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

# 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 = 5
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)]
# 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_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[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=0
    ):

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

    embeddings = base64_to_embedding(embeddings)
    embeddings = torch.tensor(embeddings).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)
    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 on_embeddings_changed_update_average_embeddings(last_embedding_base64):
# def on_embeddings_changed_update_average_embeddings(embedding_base64s):
def on_embeddings_changed_update_average_embeddings(embedding_base64s_state, embedding_base64, idx):
    # global embedding_base64s
    final_embedding = None
    num_embeddings = 0
    embedding_base64s_state[idx] = embedding_base64
    # for textbox in embedding_base64s:
    #     embedding_base64 = textbox.value
    for embedding_base64 in embedding_base64s_state:
        if embedding_base64 is None or embedding_base64 == "":
            continue
        embedding = base64_to_embedding(embedding_base64)
        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)
    final_embedding = final_embedding / num_embeddings
    embeddings_b64 = embedding_to_base64(final_embedding)
    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 == "":
        return gr.LinePlot.update()
        
    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])


device = torch.device("mps" if torch.backends.mps.is_available() else "cuda:0" if torch.cuda.is_available() else "cpu")
pipe = StableDiffusionPipeline.from_pretrained(
    model_id,
    custom_pipeline="pipeline.py",
    torch_dtype=torch.float16,
    # , 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 = [
    ["frog.png", 3, 1, 25, 0],
    ["img0.jpg", 3, 1, 25, 0],
    ["img1.jpg", 3, 1, 25, 0],
    ["img2.jpg", 3, 1, 25, 0],
    ["img3.jpg", 3, 1, 25, 0],
]


with gr.Blocks() as demo:
    with gr.Row():
        for i in range(max_tabs):
            with gr.Tab(f"Input {i}"):
                with gr.Row():
                    with gr.Column(scale=1, min_width=240):
                        input_images[i] = gr.Image()
                    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=1, min_width=240):
                        input_prompts[i] = gr.Textbox()
                    with gr.Column(scale=3, min_width=600):
                        with gr.Accordion("Embeddings", open=False):
                            embedding_base64s[i] = gr.Textbox(show_label=False)

    with gr.Row():
        average_embedding_plot = gr.LinePlot(show_label=False).style(container=False)
    with gr.Row():
        average_embedding_base64 = gr.Textbox(show_label=False)

    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", precision=0)
    with gr.Row():
        submit = gr.Button("Submit")
    with gr.Row():
        output = gr.Gallery(label="Generated variations")

    embedding_base64s_state = gr.State(value=[None 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].submit(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]])
        # embedding_plots[i].change(on_plot_changed, embedding_base64s[i], average_embedding_base64)
        # embedding_plots[i].change(on_embeddings_changed_update_average_embeddings, embedding_base64s[i], average_embedding_base64)
        idx_state = gr.State(value=i)
        embedding_base64s[i].change(on_embeddings_changed_update_average_embeddings, [embedding_base64s_state, embedding_base64s[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)

    

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