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( '''

Illusion Diffusion HQ 🌀

Generate stunning high quality illusion artwork with Stable Diffusion

Illusion Diffusion is back up with a safety checker! Because I have been asked, if you would like to support me, consider using deforum.studio

A space by AP Follow me on Twitter with big contributions from multimodalart

This project works by using Monster Labs QR Control Net. Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: MrUgleh for discovering the workflow :)

''' ) 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)