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Running
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Zero
| 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 tempfile | |
| 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 = Image.open(base64_image) | |
| return pil_image | |
| def convert_to_base64(pil_image): | |
| with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file: | |
| image.save(temp_file.name) | |
| return temp_file.name | |
| # Inference function | |
| 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( | |
| ''' | |
| <center><h1>Illusion Diffusion HQ 🌀</h1></span> | |
| <span font-size:16px;">Generate stunning high quality illusion artwork with Stable Diffusion</span> | |
| </center> | |
| 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( | |
| 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) | |