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demo_search.py
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"""
CompoDiff
Copyright (c) 2023-present NAVER Corp.
Apache-2.0
"""
import os
import numpy as np
import base64
import requests
import json
import time
import torch
import torch.nn.functional as F
import gradio as gr
from clip_retrieval.clip_client import ClipClient
import types
from typing import Union, List, Optional, Callable
import torch
from diffusers import UnCLIPImageVariationPipeline
from torchvision import transforms
from torchvision.transforms.functional import to_pil_image, pil_to_tensor
from PIL import Image
import compodiff
def load_models():
### build model
print("\tbuilding CompoDiff")
compodiff_model, clip_model, img_preprocess, tokenizer = compodiff.build_model()
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
compodiff_model, clip_model = compodiff_model.to(device), clip_model.to(device)
if device != 'cpu':
clip_model = clip_model.half()
model_dict = {}
model_dict['compodiff'] = compodiff_model
model_dict['clip_model'] = clip_model
model_dict['img_preprocess'] = img_preprocess
model_dict['tokenizer'] = tokenizer
model_dict['device'] = device
return model_dict
@torch.no_grad()
def l2norm(features):
return features / features.norm(p=2, dim=-1, keepdim=True)
def predict(images, input_text, negative_text, step, cfg_image_scale, cfg_text_scale, do_generate, source_mixing_weight):
'''
image_source, text_input, negative_text_input, mask_text_input, steps_input, cfg_scale, do_generate, cfg_attn_target
'''
device = model_dict['device']
step = int(step)
step = step + 1 if step < 1000 else step
cfg_scale = (cfg_image_scale, cfg_text_scale)
text = input_text
if images is None:
# t2i
cfg_scale = (1.0, cfg_text_scale)
text_token_dict = model_dict['tokenizer'](text=text, return_tensors='pt', padding='max_length', truncation=True)
text_tokens, text_attention_mask = text_token_dict['input_ids'].to(device), text_token_dict['attention_mask'].to(device)
negative_text_token_dict = model_dict['tokenizer'](text=negative_text, return_tensors='pt', padding='max_length', truncation=True)
negative_text_tokens, negative_text_attention_mask = negative_text_token_dict['input_ids'].to(device), text_token_dict['attention_mask'].to(device)
with torch.no_grad():
image_cond = torch.zeros([1,1,768]).to(device)
text_cond = model_dict['clip_model'].encode_texts(text_tokens, text_attention_mask)
negative_text_cond = model_dict['clip_model'].encode_texts(negative_text_tokens, negative_text_attention_mask)
sampling_start = time.time()
mask = torch.tensor(np.zeros([64, 64], dtype='float32')).to(device).unsqueeze(0)
sampled_image_features = model_dict['compodiff'].sample(image_cond, text_cond, negative_text_cond, mask, timesteps=step, cond_scale=cfg_scale, num_samples_per_batch=2)
sampling_end = time.time()
sampled_image_features_org = sampled_image_features
sampled_image_features = l2norm(sampled_image_features)
else:
# CIR
image_source = images['image'].resize((512, 512))
mask = images['mask'].resize((512, 512))
mask = model_dict['img_preprocess'](mask, do_normalize=False, return_tensors='pt')['pixel_values']
mask = mask[:,:1,:,:]
## preprocess
image_source = model_dict['img_preprocess'](image_source, return_tensors='pt')['pixel_values'].to(device)
mask = (mask > 0.5).float().to(device)
image_source = image_source * (1 - mask)
text_token_dict = model_dict['tokenizer'](text=text, return_tensors='pt', padding='max_length', truncation=True)
text_tokens, text_attention_mask = text_token_dict['input_ids'].to(device), text_token_dict['attention_mask'].to(device)
negative_text_token_dict = model_dict['tokenizer'](text=negative_text, return_tensors='pt', padding='max_length', truncation=True)
negative_text_tokens, negative_text_attention_mask = negative_text_token_dict['input_ids'].to(device), text_token_dict['attention_mask'].to(device)
with torch.no_grad():
image_cond = model_dict['clip_model'].encode_images(image_source)
text_cond = model_dict['clip_model'].encode_texts(text_tokens, text_attention_mask)
