Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import numpy as np | |
| import time | |
| import math | |
| import random | |
| import torch | |
| try: | |
| import spaces | |
| except: | |
| class spaces(): | |
| def GPU(*args, **kwargs): | |
| def decorator(function): | |
| return lambda *dummy_args, **dummy_kwargs: function(*dummy_args, **dummy_kwargs) | |
| return decorator | |
| from diffusers import ( | |
| ControlNetModel, | |
| StableDiffusionControlNetPipeline, | |
| ) | |
| from PIL import Image | |
| from pillow_heif import register_heif_opener | |
| register_heif_opener() | |
| max_64_bit_int = np.iinfo(np.int32).max | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| floatType = torch.float16 | |
| else: | |
| device = "cpu" | |
| floatType = torch.float32 | |
| controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_ip2p", torch_dtype = floatType) | |
| pipe = StableDiffusionControlNetPipeline.from_pretrained( | |
| "botp/stable-diffusion-v1-5", safety_checker = None, controlnet = controlnet, torch_dtype = floatType | |
| ) | |
| pipe = pipe.to(device) | |
| def update_seed(is_randomize_seed, seed): | |
| if is_randomize_seed: | |
| return random.randint(0, max_64_bit_int) | |
| return seed | |
| def check( | |
| input_image, | |
| prompt, | |
| negative_prompt, | |
| denoising_steps, | |
| num_inference_steps, | |
| guidance_scale, | |
| image_guidance_scale, | |
| is_randomize_seed, | |
| seed, | |
| progress = gr.Progress()): | |
| if input_image is None: | |
| raise gr.Error("Please provide an image.") | |
| if prompt is None or prompt == "": | |
| raise gr.Error("Please provide a prompt input.") | |
| def pix2pix( | |
| input_image, | |
| prompt, | |
| negative_prompt, | |
| denoising_steps, | |
| num_inference_steps, | |
| guidance_scale, | |
| image_guidance_scale, | |
| is_randomize_seed, | |
| seed, | |
| progress = gr.Progress()): | |
| check( | |
| input_image, | |
| prompt, | |
| negative_prompt, | |
| denoising_steps, | |
| num_inference_steps, | |
| guidance_scale, | |
| image_guidance_scale, | |
| is_randomize_seed, | |
| seed | |
| ) | |
| start = time.time() | |
| progress(0, desc = "Preparing data...") | |
| if negative_prompt is None: | |
| negative_prompt = "" | |
| if denoising_steps is None: | |
| denoising_steps = 0 | |
| if num_inference_steps is None: | |
| num_inference_steps = 20 | |
| if guidance_scale is None: | |
| guidance_scale = 5 | |
| if image_guidance_scale is None: | |
| image_guidance_scale = 1.5 | |
| if seed is None: | |
| seed = random.randint(0, max_64_bit_int) | |
| random.seed(seed) | |
| torch.manual_seed(seed) | |
| original_height, original_width, dummy_channel = np.array(input_image).shape | |
| output_width = original_width | |
| output_height = original_height | |
| mask_image = Image.new(mode = input_image.mode, size = (output_width, output_height), color = "white") | |
| limitation = ""; | |
| # Limited to 1 million pixels | |
| if 1024 * 1024 < output_width * output_height: | |
| factor = ((1024 * 1024) / (output_width * output_height))**0.5 | |
| output_width = math.floor(output_width * factor) | |
| output_height = math.floor(output_height * factor) | |
| limitation = " Due to technical limitation, the image have been downscaled and then upscaled."; | |
| # Width and height must be multiple of 8 | |
| output_width = output_width - (output_width % 8) | |
| output_height = output_height - (output_height % 8) | |
| progress(None, desc = "Processing...") | |
| output_image = pipe( | |
| seeds=[seed], | |
| width = output_width, | |
| height = output_height, | |
| prompt = prompt, | |
| negative_prompt = negative_prompt, | |
| image = input_image, | |
| mask_image = mask_image, | |
