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import gradio as gr
import torch
from PIL import Image
from torchvision import transforms
# from diffusers import StableDiffusionPipeline, StableDiffusionImageVariationPipeline, DiffusionPipeline
import numpy as np
import pandas as pd
import math
from transformers import CLIPTextModel, CLIPTokenizer
import os
from clip_retrieval.clip_client import ClipClient, Modality
# clip_model_id = "openai/clip-vit-large-patch14-336"
# clip_retrieval_indice_name, clip_model_id ="laion5B-L-14", "/laion/CLIP-ViT-L-14-laion2B-s32B-b82K"
clip_retrieval_service_url = "https://knn.laion.ai/knn-service"
# available models = ['RN50', 'RN101', 'RN50x4', 'RN50x16', 'RN50x64', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px']
# clip_model="ViT-B/32"
clip_model="ViT-L/14"
clip_model_id ="laion5B-L-14"
max_tabs = 10
input_images = [None for i in range(max_tabs)]
input_prompts = [None for i in range(max_tabs)]
embedding_plots = [None for i in range(max_tabs)]
embedding_powers = [1. for i in range(max_tabs)]
# global embedding_base64s
embedding_base64s = [None for i in range(max_tabs)]
# embedding_base64s = gr.State(value=[None for i in range(max_tabs)])
def image_to_embedding(input_im):
input_im = Image.fromarray(input_im)
prepro = preprocess(input_im).unsqueeze(0).to(device)
with torch.no_grad():
image_embeddings = model.encode_image(prepro)
image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
image_embeddings_np = image_embeddings.cpu().to(torch.float32).detach().numpy()
return image_embeddings_np
def prompt_to_embedding(prompt):
text = tokenizer([prompt]).to(device)
with torch.no_grad():
prompt_embededdings = model.encode_text(text)
prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
prompt_embededdings_np = prompt_embededdings.cpu().to(torch.float32).detach().numpy()
return prompt_embededdings_np
def embedding_to_image(embeddings):
size = math.ceil(math.sqrt(embeddings.shape[0]))
image_embeddings_square = np.pad(embeddings, (0, size**2 - embeddings.shape[0]), 'constant')
image_embeddings_square.resize(size,size)
embedding_image = Image.fromarray(image_embeddings_square, mode="L")
return embedding_image
def embedding_to_base64(embeddings):
import base64
# ensure float32
embeddings = embeddings.astype(np.float32)
embeddings_b64 = base64.urlsafe_b64encode(embeddings).decode()
return embeddings_b64
def base64_to_embedding(embeddings_b64):
import base64
embeddings = base64.urlsafe_b64decode(embeddings_b64)
embeddings = np.frombuffer(embeddings, dtype=np.float32)
# embeddings = torch.tensor(embeddings)
return embeddings
def main(
# input_im,
embeddings,
n_samples=4,
):
embeddings = base64_to_embedding(embeddings)
# convert to python array
embeddings = embeddings.tolist()
results = clip_retrieval_client.query(embedding_input=embeddings)
images = []
for result in results:
if len(images) >= n_samples:
break
# dowload image
import requests
from io import BytesIO
try:
response = requests.get(result["url"])
if not response.ok:
continue
bytes = BytesIO(response.content)
image = Image.open(bytes)
image.title = str(result["similarity"]) + ' ' + result["caption"]
