app.py

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

# model_id = "stabilityai/stable-diffusion-2-1-base"
# text_model_id = "CompVis/stable-diffusion-v-1-4-original"
# text_model_id = "CompVis/stable-diffusion-v1-4"
text_model_id = "runwayml/stable-diffusion-v1-5"
# text_model_id = "stabilityai/stable-diffusion-2-1-base"
model_id = "lambdalabs/sd-image-variations-diffusers"
clip_model_id = "openai/clip-vit-large-patch14-336"

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):
    tform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize(
        (224, 224),
        interpolation=transforms.InterpolationMode.BICUBIC,
        antialias=False,
        ),
        transforms.Normalize(
          [0.48145466, 0.4578275, 0.40821073],
          [0.26862954, 0.26130258, 0.27577711]),
    ])

    inp = tform(input_im).to(device)
    dtype = next(pipe.image_encoder.parameters()).dtype
    image = inp.tile(1, 1, 1, 1).to(device=device, dtype=dtype)
    image_embeddings = pipe.image_encoder(image).image_embeds
    image_embeddings = image_embeddings[0]
    # image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
    image_embeddings_np = image_embeddings.cpu().detach().numpy()
    return image_embeddings_np

def prompt_to_embedding(prompt):
    # inputs = processor(prompt, images=imgs, return_tensors="pt", padding=True)
    inputs = processor(prompt, return_tensors="pt", padding='max_length', max_length=77)
    # labels = torch.tensor(labels)
    # prompt_tokens = inputs.input_ids[0]
    prompt_tokens = inputs.input_ids
    # image = inputs.pixel_values
    with torch.no_grad():
        prompt_embededdings = model.get_text_features(prompt_tokens.to(device))
    # prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
    prompt_embededdings = prompt_embededdings[0].cpu().detach().numpy()
    return prompt_embededdings

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 float16
    embeddings = embeddings.astype(np.float16)
    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.float16)
    # embeddings = torch.tensor(embeddings)
    return embeddings

def main(
    # input_im,
    embeddings,
    scale=3.0,
    n_samples=4,
    steps=25,
    seed=None
    ):

    if seed == None:
        seed = np.random.randint(2147483647)
    # if device contains cuda
    if device.type == 'cuda':
        generator = torch.Generator(device=device).manual_seed(int(seed))
    else:
        generator = torch.Generator().manual_seed(int(seed)) # use cpu as does not work on mps

    embeddings = base64_to_embedding(embeddings)
    embeddings = torch.tensor(embeddings, dtype=torch_size).to(device)

    images_list = pipe(
        # inp.tile(n_samples, 1, 1, 1),
        # [embeddings * n_samples],
        embeddings,
        guidance_scale=scale,
        num_inference_steps=steps,
        generator=generator,
        )

    images = []
    for i, image in enumerate(images_list["images"]):
        images.append(image)
    # images.append(embedding_image)
    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
    
    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")
torch_size = torch.float16 if device == ('cuda') else torch.float32
# torch_size = torch.float32
pipe = StableDiffusionPipeline.from_pretrained(
    model_id,
    custom_pipeline="pipeline.py",
    torch_dtype=torch_size,
    # , revision="fp16",
    requires_safety_checker = False, safety_checker=None,
    text_encoder = CLIPTextModel,
    tokenizer = CLIPTokenizer,
    )
pipe = pipe.to(device)

from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained(clip_model_id)
model = AutoModel.from_pretrained(clip_model_id)
model = model.to(device)

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

A tool for exploring CLIP embedding spaces.

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():
        submit = gr.Button("Generate images")
    with gr.Row():
        with gr.Column(scale=1, min_width=200):
            scale = gr.Slider(0, 25, value=3, step=1, label="Guidance scale")
        with gr.Column(scale=1, min_width=200):
            n_samples = gr.Slider(1, 4, value=1, step=1, label="Number images")
        with gr.Column(scale=1, min_width=200):
            steps = gr.Slider(5, 50, value=25, step=5, label="Steps")
        with gr.Column(scale=1, min_width=200):
            seed = gr.Number(None, label="Seed (blank = random)", precision=0)
    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, scale, n_samples, steps, seed], 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.
- View embedding spaces as graph
- Generate a new image based on the average embedding space

### Known limitations

- Text input is a little off (requires fine tuning and I'm having issues with that at the moment)
- It can only generate a single image at a time
- Not easy to use the sample images

### Acknowledgements

- I heavily build on Justin Pinkney's [Experiments in Image Variation](https://www.justinpinkney.com/image-variation-experiments). Please credit them if you use this work.
- [CLIP](https://openai.com/blog/clip/)
- [Stable Diffusion](https://github.com/CompVis/stable-diffusion)

""")

# ![Alt Text](file/pup1.jpg)

# <img src="file/pup1.jpg" width="100" height="100">

# ![Alt Text](file/pup1.jpg){height=100 width=100}    

if __name__ == "__main__":
    demo.launch()