src/pipelines/interpolate.py

import torch
import spaces
import gradio as gr
from src.util.base import *
from src.util.params import *


def interpolate_prompts(promptA, promptB, num_interpolation_steps):
    text_embeddingsA = get_text_embeddings(promptA)
    text_embeddingsB = get_text_embeddings(promptB)

    interpolated_embeddings = []

    for i in range(num_interpolation_steps):
        alpha = i / num_interpolation_steps
        interpolated_embedding = torch.lerp(text_embeddingsA, text_embeddingsB, alpha)
        interpolated_embeddings.append(interpolated_embedding)

    return interpolated_embeddings

@spaces.GPU(enable_queue=True)
def display_interpolate_images(
    seed, promptA, promptB, num_inference_steps, num_images, progress=gr.Progress()
):
    latents = generate_latents(seed)
    num_images = num_images + 2  # add 2 for first and last image
    text_embeddings = interpolate_prompts(promptA, promptB, num_images)
    images = []
    progress(0)

    for i in range(num_images):
        progress(i / num_images)
        image = generate_images(latents, text_embeddings[i], num_inference_steps)
        images.append((image, "{}".format(i + 1)))

    progress(1, desc="Exporting as gif")
    export_as_gif(images, filename="interpolate.gif", reverse=True)

    fname = "interpolate"
    tab_config = {
        "Tab": "Interpolate",
        "First Prompt": promptA,
        "Second Prompt": promptB,
        "Number of Interpolation Steps": num_images,
        "Number of Inference Steps per Image": num_inference_steps,
        "Seed": seed,
    }
    export_as_zip(images, fname, tab_config)
    return images, "outputs/interpolate.gif", f"outputs/{fname}.zip"


__all__ = ["display_interpolate_images"]