images.append(image)
except Exception as e:
print(e)
return images
def on_image_load_update_embeddings(image_data):
# image to embeddings
if image_data is None:
# embeddings = prompt_to_embedding('')
# embeddings_b64 = embedding_to_base64(embeddings)
# return gr.Text.update(embeddings_b64)
return gr.Text.update('')
embeddings = image_to_embedding(image_data)
embeddings_b64 = embedding_to_base64(embeddings)
return gr.Text.update(embeddings_b64)
def on_prompt_change_update_embeddings(prompt):
# prompt to embeddings
if prompt is None or prompt == "":
embeddings = prompt_to_embedding('')
embeddings_b64 = embedding_to_base64(embeddings)
return gr.Text.update(embedding_to_base64(embeddings))
embeddings = prompt_to_embedding(prompt)
embeddings_b64 = embedding_to_base64(embeddings)
return gr.Text.update(embeddings_b64)
def update_average_embeddings(embedding_base64s_state, embedding_powers):
final_embedding = None
num_embeddings = 0
for i, embedding_base64 in enumerate(embedding_base64s_state):
if embedding_base64 is None or embedding_base64 == "":
continue
embedding = base64_to_embedding(embedding_base64)
embedding = embedding * embedding_powers[i]
if final_embedding is None:
final_embedding = embedding
else:
final_embedding = final_embedding + embedding
num_embeddings += 1
if final_embedding is None:
# embeddings = prompt_to_embedding('')
# embeddings_b64 = embedding_to_base64(embeddings)
# return gr.Text.update(embeddings_b64)
return gr.Text.update('')
# TODO toggle this to support average or sum
# final_embedding = final_embedding / num_embeddings
# normalize embeddings in numpy
final_embedding /= np.linalg.norm(final_embedding)
embeddings_b64 = embedding_to_base64(final_embedding)
return embeddings_b64
def on_power_change_update_average_embeddings(embedding_base64s_state, embedding_power_state, power, idx):
embedding_power_state[idx] = power
embeddings_b64 = update_average_embeddings(embedding_base64s_state, embedding_power_state)
return gr.Text.update(embeddings_b64)
def on_embeddings_changed_update_average_embeddings(embedding_base64s_state, embedding_power_state, embedding_base64, idx):
embedding_base64s_state[idx] = embedding_base64 if embedding_base64 != '' else None
embeddings_b64 = update_average_embeddings(embedding_base64s_state, embedding_power_state)
return gr.Text.update(embeddings_b64)
def on_embeddings_changed_update_plot(embeddings_b64):
# plot new embeddings
if embeddings_b64 is None or embeddings_b64 == "":
data = pd.DataFrame({
'embedding': [],
'index': []})
return gr.LinePlot.update(data,
x="index",
y="embedding",
# color="country",
title="Embeddings",
# stroke_dash="cluster",
# x_lim=[1950, 2010],
tooltip=['index', 'embedding'],
# stroke_dash_legend_title="Country Cluster",
# height=300,
width=0)
embeddings = base64_to_embedding(embeddings_b64)
data = pd.DataFrame({
'embedding': embeddings,
'index': [n for n in range(len(embeddings))]})
return gr.LinePlot.update(data,
x="index",
y="embedding",
# color="country",
title="Embeddings",
# stroke_dash="cluster",
# x_lim=[1950, 2010],
tooltip=['index', 'embedding'],
