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DEVICE = 'cpu'
import gradio as gr
import numpy as np
from sklearn.svm import LinearSVC
from sklearn import preprocessing
import pandas as pd
import random
import time
import replicate
import torch
import pickle
from urllib.request import urlopen
from PIL import Image
import requests
from io import BytesIO
prompt_list = [p for p in list(set(
pd.read_csv('./twitter_prompts.csv').iloc[:, 1].tolist())) if type(p) == str]
calibrate_prompts = [
"4k photo",
'surrealist art',
'a psychedelic, fractal view',
'a beautiful collage',
'abstract art',
'an eldritch image',
'a sketch',
'a city full of darkness and graffiti',
'',
]
embs = []
ys = []
start_time = time.time()
glob_idx = 0
def next_image():
global glob_idx
glob_idx = glob_idx + 1
with torch.no_grad():
if len(calibrate_prompts) > 0:
print('######### Calibrating with sample prompts #########')
prompt = calibrate_prompts.pop(0)
print(prompt)
output = replicate.run(
"rynmurdock/zahir:49ebb1916c4baae35884ebfa16b092cf45d086c1913b53f62bb07d575cdbe683",
input={"prompt": prompt,}
)
response = requests.get(output['file1'])
image = Image.open(BytesIO(response.content))
embs.append(torch.tensor([float(i) for i in urlopen(output['file2']).read().decode('utf-8').split(', ')]).unsqueeze(0))
return image
else:
print('######### Roaming #########')
# sample only as many negatives as there are positives
indices = range(len(ys))
pos_indices = [i for i in indices if ys[i] == 1]
neg_indices = [i for i in indices if ys[i] == 0]
lower = min(len(pos_indices), len(neg_indices))
neg_indices = random.sample(neg_indices, lower)
pos_indices = random.sample(pos_indices, lower)
cut_embs = [embs[i] for i in neg_indices] + [embs[i] for i in pos_indices]
cut_ys = [ys[i] for i in neg_indices] + [ys[i] for i in pos_indices]
feature_embs = torch.stack([e[0].detach().cpu() for e in cut_embs])
scaler = preprocessing.StandardScaler().fit(feature_embs)
feature_embs = scaler.transform(feature_embs)
print(np.array(feature_embs).shape, np.array(ys).shape)
lin_class = LinearSVC(max_iter=50000, dual='auto', class_weight='balanced').fit(np.array(feature_embs), np.array(cut_ys))
lin_class.coef_ = torch.tensor(lin_class.coef_, dtype=torch.double)
lin_class.coef_ = (lin_class.coef_.flatten() / (lin_class.coef_.flatten().norm())).unsqueeze(0)
rng_prompt = random.choice(prompt_list)
w = 1# if len(embs) % 2 == 0 else 0
im_emb = w * lin_class.coef_.to(device=DEVICE, dtype=torch.float16)
prompt= 'an image' if glob_idx % 2 == 0 else rng_prompt
print(prompt)
output = replicate.run(
"rynmurdock/zahir:49ebb1916c4baae35884ebfa16b092cf45d086c1913b53f62bb07d575cdbe683",
input={"prompt": prompt, 'im_emb': im_emb.tolist()}
)
response = requests.get(output['file1'])
image = Image.open(BytesIO(response.content))
im_emb = torch.tensor([float(i) for i in urlopen(output['file2']).read().decode('utf-8').split(', ')]).unsqueeze(0)
embs.append(im_emb)
torch.save(lin_class.coef_, f'./{start_time}.pt')
return image
def start(_):
return [
gr.Button(value='Like', interactive=True),
gr.Button(value='Neither', interactive=True),
gr.Button(value='Dislike', interactive=True),
gr.Button(value='Start', interactive=False),
next_image()
]
def choose(choice):
if choice == 'Like':
choice = 1
elif choice == 'Neither':
_ = embs.pop(-1)
return next_image()
else:
choice = 0
ys.append(choice)
return next_image()
css = "div#output-image {height: 768px !important; width: 768px !important; margin:auto;}"
with gr.Blocks(css=css) as demo:
with gr.Row():
html = gr.HTML('''<div style='text-align:center; font-size:32'>You will callibrate for several prompts and then roam.</ div>''')
with gr.Row(elem_id='output-image'):
img = gr.Image(interactive=False, elem_id='output-image',)
with gr.Row(equal_height=True):
b3 = gr.Button(value='Dislike', interactive=False,)
b2 = gr.Button(value='Neither', interactive=False,)
b1 = gr.Button(value='Like', interactive=False,)
b1.click(
choose,
[b1],
[img]
)
b2.click(
choose,
[b2],
[img]
)
b3.click(
choose,
[b3],
[img]
)
with gr.Row():
b4 = gr.Button(value='Start')
b4.click(start,
[b4],
[b1, b2, b3, b4, img,])
demo.launch() # Share your demo with just 1 extra parameter 🚀 |