import spaces import torch import gradio as gr from gradio import processing_utils, utils from PIL import Image import random from diffusers import ( DiffusionPipeline, AutoencoderKL, StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionLatentUpscalePipeline, StableDiffusionImg2ImgPipeline, StableDiffusionControlNetImg2ImgPipeline, DPMSolverMultistepScheduler, # <-- Added import EulerDiscreteScheduler # <-- Added import ) import time from share_btn import community_icon_html, loading_icon_html, share_js import user_history from illusion_style import css BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE" # Initialize both pipelines vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16) #init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", torch_dtype=torch.float16) controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)#, torch_dtype=torch.float16) main_pipe = StableDiffusionControlNetPipeline.from_pretrained( BASE_MODEL, controlnet=controlnet, vae=vae, safety_checker=None, torch_dtype=torch.float16, ).to("cuda") #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True) #main_pipe.unet.to(memory_format=torch.channels_last) #main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True) #model_id = "stabilityai/sd-x2-latent-upscaler" image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components) #image_pipe.unet = torch.compile(image_pipe.unet, mode="reduce-overhead", fullgraph=True) #upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) #upscaler.to("cuda") # 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 # Calculate dimensions to crop to the center 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 # Crop and resize 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): #s = samples.copy() 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") def convert_to_pil(base64_image): pil_image = processing_utils.decode_base64_to_image(base64_image) return pil_image def convert_to_base64(pil_image): base64_image = processing_utils.encode_pil_to_base64(pil_image) return base64_image # 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), profile: gr.OAuthProfile | None = None, ): 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}") # Generate the initial image #init_image = init_pipe(prompt).images[0] # Rest of your existing code 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") # Save image + metadata 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": seed, "sampler": sampler, }, ) return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed with gr.Blocks() as app: gr.Markdown( '''

Illusion Diffusion HQ 🌀

Generate stunning high quality illusion artwork with Stable Diffusion
A space by AP [Follow me on Twitter](https://twitter.com/angrypenguinPNG) with big contributions from [multimodalart](https://twitter.com/multimodalart) This project works by using [Monster Labs QR Control Net](https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster). Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: [MrUgleh](https://twitter.com/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( convert_to_pil, inputs=[control_image], outputs=[state_img_input], queue=False, preprocess=False, ).success( inference, inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler], outputs=[state_img_output, result_image, share_group, used_seed] ).success( convert_to_base64, inputs=[state_img_output], outputs=[result_image], queue=False, postprocess=False ) run_btn.click( check_inputs, inputs=[prompt, control_image], queue=False ).success( convert_to_pil, inputs=[control_image], outputs=[state_img_input], queue=False, preprocess=False, ).success( inference, inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler], outputs=[state_img_output, result_image, share_group, used_seed] ).success( convert_to_base64, inputs=[state_img_output], outputs=[result_image], queue=False, postprocess=False ) 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)