| num_inference_steps = num_inference_steps, | |
| guidance_scale = guidance_scale, | |
| image_guidance_scale = image_guidance_scale, | |
| denoising_steps = denoising_steps, | |
| show_progress_bar = True | |
| ).images[0] | |
| if limitation != "": | |
| output_image = output_image.resize((original_width, original_height)) | |
| end = time.time() | |
| secondes = int(end - start) | |
| minutes = math.floor(secondes / 60) | |
| secondes = secondes - (minutes * 60) | |
| hours = math.floor(minutes / 60) | |
| minutes = minutes - (hours * 60) | |
| return [ | |
| output_image, | |
| ("Start again to get a different result. " if is_randomize_seed else "") + "The image has been generated in " + ((str(hours) + " h, ") if hours != 0 else "") + ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + str(secondes) + " sec." + limitation | |
| ] | |
| with gr.Blocks() as interface: | |
| gr.HTML( | |
| """ | |
| <h1 style="text-align: center;">Instruct Pix2Pix demo</h1> | |
| <p style="text-align: center;">Modifies your image using a textual instruction, freely, without account, without watermark, without installation, which can be downloaded</p> | |
| <br/> | |
| <br/> | |
| ✨ Powered by <i>SD 1.5</i> and <i>ControlNet</i>. You'd rather use modern AIs like <i>Flux Kontext</i> or <i>Qwen Image Edit</i>. The result quality extremely varies depending on what we ask. | |
| <br/> | |
| <ul> | |
| <li>To change the <b>view angle</b> of your image, I recommend to use <i>Zero123</i>,</li> | |
| <li>To <b>upscale</b> your image, I recommend to use <i><a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR">SUPIR</a></i>,</li> | |
| <li>To <b>slightly change</b> your image, I recommend to use <i>Image-to-Image SDXL</i>,</li> | |
| <li>To change <b>one detail</b> on your image, I recommend to use <i>Inpaint SDXL</i>,</li> | |
| <li>To remove the <b>background</b> of your image, I recommend to use <i>BRIA</i>,</li> | |
| <li>To enlarge the <b>viewpoint</b> of your image, I recommend to use <i>Uncrop</i>,</li> | |
| <li>To make a <b>tile</b> of your image, I recommend to use <i>Make My Image Tile</i>,</li> | |
| </ul> | |
| <br/> | |
| """ + ("🏃♀️ Estimated time: few minutes." if torch.cuda.is_available() else "🐌 Slow process... ~1 hour.") + """ | |
| Your computer must not enter into standby mode. You can launch several generations in different browser tabs when you're gone. If this space does not work or you want a faster run, use <i>Instruct Pix2Pix</i> available on hysts's <i>ControlNet-v1-1</i> space (last tab) or on <i>Dezgo</i> site.<br>You can duplicate this space on a free account, it's designed to work on CPU, GPU and ZeroGPU.<br/> | |
| <a href='https://huggingface.co/spaces/Fabrice-TIERCELIN/Instruct-Pix2Pix?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14'></a> | |
| <br/> | |
| ⚖️ You can use, modify and share the generated images but not for commercial uses. | |
| """ | |
| ) | |
| with gr.Column(): | |
| input_image = gr.Image(label = "Your image", sources = ["upload", "webcam", "clipboard"], type = "pil") | |
| prompt = gr.Textbox(label = "Prompt", info = "Instruct what to change in the image", placeholder = "Order the AI what to change in the image", lines = 2) | |
| with gr.Accordion("Advanced options", open = False): | |
| negative_prompt = gr.Textbox(label = "Negative prompt", placeholder = "Describe what you do NOT want to see in the image", value = '' | |
| 'blurring, aliasing, unsharp, weird textures, ugly, dirty, messy, ' | |
| 'worst quality, low quality, frames, watermark, signature, jpeg artifacts, ' | |
| 'deformed, lowres, over-smooth') | |
| denoising_steps = gr.Slider(minimum = 0, maximum = 1000, value = 0, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result") | |