negative_text_cond = model_dict['clip_model'].encode_texts(negative_text_tokens, negative_text_attention_mask)
sampling_start = time.time()
mask = transforms.Resize([64, 64])(mask)[:,0,:,:]
mask = (mask > 0.5).float()
if torch.sum(mask).item() == 0.0:
mask = torch.tensor(np.zeros([64, 64], dtype='float32')).to(device).unsqueeze(0)
sampled_image_features = model_dict['compodiff'].sample(image_cond, text_cond, negative_text_cond, mask, timesteps=step, cond_scale=cfg_scale, num_samples_per_batch=2)
sampling_end = time.time()
sampled_image_features_org = (1 - source_mixing_weight) * sampled_image_features + source_mixing_weight * image_cond[0]
sampled_image_features = l2norm(sampled_image_features_org)
if do_generate and image_decoder is not None:
images = image_decoder(image_embeddings=sampled_image_features_org.half(), num_images_per_prompt=2).images
else:
images = [Image.fromarray(np.zeros([256,256,3], dtype='uint8'))]
do_list = [['KNN results', sampled_image_features],
]
output = ''
top1_list = []
search_start = time.time()
for name, features in do_list:
results = client.query(embedding_input=features[0].tolist())[:15]
output += f'<details open><summary>{name} outputs</summary>\n\n'
for idx, result in enumerate(results):
image_url = result['url']
if idx == 0:
top1_list.append(f'{image_url}')
output += f'\n'
output += '\n</details>\n\n'
search_end = time.time()
return output, images
if __name__ == "__main__":
global model_dict, client, image_decoder
model_dict = load_models()
if 'cuda' in model_dict['device']:
image_decoder = UnCLIPImageVariationPipeline.from_pretrained("kakaobrain/karlo-v1-alpha-image-variations", torch_dtype=torch.float16).to('cuda:0')
else:
image_decoder = None
client = ClipClient(url="https://knn.laion.ai/knn-service",
indice_name="laion5B-L-14",
)
### define gradio demo
title = 'CompoDiff demo'
md_title = f'''# {title}
Diffusion on {model_dict["device"]}, K-NN Retrieval using https://rom1504.github.io/clip-retrieval.
'''
md_below = f'''### Tips:
Here are some tips for using the demo:
+ If you want to apply more of the original image's context, increase the source weight in the Advanced options from 0.1. This will convey the context of the original image as a strong signal.
+ If you want to exclude specific keywords, you can add them to the Negative text input.
+ Try using "generate image with unCLIP" to create images. You can see some interesting generated images that are as fascinating as search results.
+ If you only input text and no image, it will work like the prior of unCLIP.
'''
with gr.Blocks(title=title) as demo:
gr.Markdown(md_title)
with gr.Row():
with gr.Column():
image_source = gr.Image(type='pil', label='Source image', tool='sketch')
with gr.Row():
steps_input = gr.Radio(['2', '3', '5', '10'], value='10', label='denoising steps')
if model_dict['device'] == 'cpu':
do_generate = gr.Checkbox(value=False, label='generate image with unCLIP', visible=False)
else:
do_generate = gr.Checkbox(value=False, label='generate image with unCLIP', visible=True)
with gr.Accordion('Advanced options', open=False):
with gr.Row():
cfg_image_scale = gr.Number(value=1.5, label='image condition scale')
cfg_text_scale = gr.Number(value=7.5, label='text condition scale')
source_mixing_weight = gr.Number(value=0.1, label='source weight (0.0~1.0)')
text_input = gr.Textbox(value='', label='Input text guidance')
negative_text_input = gr.Textbox(value='', label='Negative text') # low quality, text overlay, logo
submit_button = gr.Button('Submit')
gr.Markdown(md_below)
with gr.Column():
if model_dict['device'] == 'cpu':
gallery = gr.Gallery(label='Generated images', visible=False).style(grid=[2])
else:
gallery = gr.Gallery(label='Generated images', visible=True).style(grid=[2])
md_output = gr.Markdown(label='Output')
submit_button.click(predict, inputs=[image_source, text_input, negative_text_input, steps_input, cfg_image_scale, cfg_text_scale, do_generate, source_mixing_weight], outputs=[md_output, gallery])
demo.launch(server_name='0.0.0.0',
server_port=8000)