_guidance_end),
        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
    )
    end_time = time.time()
    end_time_struct = time.localtime(end_time)
    end_time_formatted = time.strftime("%H:%M:%S", end_time_struct)
    print(f"Inference ended at {end_time_formatted}, taking {end_time-start_time}s")

    return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed


with gr.Blocks(css=css) as app:
    gr.Markdown(
        '''
        <div style="text-align: center;">
            <h1>Illusion Diffusion HQ 🌀</h1>
            <p style="font-size:16px;">Generate stunning high quality illusion artwork with Stable Diffusion</p>
            <p>Illusion Diffusion is back up with a safety checker! Because I have been asked, if you would like to support me, consider using <a href="https://deforum.studio">deforum.studio</a></p>
            <p>A space by AP <a href="https://twitter.com/angrypenguinPNG">Follow me on Twitter</a> with big contributions from <a href="https://twitter.com/multimodalart">multimodalart</a></p>
            <p>This project works by using <a href="https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster">Monster Labs QR Control Net</a>. Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: <a href="https://twitter.com/MrUgleh">MrUgleh</a> for discovering the workflow :)</p>
        </div>
        '''
    )

    state_img_input = gr.State()
    state_img_output = gr.State()
    with gr.Row():
        with gr.Column():
            control_image = gr.Image(label="Input Illusion", type="pil", elem_id="control_image")
            controlnet_conditioning_scale = gr.Slider(
                minimum=0.0,
                maximum=5.0,
                step=0.01,
                value=0.8,
                label="Illusion strength",
                elem_id="illusion_strength",
                info="ControlNet conditioning scale",
            )
            gr.Examples(
                examples=[
                    "checkers.png",
                    "checkers_mid.jpg",
                    "pattern.png",
                    "ultra_checkers.png",
                    "spiral.jpeg",
                    "funky.jpeg",
                ],
                inputs=control_image,
            )
            prompt = gr.Textbox(
                label="Prompt",
                elem_id="prompt",
                info="Type what you want to generate",
                placeholder="Medieval village scene with busy streets and castle in the distance",
            )
            negative_prompt = gr.Textbox(
                label="Negative Prompt",
                info="Type what you don't want to see",
                value="low quality",
                elem_id="negative_prompt",
            )
            with gr.Accordion(label="Advanced Options", open=False):
                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="Euler")
                control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0, label="Start of ControlNet")
                control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="End of ControlNet")
                strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="Strength of the upscaler")
                seed = gr.Slider(
                    minimum=-1,
                    maximum=9999999999,
                    step=1,
                    value=-1,
                    label="Seed",
                    info="-1 means random seed",
                )
                used_seed = gr.Number(label="Last seed used", interactive=False)
            run_btn = gr.Button("Run")
        with gr.Column():
            result_image = gr.Image(label="Illusion Diffusion Output", interactive=False, elem_id="output")
            with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
                community_icon = gr.HTML(community_icon_html)
                loading_icon = gr.HTML(loading_icon_html)
                share_button = gr.Button("Share to community", elem_id="share-btn")

    prompt.submit(
        check_inputs,
        inputs=[prompt, control_image],
        queue=False,
    ).success(
        inference,
        inputs=[
            control_image,
            prompt,
            negative_prompt,
            guidance_scale,
            controlnet_conditioning_scale,
            control_start,
            control_end,
            strength,
            seed,
            sampler,
        ],
        outputs=[result_image, result_image, share_group, used_seed],
    )

    run_btn.click(
        check_inputs,
        inputs=[prompt, control_image],
        queue=False,
    ).success(
        inference,
        inputs=[
            control_image,
            prompt,
            negative_prompt,
            guidance_scale,
            controlnet_conditioning_scale,
            control_start,
            control_end,
            strength,
            seed,
            sampler,
        ],
        outputs=[result_image, result_image, share_group, used_seed],
    )

    share_button.click(None, [], [], js=share_js)

app.queue(max_size=20, api_open=False)

if __name__ == "__main__":
    app.launch(max_threads=400)