from diffusers import ( ControlNetModel, DiffusionPipeline, StableDiffusionControlNetPipeline, ) import gradio as gr import numpy as np import os import time import math import random import imageio from PIL import Image, ImageFilter import torch max_64_bit_int = 2**63 - 1 device = "cuda" if torch.cuda.is_available() else "cpu" controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_ip2p", torch_dtype = torch.float32) pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker = None, controlnet = controlnet, torch_dtype = torch.float32 ) pipe = pipe.to(device) def check( source_img, prompt, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, randomize_seed, seed, progress = gr.Progress()): if source_img 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( source_img, prompt, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, randomize_seed, seed, progress = gr.Progress()): check( source_img, prompt, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, 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 randomize_seed: seed = random.randint(0, max_64_bit_int) random.seed(seed) #pipe = pipe.manual_seed(seed) try: imageio.imwrite("data.png", source_img) except: raise gr.Error("Can't read input image. You can try to first save your image in another format (.webp, .png, .jpeg, .bmp...).") # Input image try: input_image = Image.open("data.png").convert("RGB") except: raise gr.Error("Can't open input image. You can try to first save your image in another format (.webp, .png, .jpeg, .bmp...).") 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 = secondes // 60 secondes = secondes - (minutes * 60) hours = minutes // 60 minutes = minutes - (hours * 60) return [ output_image, "Start again to get a different result. The new image is " + str(output_width) + " pixels large and " + str(output_height) + " pixels high, so an image of " + f'{output_width * output_height:,}' + " pixels. The image have been generated in " + str(hours) + " h, " + str(minutes) + " min, " + str(secondes) + " sec." + limitation ] with gr.Blocks() as interface: gr.Markdown( """

Instruct Pix2Pix demo

Modifies your image using a textual instruction, up to 1 million pixels, freely, without account, without watermark, without installation, which can be downloaded



🚀 Powered by SD 1.5 and ControlNet

🐌 Slow process... ~1 hour. If this space does not work or you want a faster run, use Instruct Pix2Pix available on terrapretapermaculture's ControlNet-v1-1 space (last tab) or on Dezgo site.
You can duplicate this space on a free account, it works on CPU.

⚖️ You can use, modify and share the generated images but not for commercial uses. """ ) with gr.Column(): source_img = gr.Image(label = "Your image", sources = ["upload"], type = "numpy") prompt = gr.Textbox(label = 'Prompt', info = "Instruct what to change in the image", placeholder = 'Order the AI what to change in the image') 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 = 'Watermark') 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 = "Classifier-Free 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 (not working, always checked)", 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 (if not randomized)") submit = gr.Button("Modify", variant = "primary") modified_image = gr.Image(label = "Modified image") information = gr.Label(label = "Information") submit.click(check, inputs = [ source_img, 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 = [ source_img, 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( inputs = [ source_img, prompt, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, randomize_seed, seed ], outputs = [ modified_image, information ], examples = [ [ "Example1.webp", "What if it's snowing?", "Watermark", 1, 20, 5, 1.5, True, 42 ], [ "Example2.png", "What if this woman had brown hair?", "Watermark", 1, 20, 5, 1.5, True, 42 ], [ "Example3.jpeg", "Replace the house by a windmill", "Watermark", 1, 20, 5, 1.5, True, 42 ], ], cache_examples = False, ) interface.queue().launch()