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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
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',
'an intricate portrait',
'an impressionist painting',
'abstract art',
'an eldritch image',
'a sketch',
'a city full of darkness and graffiti',
'a black & white photo',
'a brilliant, timeless tarot card of the world',
'a photo of a woman',
'',
]
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)
image = pipe(
prompt=prompt,
height=1024,
width=1024,
num_inference_steps=8,
guidance_scale=0,
ip_adapter_emb=torch.zeros(1, 1, 1280, device=DEVICE, dtype=torch.float16),
).images
pooled_embeds, _ = pipe.encode_image(
image[0], DEVICE, 1, output_hidden_state
)
embs.append(pooled_embeds)
return image[0]
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)
image, im_emb = replicate.run(
"rynmurdock/zahir:43177e0594f3bc2e3560170ff0ffb6d1cacdddda1be25fbcd4348ef02b0b7d0f",
input={"prompt": prompt, 'im_emg': pickle.dumps(im_emb)}
)
embs.append(im_emb)
torch.save(lin_class.coef_, f'./{start_time}.pt')
return image[0]
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 🚀 |