app.py

import os
import time
import random
import tempfile
import torch
import gradio as gr
from PIL import Image

import spaces
from gradio import processing_utils, utils

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    StableDiffusionControlNetPipeline,
    StableDiffusionControlNetImg2ImgPipeline,
    StableDiffusionLatentUpscalePipeline,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler,
)

from share_btn import community_icon_html, loading_icon_html, share_js
import user_history
from illusion_style import css

# -----------------------------
# Device & dtype (GPU/CPU auto)
# -----------------------------
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

# -----------------------------
# Base / ControlNet models
# -----------------------------
BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
VAE_ID = "stabilityai/sd-vae-ft-mse"
CONTROLNET_ID = "monster-labs/control_v1p_sd15_qrcode_monster"

# -----------------------------
# Load components
# -----------------------------
vae = AutoencoderKL.from_pretrained(VAE_ID, torch_dtype=dtype)
controlnet = ControlNetModel.from_pretrained(CONTROLNET_ID, torch_dtype=dtype)

# ⚠️ safety checker & clip feature extractor removed
main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
    BASE_MODEL,
    controlnet=controlnet,
    vae=vae,
    safety_checker=None,      # <= important
    feature_extractor=None,   # <= important
    torch_dtype=dtype,
)
main_pipe = main_pipe.to(device)

# Img2Img pipe reusing components
image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
image_pipe = image_pipe.to(device)

# -----------------------------
# 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),
}

# -----------------------------
# Helpers
# -----------------------------
def center_crop_resize(img: Image.Image, output_size=(512, 512)):
    width, height = img.size
    new_dim = min(width, height)
    left = (width - new_dim) / 2
    top = (height - new_dim) / 2
    right = (width + new_dim) / 2
    bottom = (height + new_dim) / 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_w = samples.shape[3]
        old_h = samples.shape[2]
        old_aspect = old_w / old_h
        new_aspect = width / height
        x = 0
        y = 0
        if old_aspect > new_aspect:
            x = round((old_w - old_w * (new_aspect / old_aspect)) / 2)
        elif old_aspect < new_aspect:
            y = round((old_h - old_h * (old_aspect / new_aspect)) / 2)
        s = samples[:, :, y : old_h - y, x : old_w - 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 not prompt:
        raise gr.Error("Prompt is required")

# -----------------------------
# Inference
# -----------------------------
@spaces.GPU
def inference(
    control_image: Image.Image,
    prompt: str,
    negative_prompt: str,
    guidance_scale: float = 8.0,
    controlnet_conditioning_scale: float = 1.0,
    control_guidance_start: float = 1.0,
    control_guidance_end: float = 1.0,
    upscaler_strength: float = 0.5,
    seed: int = -1,
    sampler: str = "DPM++ Karras SDE",
    progress = gr.Progress(track_tqdm=True),
    profile: gr.OAuthProfile | None = None,
):
    start_time = time.time()

    control_image_small = center_crop_resize(control_image, (512, 512))
    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 int(seed)
    generator = torch.Generator(device=device).manual_seed(my_seed)

    # First pass -> latents
    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",
    )

    # Upscale latents
    upscaled_latents = upscale(out, "nearest-exact", 2)

    # Second pass -> image
    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=float(upscaler_strength),
        control_guidance_start=float(control_guidance_start),
        control_guidance_end=float(control_guidance_end),
        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
    )

    # Save history
    user_history.save_image(
        label=prompt,
        image=out_image["images"][0],
        profile=profile,
        metadata={
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "guidance_scale": guidance_scale,
            "controlnet_conditioning_scale": controlnet_conditioning_scale,
            "control_guidance_start": control_guidance_start,
            "control_guidance_end": control_guidance_end,
            "upscaler_strength": upscaler_strength,
            "seed": my_seed,
            "sampler": sampler,
        },
    )

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

# -----------------------------
# UI
# -----------------------------
with gr.Blocks() as app:
    gr.Markdown(
        '''
        <div style="text-align: center;">
            <h1>Illusion Diffusion HQ 🌀</h1>
            <p style="font-size:16px;">Generate high-quality illusion artwork with Stable Diffusion + ControlNet</p>
            <p>A space by AP with contributions from the community.</p>
            <p>This uses <a href="https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster">Monster Labs QR ControlNet</a>.</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 a castle in the distance")
            negative_prompt = gr.Textbox(label="Negative Prompt", info="What you do NOT want", value="low quality, blurry", 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", label="Sampler")
                control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.0, label="Start of ControlNet")
                control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="End of ControlNet")
                strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="Strength of the upscaler")
                seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=-1, label="Seed", info="-1 = random")
                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")

    # Wire up
    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)

with gr.Blocks(css=css) as app_with_history:
    with gr.Tab("Demo"):
        app.render()
    with gr.Tab("Past generations"):
        user_history.render()

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

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