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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 = '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=["image",
gr.Textbox(label="Original Description"),
gr.Textbox(label="Edit Description"),
# 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()
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