# stroke_dash_legend_title="Country Cluster",
# height=300,
width=embeddings.shape[0])
def on_example_image_click_set_image(input_image, image_url):
input_image.value = image_url
# device = torch.device("mps" if torch.backends.mps.is_available() else "cuda:0" if torch.cuda.is_available() else "cpu")
device = "cuda:0" if torch.cuda.is_available() else "cpu"
from clip_retrieval.load_clip import load_clip, get_tokenizer
# model, preprocess = load_clip(clip_model, use_jit=True, device=device)
model, preprocess = load_clip(clip_model, use_jit=True, device=device)
tokenizer = get_tokenizer(clip_model)
clip_retrieval_client = ClipClient(
url=clip_retrieval_service_url,
indice_name=clip_model_id,
use_safety_model = False,
use_violence_detector = False,
)
# results = clip_retrieval_client.query(text="an image of a cat")
# results[0]
examples = [
["SohoJoeEth.jpeg", "Ray-Liotta-Goodfellas.jpg", "SohoJoeEth + Ray.jpeg"],
# ["SohoJoeEth.jpeg", "Donkey.jpg", "SohoJoeEth + Donkey.jpeg"],
# ["SohoJoeEth.jpeg", "Snoop Dogg.jpg", "SohoJoeEth + Snoop Dogg.jpeg"],
]
tile_size = 100
# image_folder = os.path.join("file", "images")
image_folder ="images"
# image_examples = {
# "452650": "452650.jpeg",
# "Prompt 1": "a college dorm with a desk and bunk beds",
# "371739": "371739.jpeg",
# "Prompt 2": "a large banana is placed before a stuffed monkey.",
# "557922": "557922.jpeg",
# "Prompt 3": "a person sitting on a bench using a cell phone",
# }
tabbed_examples = {
"CoCo": {
"452650": "452650.jpeg",
"Prompt 1": "a college dorm with a desk and bunk beds",
"371739": "371739.jpeg",
"Prompt 2": "a large banana is placed before a stuffed monkey.",
"557922": "557922.jpeg",
"Prompt 3": "a person sitting on a bench using a cell phone",
"540554": "540554.jpeg",
"Prompt 4": "two trains are coming down the tracks, a steam engine and a modern train.",
},
"Transforms": {
"ColorWheel001": "ColorWheel001.jpg",
"ColorWheel001 BW": "ColorWheel001 BW.jpg",
"ColorWheel002": "ColorWheel002.jpg",
"ColorWheel002 BW": "ColorWheel002 BW.jpg",
},
"Portraits": {
"Snoop": "Snoop Dogg.jpg",
"Snoop Prompt": "Snoop Dogg",
"Ray": "Ray-Liotta-Goodfellas.jpg",
"Ray Prompt": "Ray Liotta, Goodfellas",
"Anya": "Anya Taylor-Joy 003.jpg",
"Anya Prompt": "Anya Taylor-Joy, The Queen's Gambit",
"Billie": "billie eilish 004.jpeg",
"Billie Prompt": "Billie Eilish, blonde hair",
"Lizzo": "Lizzo 001.jpeg",
"Lizzo Prompt": "Lizzo,",
"Donkey": "Donkey.jpg",
"Donkey Prompt": "Donkey, from Shrek",
},
"NFT's": {
"SohoJoe": "SohoJoeEth.jpeg",
"SohoJoe Prompt": "SohoJoe.Eth",
"Mirai": "Mirai.jpg",
"Mirai Prompt": "Mirai from White Rabbit, @shibuyaxyz",
"OnChainMonkey": "OnChainMonkey-2278.jpg",
"OCM Prompt": "On Chain Monkey",
"Wassie": "Wassie 4498.jpeg",
"Wassie Prompt": "Wassie by Wassies",
},
"Pups": {
"Pup1": "pup1.jpg",
"Prompt": "Teacup Yorkies",
"Pup2": "pup2.jpg",
"Pup3": "pup3.jpg",
"Pup4": "pup4.jpeg",
"Pup5": "pup5.jpg",
},
}
image_examples_tile_size = 50
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=5):
gr.Markdown(
"""
# Soho-Clip Embeddings Explorer
A tool for exploring CLIP embedding space.
Try uploading a few images and/or add some text prompts and click generate images.
