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( '''

Illusion Diffusion HQ 🌀

Generate high-quality illusion artwork with Stable Diffusion + ControlNet

A space by AP with contributions from the community.

This uses Monster Labs QR ControlNet.

''' ) 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)