import PIL import requests import torch import gradio as gr import random from PIL import Image import os import time from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler #Loading from Diffusers Library model_id = "timbrooks/instruct-pix2pix" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") #, safety_checker=None) pipe.to("cuda") pipe.enable_attention_slicing() counter = 0 help_text = """ Some notes from the official [instruct-pix2pix](https://huggingface.co/spaces/timbrooks/instruct-pix2pix) Space by the authors and from the official [Diffusers docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix) - If you're not getting what you want, there may be a few reasons: 1. Is the image not changing enough? Your guidance_scale may be too low. It should be >1. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. This value dictates how similar the output should be to the input. This pipeline requires a value of at least `1`. It's possible your edit requires larger changes from the original image. 2. Alternatively, you can toggle image_guidance_scale. Image guidance scale is to push the generated image towards the inital image. Image guidance scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to generate images that are closely linked to the source image `image`, usually at the expense of lower image quality. 3. I have observed that rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog"). 4. Increasing the number of steps sometimes improves results. 5. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try: * Cropping the image so the face takes up a larger portion of the frame. """ def previous(image): return image def chat(image_in, in_steps, in_guidance_scale, in_img_guidance_scale, image_hid, img_name, counter_out, image_oneup, prompt, history, progress=gr.Progress(track_tqdm=True)): progress(0, desc="Starting...") if prompt.lower() == 'reverse' : #--to add revert functionality later history = history or [] temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' image_oneup.save(temp_img_name) response = 'Reverted to the last image ' + '' history.append((prompt, response)) return history, history, image_oneup, temp_img_name, counter_out if prompt.lower() == 'restart' : #--to add revert functionality later history = history or [] temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' image_in.save(temp_img_name) response = 'Reverted to the last image ' + '' history.append((prompt, response)) return history, history, image_in, temp_img_name, counter_out if counter_out > 0: edited_image = pipe(prompt, image=image_hid, num_inference_steps=int(in_steps), guidance_scale=float(in_guidance_scale), image_guidance_scale=float(in_img_guidance_scale)).images[0] if os.path.exists(img_name): os.remove(img_name) temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' # Create a file-like object with open(temp_img_name, "wb") as fp: # Save the image to the file-like object edited_image.save(fp) #Get the name of the saved image saved_image_name = fp.name #edited_image.save(temp_img_name) #, overwrite=True) counter_out += 1 else: seed = random.randint(0, 1000000) img_name = f"./edited_image_{seed}.png" #Resizing the image basewidth = 512 wpercent = (basewidth/float(image_in.size[0])) hsize = int((float(image_in.size[1])*float(wpercent))) image_in = image_in.resize((basewidth,hsize), Image.Resampling.LANCZOS) edited_image = pipe(prompt, image=image_in, num_inference_steps=int(in_steps), guidance_scale=float(in_guidance_scale), image_guidance_scale=float(in_img_guidance_scale)).images[0] if os.path.exists(img_name): os.remove(img_name) with open(img_name, "wb") as fp: # Save the image to the file-like object edited_image.save(fp) #Get the name of the saved image saved_image_name2 = fp.name history = history or [] #Resizing (or not) the image for better display and adding supportive sample text add_text_list = ["There you go", "Enjoy your image!", "Nice work! Wonder what you gonna do next!", "Way to go!", "Does this work for you?", "Something like this?"] if counter_out > 0: response = random.choice(add_text_list) + '' history.append((prompt, response)) return history, history, edited_image, temp_img_name, counter_out else: response = random.choice(add_text_list) + '' #IMG_NAME history.append((prompt, response)) counter_out += 1 return history, history, edited_image, img_name, counter_out with gr.Blocks() as demo: gr.Markdown("""

Chat Interface with InstructPix2Pix: Give Image Editing Instructions

For faster inference without waiting in the queue, you may duplicate the space and upgrade to GPU in settings.
Duplicate Space **Note: Please be advised that a safety checker has been implemented in this public space. Any attempts to generate inappropriate or NSFW images will result in the display of a black screen as a precautionary measure to protect all users. We appreciate your cooperation in maintaining a safe and appropriate environment for all members of our community.** New features and bug-fixes: 1. Now use 'reverse' as prompt to get back the previous image after an unwanted edit 2. Use 'restart' as prompt to get back to original image and start over! 3. Now you can load larger images (~5 mb) as well

""") with gr.Row(): with gr.Column(): image_in = gr.Image(type='pil', label="Original Image") text_in = gr.Textbox() state_in = gr.State() #with gr.Row(): b1 = gr.Button('Edit the image!') #b2 = gr.Button('Revert!') with gr.Accordion("Advance settings for Training and Inference", open=False): gr.Markdown("Advance settings for - Number of Inference steps, Guidanace scale, and Image guidance scale.") in_steps = gr.Number(label="Enter the number of Inference steps", value = 20) in_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Guidance scale", value=7.5) in_img_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Image Guidance scale", value=1.5) image_hid = gr.Image(type='pil', visible=False) image_oneup = gr.Image(type='pil', visible=False) img_name_temp_out = gr.Textbox(visible=False) #img_revert = gr.Checkbox(visible=True, value=False,label=to track a revert message) counter_out = gr.Number(visible=False, value=0, precision=0) chatbot = gr.Chatbot() b1.click(chat,[image_in, in_steps, in_guidance_scale, in_img_guidance_scale, image_hid, img_name_temp_out,counter_out, image_oneup, text_in, state_in], [chatbot, state_in, image_hid, img_name_temp_out, counter_out]) #, queue=True) b1.click(previous, [image_hid], [image_oneup]) #b2.click(previous, image_oneup, image_hid) gr.Markdown(help_text) demo.queue(concurrency_count=10) demo.launch(debug=True, width="80%", height=2000)