app.py

DEVICE = 'cpu'

import gradio as gr
import numpy as np
from sklearn.svm import LinearSVC
from sklearn import preprocessing
import pandas as pd

from diffusers import LCMScheduler, AutoencoderTiny, EulerDiscreteScheduler, UNet2DConditionModel
from diffusers.models import ImageProjection
from patch_sdxl import SDEmb
import torch
import spaces

import random
import time

import torch
from urllib.request import urlopen

from PIL import Image
import requests
from io import BytesIO, StringIO

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

prompt_list = [p for p in list(set(
                pd.read_csv('./twitter_prompts.csv').iloc[:, 1].tolist())) if type(p) == str]

start_time = time.time()

####################### Setup Model
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
sdxl_lightening = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_2step_unet.safetensors"
unet = UNet2DConditionModel.from_config(model_id, subfolder="unet").to("cuda", torch.float16)
unet.load_state_dict(load_file(hf_hub_download(sdxl_lightening, ckpt), device="cuda"))
pipe = SDEmb.from_pretrained(model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe.to(device='cuda')
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")

output_hidden_state = False
#######################

@spaces.GPU
def predict(
        prompt,
        im_emb=None,
        progress=gr.Progress(track_tqdm=True)
    ):
    """Run a single prediction on the model"""
    with torch.no_grad():
        if im_emb == None:
            im_emb = torch.zeros(1, 1280, dtype=torch.float16, device='cuda')
        image = pipe(
            prompt=prompt,
            ip_adapter_emb=im_emb.to('cuda'),
            height=1024,
            width=1024,                                                                                                             
            num_inference_steps=2,
            guidance_scale=0,
            ).images[0]
        im_emb, _ = pipe.encode_image(
                image, 'cuda', 1, output_hidden_state
            )
        return image, im_emb.to(DEVICE)

# TODO add to state instead of shared across all
glob_idx = 0

def next_image(embs, ys, calibrate_prompts):
    global glob_idx
    glob_idx = glob_idx + 1

    # handle case where every instance of calibration prompts is 'Neither' or 'Like' or 'Dislike'
    if len(calibrate_prompts) == 0 and len(list(set(ys))) <= 1:
        embs.append(.01*torch.randn(1, 1280))
        embs.append(.01*torch.randn(1, 1280))
        ys.append(0)
        ys.append(1)
        
    with torch.no_grad():
        if len(calibrate_prompts) > 0:
            print('######### Calibrating with sample prompts #########')
            prompt = calibrate_prompts.pop(0)
            print(prompt)
            image, img_emb = predict(prompt)
            embs.append(img_emb)
            return image, embs, ys, calibrate_prompts
        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 = predict(prompt, im_emb)
            embs.append(im_emb)
            return image, embs, ys, calibrate_prompts









def start(_, embs, ys, calibrate_prompts):
    image, embs, ys, calibrate_prompts = next_image(embs, ys, calibrate_prompts)
    return [
            gr.Button(value='Like (L)', interactive=True), 
            gr.Button(value='Neither (Space)', interactive=True), 
            gr.Button(value='Dislike (A)', interactive=True),
            gr.Button(value='Start', interactive=False),
            image,
            embs,
            ys,
            calibrate_prompts
            ]


def choose(choice, embs, ys, calibrate_prompts):
    if choice == 'Like (L)':
        choice = 1
    elif choice == 'Neither (Space)':
        _ = embs.pop(-1)
        img, embs, ys, calibrate_prompts = next_image(embs, ys, calibrate_prompts)
        return img, embs, ys, calibrate_prompts
    else:
        choice = 0
    ys.append(choice)
    img, embs, ys, calibrate_prompts = next_image(embs, ys, calibrate_prompts)
    return img, embs, ys, calibrate_prompts

css = '''.gradio-container{max-width: 700px !important}
#description{text-align: center}
#description h1{display: block}
#description p{margin-top: 0}
.fade-in-out {animation: fadeInOut 3s forwards}
@keyframes fadeInOut {
    0% {
      background: var(--bg-color);
    }
    100% {
      background: var(--button-secondary-background-fill);
    }
}
'''
js_head = '''
<script>
document.addEventListener('keydown', function(event) {
    if (event.key === 'a' || event.key === 'A') {
        // Trigger click on 'dislike' if 'A' is pressed
        document.getElementById('dislike').click();
    } else if (event.key === ' ' || event.keyCode === 32) {
        // Trigger click on 'neither' if Spacebar is pressed
        document.getElementById('neither').click();
    } else if (event.key === 'l' || event.key === 'L') {
        // Trigger click on 'like' if 'L' is pressed
        document.getElementById('like').click();
    }
});
function fadeInOut(button, color) {
  button.style.setProperty('--bg-color', color);
  button.classList.remove('fade-in-out');
  void button.offsetWidth; // This line forces a repaint by accessing a DOM property
  
  button.classList.add('fade-in-out');
  button.addEventListener('animationend', () => {
    button.classList.remove('fade-in-out'); // Reset the animation state
  }, {once: true});
}
document.body.addEventListener('click', function(event) {
    const target = event.target;
    if (target.id === 'dislike') {
      fadeInOut(target, '#ff1717');
    } else if (target.id === 'like') {
      fadeInOut(target, '#006500');
    } else if (target.id === 'neither') {
      fadeInOut(target, '#cccccc');
    }
});
</script>
'''

with gr.Blocks(css=css, head=js_head) as demo:
    gr.Markdown('''# Generative Recommenders
    Explore the latent space without text prompts, based on your preferences. [Learn more on the blog](https://rynmurdock.github.io/posts/2024/3/generative_recomenders/)
    ''', elem_id="description")
    embs = gr.State([])
    ys = gr.State([])
    calibrate_prompts = gr.State([
    "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',
    '',
    ])

    with gr.Row(elem_id='output-image'):
        img = gr.Image(interactive=False, elem_id='output-image',width=700)
    with gr.Row(equal_height=True):
        b3 = gr.Button(value='Dislike (A)', interactive=False, elem_id="dislike")
        b2 = gr.Button(value='Neither (Space)', interactive=False, elem_id="neither")
        b1 = gr.Button(value='Like (L)', interactive=False, elem_id="like")
        b1.click(
        choose, 
        [b1, embs, ys, calibrate_prompts],
        [img, embs, ys, calibrate_prompts]
        )
        b2.click(
        choose, 
        [b2, embs, ys, calibrate_prompts],
        [img, embs, ys, calibrate_prompts]
        )
        b3.click(
        choose, 
        [b3, embs, ys, calibrate_prompts],
        [img, embs, ys, calibrate_prompts]
        )
    with gr.Row():
        b4 = gr.Button(value='Start')
        b4.click(start,
                 [b4, embs, ys, calibrate_prompts],
                 [b1, b2, b3, b4, img, embs, ys, calibrate_prompts])
    with gr.Row():
        html = gr.HTML('''<div style='text-align:center; font-size:20px'>You will calibrate for several prompts and then roam.</ div>''')

demo.launch()  # Share your demo with just 1 extra parameter 🚀