""")
with gr.Column(scale=2, min_width=(tile_size+20)*3):
with gr.Row():
with gr.Column(scale=1, min_width=tile_size):
gr.Markdown("## Input 1")
with gr.Column(scale=1, min_width=tile_size):
gr.Markdown("## Input 2")
with gr.Column(scale=1, min_width=tile_size):
gr.Markdown("## Generates:")
for example in examples:
with gr.Row():
for example in example:
with gr.Column(scale=1, min_width=tile_size):
local_path = os.path.join(image_folder, example)
gr.Image(
value = local_path, shape=(tile_size,tile_size),
show_label=False, interactive=False) \
.style(height=tile_size, width=tile_size)
with gr.Row():
for i in range(max_tabs):
with gr.Tab(f"Input {i+1}"):
with gr.Row():
with gr.Column(scale=1, min_width=240):
input_images[i] = gr.Image(label="Image Prompt", show_label=True)
with gr.Column(scale=3, min_width=600):
embedding_plots[i] = gr.LinePlot(show_label=False).style(container=False)
# input_image.change(on_image_load, inputs= [input_image, plot])
with gr.Row():
with gr.Column(scale=2, min_width=240):
input_prompts[i] = gr.Textbox(label="Text Prompt", show_label=True)
with gr.Column(scale=3, min_width=600):
with gr.Row():
# with gr.Slider(min=-5, max=5, value=1, label="Power", show_label=True):
# embedding_powers[i] = gr.Slider.value
embedding_powers[i] = gr.Slider(minimum=-3, maximum=3, value=1, label="Power", show_label=True, interactive=True)
with gr.Row():
with gr.Accordion(f"Embeddings (base64)", open=False):
embedding_base64s[i] = gr.Textbox(show_label=False)
for idx, (tab_title, examples) in enumerate(tabbed_examples.items()):
with gr.Tab(tab_title):
with gr.Row():
for idx, (title, example) in enumerate(examples.items()):
if example.endswith(".jpg") or example.endswith(".jpeg"):
# add image example
local_path = os.path.join(image_folder, example)
with gr.Column(scale=1, min_width=image_examples_tile_size):
gr.Examples(
examples=[local_path],
inputs=input_images[i],
label=title,
)
else:
# add text example
with gr.Column(scale=1, min_width=image_examples_tile_size*2):
gr.Examples(
examples=[example],
inputs=input_prompts[i],
label=title,
)
with gr.Row():
average_embedding_plot = gr.LinePlot(show_label=True, label="Average Embeddings (base64)").style(container=False)
with gr.Row():
with gr.Accordion(f"Avergage embeddings in base 64", open=False):
average_embedding_base64 = gr.Textbox(show_label=False)
with gr.Row():
with gr.Column(scale=1, min_width=200):
n_samples = gr.Slider(1, 16, value=4, step=1, label="Number images")
with gr.Column(scale=3, min_width=200):
submit = gr.Button("Search embedding space")
with gr.Row():
output = gr.Gallery(label="Generated variations")
embedding_base64s_state = gr.State(value=[None for i in range(max_tabs)])
embedding_power_state = gr.State(value=[1. for i in range(max_tabs)])
for i in range(max_tabs):
input_images[i].change(on_image_load_update_embeddings, input_images[i], [embedding_base64s[i]])
input_prompts[i].change(on_prompt_change_update_embeddings, input_prompts[i], [embedding_base64s[i]])
embedding_base64s[i].change(on_embeddings_changed_update_plot, embedding_base64s[i], [embedding_plots[i]])
idx_state = gr.State(value=i)
embedding_base64s[i].change(on_embeddings_changed_update_average_embeddings, [embedding_base64s_state, embedding_power_state, embedding_base64s[i], idx_state], average_embedding_base64)
embedding_powers[i].change(on_power_change_update_average_embeddings, [embedding_base64s_state, embedding_power_state, embedding_powers[i], idx_state], average_embedding_base64)
average_embedding_base64.change(on_embeddings_changed_update_plot, average_embedding_base64, average_embedding_plot)
# submit.click(main, inputs= [embedding_base64s[0], scale, n_samples, steps, seed], outputs=output)
submit.click(main, inputs= [average_embedding_base64, n_samples], outputs=output)
output.style(grid=2)
with gr.Row():
gr.Markdown(
"""
My interest is to use CLIP for image/video understanding (see [CLIP_visual-spatial-reasoning](https://github.com/Sohojoe/CLIP_visual-spatial-reasoning).)
### Initial Features
- Combine up to 10 Images and/or text inputs to create an average embedding space.
- Search the laion 5b immages via a knn search
### Known limitations
- ...
### Acknowledgements
- I heavily build on [clip-retrieval](https://rom1504.github.io/clip-retrieval/) and use their API. Please [citate](https://github.com/rom1504/clip-retrieval#citation) the authors if you use this work.
- [CLIP](https://openai.com/blog/clip/)
- [Stable Diffusion](https://github.com/CompVis/stable-diffusion)
""")
# 
# <img src="file/pup1.jpg" width="100" height="100">
# {height=100 width=100}
if __name__ == "__main__":
demo.launch() |