| num_inference_steps = gr.Slider(minimum = 10, maximum = 500, value = 20, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality") | |
| guidance_scale = gr.Slider(minimum = 1, maximum = 13, value = 5, step = 0.1, label = "Guidance Scale", info = "lower=image quality, higher=follow the prompt") | |
| image_guidance_scale = gr.Slider(minimum = 1, value = 1.5, step = 0.1, label = "Image Guidance Scale", info = "lower=image quality, higher=follow the image") | |
| randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different") | |
| seed = gr.Slider(minimum = 0, maximum = max_64_bit_int, step = 1, randomize = True, label = "Seed") | |
| submit = gr.Button("🚀 Modify", variant = "primary") | |
| modified_image = gr.Image(label = "Modified image") | |
| information = gr.HTML() | |
| submit.click(fn = update_seed, inputs = [ | |
| randomize_seed, | |
| seed | |
| ], outputs = [ | |
| seed | |
| ], queue = False, show_progress = False).then(check, inputs = [ | |
| input_image, | |
| prompt, | |
| negative_prompt, | |
| denoising_steps, | |
| num_inference_steps, | |
| guidance_scale, | |
| image_guidance_scale, | |
| randomize_seed, | |
| seed | |
| ], outputs = [], queue = False, show_progress = False).success(pix2pix, inputs = [ | |
| input_image, | |
| prompt, | |
| negative_prompt, | |
| denoising_steps, | |
| num_inference_steps, | |
| guidance_scale, | |
| image_guidance_scale, | |
| randomize_seed, | |
| seed | |
| ], outputs = [ | |
| modified_image, | |
| information | |
| ], scroll_to_output = True) | |
| gr.Examples( | |
| run_on_click = True, | |
| fn = pix2pix, | |
| inputs = [ | |
| input_image, | |
| prompt, | |
| negative_prompt, | |
| denoising_steps, | |
| num_inference_steps, | |
| guidance_scale, | |
| image_guidance_scale, | |
| randomize_seed, | |
| seed | |
| ], | |
| outputs = [ | |
| modified_image, | |
| information | |
| ], | |
| examples = [ | |
| [ | |
| "./Examples/Example1.webp", | |
| "What if it's snowing?", | |
| "blurring, aliasing, unsharp, weird textures, ugly, dirty, messy, " | |
| "worst quality, low quality, frames, watermark, signature, jpeg artifacts, " | |
| "deformed, lowres, over-smooth", | |
| 1, | |
| 20, | |
| 5, | |
| 1.5, | |
| False, | |
| 42 | |
| ], | |
| [ | |
| "./Examples/Example2.png", | |
| "What if this woman had brown hair?", | |
| "blurring, aliasing, unsharp, weird textures, ugly, dirty, messy, " | |
| "worst quality, low quality, frames, watermark, signature, jpeg artifacts, " | |
| "deformed, lowres, over-smooth", | |
| 1, | |
| 20, | |
| 5, | |
| 1.5, | |
| False, | |
| 42 | |
| ], | |
| [ | |
| "./Examples/Example3.jpeg", | |
| "Replace the house by a windmill", | |
| "blurring, aliasing, unsharp, weird textures, ugly, dirty, messy, " | |
| "worst quality, low quality, frames, watermark, signature, jpeg artifacts, " | |
| "deformed, lowres, over-smooth", | |
| 1, | |
| 20, | |
| 5, | |
| 1.5, | |
| False, | |
| 42 | |
| ], | |
| [ | |
| "./Examples/Example4.gif", | |
| "What if the camera was in opposite side?", | |
| "blurring, aliasing, unsharp, weird textures, ugly, dirty, messy, " | |
| "worst quality, low quality, frames, watermark, signature, jpeg artifacts, " | |
| "deformed, lowres, over-smooth", | |
| 1, | |
| 20, | |
| 5, | |
| 1.5, | |
| False, | |
| 42 | |
| ], | |
| [ | |
| "./Examples/Example5.bmp", | |
| "Turn him into cyborg", | |
| "blurring, aliasing, unsharp, weird textures, ugly, dirty, messy, " | |
| "worst quality, low quality, frames, watermark, signature, jpeg artifacts, " | |
| "deformed, lowres, over-smooth", | |
| 1, | |
| 20, | |
| 5, | |
| 25, | |
| False, | |
| 42 | |
| ], | |
| ], | |
| cache_examples = False, | |
| ) | |
| interface.queue().launch() |