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import PIL |
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import requests |
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import torch |
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import gradio as gr |
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import random |
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from PIL import Image |
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import os |
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import time |
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from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler |
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model_id = "timbrooks/instruct-pix2pix" |
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pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") |
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pipe.to("cuda") |
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pipe.enable_xformers_memory_efficient_attention() |
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pipe.unet.to(memory_format=torch.channels_last) |
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counter = 0 |
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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) - |
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If you're not getting what you want, there may be a few reasons: |
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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 |
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that are closely linked to the text `prompt`, usually at the expense of lower image quality. This value dictates how similar the output should |
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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. |
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2. Alternatively, you can toggle image_guidance_scale. Image guidance scale is to push the generated image towards the inital image. Image guidance |
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scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to generate images that are closely |
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linked to the source image `image`, usually at the expense of lower image quality. |
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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"). |
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4. Increasing the number of steps sometimes improves results. |
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5. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try: |
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* Cropping the image so the face takes up a larger portion of the frame. |
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""" |
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def previous(image): |
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return image |
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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)): |
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progress(0, desc="Starting...") |
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if prompt.lower() == 'reverse' : |
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history = history or [] |
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temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' |
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image_oneup.save(temp_img_name) |
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response = 'Reverted to the last image ' + '<img src="/file=' + temp_img_name + '">' |
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history.append((prompt, response)) |
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return history, history, image_oneup, temp_img_name, counter_out |
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if prompt.lower() == 'restart' : |
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history = history or [] |
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temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' |
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image_in.save(temp_img_name) |
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response = 'Reverted to the last image ' + '<img src="/file=' + temp_img_name + '">' |
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history.append((prompt, response)) |
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return history, history, image_in, temp_img_name, counter_out |
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if counter_out > 0: |
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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] |
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if os.path.exists(img_name): |
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os.remove(img_name) |
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temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' |
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with open(temp_img_name, "wb") as fp: |
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edited_image.save(fp) |
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saved_image_name = fp.name |
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counter_out += 1 |
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else: |
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seed = random.randint(0, 1000000) |
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img_name = f"./edited_image_{seed}.png" |
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basewidth = 512 |
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wpercent = (basewidth/float(image_in.size[0])) |
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hsize = int((float(image_in.size[1])*float(wpercent))) |
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image_in = image_in.resize((basewidth,hsize), Image.Resampling.LANCZOS) |
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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] |
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if os.path.exists(img_name): |
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os.remove(img_name) |
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with open(img_name, "wb") as fp: |
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edited_image.save(fp) |
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saved_image_name2 = fp.name |
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history = history or [] |
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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?"] |
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if counter_out > 0: |
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response = random.choice(add_text_list) + '<img src="/file=' + saved_image_name + '">' |
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history.append((prompt, response)) |
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return history, history, edited_image, temp_img_name, counter_out |
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else: |
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response = random.choice(add_text_list) + '<img src="/file=' + saved_image_name2 + '">' |
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history.append((prompt, response)) |
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counter_out += 1 |
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return history, history, edited_image, img_name, counter_out |
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with gr.Blocks() as demo: |
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gr.Markdown("""<h1><center>dummy</h1></center> """) |
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with gr.Row(): |
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with gr.Column(): |
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image_in = gr.Image(type='pil', label="Original Image") |
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text_in = gr.Textbox() |
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state_in = gr.State() |
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b1 = gr.Button('Edit the image!') |
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with gr.Accordion("Advance settings for Training and Inference", open=False): |
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gr.Markdown("Advance settings for - Number of Inference steps, Guidanace scale, and Image guidance scale.") |
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in_steps = gr.Number(label="Enter the number of Inference steps", value = 20) |
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in_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Guidance scale", value=7.5) |
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in_img_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Image Guidance scale", value=1.5) |
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image_hid = gr.Image(type='pil', visible=False) |
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image_oneup = gr.Image(type='pil', visible=False) |
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img_name_temp_out = gr.Textbox(visible=False) |
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counter_out = gr.Number(visible=False, value=0, precision=0) |
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chatbot = gr.Chatbot() |
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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]) |
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b1.click(previous, [image_hid], [image_oneup]) |
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gr.Markdown(help_text) |
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demo.queue(concurrency_count=10) |
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demo.launch(debug=True, width="80%", height=2000) |
