import gradio as gr import numpy as np # from edict_functions import EDICT_editing from PIL import Image from utils import Endpoint, get_token from io import BytesIO import requests endpoint = Endpoint() def local_edict(x, source_text, edit_text, edit_strength, guidance_scale, steps=50, mix_weight=0.93, ): x = Image.fromarray(x) return_im = EDICT_editing(x, source_text, edit_text, steps=steps, mix_weight=mix_weight, init_image_strength=edit_strength, guidance_scale=guidance_scale )[0] return np.array(return_im) def encode_image(image): buffered = BytesIO() image.save(buffered, format="JPEG", quality=95) buffered.seek(0) return buffered def decode_image(img_obj): img = Image.open(img_obj).convert("RGB") return img def edict(x, source_text, edit_text, edit_strength, guidance_scale, steps=50, mix_weight=0.93, ): url = endpoint.url url = url + "/api/edit" headers = {### Misc. "User-Agent": "EDICT HuggingFace Space", "Auth-Token": get_token(), } data = { "source_text": source_text, "edit_text": edit_text, "edit_strength": edit_strength, "guidance_scale": guidance_scale, } image = encode_image(Image.fromarray(x)) files = {"image": image} response = requests.post(url, data=data, files=files, headers=headers) if response.status_code == 200: return np.array(decode_image(BytesIO(response.content))) else: return "Error: " + response.text # x = decode_image(response) # return np.array(x) examples = [ ['square_ims/american_gothic.jpg', 'A painting of two people frowning', 'A painting of two people smiling', 0.5, 3], ['square_ims/colloseum.jpg', 'An old ruined building', 'A new modern office building', 0.8, 3], ] examples.append(['square_ims/scream.jpg', 'A painting of someone screaming', 'A painting of an alien', 0.5, 3]) examples.append(['square_ims/yosemite.jpg', 'Granite forest valley', 'Granite desert valley', 0.8, 3]) examples.append(['square_ims/einstein.jpg', 'Mouth open', 'Mouth closed', 0.8, 3]) examples.append(['square_ims/einstein.jpg', 'A man', 'A man in K.I.S.S. facepaint', 0.8, 3]) """ examples.extend([ ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A Chinese New Year cupcake', 0.8, 3], ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A Union Jack cupcake', 0.8, 3], ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A Nigerian flag cupcake', 0.8, 3], ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A Santa Claus cupcake', 0.8, 3], ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'An Easter cupcake', 0.8, 3], ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A hedgehog cupcake', 0.8, 3], ['square_ims/imagenet_cake_2.jpg', 'A cupcake', 'A rose cupcake', 0.8, 3], ]) """ for dog_i in [1, 2]: for breed in ['Golden Retriever', 'Chihuahua', 'Dalmatian']: examples.append([f'square_ims/imagenet_dog_{dog_i}.jpg', 'A dog', f'A {breed}', 0.8, 3]) description = """ **We have disabled image uploading from March 22. 2023.** **Please try examples provided below.** A gradio demo for [EDICT](https://arxiv.org/abs/2211.12446) (CVPR23) """ # description = gr.Markdown(description) article = """ ### Prompting Style As with many text-to-image methods, the prompting style of EDICT can make a big difference. When in doubt, experiment! Some guidance: * Parallel *Original Description* and *Edit Description* construction as much as possible. Inserting/editing single words often is enough to affect a change while maintaining a lot of the original structure * Words that will affect the entire setting (e.g. "A photo of " vs. "A painting of") can make a big difference. Playing around with them can help a lot ### Parameters Both `edit_strength` and `guidance_scale` have similar properties qualitatively: the higher the value the more the image will change. We suggest * Increasing/decreasing `edit_strength` first, particularly to alter/preserve more of the original structure/content * Then changing `guidance_scale` to make the change in the edited region more or less pronounced. Usually we find changing `edit_strength` to be enough, but feel free to play around (and report any interesting results)! ### Misc. Having difficulty coming up with a caption? Try [BLIP](https://huggingface.co/spaces/Salesforce/BLIP2) to automatically generate one! As with most StableDiffusion approaches, faces/text are often problematic to render, especially if they're small. Having these in the foreground will help keep them cleaner. A returned black image means that the [Safety Checker](https://huggingface.co/CompVis/stable-diffusion-safety-checker) triggered on the photo. This happens in odd cases sometimes (it often rejects the huggingface logo or variations), but we need to keep it in for obvious reasons. """ # article = gr.Markdown(description) iface = gr.Interface(fn=edict, inputs=[gr.Image(interactive=False), gr.Textbox(label="Original Description", interactive=False), gr.Textbox(label="Edit Description", interactive=False), # 50, # gr.Slider(5, 50, value=20, step=1), # 0.93, # gr.Slider(0.5, 1, value=0.7, step=0.05), gr.Slider(0.0, 1, value=0.8, step=0.05), gr.Slider(0, 10, value=3, step=0.5), ], examples = examples, outputs="image", description=description, article=article, cache_examples=True ) iface.launch()